commit ea62ca237c02b1a488145258f5c99634b79e34a9 Author: GG <905865530@qq.com> Date: Fri May 10 20:12:43 2024 +0800 feat:20240410RGBtest1为socket通信下包含番茄rgb检测模型的全流程上机代码；20240419RGBtest2为pipe命名管道通信下包含番茄rgb检测模型的全流程上机代码；spectrum为光谱数据预处理代码（果子区域谱段均值） diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..01b5f8c --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,10 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml +# 基于编辑器的 HTTP 客户端请求 +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml +# GitHub Copilot persisted chat sessions +/copilot/chatSessions diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..2c0499a --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..50b176a --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/supermachine--tomato-passion_fruit.iml b/.idea/supermachine--tomato-passion_fruit.iml new file mode 100644 index 0000000..d0876a7 --- /dev/null +++ b/.idea/supermachine--tomato-passion_fruit.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/20240410RGBtest1/01picturemask.ipynb b/20240410RGBtest1/01picturemask.ipynb new file mode 100644 index 0000000..650a395 --- /dev/null +++ b/20240410RGBtest1/01picturemask.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-04-10T14:49:51.025756600Z", + "start_time": "2024-04-10T14:49:51.021739700Z" + } + }, + "source": [], + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + " import cv2\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + " image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\TopImage_32.jpg')\n", + " r_channel = image[:, :, 2]\n", + " b_channel = image[:, :, 0]\n", + " gray_image = r_channel - b_channel\n", + " gray_image = np.clip(gray_image, 0, 255)\n", + " gray_image = np.uint8(gray_image)\n", + "\n", + " # 显示新生成的灰度图像\n", + " plt.imshow(gray_image, cmap='gray')\n", + " plt.axis('off')\n", + " plt.show()\n", + " " + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T14:53:49.016762Z", + "start_time": "2024-04-10T14:53:48.426912500Z" + } + }, + "id": "206810a054630770", + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# 读取RGB图像\n", + "img = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\TopImage_32.jpg')\n", + "\n", + "\n", + "# 获取图像的高度和宽度\n", + "height, width, _ = img.shape\n", + "\n", + "# 创建一个新的灰度图像\n", + "gray_img = np.zeros((height, width), dtype=np.uint8)\n", + "\n", + "# 遍历每个像素,计算R-B作为新的灰度值\n", + "for y in range(height):\n", + " for x in range(width):\n", + " r, g, b = img[y, x]\n", + " gray_value = r - b\n", + " if gray_value < 0:\n", + " gray_value = 0\n", + " elif gray_value > 255:\n", + " gray_value = 255\n", + " gray_img[y, x] = int(gray_value)\n", + "\n", + "\n", + "plt.imshow(gray_image, cmap='gray')\n", + "plt.axis('off')\n", + "plt.show()\n", + " \n", + "# 保存灰度图像\n", + "# cv2.imwrite('output_image.jpg', gray_img)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T15:04:30.607530400Z", + "start_time": "2024-04-10T15:03:55.344343200Z" + } + }, + "id": "ba82f5c2fd049821", + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def extract_r_minus_b(image):\n", + " img_tensor = torch.tensor(image, dtype=torch.float32).to('cuda')\n", + "\n", + " r_channel = img_tensor[:, :, 0]\n", + " b_channel = img_tensor[:, :, 2]\n", + "\n", + " # 检查是否存在 NoneType 值，并将其替换为 0\n", + " r_channel = torch.nan_to_num(r_channel, nan=0)\n", + " b_channel = torch.nan_to_num(b_channel, nan=0)\n", + "\n", + " r_minus_b = torch.clamp(r_channel - b_channel, 0, 255).to('cpu').numpy().astype(np.uint8)\n", + "\n", + " return r_minus_b\n", + "\n", + "# 测试图像路径\n", + "image_path = '20240410tomatoRGBtest2/data/39.bmp'\n", + "\n", + "# 读取图像\n", + "image = cv2.imread(image_path)\n", + "\n", + "# 测试 extract_r_minus_b() 函数\n", + "r_minus_b = extract_r_minus_b(image)\n", + "\n", + "# 显示结果\n", + "plt.imshow(r_minus_b, cmap='gray')\n", + "plt.title('R-B Channel Difference')\n", + "plt.axis('off')\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T16:01:24.857246200Z", + "start_time": "2024-04-10T16:01:24.805385100Z" + } + }, + "id": "7f09896cca12ec80", + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### 自适应阈值分割\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 读取R通道减去B通道的灰度图\n", + "gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 自适应阈值分割\n", + "adaptive_threshold = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)\n", + "\n", + "# 显示结果\n", + "plt.imshow(adaptive_threshold, cmap='gray')\n", + "plt.axis('off')\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T14:49:51.023762Z", + "start_time": "2024-04-10T14:49:47.876441200Z" + } + }, + "id": "b9a2db758d89860e", + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### 带滑动条调节的阈值分割\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "# 读取R通道减去B通道的灰度图\n", + "gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 定义回调函数\n", + "def threshold_callback(threshold):\n", + " _, thresholded_image = cv2.threshold(gray_image, threshold, 255, cv2.THRESH_BINARY)\n", + " plt.imshow(thresholded_image, cmap='gray')\n", + " plt.axis('off')\n", + " plt.show()\n", + "\n", + "# 创建滑动条\n", + "threshold_slider = widgets.IntSlider(min=0, max=255, step=1, value=128, description='Threshold:')\n", + "display(threshold_slider)\n", + "\n", + "# 调用回调函数\n", + "threshold_callback(threshold_slider.value)\n", + "\n", + "# 连接滑动条和回调函数\n", + "widgets.interactive(threshold_callback, threshold=threshold_slider)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T09:49:57.666748700Z", + "start_time": "2024-04-10T09:49:56.140521900Z" + } + }, + "id": "6ff8e4e22d034f53", + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### Otsu阈值分割\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 读取R通道减去B通道的灰度图\n", + "gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 使用Otsu阈值分割\n", + "_, otsu_threshold = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n", + "\n", + "# 显示结果\n", + "plt.imshow(otsu_threshold, cmap='gray')\n", + "plt.axis('off')\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T09:54:09.077152500Z", + "start_time": "2024-04-10T09:54:08.502123300Z" + } + }, + "id": "6207115686820296", + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### Sobel边缘检测\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 读取R通道减去B通道的灰度图\n", + "gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 使用Sobel边缘检测\n", + "sobel_x = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=5)\n", + "sobel_y = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=5)\n", + "edges = np.sqrt(sobel_x**2 + sobel_y**2)\n", + "\n", + "# 显示结果\n", + "plt.imshow(edges, cmap='gray')\n", + "plt.axis('off')\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T09:55:13.525305800Z", + "start_time": "2024-04-10T09:55:12.652295600Z" + } + }, + "id": "f3bd38e5e62cbd3c", + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### 形态学处理\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 读取R通道减去B通道的灰度图\n", + "gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 定义结构元素\n", + "kernel = np.ones((2,2),np.uint8)\n", + "\n", + "# 闭运算\n", + "closing = cv2.morphologyEx(gray_image, cv2.MORPH_CLOSE, kernel)\n", + "\n", + "# 显示结果\n", + "plt.imshow(closing, cmap='gray')\n", + "plt.axis('off')\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T09:57:05.878548800Z", + "start_time": "2024-04-10T09:57:05.060591800Z" + } + }, + "id": "7395629910ee8620", + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### 角点检测\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 读取R通道减去B通道的灰度图\n", + "gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# Shi-Tomasi角点检测\n", + "corners = cv2.goodFeaturesToTrack(gray_image, 100, 0.01, 10)\n", + "\n", + "# 绘制角点\n", + "corners = np.int0(corners)\n", + "for i in corners:\n", + " x, y = i.ravel()\n", + " cv2.circle(gray_image, (x, y), 3, 255, -1)\n", + "\n", + "# 显示结果\n", + "plt.imshow(gray_image, cmap='gray')\n", + "plt.axis('off')\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T09:59:32.789829500Z", + "start_time": "2024-04-10T09:59:31.769444500Z" + } + }, + "id": "990a0ecebe27d9a0", + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### 二值化对象\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "\n", + "# 读取原始图像\n", + "img = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\output\\r_minus_b_26.jpg', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 使用Otsu's二值化方法\n", + "_, binary_img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n", + "\n", + "# # 显示二值化后的图像\n", + "# cv2.imshow('Binary Image', binary_img)\n", + "# cv2.waitKey(0)\n", + "# cv2.destroyAllWindows()\n", + "# 保存二值化后的图像\n", + "cv2.imwrite('06.jpg', binary_img)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-12T06:34:26.530387300Z", + "start_time": "2024-04-12T06:34:26.132598800Z" + } + }, + "id": "c4a6c0bfd05d3283", + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "### 光滑边缘\n", + "import cv2\n", + "import numpy as np\n", + "\n", + "# 假设您的两张图像已经保存为 image1.png 和 image2.png\n", + "image1 = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\06.jpg', cv2.IMREAD_GRAYSCALE)\n", + "image2 = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\010.png', cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 应用高斯模糊来平滑边缘\n", + "smoothed_image1 = cv2.GaussianBlur(image1, (9, 9), 0)\n", + "smoothed_image2 = cv2.GaussianBlur(image2, (9, 9), 0)\n", + "\n", + "# 将处理后的图像转换为二值图像\n", + "_, binary_image1 = cv2.threshold(smoothed_image1, 128, 255, cv2.THRESH_BINARY)\n", + "_, binary_image2 = cv2.threshold(smoothed_image2, 128, 255, cv2.THRESH_BINARY)\n", + "\n", + "# 保存二值化后的图像\n", + "cv2.imwrite('smoothed_binary_image06.png', binary_image1)\n", + "cv2.imwrite('smoothed_binary_image2.png', binary_image2)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-12T06:35:18.063684900Z", + "start_time": "2024-04-12T06:35:17.935028900Z" + } + }, + "id": "1f6ac60b3150fc9e", + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "collapsed": false + }, + "id": "3749d97ac3719946", + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/20240410RGBtest1/image_preprocessing.py b/20240410RGBtest1/image_preprocessing.py new file mode 100644 index 0000000..edfd7b4 --- /dev/null +++ b/20240410RGBtest1/image_preprocessing.py @@ -0,0 +1,298 @@ +import cv2 +import numpy as np +import os +import argparse +# from svm import predict_image_array, load_model + + + +def extract_s_l(image_path): + image = cv2.imread(image_path) + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab) + s_channel = hsv[:,:,1] + l_channel = lab[:,:,0] + result = cv2.add(s_channel, l_channel) + return result + +def find_reflection(image_path, threshold=190): + # 读取图像 + image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + + # 应用阈值分割 + _, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) + + return reflection + +def otsu_threshold(image): + + # 将图像转换为灰度图像 + # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # 使用Otsu阈值分割 + _, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + + return binary + +# 提取花萼，使用G-R的图像 +def extract_g_r(image): + # image = cv2.imread(image_path) + g_channel = image[:,:,1] + r_channel = image[:,:,2] + result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel) + return result + + +#提取西红柿，使用R-B的图像 +def extract_r_b(image_path): + image = cv2.imread(image_path) + r_channel = image[:,:,2] + b_channel = image[:,:,0] + result = cv2.subtract(r_channel, b_channel) + return result + +def extract_r_g(image_path): + image = cv2.imread(image_path) + r_channel = image[:,:,2] + g_channel = image[:,:,1] + result = cv2.subtract(r_channel, g_channel) + return result + +def threshold_segmentation(image, threshold, color=255): + _, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY) + return result + +def bitwise_operation(image1, image2, operation='and'): + if operation == 'and': + result = cv2.bitwise_and(image1, image2) + elif operation == 'or': + result = cv2.bitwise_or(image1, image2) + else: + raise ValueError("operation must be 'and' or 'or'") + return result + +def largest_connected_component(bin_img): + # 使用connectedComponentsWithStats函数找到连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8) + + # 如果只有背景标签,返回一个空的二值图像 + if num_labels <= 1: + return np.zeros_like(bin_img) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像,只显示最大的连通区域 + new_bin_img = np.zeros_like(bin_img) + new_bin_img[labels == largest_label] = 255 + + return new_bin_img + +def close_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel) + return closed_img + +def open_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + opened_img = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel) + return opened_img + + +def draw_tomato_edge(original_img, bin_img): + bin_img_processed = close_operation(bin_img, kernel_size=(15, 15)) + # cv2.imshow('Close Operation', bin_img_processed) + # bin_img_processed = open_operation(bin_img_processed, kernel_size=(19, 19)) + # cv2.imshow('Open Operation', bin_img_processed) + # 现在使用处理后的bin_img_processed查找轮廓 + contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 如果没有找到轮廓，直接返回原图 + if not contours: + return original_img, np.zeros_like(bin_img) # 返回原图和全黑mask + # 找到最大轮廓 + max_contour = max(contours, key=cv2.contourArea) + # 多边形近似的精度调整 + epsilon = 0.0006 * cv2.arcLength(max_contour, True) # 可以调整这个值 + approx = cv2.approxPolyDP(max_contour, epsilon, True) + # 绘制轮廓 + cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3) + mask = np.zeros_like(bin_img) + + # 使用白色填充最大轮廓 + cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED) + + return original_img, mask + +def draw_tomato_edge_convex_hull(original_img, bin_img): + bin_img_blurred = cv2.GaussianBlur(bin_img, (5, 5), 0) + contours, _ = cv2.findContours(bin_img_blurred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return original_img + max_contour = max(contours, key=cv2.contourArea) + hull = cv2.convexHull(max_contour) + cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3) + return original_img + +# 得到完整的西红柿二值图像，除了绿色花萼 +def fill_holes(bin_img): + # 复制 bin_img 到 img_filled + img_filled = bin_img.copy() + + # 获取图像的高度和宽度 + height, width = bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_d = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(bin_img, img_filled) + # 裁剪 img_filled 和 img_filled_d 到与 bin_img 相同的大小 + # img_filled = img_filled[:height, :width] + img_filled_d = img_filled_d[:height, :width] + + return img_filled, img_filled_d + +def bitwise_and_rgb_with_binary(rgb_img, bin_img): + # 将二值图像转换为三通道图像 + bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR) + + # 使用 bitwise_and 操作合并 RGB 图像和二值图像 + result = cv2.bitwise_and(rgb_img, bin_img_3channel) + + return result + + +def extract_max_connected_area(image, lower_hsv, upper_hsv): + # 读取图像 + # image = cv2.imread(image_path) + + # 将图像从BGR转换到HSV + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # 使用阈值获取指定区域的二值图像 + mask = cv2.inRange(hsv, lower_hsv, upper_hsv) + + # 找到二值图像的连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像，只显示最大的连通区域 + new_bin_img = np.zeros_like(mask) + new_bin_img[labels == largest_label] = 255 + + # 复制 new_bin_img 到 img_filled + img_filled = new_bin_img.copy() + + # 获取图像的高度和宽度 + height, width = new_bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_inv = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv) + + return img_filled + + + + +def main(): + parser = argparse.ArgumentParser(description='Process some integers.') + parser.add_argument('--dir_path', type=str, default=r'D:\project\Tomato\20240410RGBtest2\datatest', + help='the directory path of images') + parser.add_argument('--threshold_s_l', type=int, default=180, + help='the threshold for s_l') + parser.add_argument('--threshold_r_b', type=int, default=15, + help='the threshold for r_b') + + args = parser.parse_args() + + for img_file in os.listdir(args.dir_path): + if img_file.endswith('.png'): + img_path = os.path.join(args.dir_path, img_file) + s_l = extract_s_l(img_path) + otsu_thresholded = otsu_threshold(s_l) + img_fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), otsu_thresholded) + img_fore_defect = extract_g_r(img_fore) + img_fore_defect = threshold_segmentation(img_fore_defect, args.threshold_r_b) + # cv2.imshow('img_fore_defect', img_fore_defect) + thresholded_s_l = threshold_segmentation(s_l, args.threshold_s_l) + new_bin_img = largest_connected_component(thresholded_s_l) + zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE)) + cv2.imshow('zhongggggg', zhongggggg) + new_otsu_bin_img = largest_connected_component(otsu_thresholded) + filled_img, defect = fill_holes(new_bin_img) + defect = bitwise_and_rgb_with_binary(cv2.imread(img_path), defect) + cv2.imshow('defect', defect) + edge, mask = draw_tomato_edge(cv2.imread(img_path), new_bin_img) + org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img) + fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), mask) + fore_g_r_t = threshold_segmentation(extract_g_r(fore), 20) + fore_g_r_t_ture = bitwise_and_rgb_with_binary(cv2.imread(img_path), fore_g_r_t) + cv2.imwrite('defect_big.bmp', fore_g_r_t_ture) + res = cv2.bitwise_or(new_bin_img, fore_g_r_t) + white = find_reflection(img_path) + + # SVM预测 + # 加载模型 + # model, scaler = load_model('/Users/xs/PycharmProjects/super-tomato/svm_green.joblib') + + # 对图像进行预测 + # predicted_mask = predict_image_array(image, model, scaler) + + + + cv2.imshow('white', white) + + cv2.imshow('fore', fore) + cv2.imshow('fore_g_r_t', fore_g_r_t) + cv2.imshow('mask', mask) + print('mask', mask.shape) + print('filled', filled_img.shape) + print('largest', new_bin_img.shape) + print('rp', org_defect.shape) + cv2.imshow('res', res) + + # lower_hsv = np.array([19, 108, 15]) + # upper_hsv = np.array([118, 198, 134]) + # max_connected_area = extract_max_connected_area(img_path, lower_hsv, upper_hsv) + # cv2.imshow('Max Connected Area', max_connected_area) + + # 显示原始图像 + original_img = cv2.imread(img_path) + cv2.imshow('Original', original_img) + cv2.imshow('thresholded_s_l', thresholded_s_l) + cv2.imshow('Largest Connected Component', new_bin_img) + cv2.imshow('Filled', filled_img) + cv2.imshow('Defect', defect) + cv2.imshow('Org_defect', org_defect) + cv2.imshow('otsu_thresholded', new_otsu_bin_img) + + + #显示轮廓 + cv2.imshow('Edge', edge) + + # 等待用户按下任意键 + cv2.waitKey(0) + + # 关闭所有窗口 + cv2.destroyAllWindows() + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/20240410RGBtest1/main.py b/20240410RGBtest1/main.py new file mode 100644 index 0000000..8c4afb2 --- /dev/null +++ b/20240410RGBtest1/main.py @@ -0,0 +1,217 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/4/12 15:04 +# @Author : TG +# @File : main.py +# @Software: PyCharm + +import socket +import sys +import numpy as np +import cv2 +import root_dir +import time +import os +from root_dir import ROOT_DIR +from utils import PreSocket, receive_sock, parse_protocol, ack_sock, done_sock, DualSock, simple_sock, test_sock +import logging +from utils import threshold_segmentation, largest_connected_component, draw_tomato_edge, bitwise_and_rgb_with_binary, extract_s_l, get_tomato_dimensions, get_defect_info +from collections import deque +import time + + +def process_cmd(cmd: str, img: any, connected_sock: socket.socket) -> tuple: + """ + 处理指令 + + :param cmd: 指令类型 + :param data: 指令内容 + :param connected_sock: socket + :param detector: 模型 + :return: 是否处理成功 + """ + start_time = time.time() + if cmd == 'IM': + # image = cv2.imdecode(np.frombuffer(img, dtype=np.uint8), cv2.IMREAD_COLOR) + # image = img + threshold_s_l = 180 + # threshold_r_b = 15 + + s_l = extract_s_l(img) + # otsu_thresholded = ImageProcessor.otsu_threshold(s_l) + # img_fore = ImageProcessor.bitwise_and_rgb_with_binary(img, otsu_thresholded) + # img_fore_defect = ImageProcessor.extract_g_r(img_fore) + # img_fore_defect = ImageProcessor.threshold_segmentation(img_fore_defect, threshold_r_b) + + # cv2.imshow('img_fore_defect', img_fore_defect) + + thresholded_s_l = threshold_segmentation(s_l, threshold_s_l) + new_bin_img = largest_connected_component(thresholded_s_l) + # zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE)) + + # cv2.imshow('zhongggggg', zhongggggg) + + # new_otsu_bin_img = ImageProcessor.largest_connected_component(otsu_thresholded) + # filled_img, defect = ImageProcessor.fill_holes(new_bin_img) + # defect = ImageProcessor.bitwise_and_rgb_with_binary(cv2.imread(img), defect) + + # cv2.imshow('defect', defect) + + edge, mask = draw_tomato_edge(img, new_bin_img) + org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img) + # fore = ImageProcessor.bitwise_and_rgb_with_binary(cv2.imread(img), mask) + # fore_g_r_t = ImageProcessor.threshold_segmentation(ImageProcessor.extract_g_r(fore), 20) + # fore_g_r_t_ture = ImageProcessor.bitwise_and_rgb_with_binary(cv2.imread(img), fore_g_r_t) + + # cv2.imwrite('defect_big.bmp', fore_g_r_t_ture) + + # res = cv2.bitwise_or(new_bin_img, fore_g_r_t) + # white = ImageProcessor.find_reflection(img) + # cv2.imwrite('new_bin_img.bmp', new_bin_img) + + long_axis, short_axis = get_tomato_dimensions(mask) + number_defects, total_pixels = get_defect_info(new_bin_img) + rp = org_defect + rp = cv2.cvtColor(rp, cv2.COLOR_BGR2RGB) + # cv2.imwrite('rp1.bmp', rp) + + + + + # elif cmd == 'TR': + # detector = WoodClass(w=4096, h=1200, n=8000, p1=0.8, debug_mode=False) + # model_name = None + # if "$" in data: + # data, model_name = data.split("$", 1) + # model_name = model_name + ".p" + # settings.data_path = data + # settings.model_path = ROOT_DIR / 'models' / detector.fit_pictures(data_path=settings.data_path, file_name=model_name) + # response = simple_sock(connected_sock, cmd_type=cmd, result=result) + # elif cmd == 'MD': + # settings.model_path = data + # detector.load(path=settings.model_path) + # response = simple_sock(connected_sock, cmd_type=cmd, result=result) + # elif cmd == 'KM': + # x_data, y_data, labels, img_names = detector.get_luminance_data(data, plot_2d=True) + # result = detector.data_adjustments(x_data, y_data, labels, img_names) + # result = ','.join([str(x) for x in result]) + # response = simple_sock(connected_sock, cmd_type=cmd, result=result) + + + else: + logging.error(f'错误指令，指令为{cmd}') + response = False + + end_time = time.time() + elapsed_time = end_time - start_time + print(f'处理时间：{elapsed_time}秒') + return long_axis, short_axis, number_defects, total_pixels, rp + + + + +def main(is_debug=False): + file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log')) + file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) + logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s', + handlers=[file_handler, console_handler], + level=logging.DEBUG) + + dual_sock = DualSock(connect_ip='127.0.0.1') + + while not dual_sock.status: + dual_sock.reconnect() + + while True: + long_axis_list = [] + short_axis_list = [] + defect_num_sum = 0 + total_defect_area_sum = 0 + rp = None + + for i in range(5): + + start_time = time.time() + + pack, next_pack = receive_sock(dual_sock) + if pack == b"": + time.sleep(2) + dual_sock.reconnect() + continue + + cmd, img = parse_protocol(pack) + print(cmd) + print(img.shape) + + end_time = time.time() + elapsed_time = end_time - start_time + print(f'接收时间：{elapsed_time}秒') + + long_axis, short_axis, number_defects, total_pixels, rp = process_cmd(cmd=cmd, img=img, connected_sock=dual_sock) + # print(long_axis, short_axis, number_defects, type(total_pixels), rp.shape) + + if i <= 2: + long_axis_list.append(long_axis) + short_axis_list.append(short_axis) + if i == 1: + rp_result = rp + + defect_num_sum += number_defects + total_defect_area_sum += total_pixels + + + long_axis = round(sum(long_axis_list) / 3) + short_axis = round(sum(short_axis_list) / 3) + # print(type(long_axis), type(short_axis), type(defect_num_sum), type(total_defect_area_sum), type(rp_result)) + response = test_sock(dual_sock, cmd_type=cmd, long_axis=long_axis, short_axis=short_axis, + defect_num=defect_num_sum, total_defect_area=total_defect_area_sum, rp=rp_result) + print(long_axis, short_axis, defect_num_sum, total_defect_area_sum, rp_result.shape) + + # while True: + # result_buffer = [] + # for _ in range(5): + # pack, next_pack = receive_sock(dual_sock) # 接收数据，如果没有数据则阻塞，如果返回的是空字符串则表示出现错误 + # if pack == b"": # 无数据表示出现错误 + # time.sleep(5) + # dual_sock.reconnect() + # break + # + # cmd, data = parse_protocol(pack) + # print(cmd) + # print(data) + # + # result = process_cmd(cmd=cmd, data=data, connected_sock=dual_sock, detector=detector, settings=settings) + # result_buffer.append(result) # 将处理结果添加到缓冲区 + # + # # 在这里进行对5次结果的处理，可以进行合并、比较等操作 + # final_result = combine_results(result_buffer) + # + # # 发送最终结果 + # response = simple_sock(dual_sock, cmd_type=cmd, result=final_result) + # print(final_result) + # result_buffer = [] + + + +if __name__ == '__main__': + # 2个端口 + # 接受端口21122 + # 发送端口21123 + # 接收到图片 n_rows * n_bands * n_cols, float32 + # 发送图片 n_rows * n_cols, uint8 + main(is_debug=False) + # test(r"D:\build-tobacco-Desktop_Qt_5_9_0_MSVC2015_64bit-Release\calibrated15.raw") + # main() + # debug_main() + # test_run(all_data_dir=r'D:\数据') + # with open(r'D:\数据\虫子\valid2.raw', 'rb') as f: + # data = np.frombuffer(f.read(), dtype=np.float32).reshape(600, 29, 1024).transpose(0, 2, 1) + # plt.matshow(data[:, :, 10]) + # plt.show() + # detector = SpecDetector('model_spec/model_29.p') + # result = detector.predict(data) + # + # plt.matshow(result) + # plt.show() + # result = result.reshape((600, 1024)) \ No newline at end of file diff --git a/20240410RGBtest1/parameter calculation.py b/20240410RGBtest1/parameter calculation.py new file mode 100644 index 0000000..610f3f4 --- /dev/null +++ b/20240410RGBtest1/parameter calculation.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/4/11 15:18 +# @Author : TG +# @File : parameter calculation.py +# @Software: PyCharm + +import cv2 +import numpy as np +from scipy.ndimage.measurements import label, find_objects + +def get_tomato_dimensions(edge_img): + """ + 根据番茄边缘二值化轮廓图,计算番茄的长径、短径和长短径比值。 + 使用最小外接矩形和最小外接圆两种方法。 + + 参数: + edge_img (numpy.ndarray): 番茄边缘二值化轮廓图,背景为黑色,番茄区域为白色。 + + 返回: + tuple: (长径, 短径, 长短径比值) + """ + # 最小外接矩形 + rect = cv2.minAreaRect(cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0]) + major_axis, minor_axis = rect[1] + aspect_ratio = max(major_axis, minor_axis) / min(major_axis, minor_axis) + + # # 最小外接圆 + # (x, y), radius = cv2.minEnclosingCircle( + # cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0]) + # diameter = 2 * radius + # aspect_ratio_circle = 1.0 + + return (max(major_axis, minor_axis), min(major_axis, minor_axis), aspect_ratio) + +def get_defect_info(defect_img): + """ + 根据番茄区域缺陷二值化轮廓图,计算缺陷区域的个数和总面积。 + + 参数: + defect_img (numpy.ndarray): 番茄区域缺陷二值化轮廓图,背景为黑色,番茄区域为白色,缺陷区域为黑色连通域。 + + 返回: + tuple: (缺陷区域个数, 缺陷区域像素面积，缺陷像素总面积) + """ + nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(defect_img, connectivity=4) + max_area = max(stats[i, cv2.CC_STAT_AREA] for i in range(1, nb_components)) + areas = [] + for i in range(1, nb_components): + area = stats[i, cv2.CC_STAT_AREA] + if area != max_area: + areas.append(area) + number_defects = len(areas) + total_pixels = sum(areas) + return number_defects, areas, total_pixels + + + + +def connected_components_analysis(binary_image): + """ + 从二值化图像计算黑色连通域个数和各个黑色连通域像素面积及黑色像素总面积。 + + 参数: + binary_image (numpy.ndarray): 二值化图像, 其中 0 表示白色, 1 表示黑色。 + + 返回: + num_components (int): 黑色连通域的个数。 + component_areas (list): 每个黑色连通域的像素面积。 + total_black_area (int): 黑色像素的总面积。 + """ + # 标记连通域 + labeled_image, num_components = label(binary_image) + + # 获取每个连通域的像素位置 + slices = find_objects(labeled_image) + + # 计算每个连通域的像素面积 + component_areas = [] + for slice_obj in slices: + component_area = np.sum(binary_image[slice_obj]) + component_areas.append(component_area) + + # 计算黑色像素的总面积 + total_black_area = np.sum(binary_image) + + return num_components, component_areas, total_black_area + + + +def main(): + # 读取图像 + defect_image = cv2.imread(r'D:\project\Tomato\20240410tomatoRGBtest2\Largest Connected Component_screenshot_15.04.2024.png', 0) + edge_image = cv2.imread(r'D:\project\Tomato\20240410tomatoRGBtest2\mask_screenshot_15.04.2024.png', 0) + filled_image = cv2.imread(r'D:\project\Tomato\20240410tomatoRGBtest2\Filled_screenshot_15.04.2024.png', 0) + + # print(defect_image.shape) + # print(edge_image.shape) + # print(filled_image.shape) + + # 执行二值化处理 + _, thresh_defect = cv2.threshold(defect_image, 127, 255, cv2.THRESH_BINARY_INV) + _, thresh_edge = cv2.threshold(edge_image, 127, 255, cv2.THRESH_BINARY) + _, thresh_filled = cv2.threshold(filled_image, 127, 255, cv2.THRESH_BINARY) + + print(thresh_defect.shape) + print(thresh_edge.shape) + print(thresh_filled.shape) + + # # 直接使用二值图像 + # thresh_defect = defect_image + # thresh_edge = edge_image + # thresh_filled = filled_image + + # 获取番茄的长径、短径和长短径比值 + major_axis, minor_axis, aspect_ratio = get_tomato_dimensions(thresh_edge) + + # 获取缺陷区域的个数和总面积 + num_defects, areas, total_pixels = get_defect_info(thresh_defect) + + # 获取黑色连通域的个数、各个连通域的面积和总黑色面积 + num_components, component_areas, total_black_area = connected_components_analysis(thresh_filled) + + print(f'番茄的长径为{major_axis},短径为{minor_axis},长短径比值为{aspect_ratio}') + print(f'缺陷区域的个数为{num_defects},像素个数分别为{areas},缺陷总面积为{total_pixels}') + print(f'黑色连通域的个数为{num_components},像素个数分别为{component_areas},黑色像素总面积为{total_black_area}') + +if __name__ == '__main__': + main() + diff --git a/20240410RGBtest1/qt_test.py b/20240410RGBtest1/qt_test.py new file mode 100644 index 0000000..4ac0921 --- /dev/null +++ b/20240410RGBtest1/qt_test.py @@ -0,0 +1,211 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/4/12 16:54 +# @Author : TG +# @File : qt_test.py +# @Software: PyCharm + +import numpy as np +import socket +import logging +import matplotlib.pyplot as plt +import cv2 +import os +import time +from utils import DualSock, try_connect, receive_sock, parse_protocol, ack_sock, done_sock + + +def rec_socket(recv_sock: socket.socket, cmd_type: str, ack: bool) -> bool: + if ack: + cmd = 'A' + cmd_type + else: + cmd = 'D' + cmd_type + while True: + try: + temp = recv_sock.recv(1) + except ConnectionError as e: + logging.error(f'连接出错, 错误代码:\n{e}') + return False + except TimeoutError as e: + logging.error(f'超时了，错误代码: \n{e}') + return False + except Exception as e: + logging.error(f'遇见未知错误，错误代码: \n{e}') + return False + if temp == b'\xaa': + break + + # 获取报文长度 + temp = b'' + while len(temp) < 4: + try: + temp += recv_sock.recv(1) + except Exception as e: + logging.error(f'接收报文长度失败, 错误代码: \n{e}') + return False + try: + data_len = int.from_bytes(temp, byteorder='big') + except Exception as e: + logging.error(f'转换失败,错误代码 \n{e}, \n报文内容\n{temp}') + return False + + # 读取报文内容 + temp = b'' + while len(temp) < data_len: + try: + temp += recv_sock.recv(data_len) + except Exception as e: + logging.error(f'接收报文内容失败, 错误代码: \n{e}，\n报文内容\n{temp}') + return False + data = temp + if cmd.strip().upper() != data[:4].decode('ascii').strip().upper(): + logging.error(f'客户端接收指令错误,\n指令内容\n{data}') + return False + else: + if cmd == 'DIM': + print(data) + + # 进行数据校验 + temp = b'' + while len(temp) < 3: + try: + temp += recv_sock.recv(1) + except Exception as e: + logging.error(f'接收报文校验失败, 错误代码: \n{e}') + return False + if temp == b'\xff\xff\xbb': + return True + else: + logging.error(f"接收了一个完美的只错了校验位的报文，\n data: {data}") + return False + + +# def main(): +# socket_receive = socket.socket(socket.AF_INET, socket.SOCK_STREAM) +# socket_receive.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) +# socket_receive.bind(('127.0.0.1', 21123)) +# socket_receive.listen(5) +# socket_send = socket.socket(socket.AF_INET, socket.SOCK_STREAM) +# socket_send.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) +# socket_send.bind(('127.0.0.1', 21122)) +# socket_send.listen(5) +# print('等待连接') +# socket_send_1, receive_addr_1 = socket_send.accept() +# print("连接成功：", receive_addr_1) +# # socket_send_2 = socket_send_1 +# socket_send_2, receive_addr_2 = socket_receive.accept() +# print("连接成功：", receive_addr_2) +# while True: +# cmd = input('请输入指令：').strip().upper() +# if cmd == 'IM': +# with open('data/newrawfile_ref.raw', 'rb') as f: +# data = np.frombuffer(f.read(), dtype=np.float32).reshape(750, 288, 384) +# data = data[:, [91, 92, 93, 94, 95, 96, 97, 98, 99, 100], :] +# n_rows, n_bands, n_cols = data.shape[0], data.shape[1], data.shape[2] +# print(f'n_rows：{n_rows}, n_bands：{n_bands}, n_cols：{n_cols}') +# n_rows, n_cols, n_bands = [x.to_bytes(2, byteorder='big') for x in [n_rows, n_cols, n_bands]] +# data = data.tobytes() +# length = len(data) + 10 +# print(f'length: {length}') +# length = length.to_bytes(4, byteorder='big') +# msg = b'\xaa' + length + (' ' + cmd).upper().encode('ascii') + n_rows + n_cols + n_bands + data + b'\xff\xff\xbb' +# socket_send_1.send(msg) +# print('发送成功') +# result = socket_send_2.recv(5) +# length = int.from_bytes(result[1:5], byteorder='big') +# result = b'' +# while len(result) < length: +# result += socket_send_2.recv(length) +# print(result) +# data = result[4:length].decode() +# print(data) + +def main(): + socket_receive = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + socket_receive.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + socket_receive.bind(('127.0.0.1', 21123)) + socket_receive.listen(5) + socket_send = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + socket_send.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + socket_send.bind(('127.0.0.1', 21122)) + socket_send.listen(5) + print('等待连接') + socket_send_1, receive_addr_1 = socket_send.accept() + print("连接成功：", receive_addr_1) + socket_send_2, receive_addr_2 = socket_receive.accept() + print("连接成功：", receive_addr_2) + + # while True: + # cmd = input().strip().upper() + # if cmd == 'IM': + # image_dir = r'D:\project\Tomato\20240410tomatoRGBtest2\data' + # image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(".bmp")] + # for image_path in image_paths: + # img = cv2.imread(image_path) + # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + # img = np.asarray(img, dtype=np.uint8) + # width = img.shape[0] + # height = img.shape[1] + # print(width, height) + # img_bytes = img.tobytes() + # length = len(img_bytes) + 8 + # print(length) + # length = length.to_bytes(4, byteorder='big') + # width = width.to_bytes(2, byteorder='big') + # height = height.to_bytes(2, byteorder='big') + # send_message = b'\xaa' + length + (' ' + cmd).upper().encode('ascii') + width + height + img_bytes + b'\xff\xff\xbb' + # socket_send_1.send(send_message) + # print('发送成功') + # result = socket_send_2.recv(5) + # print(result) + while True: + cmd = input().strip().upper() + if cmd == 'IM': + image_dir = r'D:\project\Tomato\20240410tomatoRGBtest2\data' + send_images(image_dir, socket_send_1, socket_send_2) +def send_images(image_dir, socket_send_1, socket_send_2): + image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(".bmp")] + num_images = len(image_paths) + num_groups = (num_images + 4) // 5 + + for group_idx in range(num_groups): + start = group_idx * 5 + end = start + 5 + group_images = image_paths[start:end] + + group_start = time.time() + + for image_path in group_images: + + img_start = time.time() + + img = cv2.imread(image_path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.asarray(img, dtype=np.uint8) + width = img.shape[0] + height = img.shape[1] + print(width, height) + img_bytes = img.tobytes() + length = len(img_bytes) + 8 + print(length) + length = length.to_bytes(4, byteorder='big') + width = width.to_bytes(2, byteorder='big') + height = height.to_bytes(2, byteorder='big') + send_message = b'\xaa' + length + (' ' + 'IM').upper().encode('ascii') + width + height + img_bytes + b'\xff\xff\xbb' + socket_send_1.send(send_message) + + img_end = time.time() + print(f'图片发送时间: {img_end - img_start}秒') + + print('图片发送成功') + + group_end = time.time() + print(f'第 {group_idx + 1} 组图片发送时间: {group_end - group_start}秒') + + result = socket_send_2.recv(5) + print(f'第 {group_idx + 1} 组结果: {result}') + + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/.idea/.gitignore b/20240410RGBtest1/super-tomato/.idea/.gitignore new file mode 100644 index 0000000..13566b8 --- /dev/null +++ b/20240410RGBtest1/super-tomato/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/20240410RGBtest1/super-tomato/.idea/inspectionProfiles/Project_Default.xml b/20240410RGBtest1/super-tomato/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..89690d3 --- /dev/null +++ b/20240410RGBtest1/super-tomato/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,48 @@ + + + + \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/.idea/inspectionProfiles/profiles_settings.xml b/20240410RGBtest1/super-tomato/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/20240410RGBtest1/super-tomato/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/.idea/misc.xml b/20240410RGBtest1/super-tomato/.idea/misc.xml new file mode 100644 index 0000000..39bad22 --- /dev/null +++ b/20240410RGBtest1/super-tomato/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/.idea/modules.xml b/20240410RGBtest1/super-tomato/.idea/modules.xml new file mode 100644 index 0000000..1c5fdfb --- /dev/null +++ b/20240410RGBtest1/super-tomato/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/.idea/other.xml b/20240410RGBtest1/super-tomato/.idea/other.xml new file mode 100644 index 0000000..58daadc --- /dev/null +++ b/20240410RGBtest1/super-tomato/.idea/other.xml @@ -0,0 +1,6 @@ + + + + + \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/.idea/super-tomato.iml b/20240410RGBtest1/super-tomato/.idea/super-tomato.iml new file mode 100644 index 0000000..d0876a7 --- /dev/null +++ b/20240410RGBtest1/super-tomato/.idea/super-tomato.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/1111.py b/20240410RGBtest1/super-tomato/1111.py new file mode 100644 index 0000000..4bfc00e --- /dev/null +++ b/20240410RGBtest1/super-tomato/1111.py @@ -0,0 +1,14 @@ +import os +from PIL import Image + +def convert_images(directory): + for filename in os.listdir(directory): + if filename.endswith('.png'): + filepath = os.path.join(directory, filename) + with Image.open(filepath) as img: + if img.mode == 'RGBA': + img = img.convert('RGB') + img.save(filepath) + +# 调用函数，替换为你的目录路径 +convert_images(r'D:\同步盘\project\Tomato\20240410RGBtest2\super-tomato\images') \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/3D.py b/20240410RGBtest1/super-tomato/3D.py new file mode 100644 index 0000000..acf6477 --- /dev/null +++ b/20240410RGBtest1/super-tomato/3D.py @@ -0,0 +1,65 @@ +import cv2 +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D + +def plot_color_spaces(image_path, mask_path): + # 读取原始图像和掩码图像 + image = cv2.imread(image_path) + mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) + + # 确保掩码是二值的 + _, mask = cv2.threshold(mask, 128, 255, cv2.THRESH_BINARY) + + # 提取花萼部分和背景部分的像素 + flower_parts = image[mask == 255] + background = image[mask == 0] + background = background[::50] # 每隔三个像素取一个 + + # 转换到HSV和LAB颜色空间 + flower_parts_hsv = cv2.cvtColor(flower_parts.reshape(-1, 1, 3), cv2.COLOR_BGR2HSV).reshape(-1, 3) + flower_parts_lab = cv2.cvtColor(flower_parts.reshape(-1, 1, 3), cv2.COLOR_BGR2LAB).reshape(-1, 3) + + background_hsv = cv2.cvtColor(background.reshape(-1, 1, 3), cv2.COLOR_BGR2HSV).reshape(-1, 3) + background_lab = cv2.cvtColor(background.reshape(-1, 1, 3), cv2.COLOR_BGR2LAB).reshape(-1, 3) + + # 创建RGB空间的3D图 + fig_rgb = plt.figure() + ax_rgb = fig_rgb.add_subplot(111, projection='3d') + ax_rgb.scatter(flower_parts[:, 2], flower_parts[:, 1], flower_parts[:, 0], c='r', label='Flower Parts',alpha=0.01) + ax_rgb.scatter(background[:, 2], background[:, 1], background[:, 0], c='b', label='Background',alpha=0.01) + ax_rgb.set_title('RGB Color Space') + ax_rgb.set_xlabel('Red') + ax_rgb.set_ylabel('Green') + ax_rgb.set_zlabel('Blue') + ax_rgb.legend() + plt.show() + + # 创建HSV空间的3D图 + fig_hsv = plt.figure() + ax_hsv = fig_hsv.add_subplot(111, projection='3d') + ax_hsv.scatter(flower_parts_hsv[:, 0], flower_parts_hsv[:, 1], flower_parts_hsv[:, 2], c='r', label='Flower Parts',alpha=0.01) + ax_hsv.scatter(background_hsv[:, 0], background_hsv[:, 1], background_hsv[:, 2], c='b', label='Background',alpha=0.01) + ax_hsv.set_title('HSV Color Space') + ax_hsv.set_xlabel('Hue') + ax_hsv.set_ylabel('Saturation') + ax_hsv.set_zlabel('Value') + ax_hsv.legend() + plt.show() + + # 创建LAB空间的3D图 + fig_lab = plt.figure() + ax_lab = fig_lab.add_subplot(111, projection='3d') + ax_lab.scatter(flower_parts_lab[:, 0], flower_parts_lab[:, 1], flower_parts_lab[:, 2], c='r', label='Flower Parts',alpha=0.01) + ax_lab.scatter(background_lab[:, 0], background_lab[:, 1], background_lab[:, 2], c='b', label='Background',alpha=0.01) + ax_hsv.set_title('LAB Color Space') + ax_hsv.set_xlabel('L') + ax_hsv.set_ylabel('A') + ax_hsv.set_zlabel('B') + ax_hsv.legend() + plt.show() + + +# 调用函数，确保替换下面的路径为你的图像路径 +plot_color_spaces('/Users/xs/PycharmProjects/super-tomato/datasets_green/train/img/2.bmp', + '/Users/xs/PycharmProjects/super-tomato/datasets_green/train/label/2.png') diff --git a/20240410RGBtest1/super-tomato/CNN_green.py b/20240410RGBtest1/super-tomato/CNN_green.py new file mode 100644 index 0000000..f5794b6 --- /dev/null +++ b/20240410RGBtest1/super-tomato/CNN_green.py @@ -0,0 +1,157 @@ +import torch +import torch.nn as nn +import torch.optim as optim +from torch import device +from torch.utils.data import Dataset, DataLoader +from PIL import Image +import numpy as np +from sklearn.metrics import accuracy_score, precision_score, recall_score +import os +from tqdm import tqdm +import time +import matplotlib.pyplot as plt + + +class SimpleCNN(nn.Module): + def __init__(self): + super(SimpleCNN, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1) + self.conv2 = nn.Conv2d(64, 1, kernel_size=3, padding=1) + + def forward(self, x): + x = torch.relu(self.conv1(x)) + x = torch.sigmoid(self.conv2(x)) + return x + +class ImageDataset(Dataset): + def __init__(self, img_paths, mask_paths): + self.img_paths = img_paths + self.mask_paths = mask_paths + + def __len__(self): + return len(self.img_paths) + + def __getitem__(self, idx): + img = np.array(Image.open(self.img_paths[idx]).convert('RGB')).transpose((2, 0, 1)) # 转换为RGB图像，确保有3个通道 + if self.mask_paths[0] is not None: + mask = np.array(Image.open(self.mask_paths[idx]).convert('I')) # 转换为32位深度的灰度图像 + mask = mask / np.max(mask) # Normalize to 0-1 + return img, mask[np.newaxis, :] + else: + return img, None + +def train_model(dataloader, model, criterion, optimizer, epochs): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model.to(device) + + best_accuracy = 0.0 + for epoch in tqdm(range(epochs), desc="Training"): + for img, mask in dataloader: + img = img.float().to(device) + mask = mask.float().to(device) + + optimizer.zero_grad() + outputs = model(img) + loss = criterion(outputs, mask) + loss.backward() + optimizer.step() + + # 二值化模型的输出 + preds = outputs.detach().cpu().numpy() > 0.5 + mask = (mask.cpu().numpy() > 0.5) # Binarize the mask + + # 计算准确度、精度和召回率 + accuracy = accuracy_score(mask.flatten(), preds.flatten()) + precision = precision_score(mask.flatten(), preds.flatten()) + recall = recall_score(mask.flatten(), preds.flatten()) + + print(f'Epoch {epoch+1}/{epochs}, Loss: {loss.item()}, Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}') + + # 如果这个模型的准确度更好，就保存它 + if accuracy > best_accuracy: + best_accuracy = accuracy + torch.save(model.state_dict(), 'best_model.pth') + + return model + +def predict(model, img_path): + img = np.array(Image.open(img_path)).transpose((2, 0, 1)) # 调整维度为(C, H, W) + img = torch.from_numpy(img).float().unsqueeze(0) + model.eval() + with torch.no_grad(): + outputs = model(img) + pred = outputs.squeeze().numpy() + return pred + +def main(train_img_folder, train_mask_folder, test_img_folder, test_mask_folder, epochs, img_path='/Users/xs/PycharmProjects/super-tomato/datasets_green/test/label'): + # Define device + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + # Create model + model = SimpleCNN() + criterion = nn.BCELoss() + optimizer = optim.Adam(model.parameters()) + + # Create data loaders + train_dataset = ImageDataset(train_img_folder, train_mask_folder) + train_dataloader = DataLoader(train_dataset, batch_size=1) + + # Train model + model = train_model(train_dataloader, model, criterion, optimizer, epochs) + + # Create test data loaders + test_dataset = ImageDataset(test_img_folder, test_mask_folder) + test_dataloader = DataLoader(test_dataset, batch_size=1) + + # Use trained model to predict + for img, mask in test_dataloader: + img = img.float().to(device) + mask = mask.float().to(device) + + start_time = time.time() + outputs = model(img) + elapsed_time = time.time() - start_time + + # Binarize model's output + preds = outputs.detach().cpu().numpy() > 0.5 + mask = mask.cpu().numpy() + + # Calculate accuracy, precision and recall + accuracy = accuracy_score(mask.flatten(), preds.flatten()) + precision = precision_score(mask.flatten(), preds.flatten()) + recall = recall_score(mask.flatten(), preds.flatten()) + + print(f'Prediction for {img_path} saved, Time: {elapsed_time:.3f} seconds, Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}') +# 调用函数示例 +main('/Users/xs/PycharmProjects/super-tomato/datasets_green/train/img', + '/Users/xs/PycharmProjects/super-tomato/datasets_green/train/label', + '/Users/xs/PycharmProjects/super-tomato/datasets_green/test/img', + '/Users/xs/PycharmProjects/super-tomato/datasets_green/test/label', 1) + + + +def predict_and_display(model_path, img_paths): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + # 加载模型 + model = SimpleCNN() + model.load_state_dict(torch.load(model_path)) + model.to(device) + model.eval() + + dataset = ImageDataset(img_paths, [None]*len(img_paths)) # 我们不需要掩码，所以传入一个空列表 + dataloader = DataLoader(dataset, batch_size=1) + + for i, img in enumerate(dataloader): + img = img.float().to(device) + with torch.no_grad(): + outputs = model(img) + pred = outputs.detach().cpu().numpy() > 0.5 + + # 显示预测结果 + plt.imshow(pred[0, 0, :, :], cmap='gray') + plt.title(f'Predicted Mask for {img_paths[i]}') + plt.show() + +# 调用函数示例 +predict_and_display('best_model.pth', ['/Users/xs/PycharmProjects/super-tomato/datasets_green/test/img/5.bmp']) \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/ML_green.py b/20240410RGBtest1/super-tomato/ML_green.py new file mode 100644 index 0000000..5fd9eeb --- /dev/null +++ b/20240410RGBtest1/super-tomato/ML_green.py @@ -0,0 +1,85 @@ +import numpy as np +from PIL import Image +from sklearn.ensemble import RandomForestClassifier +import os +import time + + +def load_image_data(img_path, label_path): + """加载图像和标签，并将其转换为模型输入格式.""" + image = Image.open(img_path) + label = Image.open(label_path).convert('L') + + image_np = np.array(image) + label_np = np.array(label) + + # 转换图像数据格式 + n_samples = image_np.shape[0] * image_np.shape[1] + n_features = image_np.shape[2] # RGB通道 + image_np = image_np.reshape((n_samples, n_features)) + label_np = label_np.reshape((n_samples,)) + + # 二值化标签 + label_np = (label_np > 128).astype(int) + + return image_np, label_np + + +def train_model(X, y, n_estimators=100): + """训练模型.""" + model = RandomForestClassifier(n_estimators=n_estimators) + model.fit(X, y) + return model + + +def predict_and_save(model, image_path, output_path): + """预测并保存结果图像.""" + image = Image.open(image_path) + image_np = np.array(image) + n_samples = image_np.shape[0] * image_np.shape[1] + image_np = image_np.reshape((n_samples, -1)) + + # 预测 + predicted_labels = model.predict(image_np) + predicted_labels = predicted_labels.reshape((image.size[1], image.size[0])) # Use correct dimensions + + # 保存预测结果 + output_image = Image.fromarray((predicted_labels * 255).astype('uint8'), 'L') # 'L' for grayscale + output_image.save(output_path) + + + +def process_folder(train_folder): + X, y = [], [] + img_folder = os.path.join(train_folder, "img") + label_folder = os.path.join(train_folder, "label") + + for filename in os.listdir(img_folder): + img_path = os.path.join(img_folder, filename) + label_path = os.path.join(label_folder, filename.replace('.bmp', '.png')) + + img_data, label_data = load_image_data(img_path, label_path) + X.append(img_data) + y.append(label_data) + + # 将数据列表转换为numpy数组 + X = np.vstack(X) + y = np.concatenate(y) + + # 训练模型 + return train_model(X, y) + + +# 示例用法 +train_folder = '/Users/xs/PycharmProjects/super-tomato/datasets_green/train' +t1 = time.time() +model = process_folder(train_folder) +t2 = time.time() +print(f'训练模型所需时间: {t2 - t1:.2f}秒') + +# 测试图像处理和保存预测结果 +test_folder = '/Users/xs/PycharmProjects/super-tomato/tomato_img_25' +for test_filename in os.listdir(test_folder): + test_image_path = os.path.join(test_folder, test_filename) + output_path = os.path.join(test_folder, "predicted_" + test_filename) + predict_and_save(model, test_image_path, output_path) diff --git a/20240410RGBtest1/super-tomato/U_net.py b/20240410RGBtest1/super-tomato/U_net.py new file mode 100644 index 0000000..7c35f69 --- /dev/null +++ b/20240410RGBtest1/super-tomato/U_net.py @@ -0,0 +1,169 @@ + +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import Dataset, DataLoader +from torchvision import transforms +from PIL import Image +import os +import numpy as np + +# 定义 U-Net 的下采样（编码器）部分 +class UNetDown(nn.Module): + def __init__(self, in_size, out_size): + super(UNetDown, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d(in_size, out_size, 3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(out_size, out_size, 3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2) + ) + + def forward(self, x): + return self.conv(x) + +# 定义 U-Net 的上采样（解码器）部分 +class UNetUp(nn.Module): + def __init__(self, in_size, out_size): + super(UNetUp, self).__init__() + self.up = nn.ConvTranspose2d(in_size, out_size, 2, stride=2) + self.conv = nn.Sequential( + nn.Conv2d(in_size, out_size, 3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(out_size, out_size, 3, padding=1), + nn.ReLU(inplace=True) + ) + + def forward(self, x1, x2): + x1 = self.up(x1) + # 计算 padding + diffY = x2.size()[2] - x1.size()[2] + diffX = x2.size()[3] - x1.size()[3] + x1 = nn.functional.pad(x1, [ + diffX // 2, diffX - diffX // 2, + diffY // 2, diffY - diffY // 2 + ]) + + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + + +# 组装完整的 U-Net 模型 +class UNet(nn.Module): + def __init__(self): + super(UNet, self).__init__() + self.down1 = UNetDown(3, 64) + self.down2 = UNetDown(64, 128) + self.down3 = UNetDown(128, 256) + self.down4 = UNetDown(256, 512) + self.middle_conv = nn.Sequential( + nn.Conv2d(512, 1024, 3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(1024, 1024, 3, padding=1), + nn.ReLU(inplace=True) + ) + self.up1 = UNetUp(1024, 512) + self.up2 = UNetUp(512, 256) + self.up3 = UNetUp(256, 128) + self.up4 = UNetUp(128, 64) + self.final_conv = nn.Conv2d(64, 1, 1) + + def forward(self, x): + x1 = self.down1(x) + x2 = self.down2(x1) + x3 = self.down3(x2) + x4 = self.down4(x3) + x_middle = self.middle_conv(x4) + x = self.up1(x_middle, x4) + x = self.up2(x, x3) + x = self.up3(x, x2) + x = self.up4(x, x1) + x = self.final_conv(x) + return torch.sigmoid(x) + +# 创建模型 +model = UNet() +print(model) + + +class ImageDataset(Dataset): + def __init__(self, image_dir, mask_dir, transform=None, size=(320, 458)): + self.image_dir = image_dir + self.mask_dir = mask_dir + self.transform = transform + self.size = size + self.images = os.listdir(image_dir) + + def __len__(self): + return len(self.images) + + def __getitem__(self, idx): + img_name = self.images[idx] + img_path = os.path.join(self.image_dir, img_name) + mask_path = os.path.join(self.mask_dir, img_name.replace('.bmp', '.png')) + image = Image.open(img_path).convert("RGB") + mask = Image.open(mask_path).convert("L") # 确保是单通道的灰度图 + + # Resize image and mask + resize = transforms.Resize(self.size) + image = resize(image) + mask = resize(mask) + + if self.transform: + image = self.transform(image) + mask = self.transform(mask) + + mask = (mask > 128).type(torch.FloatTensor) # 二值化标签 + return image, mask + +transform = transforms.Compose([ + transforms.ToTensor(), +]) + + +def test_and_save(model, test_dir, save_dir): + model.eval() + transform = transforms.Compose([transforms.ToTensor()]) + test_images = os.listdir(test_dir) + for img_name in test_images: + img_path = os.path.join(test_dir, img_name) + image = Image.open(img_path).convert("RGB") + image = transform(image).unsqueeze(0) # 添加 batch dimension + + with torch.no_grad(): + prediction = model(image) + + prediction = prediction.squeeze(0).squeeze(0) # 去掉 batch 和 channel dimension + prediction = (prediction > 0.5).type(torch.uint8) # 二值化 + save_image = Image.fromarray(prediction.numpy() * 255, 'L') + save_image.save(os.path.join(save_dir, img_name)) + + +def train(model, loader, optimizer, criterion, epochs): + model.train() + for epoch in range(epochs): + for images, masks in loader: + optimizer.zero_grad() + outputs = model(images) + loss = criterion(outputs, masks) + loss.backward() + optimizer.step() + print(f"Epoch {epoch+1}, Loss: {loss.item()}") + +train_dataset = ImageDataset('/Users/xs/PycharmProjects/super-tomato/datasets_green/train/img', '/Users/xs/PycharmProjects/super-tomato/datasets_green/train/label', transform=transform) +train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True) + +# 上面定义的 UNet 类的代码 +model = UNet() +criterion = nn.BCELoss() +optimizer = optim.Adam(model.parameters(), lr=0.001) + +# 训练模型 + + +train(model, train_loader, optimizer, criterion, epochs=50) + + + +test_and_save(model, '/Users/xs/PycharmProjects/super-tomato/tomato_img_25', '/Users/xs/PycharmProjects/super-tomato/tomato_img_25/pre') diff --git a/20240410RGBtest1/super-tomato/best_model.pth b/20240410RGBtest1/super-tomato/best_model.pth new file mode 100644 index 0000000..648274e Binary files /dev/null and b/20240410RGBtest1/super-tomato/best_model.pth differ diff --git a/20240410RGBtest1/super-tomato/defect.py b/20240410RGBtest1/super-tomato/defect.py new file mode 100644 index 0000000..9b11592 --- /dev/null +++ b/20240410RGBtest1/super-tomato/defect.py @@ -0,0 +1,57 @@ +import cv2 +import numpy as np + + +def segment_image_by_variance(image_path, m, n, variance_threshold): + # 读取图像 + image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + if image is None: + print("Error loading image") + return None + + # 图像的高度和宽度 + h, w = image.shape + + # 计算每个块的尺寸 + block_h, block_w = h // m, w // n + + # 创建空白图像 + segmented_image = np.zeros_like(image) + + # 遍历每个块 + for row in range(m): + for col in range(n): + # 计算块的位置 + y1, x1 = row * block_h, col * block_w + y2, x2 = y1 + block_h, x1 + block_w + + # 提取块 + block = image[y1:y2, x1:x2] + + # 计算方差 + variance = np.var(block) + + # 根据方差设置新图像的对应区块 + if variance > variance_threshold: + segmented_image[y1:y2, x1:x2] = 1 + else: + segmented_image[y1:y2, x1:x2] = 0 + + # 将新图像的值扩展到0-255范围，以便可视化 + segmented_image *= 255 + + return segmented_image + + +# 示例用法 +image_path = '/Users/xs/PycharmProjects/super-tomato/tomato_img_25/60.bmp' # 替换为你的番茄图像路径 +m, n = 300, 300 # 划分的区块数 +variance_threshold = 80 # 方差的阈值 + +segmented_image = segment_image_by_variance(image_path, m, n, variance_threshold) + +if segmented_image is not None: + cv2.imshow("Segmented Image", segmented_image) + cv2.imshow("Original Image", cv2.imread(image_path)) + cv2.waitKey(0) + cv2.destroyAllWindows() diff --git a/20240410RGBtest1/super-tomato/defect_big.bmp b/20240410RGBtest1/super-tomato/defect_big.bmp new file mode 100644 index 0000000..bd07045 Binary files /dev/null and b/20240410RGBtest1/super-tomato/defect_big.bmp differ diff --git a/20240410RGBtest1/super-tomato/image_preprocessing.py b/20240410RGBtest1/super-tomato/image_preprocessing.py new file mode 100644 index 0000000..11cd38d --- /dev/null +++ b/20240410RGBtest1/super-tomato/image_preprocessing.py @@ -0,0 +1,295 @@ +import cv2 +import numpy as np +import os +import argparse +# from svm import predict_image_array, load_model + + + +#提取西红柿，使用S+L的图像 +def extract_s_l(image_path): + image = cv2.imread(image_path) + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab) + s_channel = hsv[:,:,1] + l_channel = lab[:,:,0] + result = cv2.add(s_channel, l_channel) + return result + +def find_reflection(image_path, threshold=190): + # 读取图像 + image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + + # 应用阈值分割 + _, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) + + return reflection + +def otsu_threshold(image): + + # 将图像转换为灰度图像 + # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # 使用Otsu阈值分割 + _, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + + return binary + +# 提取花萼，使用G-R的图像 +def extract_g_r(image): + # image = cv2.imread(image_path) + g_channel = image[:,:,1] + r_channel = image[:,:,2] + result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel) + return result + + +#提取西红柿，使用R-B的图像 +def extract_r_b(image_path): + image = cv2.imread(image_path) + r_channel = image[:,:,2] + b_channel = image[:,:,0] + result = cv2.subtract(r_channel, b_channel) + return result + +def extract_r_g(image_path): + image = cv2.imread(image_path) + r_channel = image[:,:,2] + g_channel = image[:,:,1] + result = cv2.subtract(r_channel, g_channel) + return result + +def threshold_segmentation(image, threshold, color=255): + _, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY) + return result + +def bitwise_operation(image1, image2, operation='and'): + if operation == 'and': + result = cv2.bitwise_and(image1, image2) + elif operation == 'or': + result = cv2.bitwise_or(image1, image2) + else: + raise ValueError("operation must be 'and' or 'or'") + return result + +def largest_connected_component(bin_img): + # 使用connectedComponentsWithStats函数找到连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像，只显示最大的连通区域 + new_bin_img = np.zeros_like(bin_img) + new_bin_img[labels == largest_label] = 255 + + return new_bin_img + +def close_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel) + return closed_img + +def open_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + opened_img = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel) + return opened_img + + +def draw_tomato_edge(original_img, bin_img): + bin_img_processed = close_operation(bin_img, kernel_size=(15, 15)) + # cv2.imshow('Close Operation', bin_img_processed) + # bin_img_processed = open_operation(bin_img_processed, kernel_size=(19, 19)) + # cv2.imshow('Open Operation', bin_img_processed) + # 现在使用处理后的bin_img_processed查找轮廓 + contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 如果没有找到轮廓，直接返回原图 + if not contours: + return original_img + # 找到最大轮廓 + max_contour = max(contours, key=cv2.contourArea) + # 多边形近似的精度调整 + epsilon = 0.0006 * cv2.arcLength(max_contour, True) # 可以调整这个值 + approx = cv2.approxPolyDP(max_contour, epsilon, True) + # 绘制轮廓 + cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3) + mask = np.zeros_like(bin_img) + + # 使用白色填充最大轮廓 + cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED) + + return original_img, mask + +def draw_tomato_edge_convex_hull(original_img, bin_img): + bin_img_blurred = cv2.GaussianBlur(bin_img, (5, 5), 0) + contours, _ = cv2.findContours(bin_img_blurred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return original_img + max_contour = max(contours, key=cv2.contourArea) + hull = cv2.convexHull(max_contour) + cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3) + return original_img + +# 得到完整的西红柿二值图像，除了绿色花萼 +def fill_holes(bin_img): + # 复制 bin_img 到 img_filled + img_filled = bin_img.copy() + + # 获取图像的高度和宽度 + height, width = bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_d = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(bin_img, img_filled) + # 裁剪 img_filled 和 img_filled_d 到与 bin_img 相同的大小 + # img_filled = img_filled[:height, :width] + img_filled_d = img_filled_d[:height, :width] + + return img_filled, img_filled_d + +def bitwise_and_rgb_with_binary(rgb_img, bin_img): + # 将二值图像转换为三通道图像 + bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR) + + # 使用 bitwise_and 操作合并 RGB 图像和二值图像 + result = cv2.bitwise_and(rgb_img, bin_img_3channel) + + return result + + +def extract_max_connected_area(image_path, lower_hsv, upper_hsv): + # 读取图像 + image = cv2.imread(image_path) + + # 将图像从BGR转换到HSV + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # 使用阈值获取指定区域的二值图像 + mask = cv2.inRange(hsv, lower_hsv, upper_hsv) + + # 找到二值图像的连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像，只显示最大的连通区域 + new_bin_img = np.zeros_like(mask) + new_bin_img[labels == largest_label] = 255 + + # 复制 new_bin_img 到 img_filled + img_filled = new_bin_img.copy() + + # 获取图像的高度和宽度 + height, width = new_bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_inv = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv) + + return img_filled + + + + +def main(): + parser = argparse.ArgumentParser(description='Process some integers.') + parser.add_argument('--dir_path', type=str, default=r'D:\project\Tomato\20240410RGBtest2\data', + help='the directory path of images') + parser.add_argument('--threshold_s_l', type=int, default=180, + help='the threshold for s_l') + parser.add_argument('--threshold_r_b', type=int, default=15, + help='the threshold for r_b') + + args = parser.parse_args() + + for img_file in os.listdir(args.dir_path): + if img_file.endswith('.bmp'): + img_path = os.path.join(args.dir_path, img_file) + s_l = extract_s_l(img_path) + otsu_thresholded = otsu_threshold(s_l) + img_fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), otsu_thresholded) + img_fore_defect = extract_g_r(img_fore) + img_fore_defect = threshold_segmentation(img_fore_defect, args.threshold_r_b) + # cv2.imshow('img_fore_defect', img_fore_defect) + thresholded_s_l = threshold_segmentation(s_l, args.threshold_s_l) + new_bin_img = largest_connected_component(thresholded_s_l) + zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE)) + cv2.imshow('zhongggggg', zhongggggg) + new_otsu_bin_img = largest_connected_component(otsu_thresholded) + filled_img, defect = fill_holes(new_bin_img) + defect = bitwise_and_rgb_with_binary(cv2.imread(img_path), defect) + cv2.imshow('defect', defect) + edge, mask = draw_tomato_edge(cv2.imread(img_path), new_bin_img) + org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img) + fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), mask) + fore_g_r_t = threshold_segmentation(extract_g_r(fore), 20) + fore_g_r_t_ture = bitwise_and_rgb_with_binary(cv2.imread(img_path), fore_g_r_t) + cv2.imwrite('defect_big.bmp', fore_g_r_t_ture) + res = cv2.bitwise_or(new_bin_img, fore_g_r_t) + white = find_reflection(img_path) + + # SVM预测 + # 加载模型 + # model, scaler = load_model('/Users/xs/PycharmProjects/super-tomato/svm_green.joblib') + + # 对图像进行预测 + # predicted_mask = predict_image_array(image, model, scaler) + + + + cv2.imshow('white', white) + + cv2.imshow('fore', fore) + cv2.imshow('fore_g_r_t', fore_g_r_t) + cv2.imshow('mask', mask) + print('mask', mask.shape) + print('filled', filled_img.shape) + print('largest', new_bin_img.shape) + print('rp', org_defect.shape) + cv2.imshow('res', res) + + # lower_hsv = np.array([19, 108, 15]) + # upper_hsv = np.array([118, 198, 134]) + # max_connected_area = extract_max_connected_area(img_path, lower_hsv, upper_hsv) + # cv2.imshow('Max Connected Area', max_connected_area) + + # 显示原始图像 + original_img = cv2.imread(img_path) + cv2.imshow('Original', original_img) + cv2.imshow('thresholded_s_l', thresholded_s_l) + cv2.imshow('Largest Connected Component', new_bin_img) + cv2.imshow('Filled', filled_img) + cv2.imshow('Defect', defect) + cv2.imshow('Org_defect', org_defect) + cv2.imshow('otsu_thresholded', new_otsu_bin_img) + + + #显示轮廓 + cv2.imshow('Edge', edge) + + # 等待用户按下任意键 + cv2.waitKey(0) + + # 关闭所有窗口 + cv2.destroyAllWindows() + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/20240410RGBtest1/super-tomato/svm.py b/20240410RGBtest1/super-tomato/svm.py new file mode 100644 index 0000000..4939316 --- /dev/null +++ b/20240410RGBtest1/super-tomato/svm.py @@ -0,0 +1,105 @@ +import cv2 +import numpy as np +from sklearn import svm +from sklearn.preprocessing import StandardScaler +import time +import os +import joblib + + +def load_model(model_path): + # 加载模型和标准化器 + model, scaler = joblib.load(model_path) + return model, scaler + +def predict_image_array(image_array, model_path): + # 加载模型和标准化器 + model, scaler = load_model(model_path) + + # 将图像转换为像素数组 + test_pixels = image_array.reshape(-1, 3) + + # 标准化 + test_pixels_scaled = scaler.transform(test_pixels) + + # 预测 + predictions = model.predict(test_pixels_scaled) + + # 转换预测结果为图像 + mask_predicted = predictions.reshape(image_array.shape[0], image_array.shape[1]) + + return mask_predicted +def prepare_data(image_dir, mask_dir): + # 初始化像素和标签列表 + all_pixels = [] + all_labels = [] + + # 获取图像和掩码文件名列表 + image_files = sorted(os.listdir(image_dir)) + mask_files = sorted(os.listdir(mask_dir)) + + # 遍历所有图像和掩码文件 + for image_file, mask_file in zip(image_files, mask_files): + # 读取原始图像和掩码图像 + image = cv2.imread(os.path.join(image_dir, image_file)) + mask = cv2.imread(os.path.join(mask_dir, mask_file), cv2.IMREAD_GRAYSCALE) + + # 提取像素 + pixels = image.reshape(-1, 3) # 将图像转换为(n_pixels, 3) + labels = (mask.reshape(-1) > 128).astype(int) # 标记为0或1 + + # 添加到列表 + all_pixels.append(pixels) + all_labels.append(labels) + + # 将列表转换为NumPy数组 + all_pixels = np.concatenate(all_pixels, axis=0) + all_labels = np.concatenate(all_labels, axis=0) + + return all_pixels, all_labels + +# 加载数据 +train_pixels, train_labels = prepare_data('/Users/xs/PycharmProjects/super-tomato/datasets_green/train-2/img', + '/Users/xs/PycharmProjects/super-tomato/datasets_green/train-2/label') + +# 数据标准化 +scaler = StandardScaler() +train_pixels_scaled = scaler.fit_transform(train_pixels) + +# 创建SVM模型 +# model = svm.SVC(kernel='linear', C=1.0) +# model.fit(train_pixels_scaled, train_labels) +# # 在训练模型后保存模型 +# joblib.dump((model, scaler), '/Users/xs/PycharmProjects/super-tomato/svm_green.joblib') # 替换为你的模型文件路径 + +print('模型训练完成！') + +def predict_image(image_path, model, scaler): + # 读取图像 + image = cv2.imread(image_path) + test_pixels = image.reshape(-1, 3) + + # 标准化 + test_pixels_scaled = scaler.transform(test_pixels) + + # 预测 + predictions = model.predict(test_pixels_scaled) + + # 转换预测结果为图像 + mask_predicted = predictions.reshape(image.shape[0], image.shape[1]) + + return mask_predicted + + +# 对一个新的图像进行预测 +time1 = time.time() +model, scaler = load_model('/Users/xs/PycharmProjects/super-tomato/svm_green.joblib') + +predicted_mask = predict_image('/Users/xs/PycharmProjects/super-tomato/defect_big.bmp', model, scaler) +cv2.imwrite('/Users/xs/PycharmProjects/super-tomato/defect_mask.bmp', (predicted_mask * 255).astype('uint8')) +cv2.imshow('Predicted Mask', (predicted_mask * 255).astype('uint8')) +cv2.waitKey(0) +cv2.destroyAllWindows() + +time2 = time.time() +print(f'预测时间: {time2 - time1:.2f}秒') diff --git a/20240410RGBtest1/super-tomato/svm_green.joblib b/20240410RGBtest1/super-tomato/svm_green.joblib new file mode 100644 index 0000000..e31bfc2 Binary files /dev/null and b/20240410RGBtest1/super-tomato/svm_green.joblib differ diff --git a/20240410RGBtest1/super-tomato/test.py b/20240410RGBtest1/super-tomato/test.py new file mode 100644 index 0000000..3eaca45 --- /dev/null +++ b/20240410RGBtest1/super-tomato/test.py @@ -0,0 +1,56 @@ +import cv2 +import numpy as np + + +def find_reflection(image_path, threshold=190): + # 读取图像 + image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + + # 应用阈值分割 + _, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) + + return reflection + +def repair_reflection_telea(image_path, reflection, inpaint_radius=20): + # 读取图像 + image = cv2.imread(image_path) + + # 将高亮区域转换为二值图像 + _, reflection_binary = cv2.threshold(reflection, 1, 255, cv2.THRESH_BINARY) + + # 使用inpaint函数修复高亮区域 + repaired_image = cv2.inpaint(image, reflection_binary, inpaint_radius, cv2.INPAINT_TELEA) + + return repaired_image + +# 读取图像 +image_path = '/Users/xs/PycharmProjects/super-tomato/tomato_img_25/60.bmp' # 替换为你的图像路径 +image = find_reflection(image_path) +cv2.imshow('image', image) +cv2.waitKey(0) +cv2.destroyAllWindows() +# 修复反光 +image = repair_reflection_telea(image_path, image) +cv2.imshow('ima11ge', cv2.imread(image_path)) +# 创建窗口 +cv2.namedWindow('image') + +# 创建滑动条 +cv2.createTrackbar('Threshold', 'image', 0, 255, lambda x: None) + +while True: + # 获取滑动条的值 + threshold = cv2.getTrackbarPos('Threshold', 'image') + + # 使用阈值进行分割 + _, thresholded_image = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) + + # 显示二值图像 + cv2.imshow('image', thresholded_image) + + # 按下'q'键退出循环 + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +# 销毁所有窗口 +cv2.destroyAllWindows() \ No newline at end of file diff --git a/20240410RGBtest1/utils.py b/20240410RGBtest1/utils.py new file mode 100644 index 0000000..e0241d0 --- /dev/null +++ b/20240410RGBtest1/utils.py @@ -0,0 +1,660 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/4/12 14:53 +# @Author : TG +# @File : utils.py +# @Software: PyCharm + +import os +import socket +import time +import logging +import numpy as np +import shutil +import cv2 +import matplotlib.pyplot as plt +from typing import Tuple +import pickle +import sys +import os +from scipy.ndimage.measurements import label, find_objects + + +class PreSocket(socket.socket): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.pre_pack = b'' + self.settimeout(5) + + def receive(self, *args, **kwargs): + if self.pre_pack == b'': + return self.recv(*args, **kwargs) + else: + data_len = args[0] + required, left = self.pre_pack[:data_len], self.pre_pack[data_len:] + self.pre_pack = left + return required + + def set_prepack(self, pre_pack: bytes): + temp = self.pre_pack + self.pre_pack = temp + pre_pack + + +class DualSock(PreSocket): + def __init__(self, connect_ip='127.0.0.1', recv_port: int = 21122, send_port: int = 21123): + super().__init__() + received_status, self.received_sock = try_connect(connect_ip=connect_ip, port_number=recv_port) + send_status, self.send_sock = try_connect(connect_ip=connect_ip, port_number=send_port) + self.status = received_status and send_status + + def send(self, *args, **kwargs) -> int: + return self.send_sock.send(*args, **kwargs) + + def receive(self, *args, **kwargs) -> bytes: + return self.received_sock.receive(*args, **kwargs) + + def set_prepack(self, pre_pack: bytes): + self.received_sock.set_prepack(pre_pack) + + def reconnect(self, connect_ip='127.0.0.1', recv_port:int = 21122, send_port: int = 21123): + received_status, self.received_sock = try_connect(connect_ip=connect_ip, port_number=recv_port) + send_status, self.send_sock = try_connect(connect_ip=connect_ip, port_number=send_port) + return received_status and send_status + + +def receive_sock(recv_sock: PreSocket, pre_pack: bytes = b'', time_out: float = -1.0, time_out_single=5e20) -> ( +bytes, bytes): + """ + 从指定的socket中读取数据.自动阻塞，如果返回的数据为空则说明连接出现问题，需要重新连接。 + + :param recv_sock: 指定sock + :param pre_pack: 上一包的粘包内容 + :param time_out: 每隔time_out至少要发来一次指令,否则认为出现问题进行重连，小于0则为一直等 + :param time_out_single: 单次指令超时时间，单位是秒 + :return: data, next_pack + """ + recv_sock.set_prepack(pre_pack) + # 开头校验 + time_start_recv = time.time() + while True: + if time_out > 0: + if (time.time() - time_start_recv) > time_out: + logging.error(f'指令接收超时') + return b'', b'' + try: + temp = recv_sock.receive(1) + except ConnectionError as e: + logging.error(f'连接出错, 错误代码:\n{e}') + return b'', b'' + except TimeoutError as e: + # logging.error(f'超时了，错误代码: \n{e}') + logging.info('运行中,等待指令..') + continue + except socket.timeout as e: + logging.info('运行中,等待指令..') + continue + except Exception as e: + logging.error(f'遇见未知错误，错误代码: \n{e}') + return b'', b'' + if temp == b'\xaa': + break + + # 接收开头后，开始进行时间记录 + time_start_recv = time.time() + + # 获取报文长度 + temp = b'' + while len(temp) < 4: + if (time.time() - time_start_recv) > time_out_single: + logging.error(f'单次指令接收超时') + return b'', b'' + try: + temp += recv_sock.receive(1) + except Exception as e: + logging.error(f'接收报文的长度不正确, 错误代码: \n{e}') + return b'', b'' + try: + data_len = int.from_bytes(temp, byteorder='big') + except Exception as e: + logging.error(f'转换失败,错误代码 \n{e}') + return b'', b'' + + # 读取报文内容 + temp = b'' + while len(temp) < data_len: + if (time.time() - time_start_recv) > time_out_single: + logging.error(f'单次指令接收超时') + return b'', b'' + try: + temp += recv_sock.receive(data_len) + except Exception as e: + logging.error(f'接收报文内容失败, 错误代码: \n{e}') + return b'', b'' + data, next_pack = temp[:data_len], temp[data_len:] + recv_sock.set_prepack(next_pack) + next_pack = b'' + + # 进行数据校验 + temp = b'' + while len(temp) < 3: + if (time.time() - time_start_recv) > time_out_single: + logging.error(f'单次指令接收超时') + return b'', b'' + try: + temp += recv_sock.receive(1) + except Exception as e: + logging.error(f'接收报文校验失败, 错误代码: \n{e}') + return b'', b'' + if temp == b'\xff\xff\xbb': + return data, next_pack + else: + logging.error(f"接收了一个完美的只错了校验位的报文") + return b'', b'' + + +def parse_protocol(data: bytes) -> (str, any): + ''' + 指令转换 + :param data: 接收到的报文 + :return: 指令类型，指令内容 + ''' + try: + assert len(data) > 4 + except AssertionError: + logging.error('指令转换失败，长度不足5') + return '', None + cmd, data = data[:4], data[4:] + cmd = cmd.decode('ascii').strip().upper() + if cmd == 'IM': + n_rows, n_cols, img = data[:2], data[2:4], data[4:] + try: + n_rows, n_cols = [int.from_bytes(x, byteorder='big') for x in [n_rows, n_cols]] + except Exception as e: + logging.error(f'长宽转换失败, 错误代码{e}, 报文大小: n_rows:{n_rows}, n_cols: {n_cols}') + return '', None + try: + assert n_rows * n_cols * 3 == len(img) + except AssertionError: + logging.error('图像指令IM转换失败，数据长度错误') + return '', None + img = np.frombuffer(img, dtype=np.uint8).reshape((n_rows, n_cols, -1)) + return cmd, img + + +def ack_sock(send_sock: socket.socket, cmd_type: str) -> bool: + ''' + 发送应答 + :param cmd_type:指令类型 + :param send_sock:指定sock + :return:是否发送成功 + ''' + msg = b'\xaa\x00\x00\x00\x05' + (' A' + cmd_type).upper().encode('ascii') + b'\xff\xff\xff\xbb' + try: + send_sock.send(msg) + except Exception as e: + logging.error(f'发送应答失败，错误类型：{e}') + return False + return True + + +def done_sock(send_sock: socket.socket, cmd_type: str, result = '') -> bool: + ''' + 发送任务完成指令 + :param send_sock: 指定sock + :param cmd_type: 指令类型 + :param result: 数据 + :return: 是否发送成功 + ''' + cmd = cmd_type.strip().upper() + if cmd_type == 'IM': + result = result.encode() + # 指令4位 + length = len(result) + 4 + length = length.to_bytes(4, byteorder='big') + msg = b'\xaa' + length + (' D' + cmd).upper().encode('ascii') + result + b'\xff\xff\xbb' + try: + send_sock.send(msg) + except Exception as e: + logging.error(f'发送完成指令失败，错误类型：{e}') + return False + return True + +def simple_sock(send_sock: socket.socket, cmd_type: str, result) -> bool: + ''' + 发送任务完成指令 + :param cmd_type:指令类型 + :param send_sock:指定sock + :param result:数据 + :return:是否发送成功 + ''' + cmd_type = cmd_type.strip().upper() + if cmd_type == 'IM': + if result == 0: + msg = b'S' + elif result == 1: + msg = b'Z' + elif result == 2: + msg = b'Q' + elif cmd_type == 'TR': + msg = b'A' + elif cmd_type == 'MD': + msg = b'D' + elif cmd_type == 'KM': + msg = b'K' + result = result.encode('ascii') + result = b',' + result + length = len(result) + msg = msg + length.to_bytes(4, 'big') + result + try: + send_sock.send(msg) + except Exception as e: + logging.error(f'发送完成指令失败，错误类型：{e}') + return False + return True + +def test_sock(send_sock: socket.socket, cmd_type: str, long_axis, short_axis, defect_num, total_defect_area, rp) -> bool: + start_time = time.time() + + cmd_type = cmd_type.strip().upper() + if cmd_type == 'IM': + # image_dir = r'D:\project\Tomato\20240410tomatoRGBtest2\huifu' + # image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(".jpg")] + # for image_path in image_paths: + # img = cv2.imread(r'D:\project\Tomato\20240410tomatoRGBtest2\huifu\thresholded_6.jpg') + # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + # img = np.asarray(img, dtype=np.uint8) + width = rp.shape[0] + height = rp.shape[1] + # print(width, height) + img_bytes = rp.tobytes() + length = len(img_bytes) + 18 + # print(length) + length = length.to_bytes(4, byteorder='big') + width = width.to_bytes(2, byteorder='big') + height = height.to_bytes(2, byteorder='big') + long_axis = long_axis.to_bytes(2, byteorder='big') + short_axis = short_axis.to_bytes(2, byteorder='big') + defect_num = defect_num.to_bytes(2, byteorder='big') + total_defect_area = int(total_defect_area).to_bytes(4, byteorder='big') + cmd_type = 'RIM' + # result = result.encode('ascii') + send_message = b'\xaa' + length + (' ' + cmd_type).upper().encode('ascii') + long_axis + short_axis + defect_num + total_defect_area + width + height + img_bytes + b'\xff\xff\xbb' + # print(long_axis) + # print(short_axis) + # print(defect_num) + # print(total_defect_area) + # print(width) + # print(height) + + try: + send_sock.send(send_message) + print('发送成功') + # print(send_message) + except Exception as e: + logging.error(f'发送完成指令失败，错误类型：{e}') + return False + + end_time = time.time() + print(f'发送时间：{end_time - start_time}秒') + + return True + + + + + +def try_connect(connect_ip: str, port_number: int, is_repeat: bool = False, max_reconnect_times: int = 50) -> ( + bool, socket.socket): + """ + 尝试连接. + + :param is_repeat: 是否是重新连接 + :param max_reconnect_times:最大重连次数 + :return: (连接状态True为成功, Socket / None) + """ + reconnect_time = 0 + while reconnect_time < max_reconnect_times: + logging.warning(f'尝试{"重新" if is_repeat else ""}发起第{reconnect_time + 1}次连接...') + try: + connected_sock = PreSocket(socket.AF_INET, socket.SOCK_STREAM) + connected_sock.connect((connect_ip, port_number)) + except Exception as e: + reconnect_time += 1 + logging.error(f'第{reconnect_time}次连接失败... 5秒后重新连接...\n {e}') + time.sleep(5) + continue + logging.warning(f'{"重新" if is_repeat else ""}连接成功') + return True, connected_sock + return False, None + + +def mkdir_if_not_exist(dir_name, is_delete=False): + """ + 创建文件夹 + :param dir_name: 文件夹 + :param is_delete: 是否删除 + :return: 是否成功 + """ + try: + if is_delete: + if os.path.exists(dir_name): + shutil.rmtree(dir_name) + print('[Info] 文件夹 "%s" 存在, 删除文件夹.' % dir_name) + + if not os.path.exists(dir_name): + os.makedirs(dir_name) + print('[Info] 文件夹 "%s" 不存在, 创建文件夹.' % dir_name) + return True + except Exception as e: + print('[Exception] %s' % e) + return False + +def create_file(file_name): + """ + 创建文件 + :param file_name: 文件名 + :return: None + """ + if os.path.exists(file_name): + print("文件存在：%s" % file_name) + return False + # os.remove(file_name) # 删除已有文件 + if not os.path.exists(file_name): + print("文件不存在，创建文件：%s" % file_name) + open(file_name, 'a').close() + return True + + +class Logger(object): + def __init__(self, is_to_file=False, path=None): + self.is_to_file = is_to_file + if path is None: + path = "tomato.log" + self.path = path + create_file(path) + + def log(self, content): + if self.is_to_file: + with open(self.path, "a") as f: + print(time.strftime("[%Y-%m-%d_%H-%M-%S]:"), file=f) + print(content, file=f) + else: + print(content) + + + +#提取西红柿，使用S+L的图像 +def extract_s_l(image): + # image = cv2.imread(image_path) + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab) + s_channel = hsv[:,:,1] + l_channel = lab[:,:,0] + result = cv2.add(s_channel, l_channel) + return result + +def find_reflection(image, threshold=190): + # 读取图像 + # image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + + # 应用阈值分割 + _, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) + + return reflection + +def otsu_threshold(image): + + # 将图像转换为灰度图像 + # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # 使用Otsu阈值分割 + _, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + + return binary + +# 提取花萼，使用G-R的图像 +def extract_g_r(image): + # image = cv2.imread(image_path) + g_channel = image[:,:,1] + r_channel = image[:,:,2] + result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel) + return result + + +#提取西红柿，使用R-B的图像 +def extract_r_b(image): + # image = cv2.imread(image_path) + r_channel = image[:,:,2] + b_channel = image[:,:,0] + result = cv2.subtract(r_channel, b_channel) + return result + +def extract_r_g(image): + # image = cv2.imread(image_path) + r_channel = image[:,:,2] + g_channel = image[:,:,1] + result = cv2.subtract(r_channel, g_channel) + return result + +def threshold_segmentation(image, threshold, color=255): + _, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY) + return result + +def bitwise_operation(image1, image2, operation='and'): + if operation == 'and': + result = cv2.bitwise_and(image1, image2) + elif operation == 'or': + result = cv2.bitwise_or(image1, image2) + else: + raise ValueError("operation must be 'and' or 'or'") + return result + +def largest_connected_component(bin_img): + # 使用connectedComponentsWithStats函数找到连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像，只显示最大的连通区域 + new_bin_img = np.zeros_like(bin_img) + new_bin_img[labels == largest_label] = 255 + + return new_bin_img + +def close_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel) + return closed_img + +def open_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + opened_img = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel) + return opened_img + + +def draw_tomato_edge(original_img, bin_img): + bin_img_processed = close_operation(bin_img, kernel_size=(15, 15)) + # cv2.imshow('Close Operation', bin_img_processed) + # bin_img_processed = open_operation(bin_img_processed, kernel_size=(19, 19)) + # cv2.imshow('Open Operation', bin_img_processed) + # 现在使用处理后的bin_img_processed查找轮廓 + contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 如果没有找到轮廓，直接返回原图 + if not contours: + return original_img + # 找到最大轮廓 + max_contour = max(contours, key=cv2.contourArea) + # 多边形近似的精度调整 + epsilon = 0.0006 * cv2.arcLength(max_contour, True) # 可以调整这个值 + approx = cv2.approxPolyDP(max_contour, epsilon, True) + # 绘制轮廓 + cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3) + mask = np.zeros_like(bin_img) + + # 使用白色填充最大轮廓 + cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED) + + return original_img, mask + +def draw_tomato_edge_convex_hull(original_img, bin_img): + bin_img_blurred = cv2.GaussianBlur(bin_img, (5, 5), 0) + contours, _ = cv2.findContours(bin_img_blurred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return original_img + max_contour = max(contours, key=cv2.contourArea) + hull = cv2.convexHull(max_contour) + cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3) + return original_img + +# 得到完整的西红柿二值图像，除了绿色花萼 +def fill_holes(bin_img): + # 复制 bin_img 到 img_filled + img_filled = bin_img.copy() + + # 获取图像的高度和宽度 + height, width = bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_d = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(bin_img, img_filled) + # 裁剪 img_filled 和 img_filled_d 到与 bin_img 相同的大小 + # img_filled = img_filled[:height, :width] + img_filled_d = img_filled_d[:height, :width] + + return img_filled, img_filled_d + +def bitwise_and_rgb_with_binary(rgb_img, bin_img): + # 将二值图像转换为三通道图像 + bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR) + + # 使用 bitwise_and 操作合并 RGB 图像和二值图像 + result = cv2.bitwise_and(rgb_img, bin_img_3channel) + + return result + + +def extract_max_connected_area(image, lower_hsv, upper_hsv): + # 读取图像 + # image = cv2.imread(image_path) + + # 将图像从BGR转换到HSV + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # 使用阈值获取指定区域的二值图像 + mask = cv2.inRange(hsv, lower_hsv, upper_hsv) + + # 找到二值图像的连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像，只显示最大的连通区域 + new_bin_img = np.zeros_like(mask) + new_bin_img[labels == largest_label] = 255 + + # 复制 new_bin_img 到 img_filled + img_filled = new_bin_img.copy() + + # 获取图像的高度和宽度 + height, width = new_bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_inv = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv) + + return img_filled +def get_tomato_dimensions(edge_img): + """ + 根据番茄边缘二值化轮廓图,计算番茄的长径、短径和长短径比值。 + 使用最小外接矩形和最小外接圆两种方法。 + + 参数: + edge_img (numpy.ndarray): 番茄边缘二值化轮廓图,背景为黑色,番茄区域为白色。 + + 返回: + tuple: (长径, 短径, 长短径比值) + """ + # 最小外接矩形 + rect = cv2.minAreaRect(cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0]) + major_axis, minor_axis = rect[1] + aspect_ratio = max(major_axis, minor_axis) / min(major_axis, minor_axis) + + # # 最小外接圆 + # (x, y), radius = cv2.minEnclosingCircle( + # cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0]) + # diameter = 2 * radius + # aspect_ratio_circle = 1.0 + + return (max(major_axis, minor_axis), min(major_axis, minor_axis)) + +def get_defect_info(defect_img): + """ + 根据番茄区域缺陷二值化轮廓图,计算缺陷区域的个数和总面积。 + + 参数: + defect_img (numpy.ndarray): 番茄区域缺陷二值化轮廓图,背景为黑色,番茄区域为白色,缺陷区域为黑色连通域。 + + 返回: + tuple: (缺陷区域个数, 缺陷区域像素面积，缺陷像素总面积) + """ + + nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(defect_img, connectivity=4) + max_area = max(stats[i, cv2.CC_STAT_AREA] for i in range(1, nb_components)) + areas = [] + for i in range(1, nb_components): + area = stats[i, cv2.CC_STAT_AREA] + if area != max_area: + areas.append(area) + number_defects = len(areas) + total_pixels = sum(areas) + return number_defects, total_pixels + + + + +def connected_components_analysis(binary_image): + """ + 从二值化图像计算黑色连通域个数和各个黑色连通域像素面积及黑色像素总面积。 + + 参数: + binary_image (numpy.ndarray): 二值化图像, 其中 0 表示白色, 1 表示黑色。 + + 返回: + num_components (int): 黑色连通域的个数。 + component_areas (list): 每个黑色连通域的像素面积。 + total_black_area (int): 黑色像素的总面积。 + """ + # 标记连通域 + labeled_image, num_components = label(binary_image) + + # 获取每个连通域的像素位置 + slices = find_objects(labeled_image) + + # 计算每个连通域的像素面积 + component_areas = [] + for slice_obj in slices: + component_area = np.sum(binary_image[slice_obj]) + component_areas.append(component_area) + + # 计算黑色像素的总面积 + total_black_area = np.sum(binary_image) + + return num_components, component_areas, total_black_area \ No newline at end of file diff --git a/20240410RGBtest1/新建 Markdown.md b/20240410RGBtest1/新建 Markdown.md new file mode 100644 index 0000000..e69de29 diff --git a/20240410RGBtest1/通信协议(旻哥暂时同意版).md b/20240410RGBtest1/通信协议(旻哥暂时同意版).md new file mode 100644 index 0000000..af31774 --- /dev/null +++ b/20240410RGBtest1/通信协议(旻哥暂时同意版).md @@ -0,0 +1,65 @@ +# 百香果&西红柿通信 + +OSI5~7层，基于单播TCP/IP，一包数据由8'haa打头，8'hbb结束，共6个字段： + +| 起始 | 长度1 | 长度2 | 长度3 | 长度4 | 类型1 | 类型2 | 类型3 | 类型4 | 数据1 | 数据2 | ... | 数据i | 校验1 | 校验2 | 结束 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--: | :---: | :---: | :---: | :---: | +| 8'haa | 8'hzz | 8'hzz | 8'hzz | 8'hzz | 8'hzz | 8'hzz | 8'hzz | 8'hzz | 8'hzz | 8'hzz | ... | 8'hzz | 8'hff | 8'hff | 8'hbb | + +### 起始 + +1字节，8'haa + +### 长度 + +一个32位无符号数length，长度 = 数据字节数i + 4 。
`长度1`指length[31:24]，`长度2`指length[23:16]，`长度3`指length[15:8]，`长度4`指length[7:0] + +### 类型&数据 + +ASCII字符，比如`类型1`为' '(空格)，`类型2`为' '(空格)，`类型3`为'I'，`类型4`为'M'，代表RGB图像数据包 + +- **RGB图像数据包' (空格)''(空格) ''I''M'**，`数据1`~`数据i`包含了图像的行数rows(高度)、列数cols(宽度)、以及图像的RGB数据 + + $i-4=rows \times cols \times 3$具体如下： + +| 行数1 | 行数2 | 列数1 | 列数2 | 图像数据1 | ... | 图像数据(i-4) | +| :--------: | :-------: | :--------: | :-------: | :-------: | :--: | :-----------: | +| rows[15:8] | rows[7:0] | cols[15:8] | cols[7:0] | | ... | | + + **返回结果数据包' (空格)''R''I''M'**，`数据1`~`数据i`包含了长径long、短径short、缺陷个数num、缺陷面积area、结果图像的行数rows(高度)、列数cols(宽度)、以及结果图像的RGB数据 + + $i-14=rows \times cols \times 3$具体如下： + +| 长径1 | 长径2 | 短径1 | 短径2 | 缺陷个数1 | 缺陷个数2 | 缺陷面积1 | 缺陷面积2 | 缺陷面积3 | 缺陷面积4 | 行数1 | 行数2 | 列数1 | 列数2 | 图像数据1 | 图像数据(i-14) | +| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | +| long[15:8] | long[7:0] | short[15:8] | short[7:0] | num[15:8] | num[7:0] | area[31:24] | area[23:16] | area[15:8] | area[7:0] | rows[15:8] | rows[7:0] | cols[15:8] | cols[7:0] | | | + +- **光谱数据包' (空格)''(空格) ''S''P'**，`数据1`~`数据i`包含了光谱数据的行数rows(高度)、列数cols(宽度)、谱段数bands、以及图像的光谱数据 + + $i-6=rows \times cols \times bands \times 4$具体如下： + +| 行数1 | 行数2 | 列数1 | 列数2 | 谱段1 | 谱段2 | 图像数据1 | ... | 图像数据(i-6) | +| :--------: | :-------: | :--------: | :-------: | :---------: | :--------: | :-------: | :--: | :-----------: | +| rows[15:8] | rows[7:0] | cols[15:8] | cols[7:0] | bands[15:8] | bands[7:0] | | ... | | + +- **模型切换指令** + + **' (空格)''(空格) ''P''A'**，表示使用的是百香果分级模型 + +| 起始 | 长度1 | 长度2 | 长度3 | 长度4 | 类型1 | 类型2 | 类型3 | 类型4 | 数据1 | 校验1 | 校验2 | 结束 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| 8'haa | 8'd0 | 8'd0 | 8'd0 | 8'd5 | ' ' | ' ' | 'P' | 'A' | 8'hff | 8'hff | 8'hff | 8'hbb | + +​ **' (空格)''(空格) ''T''O'**，表示使用的是西红柿分级模型 + +| 起始 | 长度1 | 长度2 | 长度3 | 长度4 | 类型1 | 类型2 | 类型3 | 类型4 | 数据1 | 校验1 | 校验2 | 结束 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| 8'haa | 8'd0 | 8'd0 | 8'd0 | 8'd5 | ' ' | ' ' | 'T' | 'O' | 8'hff | 8'hff | 8'hff | 8'hbb | + +### 校验 + +2字节，`校验1`为8'hff，`校验2`为8'hff + +### 结束 + +1字节，8'hbb \ No newline at end of file diff --git a/20240410RGBtest1/通信协议(旻哥暂时同意版).pdf b/20240410RGBtest1/通信协议(旻哥暂时同意版).pdf new file mode 100644 index 0000000..df201af Binary files /dev/null and b/20240410RGBtest1/通信协议(旻哥暂时同意版).pdf differ diff --git a/20240419RGBtest2/main.py b/20240419RGBtest2/main.py new file mode 100644 index 0000000..c3999e1 --- /dev/null +++ b/20240419RGBtest2/main.py @@ -0,0 +1,228 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/4/20 18:45 +# @Author : TG +# @File : main.py +# @Software: PyCharm +# -*- coding: utf-8 -*- +# @Time : 2024/4/12 15:04 +# @Author : TG +# @File : main.py +# @Software: PyCharm + +import socket +import sys +import numpy as np +import cv2 +import root_dir +import time +import os +from root_dir import ROOT_DIR +import logging +from utils import threshold_segmentation, largest_connected_component, draw_tomato_edge, bitwise_and_rgb_with_binary, \ + extract_s_l, get_tomato_dimensions, get_defect_info, create_pipes, receive_rgb_data, send_data, receive_spec_data +from collections import deque +import time +import io +from PIL import Image +import threading +import queue + +def process_data(img: any) -> tuple: + """ + 处理指令 + + :param cmd: 指令类型 + :param data: 指令内容 + :param connected_sock: socket + :param detector: 模型 + :return: 是否处理成功 + """ + # start_time = time.time() + # if cmd == 'IM': + + threshold_s_l = 180 + # threshold_r_b = 15 + + s_l = extract_s_l(img) + + thresholded_s_l = threshold_segmentation(s_l, threshold_s_l) + new_bin_img = largest_connected_component(thresholded_s_l) + + edge, mask = draw_tomato_edge(img, new_bin_img) + org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img) + + # filled_img, defect = fill_holes(new_bin_img) + + long_axis, short_axis = get_tomato_dimensions(mask) + number_defects, total_pixels = get_defect_info(new_bin_img) + rp = org_defect + rp = cv2.cvtColor(rp, cv2.COLOR_BGR2RGB) + # cv2.imwrite('rp1.bmp', rp) + + # else: + # logging.error(f'错误指令，指令为{cmd}') + # response = False + + # end_time = time.time() + # elapsed_time = end_time - start_time + # print(f'处理时间：{elapsed_time}秒') + return long_axis, short_axis, number_defects, total_pixels, rp + + +## 20240423代码 +def main(is_debug=False): + file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log')) + file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) + logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s', + handlers=[file_handler, console_handler], + level=logging.DEBUG) + rgb_receive_name = r'\\.\pipe\rgb_receive' + rgb_send_name = r'\\.\pipe\rgb_send' + spec_receive_name = r'\\.\pipe\spec_receive' + rgb_receive, rgb_send, spec_receive = create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name) + + # data_size = 15040566 + + while True: + long_axis_list = [] + short_axis_list = [] + defect_num_sum = 0 + total_defect_area_sum = 0 + rp = None + + start_time = time.time() + + for i in range(5): + + # start_time = time.time() + + + img_data = receive_rgb_data(rgb_receive) + image = Image.open(io.BytesIO(img_data)) + img = np.array(image) + print(img.shape) + + # end_time = time.time() + # elapsed_time = end_time - start_time + # print(f'接收时间：{elapsed_time}秒') + + long_axis, short_axis, number_defects, total_pixels, rp = process_data(img=img) + # print(long_axis, short_axis, number_defects, type(total_pixels), rp.shape) + + if i <= 2: + long_axis_list.append(long_axis) + short_axis_list.append(short_axis) + if i == 1: + rp_result = rp + + defect_num_sum += number_defects + total_defect_area_sum += total_pixels + + long_axis = round(sum(long_axis_list) / 3) + short_axis = round(sum(short_axis_list) / 3) + # print(type(long_axis), type(short_axis), type(defect_num_sum), type(total_defect_area_sum), type(rp_result)) + + spec_data = receive_spec_data(spec_receive) + print(f'光谱数据接收长度：', len(spec_data)) + + + response = send_data(pipe_send=rgb_send, long_axis=long_axis, short_axis=short_axis, + defect_num=defect_num_sum, total_defect_area=total_defect_area_sum, rp=rp_result) + + + + end_time = time.time() + elapsed_time = (end_time - start_time) * 1000 + print(f'总时间：{elapsed_time}毫秒') + + print(long_axis, short_axis, defect_num_sum, total_defect_area_sum, rp_result.shape) + + + +if __name__ == '__main__': + # 2个pipe管道 + # 接收到图片 n_rows * n_cols * 3， uint8 + # 发送long_axis, short_axis, defect_num_sum, total_defect_area_sum, rp_result + main(is_debug=False) + + + + +### 多线程版本 + +# def receive_spec_data_thread(spec_receive, spec_queue): +# while True: +# spec_data = receive_spec_data(spec_receive) +# spec_queue.put(spec_data) +# +# def receive_process_rgb_data_thread(rgb_receive, img_queue, result_queue): +# while True: +# +# long_axis_list = [] +# short_axis_list = [] +# defect_num_sum = 0 +# total_defect_area_sum = 0 +# rp = None +# +# for i in range(5): +# img_data = receive_rgb_data(rgb_receive) +# image = Image.open(io.BytesIO(img_data)) +# img = np.array(image) +# +# long_axis, short_axis, number_defects, total_pixels, rp = process_data(img=img) +# +# if i <= 2: +# long_axis_list.append(long_axis) +# short_axis_list.append(short_axis) +# if i == 1: +# rp_result = rp +# +# defect_num_sum += number_defects +# total_defect_area_sum += total_pixels +# +# long_axis = round(sum(long_axis_list) / 3) +# short_axis = round(sum(short_axis_list) / 3) +# +# result = (long_axis, short_axis, defect_num_sum, total_defect_area_sum, rp_result) +# result_queue.put(result) +# +# def main(is_debug=False): +# file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log')) +# file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) +# console_handler = logging.StreamHandler(sys.stdout) +# console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) +# logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s', +# handlers=[file_handler, console_handler], +# level=logging.DEBUG) +# rgb_receive_name = r'\\.\pipe\rgb_receive' +# rgb_send_name = r'\\.\pipe\rgb_send' +# spec_receive_name = r'\\.\pipe\spec_receive' +# rgb_receive, rgb_send, spec_receive = create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name) +# +# spec_queue = queue.Queue() +# img_queue = queue.Queue() +# result_queue = queue.Queue() +# +# # 创建并启动线程 +# spec_thread = threading.Thread(target=receive_spec_data_thread, args=(spec_receive, spec_queue)) +# rgb_thread = threading.Thread(target=receive_process_rgb_data_thread, args=(rgb_receive, img_queue, result_queue)) +# spec_thread.start() +# rgb_thread.start() +# +# while True: +# spec_data = spec_queue.get() +# print(f'spec_data长度：', len(spec_data)) +# long_axis, short_axis, defect_num_sum, total_defect_area_sum, rp_result = result_queue.get() +# +# response = send_data(pipe_send=rgb_send, long_axis=long_axis, short_axis=short_axis, +# defect_num=defect_num_sum, total_defect_area=total_defect_area_sum, rp=rp_result) +# +# print(long_axis, short_axis, defect_num_sum, total_defect_area_sum, rp_result.shape) +# +# if __name__ == '__main__': +# main(is_debug=False) + + + diff --git a/20240419RGBtest2/pipe.py b/20240419RGBtest2/pipe.py new file mode 100644 index 0000000..2583942 --- /dev/null +++ b/20240419RGBtest2/pipe.py @@ -0,0 +1,124 @@ +import win32file +import win32pipe +import io +from PIL import Image +import time +import logging + +def send_data(pipe_send, long_axis, short_axis, defect_num, total_defect_area, rp): + + + start_time = time.time() + + # width = rp.shape[0] + # height = rp.shape[1] + # print(width, height) + img_bytes = rp.tobytes() + # length = len(img_bytes) + 18 + # print(length) + # length = length.to_bytes(4, byteorder='big') + # width = width.to_bytes(2, byteorder='big') + # height = height.to_bytes(2, byteorder='big') + long_axis = long_axis.to_bytes(2, byteorder='big') + short_axis = short_axis.to_bytes(2, byteorder='big') + defect_num = defect_num.to_bytes(2, byteorder='big') + total_defect_area = int(total_defect_area).to_bytes(4, byteorder='big') + # cmd_type = 'RIM' + # result = result.encode('ascii') + # send_message = b'\xaa' + length + (' ' + cmd_type).upper().encode('ascii') + long_axis + short_axis + defect_num + total_defect_area + width + height + img_bytes + b'\xff\xff\xbb' + send_message = long_axis + short_axis + defect_num + total_defect_area + img_bytes + # print(long_axis) + # print(short_axis) + # print(defect_num) + # print(total_defect_area) + # print(width) + # print(height) + + try: + win32file.WriteFile(pipe_send, send_message) + print('发送成功') + # print(send_message) + except Exception as e: + logging.error(f'发送完成指令失败，错误类型：{e}') + return False + + end_time = time.time() + print(f'发送时间：{end_time - start_time}秒') + + return True + + +def receive_data(pipe, data_size): + try: + # 读取图片数据 + result, img_data = win32file.ReadFile(pipe, data_size, None) + return img_data + except Exception as e: + print(f"Failed to receive data. Error: {e}") + return None + + +def create_pipes(pipe_receive_name, pipe_send_name): + # 打开或创建命名管道 + pipe_receive = win32pipe.CreateNamedPipe( + pipe_receive_name, + win32pipe.PIPE_ACCESS_INBOUND, + win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT, + 1, 80000000, 80000000, 0, None + ) + pipe_send = win32pipe.CreateNamedPipe( + pipe_send_name, + win32pipe.PIPE_ACCESS_OUTBOUND, # 修改为输出模式 + win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT, + 1, 80000000, 80000000, 0, None + ) + + # 等待发送端连接 + win32pipe.ConnectNamedPipe(pipe_receive, None) + # 等待发送端连接 + win32pipe.ConnectNamedPipe(pipe_send, None) + print("Sender connected.") + print("receive connected.") + + return pipe_receive, pipe_send + +def process_images(pipe_receive, pipe_send): + image_count = 0 + batch_size = 5 + images = [] + + while True: + for i in range(5): + start_time = time.time() # 记录开始时间 + img_data = receive_data(pipe_receive) + + if img_data: + image = Image.open(io.BytesIO(img_data)) + image = image.convert("L") # 示例处理：转换为灰度图 + buf = io.BytesIO() + image.save(buf, format='JPEG') + buf.seek(0) # 重置buffer位置到开始 + processed_data = buf.getvalue() + + images.append(buf) # 存储 BytesIO 对象而不是 Image 对象 + + if len(images) >= batch_size: + # 发送最后一个处理后的图像 + send_image_back_to_qt(pipe_send, images[-1].getvalue()) + images = [] # 清空列表以开始新的批处理 + time.sleep(0.01) # 添加适当的延迟,降低CPU使用率 + print("Image received and saved.") + end_time = time.time() # 记录结束时间 + duration_ms = (end_time - start_time) * 1000 # 转换为毫秒 + print(f"Image {i + 1} received and displayed in {duration_ms:.2f} ms.") # 打印毫秒级的时间 + image_count += 1 # 图片计数器增加 + print(f"Image {image_count} received and displayed.") + +def main(): + pipe_receive_name = r'\\.\pipe\pipe_receive' + pipe_send_name = r'\\.\pipe\pipe_send' + pipe_receive, pipe_send = create_pipes(pipe_receive_name, pipe_send_name) + process_images(pipe_receive, pipe_send) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/20240419RGBtest2/qt_test.py b/20240419RGBtest2/qt_test.py new file mode 100644 index 0000000..962083c --- /dev/null +++ b/20240419RGBtest2/qt_test.py @@ -0,0 +1,123 @@ +import sys +import os +from PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog, QLabel, QVBoxLayout, QWidget +from PyQt5.QtGui import QPixmap, QImage +import win32pipe +import win32file +import struct +from PIL import Image +import io + +class MainWindow(QMainWindow): + def __init__(self): + super().__init__() + self.setWindowTitle("Tomato Image Sender") + self.setGeometry(100, 100, 800, 600) + + central_widget = QWidget() + self.setCentralWidget(central_widget) + + layout = QVBoxLayout() + central_widget.setLayout(layout) + + self.image_label = QLabel() + layout.addWidget(self.image_label) + + self.rgb_send_name = r'\\.\pipe\rgb_receive' # 发送数据管道名对应 main.py 的接收数据管道名 + self.rgb_receive_name = r'\\.\pipe\rgb_send' # 接收数据管道名对应 main.py 的发送数据管道名 + self.spec_send_name = r'\\.\pipe\spec_receive' # 发送数据管道名对应 main.py 的接收数据管道名 + + # 连接main.py创建的命名管道 + self.rgb_send = win32file.CreateFile( + self.rgb_send_name, + win32file.GENERIC_WRITE, + 0, + None, + win32file.OPEN_EXISTING, + 0, + None + ) + + self.rgb_receive = win32file.CreateFile( + self.rgb_receive_name, + win32file.GENERIC_READ, + 0, + None, + win32file.OPEN_EXISTING, + 0, + None + ) + + self.spec_send = win32file.CreateFile( + self.spec_send_name, + win32file.GENERIC_WRITE, + 0, + None, + win32file.OPEN_EXISTING, + 0, + None + ) + + def send_image_group(self, image_dir): + rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.bmp'))][:5] + spec_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.raw')][:1] + + for image_path in rgb_files: + with open(image_path, 'rb') as f: + img_data = f.read() + + try: + win32file.WriteFile(self.rgb_send, len(img_data).to_bytes(4, byteorder='big')) + win32file.WriteFile(self.rgb_send, img_data) + except Exception as e: + print(f"数据发送失败. 错误原因: {e}") + + if spec_files: + spec_file = spec_files[0] + with open(spec_file, 'rb') as f: + spec_data = f.read() + + try: + win32file.WriteFile(self.spec_send, len(spec_data).to_bytes(4, byteorder='big')) + print(f"发送的光谱数据长度: {len(spec_data)}") + win32file.WriteFile(self.spec_send, spec_data) + print(f'发送的光谱数据长度: {len(spec_data)}') + except Exception as e: + print(f"数据发送失败. 错误原因: {e}") + + self.receive_result() + + def receive_result(self): + try: + # 读取结果数据 + long_axis = int.from_bytes(win32file.ReadFile(self.rgb_receive, 2)[1], byteorder='big') + short_axis = int.from_bytes(win32file.ReadFile(self.rgb_receive, 2)[1], byteorder='big') + defect_num = int.from_bytes(win32file.ReadFile(self.rgb_receive, 2)[1], byteorder='big') + total_defect_area = int.from_bytes(win32file.ReadFile(self.rgb_receive, 4)[1], byteorder='big') + len_img = int.from_bytes(win32file.ReadFile(self.rgb_receive, 4)[1], byteorder='big') + img_data = win32file.ReadFile(self.rgb_receive, len_img)[1] + + print(f"长径: {long_axis}, 短径: {short_axis}, 缺陷个数: {defect_num}, 缺陷面积: {total_defect_area}") + + # 显示结果图像 + image = Image.open(io.BytesIO(img_data)) + qimage = QImage(image.tobytes(), image.size[0], image.size[1], QImage.Format_RGB888) + pixmap = QPixmap.fromImage(qimage) + self.image_label.setPixmap(pixmap) + + except Exception as e: + print(f"数据接收失败. 错误原因: {e}") + + def open_file_dialog(self): + directory_dialog = QFileDialog() + directory_dialog.setFileMode(QFileDialog.Directory) + if directory_dialog.exec_(): + selected_directory = directory_dialog.selectedFiles()[0] + self.send_image_group(selected_directory) + +if __name__ == "__main__": + app = QApplication(sys.argv) + main_window = MainWindow() + main_window.show() + main_window.open_file_dialog() + sys.exit(app.exec_()) \ No newline at end of file diff --git a/20240419RGBtest2/utils.py b/20240419RGBtest2/utils.py new file mode 100644 index 0000000..edb5968 --- /dev/null +++ b/20240419RGBtest2/utils.py @@ -0,0 +1,579 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/4/20 18:24 +# @Author : TG +# @File : utils.py +# @Software: PyCharm + +import socket +import time +import logging +import numpy as np +import shutil +import cv2 +import os +from scipy.ndimage.measurements import label, find_objects +import win32pipe +import win32file +import io +from PIL import Image +import select +import msvcrt + +def receive_rgb_data(pipe): + try: + # 读取图片数据 + len_img = win32file.ReadFile(pipe, 4, None) + data_size = int.from_bytes(len_img[1], byteorder='big') + result, img_data = win32file.ReadFile(pipe, data_size, None) + return img_data + except Exception as e: + print(f"数据接收失败，错误原因: {e}") + return None + + +def receive_spec_data(pipe): + try: + # 读取图片数据长度 + len_spec = win32file.ReadFile(pipe, 4, None) + if len_spec is None: + # 未能读取到数据长度,返回"0" + return "0" + data_size = int.from_bytes(len_spec[1], byteorder='big') + if data_size == 0: + # 接收到空数据,返回"0" + return "0" + + # 读取图片数据 + result, spec_data = win32file.ReadFile(pipe, data_size, None) + return spec_data + except Exception as e: + print(f"数据接收失败，错误原因: {e}") + return '0' + +# def receive_spec_data(pipe): +# try: +# # 读取图片数据 +# len_spec = win32file.ReadFile(pipe, 4, None) +# data_size = int.from_bytes(len_spec[1], byteorder='big') +# result, spec_data = win32file.ReadFile(pipe, data_size, None) +# return spec_data +# except Exception as e: +# print(f"数据接收失败，错误原因: {e}") +# return None + + + + +# def create_pipes(pipe_receive_name, pipe_send_name): +# while True: +# try: +# # 打开或创建命名管道 +# pipe_receive = win32pipe.CreateNamedPipe( +# pipe_receive_name, +# win32pipe.PIPE_ACCESS_INBOUND, +# win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT, +# 1, 80000000, 80000000, 0, None +# ) +# pipe_send = win32pipe.CreateNamedPipe( +# pipe_send_name, +# win32pipe.PIPE_ACCESS_OUTBOUND, # 修改为输出模式 +# win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT, +# 1, 80000000, 80000000, 0, None +# ) +# +# # 等待发送端连接 +# win32pipe.ConnectNamedPipe(pipe_receive, None) +# # 等待发送端连接 +# win32pipe.ConnectNamedPipe(pipe_send, None) +# print("Sender connected.") +# print("receive connected.") +# return pipe_receive, pipe_send +# +# except Exception as e: +# print(f"Error occurred while creating pipes: {e}") +# print("Waiting for 5 seconds before retrying...") +# time.sleep(5) + + + +def create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name): + while True: + try: + # 打开或创建命名管道 + rgb_receive = win32pipe.CreateNamedPipe( + rgb_receive_name, + win32pipe.PIPE_ACCESS_INBOUND, + win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT, + 1, 80000000, 80000000, 0, None + ) + rgb_send = win32pipe.CreateNamedPipe( + rgb_send_name, + win32pipe.PIPE_ACCESS_OUTBOUND, # 修改为输出模式 + win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT, + 1, 80000000, 80000000, 0, None + ) + spec_receive = win32pipe.CreateNamedPipe( + spec_receive_name, + win32pipe.PIPE_ACCESS_INBOUND, + win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT, + 1, 200000000, 200000000, 0, None + ) + print("pipe管道创建成功，等待连接...") + # 等待发送端连接 + win32pipe.ConnectNamedPipe(rgb_receive, None) + print("rgb_receive connected.") + # 等待发送端连接 + win32pipe.ConnectNamedPipe(rgb_send, None) + print("rgb_send connected.") + win32pipe.ConnectNamedPipe(rgb_receive, None) + print("spec_receive connected.") + return rgb_receive, rgb_send, spec_receive + + except Exception as e: + print(f"管道创建连接失败，失败原因: {e}") + print("等待5秒后重试...") + time.sleep(5) + + +# def send_data(pipe_send, long_axis, short_axis, defect_num, total_defect_area, rp): +# +# # start_time = time.time() +# +# # width = rp.shape[0] +# # height = rp.shape[1] +# # print(width, height) +# img_bytes = rp.tobytes() +# # length = len(img_bytes) + 18 +# # print(length) +# # length = length.to_bytes(4, byteorder='big') +# # width = width.to_bytes(2, byteorder='big') +# # height = height.to_bytes(2, byteorder='big') +# length = (len(img_bytes) + 10).to_bytes(4, byteorder='big') +# long_axis = long_axis.to_bytes(2, byteorder='big') +# short_axis = short_axis.to_bytes(2, byteorder='big') +# defect_num = defect_num.to_bytes(2, byteorder='big') +# total_defect_area = int(total_defect_area).to_bytes(4, byteorder='big') +# # cmd_type = 'RIM' +# # result = result.encode('ascii') +# # send_message = b'\xaa' + length + (' ' + cmd_type).upper().encode('ascii') + long_axis + short_axis + defect_num + total_defect_area + width + height + img_bytes + b'\xff\xff\xbb' +# send_message = length + long_axis + short_axis + defect_num + total_defect_area + img_bytes +# # print(long_axis) +# # print(short_axis) +# # print(defect_num) +# # print(total_defect_area) +# # print(width) +# # print(height) +# +# try: +# win32file.WriteFile(pipe_send, send_message) +# print('发送成功') +# # print(send_message) +# except Exception as e: +# logging.error(f'发送完成指令失败，错误类型：{e}') +# return False +# +# # end_time = time.time() +# # print(f'发送时间：{end_time - start_time}秒') +# +# return True + + +def send_data(pipe_send, long_axis, short_axis, defect_num, total_defect_area, rp): + # start_time = time.time() + # + rp1 = Image.fromarray(rp.astype(np.uint8)) + # cv2.imwrite('rp1.bmp', rp1) + + # 将 Image 对象保存到 BytesIO 流中 + img_bytes = io.BytesIO() + rp1.save(img_bytes, format='BMP') + img_bytes = img_bytes.getvalue() + + # width = rp.shape[0] + # height = rp.shape[1] + # print(width, height) + # img_bytes = rp.tobytes() + # length = len(img_bytes) + 18 + # print(length) + # length = length.to_bytes(4, byteorder='big') + # width = width.to_bytes(2, byteorder='big') + # height = height.to_bytes(2, byteorder='big') + + print(f'原始长度:', len(rp.tobytes())) + print(f'发送长度:', len(img_bytes)) + + long_axis = long_axis.to_bytes(2, byteorder='big') + short_axis = short_axis.to_bytes(2, byteorder='big') + defect_num = defect_num.to_bytes(2, byteorder='big') + total_defect_area = int(total_defect_area).to_bytes(4, byteorder='big') + length = (len(img_bytes) + 4).to_bytes(4, byteorder='big') + # cmd_type = 'RIM' + # result = result.encode('ascii') + # send_message = b'\xaa' + length + (' ' + cmd_type).upper().encode('ascii') + long_axis + short_axis + defect_num + total_defect_area + width + height + img_bytes + b'\xff\xff\xbb' + # send_message = long_axis + short_axis + defect_num + total_defect_area + img_bytes + send_message = long_axis + short_axis + defect_num + total_defect_area + length + img_bytes + # print(long_axis) + # print(short_axis) + # print(defect_num) + # print(total_defect_area) + # print(width) + # print(height) + + try: + win32file.WriteFile(pipe_send, send_message) + time.sleep(0.01) + print('发送成功') + # print(len(send_message)) + except Exception as e: + logging.error(f'发送完成指令失败，错误类型：{e}') + return False + + # end_time = time.time() + # print(f'发送时间：{end_time - start_time}秒') + + return True + + + +def mkdir_if_not_exist(dir_name, is_delete=False): + """ + 创建文件夹 + :param dir_name: 文件夹 + :param is_delete: 是否删除 + :return: 是否成功 + """ + try: + if is_delete: + if os.path.exists(dir_name): + shutil.rmtree(dir_name) + print('[Info] 文件夹 "%s" 存在, 删除文件夹.' % dir_name) + + if not os.path.exists(dir_name): + os.makedirs(dir_name) + print('[Info] 文件夹 "%s" 不存在, 创建文件夹.' % dir_name) + return True + except Exception as e: + print('[Exception] %s' % e) + return False + +def create_file(file_name): + """ + 创建文件 + :param file_name: 文件名 + :return: None + """ + if os.path.exists(file_name): + print("文件存在：%s" % file_name) + return False + # os.remove(file_name) # 删除已有文件 + if not os.path.exists(file_name): + print("文件不存在，创建文件：%s" % file_name) + open(file_name, 'a').close() + return True + + +class Logger(object): + def __init__(self, is_to_file=False, path=None): + self.is_to_file = is_to_file + if path is None: + path = "tomato.log" + self.path = path + create_file(path) + + def log(self, content): + if self.is_to_file: + with open(self.path, "a") as f: + print(time.strftime("[%Y-%m-%d_%H-%M-%S]:"), file=f) + print(content, file=f) + else: + print(content) + + + +#提取西红柿，使用S+L的图像 +def extract_s_l(image): + # image = cv2.imread(image_path) + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab) + s_channel = hsv[:,:,1] + l_channel = lab[:,:,0] + result = cv2.add(s_channel, l_channel) + return result + +def find_reflection(image, threshold=190): + # 读取图像 + # image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + + # 应用阈值分割 + _, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) + + return reflection + +def otsu_threshold(image): + + # 将图像转换为灰度图像 + # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # 使用Otsu阈值分割 + _, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + + return binary + +# 提取花萼，使用G-R的图像 +def extract_g_r(image): + # image = cv2.imread(image_path) + g_channel = image[:,:,1] + r_channel = image[:,:,2] + result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel) + return result + + +#提取西红柿，使用R-B的图像 +def extract_r_b(image): + # image = cv2.imread(image_path) + r_channel = image[:,:,2] + b_channel = image[:,:,0] + result = cv2.subtract(r_channel, b_channel) + return result + +def extract_r_g(image): + # image = cv2.imread(image_path) + r_channel = image[:,:,2] + g_channel = image[:,:,1] + result = cv2.subtract(r_channel, g_channel) + return result + +def threshold_segmentation(image, threshold, color=255): + _, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY) + return result + +def bitwise_operation(image1, image2, operation='and'): + if operation == 'and': + result = cv2.bitwise_and(image1, image2) + elif operation == 'or': + result = cv2.bitwise_or(image1, image2) + else: + raise ValueError("operation must be 'and' or 'or'") + return result + +def largest_connected_component(bin_img): + # 使用connectedComponentsWithStats函数找到连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8) + + # 如果只有背景标签,返回一个空的二值图像 + if num_labels <= 1: + return np.zeros_like(bin_img) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像,只显示最大的连通区域 + new_bin_img = np.zeros_like(bin_img) + new_bin_img[labels == largest_label] = 255 + + return new_bin_img + +def close_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel) + return closed_img + +def open_operation(bin_img, kernel_size=(5, 5)): + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + opened_img = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel) + return opened_img + + +def draw_tomato_edge(original_img, bin_img): + bin_img_processed = close_operation(bin_img, kernel_size=(15, 15)) + # cv2.imshow('Close Operation', bin_img_processed) + # bin_img_processed = open_operation(bin_img_processed, kernel_size=(19, 19)) + # cv2.imshow('Open Operation', bin_img_processed) + # 现在使用处理后的bin_img_processed查找轮廓 + contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 如果没有找到轮廓，直接返回原图 + if not contours: + return original_img, np.zeros_like(bin_img) # 返回原图和全黑mask + # 找到最大轮廓 + max_contour = max(contours, key=cv2.contourArea) + # 多边形近似的精度调整 + epsilon = 0.0006 * cv2.arcLength(max_contour, True) # 可以调整这个值 + approx = cv2.approxPolyDP(max_contour, epsilon, True) + # 绘制轮廓 + cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3) + mask = np.zeros_like(bin_img) + + # 使用白色填充最大轮廓 + cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED) + + return original_img, mask + +def draw_tomato_edge_convex_hull(original_img, bin_img): + bin_img_blurred = cv2.GaussianBlur(bin_img, (5, 5), 0) + contours, _ = cv2.findContours(bin_img_blurred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return original_img + max_contour = max(contours, key=cv2.contourArea) + hull = cv2.convexHull(max_contour) + cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3) + return original_img + +# 得到完整的西红柿二值图像，除了绿色花萼 +def fill_holes(bin_img): + # 复制 bin_img 到 img_filled + img_filled = bin_img.copy() + + # 获取图像的高度和宽度 + height, width = bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_d = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(bin_img, img_filled) + # 裁剪 img_filled 和 img_filled_d 到与 bin_img 相同的大小 + # img_filled = img_filled[:height, :width] + img_filled_d = img_filled_d[:height, :width] + + return img_filled, img_filled_d + +def bitwise_and_rgb_with_binary(rgb_img, bin_img): + # 将二值图像转换为三通道图像 + bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR) + + # 使用 bitwise_and 操作合并 RGB 图像和二值图像 + result = cv2.bitwise_and(rgb_img, bin_img_3channel) + + return result + + +def extract_max_connected_area(image, lower_hsv, upper_hsv): + # 读取图像 + # image = cv2.imread(image_path) + + # 将图像从BGR转换到HSV + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # 使用阈值获取指定区域的二值图像 + mask = cv2.inRange(hsv, lower_hsv, upper_hsv) + + # 找到二值图像的连通区域 + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) + + # 找到最大的连通区域（除了背景） + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + + # 创建一个新的二值图像，只显示最大的连通区域 + new_bin_img = np.zeros_like(mask) + new_bin_img[labels == largest_label] = 255 + + # 复制 new_bin_img 到 img_filled + img_filled = new_bin_img.copy() + + # 获取图像的高度和宽度 + height, width = new_bin_img.shape + + # 创建一个掩码，比输入图像大两个像素点 + mask = np.zeros((height + 2, width + 2), np.uint8) + + # 使用 floodFill 函数填充黑色区域 + cv2.floodFill(img_filled, mask, (0, 0), 255) + + # 反转填充后的图像 + img_filled_inv = cv2.bitwise_not(img_filled) + + # 使用 bitwise_or 操作合并原图像和填充后的图像 + img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv) + + return img_filled +def get_tomato_dimensions(edge_img): + """ + 根据番茄边缘二值化轮廓图,计算番茄的长径、短径和长短径比值。 + 使用最小外接矩形和最小外接圆两种方法。 + + 参数: + edge_img (numpy.ndarray): 番茄边缘二值化轮廓图,背景为黑色,番茄区域为白色。 + + 返回: + tuple: (长径, 短径, 长短径比值) + """ + if edge_img is None or edge_img.any() == 0: + return (0, 0) + # 最小外接矩形 + rect = cv2.minAreaRect(cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0]) + major_axis, minor_axis = rect[1] + # aspect_ratio = max(major_axis, minor_axis) / min(major_axis, minor_axis) + + # # 最小外接圆 + # (x, y), radius = cv2.minEnclosingCircle( + # cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0]) + # diameter = 2 * radius + # aspect_ratio_circle = 1.0 + + return (max(major_axis, minor_axis), min(major_axis, minor_axis)) + +def get_defect_info(defect_img): + """ + 根据番茄区域缺陷二值化轮廓图,计算缺陷区域的个数和总面积。 + + 参数: + defect_img (numpy.ndarray): 番茄区域缺陷二值化轮廓图,背景为黑色,番茄区域为白色,缺陷区域为黑色连通域。 + + 返回: + tuple: (缺陷区域个数, 缺陷区域像素面积，缺陷像素总面积) + """ + # 检查输入是否为空 + if defect_img is None or defect_img.any() == 0: + return (0, 0) + + nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(defect_img, connectivity=4) + max_area = max(stats[i, cv2.CC_STAT_AREA] for i in range(1, nb_components)) + areas = [] + for i in range(1, nb_components): + area = stats[i, cv2.CC_STAT_AREA] + if area != max_area: + areas.append(area) + number_defects = len(areas) + total_pixels = sum(areas) + return number_defects, total_pixels + + + + + + +def connected_components_analysis(binary_image): + """ + 从二值化图像计算黑色连通域个数和各个黑色连通域像素面积及黑色像素总面积。 + + 参数: + binary_image (numpy.ndarray): 二值化图像, 其中 0 表示白色, 1 表示黑色。 + + 返回: + num_components (int): 黑色连通域的个数。 + component_areas (list): 每个黑色连通域的像素面积。 + total_black_area (int): 黑色像素的总面积。 + """ + # 标记连通域 + labeled_image, num_components = label(binary_image) + + # 获取每个连通域的像素位置 + slices = find_objects(labeled_image) + + # 计算每个连通域的像素面积 + component_areas = [] + for slice_obj in slices: + component_area = np.sum(binary_image[slice_obj]) + component_areas.append(component_area) + + # 计算黑色像素的总面积 + total_black_area = np.sum(binary_image) + + return num_components, component_areas, total_black_area \ No newline at end of file diff --git a/classfier.py b/classfier.py new file mode 100644 index 0000000..2d476fc --- /dev/null +++ b/classfier.py @@ -0,0 +1,165 @@ +import logging +import sys +from typing import Optional + +import numpy as np +import cv2 +from sklearn.tree import DecisionTreeClassifier +from sklearn.cluster import KMeans +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score, confusion_matrix +from scipy import ndimage +from sklearn.tree import DecisionTreeClassifier +from sklearn.feature_selection import SelectKBest +from sklearn.neighbors import KNeighborsClassifier +from scipy.stats import binom +import matplotlib.pyplot as plt +import time +import pickle +import os +import utils +from root_dir import ROOT_DIR + + +class Astragalin(object): + def __init__(self, load_from=None, debug_mode=False, class_weight=None): + if load_from is None: + self.model = DecisionTreeClassifier(random_state=65, class_weight=class_weight) + else: + self.load(load_from) + self.log = utils.Logger(is_to_file=debug_mode) + self.debug_mode = debug_mode + + def load(self, path=None): + if path is None: + path = os.path.join(ROOT_DIR, 'models') + model_files = os.listdir(path) + if len(model_files) == 0: + self.log.log("No model found!") + return 1 + self.log.log("./ Models Found:") + _ = [self.log.log("├--" + str(model_file)) for model_file in model_files] + file_times = [model_file[6:-2] for model_file in model_files] + latest_model = model_files[int(np.argmax(file_times))] + self.log.log("└--Using the latest model: " + str(latest_model)) + path = os.path.join(ROOT_DIR, "models", str(latest_model)) + if not os.path.isabs(path): + logging.warning('给的是相对路径') + return -1 + if not os.path.exists(path): + logging.warning('文件不存在') + return -1 + with open(path, 'rb') as f: + model_dic = pickle.load(f) + self.model = model_dic['model'] + return 0 + + def fit(self, data_x, data_y): + x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.3, random_state=65) + self.model.fit(x_train, y_train) + y_pred = self.model.predict(x_test) + print(confusion_matrix(y_test, y_pred)) + + pre_score = accuracy_score(y_test, y_pred) + self.log.log("Test accuracy is:" + str(pre_score * 100) + "%.") + y_pred = self.model.predict(x_train) + + pre_score = accuracy_score(y_train, y_pred) + self.log.log("Train accuracy is:" + str(pre_score * 100) + "%.") + y_pred = self.model.predict(data_x) + + pre_score = accuracy_score(data_y, y_pred) + self.log.log("Total accuracy is:" + str(pre_score * 100) + "%.") + + return int(pre_score * 100) + + def fit_value(self, file_name=None, data_path='data/1.txt', select_bands=[91, 92, 93, 94, 95, 96, 97, 98, 99, 100]): + data_x, data_y = self.data_construction(data_path, select_bands) + score = self.fit(data_x, data_y) + print('score:', score) + model_name = self.save(file_name=file_name) + return score, model_name + + def save(self, file_name): + # 保存模型 + if file_name is None: + file_name = "model_" + time.strftime("%Y-%m-%d_%H-%M") + ".p" + file_name = os.path.join(ROOT_DIR, "models", file_name) + model_dic = {'model': self.model} + with open(file_name, 'wb') as f: + pickle.dump(model_dic, f) + self.log.log("Model saved to '" + str(file_name) + "'.") + return file_name + + # def data_construction(self, data_path, select_bands): + # data = utils.read_envi_ascii(data_path) + # beijing = data['beijing'][:, select_bands] + # zazhi1 = data['zazhi1'][:, select_bands] + # # zazhi2 = data['zazhi2'][:, select_bands] + # huangqi = data['huangqi'][:, select_bands] + # gancaopian = data['gancaopian'][:, select_bands] + # # hongqi = data['hongqi'][:, select_bands] + # beijing_y = np.zeros(beijing.shape[0]) + # zazhi1_y = np.ones(zazhi1.shape[0]) * 3 + # # zazhi2_y = np.ones(zazhi2.shape[0]) * 2 + # huangqi_y = np.ones(huangqi.shape[0]) * 1 + # gancaopian_y = np.ones(gancaopian.shape[0]) * 4 + # # hongqi_y = np.ones(hongqi.shape[0]) * 5 + # data_x = np.concatenate((beijing, zazhi1, huangqi, gancaopian), axis=0) + # data_y = np.concatenate((beijing_y, zazhi1_y, huangqi_y, gancaopian_y), axis=0) + # return data_x, data_y + + def data_construction(self, data_path='data/1.txt', select_bands=[91, 92, 93, 94, 95, 96, 97, 98, 99, 100], + type=['beijing', 'zazhi1', 'huangqi', 'gancaopian']): + ''' + :param data_path: 数据文件路径 + :param select_bands: 选择的波段 + :param type: 选择的类型 + :return: data_x, data_y + ''' + data = utils.read_envi_ascii(data_path) + # 判断读取的txt文件内是否有beijing和haungqi类型的数据 + if 'beijing' not in data or 'huangqi' not in data: + logging.error("数据文件中缺少'beijing'或'huangqi'类型标签") + raise ValueError("数据文件中缺少'beijing'或'huangqi'类型标签") + data_x = np.concatenate([data[key][:, select_bands] for key in type], axis=0) + data_y = np.concatenate([np.zeros(data[key].shape[0]) if key == 'beijing' else np.ones(data[key].shape[0]) + if key == 'huangqi' else np.ones(data[key].shape[0]) * (i + 2) for i, key in enumerate(type)], axis=0) + return data_x, data_y + + def predict(self, data_x): + ''' + 对数据进行预测 + :param data_x: 波段选择后的数据 + :return: 预测结果二值化后的数据，0为背景，1为黄芪,2为杂质2，3为杂质1，4为甘草片，5为红芪 + ''' + data_x_shape = data_x.shape + data_x = data_x.reshape(-1, data_x.shape[2]) + data_y = self.model.predict(data_x) + data_y = data_y.reshape(data_x_shape[0], data_x_shape[1]).astype(np.uint8) + data_y, centers, categories = self.connect_space(data_y) + result = {'data_y': data_y, 'centers': centers, 'categories': categories} + return result + + def connect_space(self, data_y): + # 连通域处理离散点 + labels, num_features = ndimage.label(data_y) + centers = [] + categories = [] + for i in range(1, num_features + 1): + mask = (labels == i) + counts = np.bincount(data_y[mask]) + category = np.argmax(counts) + data_y[mask] = category + center = ndimage.measurements.center_of_mass(data_y, labels, [i]) + center = list(center) + center = np.array(center).astype(int) + centers.append(center) + categories.append(category) + return data_y, centers, categories + + +if __name__ == '__main__': + detector = Astragalin() + detector.fit_value(file_name="astragalin.p", data_path="data/1.txt") \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..090ae06 --- /dev/null +++ b/main.py @@ -0,0 +1,197 @@ + +import socket +import time + +import numpy as np +import logging +import os +import sys +import cv2 as cv + +from classfier import Astragalin +from utils import DualSock, try_connect, receive_sock, parse_protocol, ack_sock, done_sock +from root_dir import ROOT_DIR +from model import resnet34 +import torch +from torchvision import transforms +from PIL import Image +import json +import matplotlib.pyplot as plt +from PIL import Image + + + +def process_cmd(cmd: str, data: any, connected_sock: socket.socket) -> tuple: + ''' + 处理指令 + :param cmd: 指令类型 + :param data: 指令内容 + :param connected_sock: socket + :param detector: 模型 + :return: 是否处理成功 + ''' + result = '' + if cmd == 'IM': + data = np.frombuffer(data, dtype=np.uint8) + data = cv.imdecode(data, cv.IMREAD_COLOR) + + # 显示图片 + cv.imshow('image', data) + cv.waitKey(0) + cv.destroyAllWindows() + + # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + # + # data_transform = transforms.Compose( + # [transforms.Resize(256), + # transforms.CenterCrop(224), + # transforms.ToTensor(), + # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) + # + # # load image + # # img_path = r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\1.jpg" + # # assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path) + # # img = Image.open(img_path) + # # plt.imshow(img) + # # [N, C, H, W] + # img = data_transform(data) + # # expand batch dimension + # img = torch.unsqueeze(img, dim=0) + # + # # read class_indict + # # json_path = './class_indices.json' + # # assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path) + # # + # # with open(json_path, "r") as f: + # # class_indict = json.load(f) + # + # # create model + # model = resnet34(num_classes=4).to(device) + # + # # load model weights + # weights_path = r"D:\project\deep-learning-for-image-processing-master\pytorch_classification\Test5_resnet\resNet34.pth" + # assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path) + # model.load_state_dict(torch.load(weights_path, map_location=device)) + # + # # prediction + # model.eval() + # with torch.no_grad(): + # # predict class + # output = torch.squeeze(model(img.to(device))).cpu() + # predict = torch.softmax(output, dim=0) + # predict_cla = torch.argmax(predict).numpy() + # result = predict_cla + # print(predict_cla) + # print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)], + # predict[predict_cla].numpy()) + # plt.title(print_res) + # for i in range(len(predict)): + # print("class: {:10} prob: {:.3}".format(class_indict[str(i)], + # predict[i].numpy())) + # plt.show() + + + # result = detector.predict(data) + # # 取出result中的字典中的centers和categories + # centers = result['centers'] + # categories = result['categories'] + # # 将centers和categories转换为字符串，每一位之间用,隔开，centers是list,每个元素为np.array，categories是1维数组 + # # centers_str = '|'.join([str(point[0][0]) + ',' + str(point[0][1]) for point in centers]) + # # categories_str = ','.join([str(i) for i in categories]) + # # # 将centers和categories的字符串拼接起来，中间用;隔开 + # # result = centers_str + ';' + categories_str + # 给result直接赋值，用于测试 + # result = 'HELLO WORLD' + # response = done_sock(connected_sock, cmd_type=cmd, result=result) + # print(result) + else: + logging.error(f'错误指令，指令为{cmd}') + # response = False + return result + +def bytes_to_img(data): + data1 = Image.frombytes('RGB', (1200, 4096), data, 'raw') + + + + + +# def main(is_debug=False): +# file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log')) +# file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) +# console_handler = logging.StreamHandler(sys.stdout) +# console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) +# logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s', +# handlers=[file_handler, console_handler], level=logging.DEBUG) +# dual_sock = DualSock(connect_ip='127.0.0.1') +# +# while not dual_sock.status: +# logging.error('连接被断开，正在重连') +# dual_sock.reconnect() +# detector = Astragalin(ROOT_DIR / 'models' / 'astragalin.p') +# # _ = detector.predict(np.ones((4096, 1200, 10), dtype=np.float32)) +# while True: +# pack, next_pack = receive_sock(dual_sock) # 接收数据，如果没有数据则阻塞，如果返回的是空字符串则表示出现错误 +# if pack == b"": # 无数据表示出现错误 +# time.sleep(5) +# dual_sock.reconnect() +# continue +# +# cmd, data = parse_protocol(pack) +# print(cmd) +# # print(data) +# +# process_cmd(cmd=cmd, data=data, connected_sock=dual_sock, detector=detector) + + +def main(is_debug=False): + file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log')) + file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) + logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s', + handlers=[file_handler, console_handler], level=logging.DEBUG) + dual_sock = DualSock(connect_ip='127.0.0.1') + + while not dual_sock.status: + logging.error('连接被断开，正在重连') + dual_sock.reconnect() + + + + # detector = Astragalin(ROOT_DIR / 'models' / 'resNet34.pth') + result_buffer = [] # 存储处理结果的缓冲区 + + while True: + for _ in range(5): + pack, next_pack = receive_sock(dual_sock) # 接收数据，如果没有数据则阻塞，如果返回的是空字符串则表示出现错误 + if pack == b"": # 无数据表示出现错误 + time.sleep(5) + dual_sock.reconnect() + break + + cmd, data = parse_protocol(pack) + print(cmd) + # print(data) + + result = process_cmd(cmd=cmd, data=data, connected_sock=dual_sock) + result_buffer.append(result) # 将处理结果添加到缓冲区 + + # 在这里进行对5次结果的处理，可以进行合并、比较等操作 + final_result = combine_results(result_buffer) + + # 发送最终结果 + response = done_sock(dual_sock, cmd_type=cmd, result=final_result) + print(final_result) + result_buffer = [] + + + +def combine_results(results): + # 在这里实现对5次结果的合并/比较等操作，根据实际需求进行修改 + # 这里只是简单地将结果拼接成一个字符串，你可能需要根据实际情况进行更复杂的处理 + return ';'.join(results) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/model.py b/model.py new file mode 100644 index 0000000..c2aa086 --- /dev/null +++ b/model.py @@ -0,0 +1,198 @@ +import torch.nn as nn +import torch + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, + kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(out_channel) + self.relu = nn.ReLU() + self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, + kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(out_channel) + self.downsample = downsample + + def forward(self, x): + identity = x + if self.downsample is not None: + identity = self.downsample(x) + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + """ + 注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。 + 但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2， + 这么做的好处是能够在top1上提升大概0.5%的准确率。 + 可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch + """ + expansion = 4 + + def __init__(self, in_channel, out_channel, stride=1, downsample=None, + groups=1, width_per_group=64): + super(Bottleneck, self).__init__() + + width = int(out_channel * (width_per_group / 64.)) * groups + + self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, + kernel_size=1, stride=1, bias=False) # squeeze channels + self.bn1 = nn.BatchNorm2d(width) + # ----------------------------------------- + self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, + kernel_size=3, stride=stride, bias=False, padding=1) + self.bn2 = nn.BatchNorm2d(width) + # ----------------------------------------- + self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion, + kernel_size=1, stride=1, bias=False) # unsqueeze channels + self.bn3 = nn.BatchNorm2d(out_channel*self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + + def forward(self, x): + identity = x + if self.downsample is not None: + identity = self.downsample(x) + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, + block, + blocks_num, + num_classes=1000, + include_top=True, + groups=1, + width_per_group=64): + super(ResNet, self).__init__() + self.include_top = include_top + self.in_channel = 64 + + self.groups = groups + self.width_per_group = width_per_group + + self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, + padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(self.in_channel) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, blocks_num[0]) + self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) + self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) + self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) + if self.include_top: + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + + def _make_layer(self, block, channel, block_num, stride=1): + downsample = None + if stride != 1 or self.in_channel != channel * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(channel * block.expansion)) + + layers = [] + layers.append(block(self.in_channel, + channel, + downsample=downsample, + stride=stride, + groups=self.groups, + width_per_group=self.width_per_group)) + self.in_channel = channel * block.expansion + + for _ in range(1, block_num): + layers.append(block(self.in_channel, + channel, + groups=self.groups, + width_per_group=self.width_per_group)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + if self.include_top: + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + + return x + + +def resnet34(num_classes=1000, include_top=True): + # https://download.pytorch.org/models/resnet34-333f7ec4.pth + return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) + + +def resnet50(num_classes=1000, include_top=True): + # https://download.pytorch.org/models/resnet50-19c8e357.pth + return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) + + +def resnet101(num_classes=1000, include_top=True): + # https://download.pytorch.org/models/resnet101-5d3b4d8f.pth + return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top) + + +def resnext50_32x4d(num_classes=1000, include_top=True): + # https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth + groups = 32 + width_per_group = 4 + return ResNet(Bottleneck, [3, 4, 6, 3], + num_classes=num_classes, + include_top=include_top, + groups=groups, + width_per_group=width_per_group) + + +def resnext101_32x8d(num_classes=1000, include_top=True): + # https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth + groups = 32 + width_per_group = 8 + return ResNet(Bottleneck, [3, 4, 23, 3], + num_classes=num_classes, + include_top=include_top, + groups=groups, + width_per_group=width_per_group) diff --git a/models/12.p b/models/12.p new file mode 100644 index 0000000..c876b0e Binary files /dev/null and b/models/12.p differ diff --git a/models/astragalin.p b/models/astragalin.p new file mode 100644 index 0000000..4428ca7 Binary files /dev/null and b/models/astragalin.p differ diff --git a/models/dt.p b/models/dt.p new file mode 100644 index 0000000..a2fd313 Binary files /dev/null and b/models/dt.p differ diff --git a/models/resNet34.pth b/models/resNet34.pth new file mode 100644 index 0000000..45154d6 Binary files /dev/null and b/models/resNet34.pth differ diff --git a/qt_test.py b/qt_test.py new file mode 100644 index 0000000..b72f5a5 --- /dev/null +++ b/qt_test.py @@ -0,0 +1,167 @@ + +import numpy as np +import socket +import logging +import matplotlib.pyplot as plt +import cv2 + +def rec_socket(recv_sock: socket.socket, cmd_type: str, ack: bool) -> bool: + if ack: + cmd = 'A' + cmd_type + else: + cmd = 'D' + cmd_type + while True: + try: + temp = recv_sock.recv(1) + except ConnectionError as e: + logging.error(f'连接出错, 错误代码:\n{e}') + return False + except TimeoutError as e: + logging.error(f'超时了，错误代码: \n{e}') + return False + except Exception as e: + logging.error(f'遇见未知错误，错误代码: \n{e}') + return False + if temp == b'\xaa': + break + + # 获取报文长度 + temp = b'' + while len(temp) < 4: + try: + temp += recv_sock.recv(1) + except Exception as e: + logging.error(f'接收报文长度失败, 错误代码: \n{e}') + return False + try: + data_len = int.from_bytes(temp, byteorder='big') + except Exception as e: + logging.error(f'转换失败,错误代码 \n{e}, \n报文内容\n{temp}') + return False + + # 读取报文内容 + temp = b'' + while len(temp) < data_len: + try: + temp += recv_sock.recv(data_len) + except Exception as e: + logging.error(f'接收报文内容失败, 错误代码: \n{e}，\n报文内容\n{temp}') + return False + data = temp + if cmd.strip().upper() != data[:4].decode('ascii').strip().upper(): + logging.error(f'客户端接收指令错误,\n指令内容\n{data}') + return False + else: + if cmd == 'DIM': + print(data) + + # 进行数据校验 + temp = b'' + while len(temp) < 3: + try: + temp += recv_sock.recv(1) + except Exception as e: + logging.error(f'接收报文校验失败, 错误代码: \n{e}') + return False + if temp == b'\xff\xff\xbb': + return True + else: + logging.error(f"接收了一个完美的只错了校验位的报文，\n data: {data}") + return False + + +# def main(): +# socket_receive = socket.socket(socket.AF_INET, socket.SOCK_STREAM) +# socket_receive.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) +# socket_receive.bind(('127.0.0.1', 21123)) +# socket_receive.listen(5) +# socket_send = socket.socket(socket.AF_INET, socket.SOCK_STREAM) +# socket_send.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) +# socket_send.bind(('127.0.0.1', 21122)) +# socket_send.listen(5) +# print('等待连接') +# socket_send_1, receive_addr_1 = socket_send.accept() +# print("连接成功：", receive_addr_1) +# # socket_send_2 = socket_send_1 +# socket_send_2, receive_addr_2 = socket_receive.accept() +# print("连接成功：", receive_addr_2) +# while True: +# cmd = input('请输入指令：').strip().upper() +# if cmd == 'IM': +# with open('data/newrawfile_ref.raw', 'rb') as f: +# data = np.frombuffer(f.read(), dtype=np.float32).reshape(750, 288, 384) +# data = data[:, [91, 92, 93, 94, 95, 96, 97, 98, 99, 100], :] +# n_rows, n_bands, n_cols = data.shape[0], data.shape[1], data.shape[2] +# print(f'n_rows：{n_rows}, n_bands：{n_bands}, n_cols：{n_cols}') +# n_rows, n_cols, n_bands = [x.to_bytes(2, byteorder='big') for x in [n_rows, n_cols, n_bands]] +# data = data.tobytes() +# length = len(data) + 10 +# print(f'length: {length}') +# length = length.to_bytes(4, byteorder='big') +# msg = b'\xaa' + length + (' ' + cmd).upper().encode('ascii') + n_rows + n_cols + n_bands + data + b'\xff\xff\xbb' +# socket_send_1.send(msg) +# print('发送成功') +# result = socket_send_2.recv(5) +# length = int.from_bytes(result[1:5], byteorder='big') +# result = b'' +# while len(result) < length: +# result += socket_send_2.recv(length) +# print(result) +# data = result[4:length].decode() +# print(data) + +def main(): + socket_receive = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + socket_receive.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + socket_receive.bind(('127.0.0.1', 21123)) + socket_receive.listen(5) + socket_send = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + socket_send.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + socket_send.bind(('127.0.0.1', 21122)) + socket_send.listen(5) + print('等待连接') + socket_send_1, receive_addr_1 = socket_send.accept() + print("连接成功：", receive_addr_1) + # socket_send_2 = socket_send_1 + socket_send_2, receive_addr_2 = socket_receive.accept() + print("连接成功：", receive_addr_2) + while True: + cmd = input().strip().upper() + if cmd == 'IM': + + image_paths = [ + r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\1.jpg", + r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\2.jpg", + r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\3.jpg", + r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\4.jpg", + r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\5.jpg", + ] + + # 连续发送五张图片 + for image_path in image_paths: + # 读取图片文件 + img = cv2.imread(image_path) + + # img = cv2.imread(r"/Users/zhouchao/Library/CloudStorage/OneDrive-macrosolid/PycharmProjects/wood_color/data/data20220919/dark/rgb60.png") + # img = cv2.imread(r"D:\project\Astragalin\data\02newrawfile_ref.png") + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.asarray(img, dtype=np.uint8) + width = img.shape[0] + height = img.shape[1] + print(width, height) + img_bytes = img.tobytes() + length = len(img_bytes) + 8 + print(length) + length = length.to_bytes(4, byteorder='big') + width = width.to_bytes(2, byteorder='big') + height = height.to_bytes(2, byteorder='big') + send_message = b'\xaa' + length + (' ' + cmd).upper().encode('ascii') + width + height + img_bytes + b'\xff\xff\xbb' + socket_send_1.send(send_message) + print('发送成功') + result = socket_send_2.recv(10) + print(result) + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/renovate.json b/renovate.json new file mode 100644 index 0000000..5db72dd --- /dev/null +++ b/renovate.json @@ -0,0 +1,6 @@ +{ + "$schema": "https://docs.renovatebot.com/renovate-schema.json", + "extends": [ + "config:recommended" + ] +} diff --git a/report.log b/report.log new file mode 100644 index 0000000..fbb5a0d --- /dev/null +++ b/report.log @@ -0,0 +1,840 @@ +2024-01-15 14:32:13,594 utils.py[line:218] - WARNING - ���Է����1������... +2024-01-15 14:32:18,595 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WARNING - ���ӳɹ� +2024-04-19 14:05:38,568 utils.py[line:293] - ERROR - �������ָ��ʧ�ܣ��������ͣ�timed out +2024-04-19 14:05:43,681 utils.py[line:293] - ERROR - �������ָ��ʧ�ܣ��������ͣ�timed out diff --git a/root_dir.py b/root_dir.py new file mode 100644 index 0000000..754b950 --- /dev/null +++ b/root_dir.py @@ -0,0 +1,5 @@ + +import pathlib + +file_path = pathlib.Path(__file__) +ROOT_DIR = file_path.parent \ No newline at end of file diff --git a/spectrum/01test.ipynb b/spectrum/01test.ipynb new file mode 100644 index 0000000..316df78 --- /dev/null +++ b/spectrum/01test.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2023-11-20T11:13:32.538976100Z", + "start_time": "2023-11-20T11:13:31.425375100Z" + } + }, + "outputs": [ + { + "ename": "AxisError", + "evalue": "axis 2 is out of bounds for array of dimension 1", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mAxisError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[8], line 49\u001B[0m\n\u001B[0;32m 46\u001B[0m tomato_spectrum_image \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mzeros_like(spectrum_image)\n\u001B[0;32m 48\u001B[0m \u001B[38;5;66;03m# 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作\u001B[39;00m\n\u001B[1;32m---> 49\u001B[0m mask \u001B[38;5;241m=\u001B[39m \u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstack\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[43mmask\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mbands\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 51\u001B[0m \u001B[38;5;66;03m# 使用掩码提取西红柿区域的光谱图像\u001B[39;00m\n\u001B[0;32m 52\u001B[0m tomato_spectrum_image \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mwhere(mask \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m255\u001B[39m, spectrum_image, \u001B[38;5;241m0\u001B[39m)\n", + "File \u001B[1;32mD:\\TG\\Miniconda3\\envs\\tengg\\lib\\site-packages\\numpy\\core\\shape_base.py:452\u001B[0m, in \u001B[0;36mstack\u001B[1;34m(arrays, axis, out, dtype, casting)\u001B[0m\n\u001B[0;32m 449\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mall input arrays must have the same shape\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 451\u001B[0m result_ndim \u001B[38;5;241m=\u001B[39m arrays[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m--> 452\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[43mnormalize_axis_index\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mresult_ndim\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 454\u001B[0m sl \u001B[38;5;241m=\u001B[39m (\u001B[38;5;28mslice\u001B[39m(\u001B[38;5;28;01mNone\u001B[39;00m),) \u001B[38;5;241m*\u001B[39m axis \u001B[38;5;241m+\u001B[39m (_nx\u001B[38;5;241m.\u001B[39mnewaxis,)\n\u001B[0;32m 455\u001B[0m expanded_arrays \u001B[38;5;241m=\u001B[39m [arr[sl] \u001B[38;5;28;01mfor\u001B[39;00m arr \u001B[38;5;129;01min\u001B[39;00m arrays]\n", + "\u001B[1;31mAxisError\u001B[0m: axis 2 is out of bounds for array of dimension 1" + ] + } + ], + "source": [ + "import numpy as np\n", + "import cv2\n", + "import os\n", + "\n", + "# 指定文件夹路径\n", + "folder_path = 'data' # 替换成你的文件夹路径\n", + "mask_folder_path = 'result' # 替换成你的掩码图像文件夹路径\n", + "\n", + "# 获取文件夹中的所有文件\n", + "files = os.listdir(folder_path)\n", + "\n", + "# 遍历每个文件\n", + "for file in files:\n", + " # 检查文件是否是hdr文件\n", + " if file.endswith('.hdr'):\n", + " # 构建完整的hdr文件路径\n", + " hdr_file_path = os.path.join(folder_path, file)\n", + "\n", + " # 读取hdr文件\n", + " with open(hdr_file_path, 'r') as hdr_file:\n", + " lines = hdr_file.readlines()\n", + " for line in lines:\n", + " if line.startswith('lines'):\n", + " height = int(line.split()[-1])\n", + " elif line.startswith('samples'):\n", + " width = int(line.split()[-1])\n", + " elif line.startswith('bands'):\n", + " bands = int(line.split()[-1])\n", + "\n", + " # 构建对应的raw文件路径\n", + " raw_file_path = os.path.splitext(hdr_file_path)[0] + '.raw'\n", + "\n", + " # 读取raw光谱图像\n", + " raw_image = np.fromfile(raw_file_path, dtype='float32')\n", + "\n", + " # 使用从hdr文件中读取的宽度、高度和波段数来重塑raw光谱图像的形状\n", + " spectrum_image = raw_image.reshape((height, width, bands))\n", + "\n", + " # 构建对应的掩码图像路径\n", + " mask_file_path = os.path.join(mask_folder_path, os.path.splitext(file)[0] + '.tiff')\n", + "\n", + " # 读取掩码图像，假设掩码图像是一个二值图像，西红柿的区域为255，其他区域为0\n", + " mask = cv2.imread(mask_file_path, cv2.IMREAD_GRAYSCALE)\n", + "\n", + " # 创建一个和光谱图像形状相同的全零数组，用于存储西红柿区域的光谱图像\n", + " tomato_spectrum_image = np.zeros_like(spectrum_image)\n", + "\n", + " # 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作\n", + " mask = np.stack([mask]*bands, axis=2)\n", + "\n", + " # 使用掩码提取西红柿区域的光谱图像\n", + " tomato_spectrum_image = np.where(mask == 255, spectrum_image, 0)\n", + "\n", + " # 计算每个谱段上西红柿的光谱信息均值\n", + " average_values_tomato = np.mean(tomato_spectrum_image, axis=(0, 1))\n", + "\n", + " # 打印结果\n", + " print(f\"文件 {file} 中西红柿区域各波段的平均值:\", average_values_tomato)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " ...\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]]\n", + "西红柿区域各波段的平均值: [0.02331245 0.02331355 0.02331434 0.02330351 0.02328778 0.02329406\n", + " 0.02326228 0.02324953 0.02320422 0.02319987 0.02320639 0.02320258\n", + " 0.02320204 0.02322337 0.02322935 0.02323991 0.02326387 0.02327245\n", + " 0.02326593 0.02327108 0.02324165 0.02325363 0.02322353 0.02321509\n", + " 0.02317422 0.02317277 0.02317923 0.02317507 0.02318152 0.02320312\n", + " 0.02320597 0.02322854 0.02325159 0.02326882 0.02326178 0.02326993\n", + " 0.02324535 0.02325787 0.02323463 0.02322856 0.02319151 0.02319442\n", + " 0.02320041 0.02319903 0.02320681 0.02323294 0.02324102 0.02326111\n", + " 0.02328818 0.02330373 0.02330286 0.02330293 0.02328876 0.02330017\n", + " 0.02327968 0.0232756 0.0232363 0.02323936 0.02324224 0.02324493\n", + " 0.02325153 0.02327927 0.02328969 0.02330778 0.02333499 0.02334016\n", + " 0.02334813 0.02334237 0.0233291 0.02333678 0.02331344 0.02330263\n", + " 0.02326433 0.02325943 0.02326216 0.02326128 0.02326241 0.02328353\n", + " 0.023289 0.02329529 0.0233208 0.02332475 0.02332811 0.02332091\n", + " 0.02330128 0.02331001 0.02327851 0.02326897 0.02322166 0.02321757\n", + " 0.02322467 0.02322131 0.02321952 0.02323928 0.02324408 0.02325601\n", + " 0.02328104 0.02329077 0.02328341 0.02329056 0.02326275 0.02327192\n", + " 0.02324721 0.02323763 0.02319732 0.02319809 0.02320318 0.02319734\n", + " 0.0232041 0.02322625 0.02322948 0.02325055 0.02327887 0.02329449\n", + " 0.02329181 0.02329812 0.02327645 0.02328718 0.02326863 0.02325965\n", + " 0.02322663 0.02322855 0.02323177 0.02323323 0.02324393 0.02327164\n", + " 0.02328169 0.0233025 0.02333108 0.02334195 0.02334395 0.0233406\n", + " 0.02332718 0.02333372 0.02331317 0.02330532 0.02326252 0.02326255\n", + " 0.02326744 0.02326685 0.02326851 0.02328742 0.02329235 0.02330284\n", + " 0.02332182 0.02332215 0.02332292 0.02331406 0.02329648 0.02330398\n", + " 0.02327215 0.02326116 0.02321737 0.02320999 0.02321728 0.02321167\n", + " 0.02320731 0.02323294 0.02323689 0.02324381 0.02326807 0.02327303\n", + " 0.02327289 0.02326986 0.02324365 0.02325905 0.02322375 0.02321331\n", + " 0.02317007 0.02316415 0.02317228 0.02316663 0.02316599 0.02318855\n", + " 0.02318928 0.02320436 0.02322696 0.02323972 0.02323037 0.02323674\n", + " 0.02321082 0.02322122 0.02319865 0.02318867 0.02314706 0.02315172\n", + " 0.02315546 0.02315242 0.02316325 0.02318312 0.02318851 0.02321093\n", + " 0.02323843 0.02325755 0.0232583 0.02326134 0.02324466 0.02325552\n", + " 0.02324029 0.02323927 0.02320264 0.0232074 0.02321127 0.02321607\n", + " 0.02322524 0.02325411 0.02326476 0.0232875 0.02331666 0.02332832\n", + " 0.02333939 0.02333211 0.02332272 0.0233288 0.02330828 0.02329813\n", + " 0.0232588 0.02325521 0.02325736 0.02325919 0.02325801 0.02327962\n", + " 0.02328656 0.02329215]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import cv2\n", + "\n", + "# 指定raw光谱图像和mask掩码图像的路径\n", + "raw_file_path = 'data/39_ref.raw' # 替换成你的raw光谱图像路径\n", + "mask_file_path = 'result/39.tiff' # 替换成你的mask掩码图像路径\n", + "\n", + "# 读取raw光谱图像\n", + "raw_image = np.fromfile(raw_file_path, dtype='float32')\n", + "\n", + "# 使用图像的宽度、高度和波段数来重塑raw光谱图像的形状\n", + "height = 756 # 替换成你的图像高度\n", + "width = 1200 # 替换成你的图像宽度\n", + "bands = 224 # 替换成你的图像波段数\n", + "spectrum_image = raw_image.reshape((height, width, bands))\n", + "\n", + "# 读取mask掩码图像，假设掩码图像是一个二值图像，西红柿的区域为255，其他区域为0\n", + "mask = cv2.imread(mask_file_path, cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# 创建一个和光谱图像形状相同的全零数组，用于存储西红柿区域的光谱图像\n", + "tomato_spectrum_image = np.zeros_like(spectrum_image)\n", + "\n", + "# 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作\n", + "mask = np.stack([mask]*bands, axis=2)\n", + "\n", + "# 使用掩码提取西红柿区域的光谱图像\n", + "tomato_spectrum_image = np.where(mask == 255, spectrum_image, 0)\n", + "print(tomato_spectrum_image)\n", + "# 计算每个谱段上西红柿的光谱信息均值\n", + "average_values_tomato = np.mean(tomato_spectrum_image, axis=(0, 1))\n", + "\n", + "# 打印结果\n", + "print(\"西红柿区域各波段的平均值:\", average_values_tomato)\n", + "import pandas as pd\n", + "\n", + "# 创建一个DataFrame来存储结果\n", + "df = pd.DataFrame(average_values_tomato, columns=['Average Spectral Values'])\n", + "\n", + "# # 将DataFrame导出为Excel文件\n", + "# df.to_excel('39.xlsx', index=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-20T13:35:21.812410400Z", + "start_time": "2023-11-20T13:35:17.418971100Z" + } + }, + "id": "8bee30bdaaf3385e" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# 创建一个DataFrame来存储结果\n", + "df = pd.DataFrame(average_values_tomato, columns=['Average Spectral Values'])\n", + "\n", + "# 将DataFrame导出为Excel文件\n", + "df.to_excel('average_values_tomato.xlsx', index=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-20T11:14:35.425251700Z", + "start_time": "2023-11-20T11:14:34.752973600Z" + } + }, + "id": "28ec09f66baecac8" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "5d455acb1d6881e0" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spectrum/02picture.ipynb b/spectrum/02picture.ipynb new file mode 100644 index 0000000..403122c --- /dev/null +++ b/spectrum/02picture.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-03-18T12:58:36.021344Z", + "start_time": "2024-03-18T12:58:35.489144Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import cv2 as cv\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "(39911424,)\n", + "(87, 2048, 224)\n", + "\n", + "[[[1686. 1654. 1654. ... 1654. 1622. 1654.]\n", + " [1689. 1673. 1673. ... 1689. 1689. 1673.]\n", + " [1673. 1657. 1657. ... 1673. 1753. 1609.]\n", + " ...\n", + " [1609. 1689. 1721. ... 1657. 1705. 1577.]\n", + " [1718. 1702. 1654. ... 1574. 1638. 1686.]\n", + " [1686. 1670. 1686. ... 1590. 1686. 1670.]]\n", + "\n", + " [[1654. 1670. 1718. ... 1686. 1654. 1734.]\n", + " [1721. 1657. 1641. ... 1689. 1657. 1721.]\n", + " [1641. 1625. 1705. ... 1705. 1689. 1609.]\n", + " ...\n", + " [1625. 1673. 1689. ... 1673. 1673. 1641.]\n", + " [1686. 1622. 1654. ... 1686. 1654. 1606.]\n", + " [1734. 1654. 1670. ... 1510. 1654. 1654.]]\n", + "\n", + " [[1718. 1702. 1670. ... 1622. 1670. 1686.]\n", + " [1657. 1641. 1641. ... 1705. 1689. 1657.]\n", + " [1673. 1673. 1689. ... 1673. 1625. 1657.]\n", + " ...\n", + " [1625. 1673. 1705. ... 1625. 1609. 1625.]\n", + " [1702. 1686. 1654. ... 1638. 1670. 1654.]\n", + " [1638. 1702. 1686. ... 1670. 1702. 1638.]]\n", + "\n", + " ...\n", + "\n", + " [[1638. 1606. 1686. ... 1654. 1670. 1638.]\n", + " [1721. 1673. 1673. ... 1689. 1641. 1625.]\n", + " [1641. 1641. 1673. ... 1657. 1641. 1625.]\n", + " ...\n", + " [1625. 1609. 1689. ... 1657. 1657. 1673.]\n", + " [1638. 1606. 1702. ... 1622. 1622. 1654.]\n", + " [1574. 1654. 1622. ... 1718. 1670. 1622.]]\n", + "\n", + " [[1590. 1686. 1638. ... 1654. 1654. 1638.]\n", + " [1641. 1657. 1609. ... 1673. 1657. 1641.]\n", + " [1721. 1657. 1641. ... 1657. 1673. 1577.]\n", + " ...\n", + " [1657. 1673. 1641. ... 1593. 1609. 1609.]\n", + " [1622. 1670. 1670. ... 1686. 1734. 1622.]\n", + " [1670. 1590. 1670. ... 1638. 1590. 1638.]]\n", + "\n", + " [[1654. 1654. 1638. ... 1606. 1606. 1558.]\n", + " [1689. 1657. 1689. ... 1657. 1625. 1673.]\n", + " [1657. 1561. 1657. ... 1657. 1673. 1609.]\n", + " ...\n", + " [1657. 1625. 1689. ... 1657. 1609. 1625.]\n", + " [1638. 1654. 1654. ... 1622. 1606. 1590.]\n", + " [1590. 1686. 1606. ... 1574. 1686. 1686.]]]\n" + ] + } + ], + "source": [ + "##合成真彩色图像\n", + "#读取raw文件\n", + "imagePath ='data/new/yellow/1.raw' \n", + "print(type(imagePath))\n", + "rawImage = np.fromfile(imagePath,\"uint16\")\n", + "print(type(rawImage))\n", + "rawShape = rawImage.shape\n", + "print(rawShape)\n", + "formatImage = np.zeros((87,2048,224))\n", + "for row in range(0,87):\n", + " for dim in range(0,224):\n", + " formatImage[row,:,dim] = rawImage[(dim + row*224) * 2048:(dim + 1 + row*224)* 2048]\n", + "print(formatImage.shape)\n", + "print(type(formatImage))\n", + "print(formatImage)\n", + "\n", + "#选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n", + "# imgR = formatImage[:,:,88]\n", + "# imgG = formatImage[:,:,58]\n", + "# imgB = formatImage[:,:,31]\n", + "# \n", + "# rgbImg = cv.merge([imgR,imgG,imgB])\n", + "# print(rgbImg.shape)\n", + "# print(type(rgbImg))\n", + "# cv.imshow('test',rgbImg)\n", + "# cv.waitKey(0)\n", + "# cv.destroyAllWindows()\n", + "# plt.imshow(rgbImg)\n", + "# #保存\n", + "# plt.savefig('result/1_ref.png')\n", + "\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T13:06:05.055643Z", + "start_time": "2024-03-18T13:06:04.869067Z" + } + }, + "id": "3443037608cc60f6" + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "(50462720,)\n" + ] + } + ], + "source": [ + "##合成真彩色图像\n", + "#读取raw文件\n", + "imagePath = 'data/数据/原光源（条光）/黄果（成熟）/1.raw' \n", + "print(type(imagePath))\n", + "rawImage = np.fromfile(imagePath,\"float16\")\n", + "print(type(rawImage))\n", + "rawShape = rawImage.shape\n", + "print(rawShape)\n", + "formatImage = np.zeros((110,2048,224))\n", + "# 假设 rawImage 已经正确读取\n", + "raw_length = len(rawImage) # 获取 rawImage 的实际长度\n", + "\n", + "for row in range(0, 110):\n", + " for dim in range(0, 224):\n", + " start_index = (dim + row*224) * 2048\n", + " end_index = (dim + 1 + row*224) * 2048\n", + " \n", + " # 检查索引是否在 rawImage 的边界内\n", + " if start_index < raw_length and end_index <= raw_length:\n", + " formatImage[row,:,dim] = rawImage[start_index:end_index]\n", + " else:\n", + " print(f\"索引超出范围: 行 {row}, 波段 {dim}\")\n", + "\n", + "# for row in range(0,110):\n", + "# for dim in range(0,224):\n", + "# # formatImage[row,:,dim] = rawImage[(dim + row*224) * 2048:(dim + 1 + row*224)* 2048]\n", + " \n", + "#选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n", + "# imgR = formatImage[:,:,31]\n", + "# imgG = formatImage[:,:,58]\n", + "# imgB = formatImage[:,:,88]\n", + "# \n", + "# rgbImg = cv.merge([imgR,imgG,imgB])\n", + "# print(rgbImg.shape)\n", + "# print(type(rgbImg))\n", + "# cv.imshow('test',rgbImg)\n", + "# cv.waitKey(0)\n", + "# cv.destroyAllWindows()\n", + "# plt.imshow(rgbImg)\n", + "# #保存\n", + "# plt.savefig('result/1_ref.png')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T03:26:37.014093Z", + "start_time": "2024-03-18T03:26:36.769031Z" + } + }, + "id": "283f370b2401221b", + "execution_count": 5 + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(928, 1200, 3)\n", + "\n", + "(848, 1200, 3)\n", + "\n", + "(902, 1200, 3)\n", + "\n", + "(795, 1200, 3)\n", + "\n", + "(783, 1200, 3)\n", + "\n", + "(708, 1200, 3)\n", + "\n", + "(747, 1200, 3)\n", + "\n", + "(630, 1200, 3)\n", + "\n", + "(709, 1200, 3)\n", + "\n", + "(699, 1200, 3)\n", + "\n", + "(796, 1200, 3)\n", + "\n", + "(865, 1200, 3)\n", + "\n", + "(791, 1200, 3)\n", + "\n", + "(723, 1200, 3)\n", + "\n", + "(797, 1200, 3)\n", + "\n", + "(760, 1200, 3)\n", + "\n", + "(701, 1200, 3)\n", + "\n", + "(770, 1200, 3)\n", + "\n", + "(637, 1200, 3)\n", + "\n", + "(729, 1200, 3)\n", + "\n", + "(762, 1200, 3)\n", + "\n", + "(732, 1200, 3)\n", + "\n", + "(839, 1200, 3)\n", + "\n", + "(760, 1200, 3)\n", + "\n", + "(711, 1200, 3)\n", + "\n", + "(736, 1200, 3)\n", + "\n", + "(758, 1200, 3)\n", + "\n", + "(772, 1200, 3)\n", + "\n", + "(744, 1200, 3)\n", + "\n", + "(775, 1200, 3)\n", + "\n", + "(821, 1200, 3)\n", + "\n", + "(775, 1200, 3)\n", + "\n", + "(820, 1200, 3)\n", + "\n", + "(772, 1200, 3)\n", + "\n", + "(772, 1200, 3)\n", + "\n", + "(747, 1200, 3)\n", + "\n", + "(770, 1200, 3)\n", + "\n", + "(645, 1200, 3)\n", + "\n", + "(756, 1200, 3)\n", + "\n" + ] + } + ], + "source": [ + "##循环合成真彩色图像\n", + "# 指定文件夹路径\n", + "folder_path = 'data' # 替换成你的文件夹路径\n", + "# mask_out_path = 'result' # 替换成你的掩码图像文件夹路径\n", + "\n", + "# 获取文件夹中的所有文件\n", + "files = os.listdir(folder_path)\n", + "\n", + "\n", + "for i in range(1, 40):\n", + " with open(folder_path + '/' + str(i) + '_ref.hdr', 'r') as hdr_file:\n", + " lines = hdr_file.readlines()\n", + " for line in lines:\n", + " if line.startswith('lines'):\n", + " height = int(line.split()[-1])\n", + " elif line.startswith('samples'):\n", + " width = int(line.split()[-1])\n", + " elif line.startswith('bands'):\n", + " bands = int(line.split()[-1])\n", + " raw_image = np.fromfile(folder_path + '/' + str(i) + '_ref.raw', dtype='float32')\n", + " \n", + " formatImage = np.zeros((height, width, bands))\n", + " for row in range(0, height):\n", + " for dim in range(0, bands):\n", + " formatImage[row,:,dim] = raw_image[(dim + row*bands) * width:(dim + 1 + row*bands)* width]\n", + " #选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n", + " imgR = formatImage[:,:,12]\n", + " imgG = formatImage[:,:,46]\n", + " imgB = formatImage[:,:,96]\n", + " \n", + " rgbImg = cv.merge([imgR,imgG,imgB])\n", + " print(rgbImg.shape)\n", + " print(type(rgbImg))\n", + " \n", + " Img_Name = \"./tcimage/\" + str(i)+ \".png\"\n", + " cv.imwrite(Img_Name, rgbImg*255)\n", + " # cv.imshow('test',rgbImg)\n", + " # cv.waitKey(0)\n", + " # cv.destroyAllWindows()\n", + "# #读取raw文件\n", + "# imagePath = 'data/1_ref.raw' \n", + "# print(type(imagePath))\n", + "# rawImage = np.fromfile(imagePath,\"float32\")\n", + "# print(type(rawImage))\n", + "# rawShape = rawImage.shape\n", + "# print(rawShape)\n", + "# formatImage = np.zeros((928,1200,224))\n", + "# for row in range(0,928):\n", + "# for dim in range(0,224):\n", + "# formatImage[row,:,dim] = rawImage[(dim + row*224) * 1200:(dim + 1 + row*224)* 1200]\n", + "# #选择通道数为12/46/96的三个通道相当于rgb三通道的图片\n", + "# imgR = formatImage[:,:,12]\n", + "# imgG = formatImage[:,:,46]\n", + "# imgB = formatImage[:,:,96]\n", + " \n", + "# rgbImg = cv.merge([imgR,imgG,imgB])\n", + "# print(rgbImg.shape)\n", + "# print(type(rgbImg))\n", + "# cv.imshow('test',rgbImg)\n", + "# cv.waitKey(0)\n", + "# cv.destroyAllWindows()\n", + "# plt.imshow(rgbImg)\n", + "# #保存\n", + "# plt.savefig('result/1_ref.png')\n", + "\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-24T02:56:13.173734400Z", + "start_time": "2023-11-24T02:50:24.958389400Z" + } + }, + "id": "15a3889f7bf1917f" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "17b929b3de3fa2ed" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "e00c8e929ee57b60" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spectrum/03mask.ipynb b/spectrum/03mask.ipynb new file mode 100644 index 0000000..356f917 --- /dev/null +++ b/spectrum/03mask.ipynb @@ -0,0 +1,789 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-03-18T07:45:52.076766Z", + "start_time": "2024-03-18T07:45:52.061770Z" + } + }, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#读取图片\n", + "img = cv2.imread('datas/39.tiff')\n", + "hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", + "cv2.imshow('HSV', hsv)\n", + "lower_red = np.array([10, 70, 70])\n", + "upper_red = np.array([255, 255, 255])\n", + "mask = cv2.inRange(hsv, lower_red, upper_red) #lower20===>0,upper200==>0,\n", + "#保存\n", + "cv2.imwrite('result/39.tiff', mask)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-20T12:33:25.384530Z", + "start_time": "2023-11-20T12:33:25.353136100Z" + } + }, + "id": "eba7a5b467780310" + }, + { + "cell_type": "code", + "execution_count": 56, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import cv2\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "#通过OpenCV读取图片信息\n", + "img = cv2.imread('data/1.tiff')\n", + "# BGR图转为HSV\n", + "hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", + "# 提取hsv中H通道数据\n", + "h = hsv[:, :, 0].ravel()\n", + "# 直方图显示\n", + "plt.hist(h, 180, [0, 180])\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T08:31:54.546425Z", + "start_time": "2024-03-18T08:31:54.363728Z" + } + }, + "id": "14abba9e7d1d9496" + }, + { + "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#亮度增强\n", + "from PIL import Image, ImageEnhance\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 打开图像\n", + "image = Image.open('data/new/yellow/1.tiff')\n", + "\n", + "# 亮度增强\n", + "enhancer = ImageEnhance.Brightness(image)\n", + "enhanced_image = enhancer.enhance(3) # 可以尝试不同的值以找到最佳效果\n", + "\n", + "# 显示图像\n", + "plt.imshow(enhanced_image)\n", + "plt.axis('off') # 关闭坐标轴\n", + "plt.show()\n", + "\n", + "# 如有需要，保存增强后的图像\n", + "enhanced_image.save('enhanced_image.png')\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T15:43:17.592593Z", + "start_time": "2024-03-18T15:43:17.520589Z" + } + }, + "id": "4bde773e19fd521e", + "execution_count": 77 + }, + { + "cell_type": "code", + "execution_count": 78, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np\n", + "\n", + "#通过OpenCV读取图片信息\n", + "img = cv2.imread('enhanced_image.png')\n", + "hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", + "cv2.imshow(\"img\", img)\n", + "\n", + "# HSV 的下界限\n", + "lower_red = np.array([133,50,50])\n", + "# HSV 的上界限\n", + "upper_red = np.array([179,255,255])\n", + "\n", + "# 通过上下限提取范围内的掩模mask\n", + "mask = cv2.inRange(hsv, lower_red, upper_red)\n", + "cv2.imshow(\"mask\", mask)\n", + "\n", + "# 腐蚀与膨胀处理\n", + "# 定义\n", + "kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(2, 2))#定义结构元素的形状和大小\n", + "\n", + "openingmask = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernel) # 闭运算\n", + "openingmask1 = cv2.morphologyEx(openingmask, cv2.MORPH_OPEN,kernel) # 开运算\n", + "cv2.imshow('mask',openingmask)\n", + "cv2.imshow('mask1',openingmask1)\n", + "\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()\n", + "\n", + "\n", + "# 找出所有白色连通区域\n", + "num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(openingmask1, 4, cv2.CV_32S)\n", + "\n", + "# 遍历所有连通区域，移除面积小于特定阈值的区域\n", + "area_threshold = 10 # 可以根据实际情况调整阈值大小\n", + "for i in range(1, num_labels): # 标签0是背景\n", + " if stats[i, cv2.CC_STAT_AREA] < area_threshold:\n", + " openingmask1[labels == i] = 0\n", + "\n", + "# 保存和显示处理后的掩膜\n", + "cv2.imwrite('cleaned_mask.png', openingmask1)\n", + "cv2.imshow('Cleaned Mask', openingmask1)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()\n", + "\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T15:43:25.341649Z", + "start_time": "2024-03-18T15:43:19.273376Z" + } + }, + "id": "a6e663957af72d33" + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T10:43:49.218155Z", + "start_time": "2024-03-18T10:43:26.620748Z" + } + }, + "id": "7264359547e5d56f", + "execution_count": 25 + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "#白色颜色带处理\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "\n", + "# 加载图像\n", + "mask_path = 'mask1/new/empty/yellow/mask_1.png'\n", + "\n", + "\n", + "\n", + "\n", + "# 边缘去除函数\n", + "def remove_edge_bands(mask, edge_threshold=10):\n", + " h, w = mask.shape[:2]\n", + "\n", + " # 去除上下边缘区域\n", + " mask[:edge_threshold, :] = 0\n", + " mask[h-edge_threshold:, :] = 0\n", + "\n", + " return mask\n", + "\n", + "# 应用边缘去除\n", + "cleaned_mask = remove_edge_bands(mask)\n", + "\n", + "# 保存处理后的图像\n", + "output_path = '测试.png'\n", + "cv2.imwrite(output_path, cleaned_mask)\n", + "# 显示处理后的图像\n", + "cv2.imshow('Cleaned Mask ', cleaned_mask)\n", + "\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()\n", + "\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T16:11:08.430381Z", + "start_time": "2024-03-18T16:11:04.710701Z" + } + }, + "id": "9eedb7f708e43473", + "execution_count": 95 + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: 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V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n", + "H: (0, 179), S: (0, 255), V: (0, 255)\n" + ] + }, + { + "ename": "error", + "evalue": "OpenCV(4.9.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\highgui\\src\\window_w32.cpp:2561: error: (-27:Null pointer) NULL window: 'HSV Thresholding' in function 'cvGetTrackbarPos'\n", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31merror\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[70], line 28\u001B[0m\n\u001B[0;32m 24\u001B[0m hsv \u001B[38;5;241m=\u001B[39m cv2\u001B[38;5;241m.\u001B[39mcvtColor(image, cv2\u001B[38;5;241m.\u001B[39mCOLOR_BGR2HSV)\n\u001B[0;32m 26\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[0;32m 27\u001B[0m \u001B[38;5;66;03m# 获取滑动条的当前位置\u001B[39;00m\n\u001B[1;32m---> 28\u001B[0m h_lower \u001B[38;5;241m=\u001B[39m \u001B[43mcv2\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgetTrackbarPos\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mH Lower\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mHSV Thresholding\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 29\u001B[0m h_upper \u001B[38;5;241m=\u001B[39m cv2\u001B[38;5;241m.\u001B[39mgetTrackbarPos(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mH Upper\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mHSV Thresholding\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 30\u001B[0m s_lower \u001B[38;5;241m=\u001B[39m cv2\u001B[38;5;241m.\u001B[39mgetTrackbarPos(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mS Lower\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mHSV Thresholding\u001B[39m\u001B[38;5;124m'\u001B[39m)\n", + "\u001B[1;31merror\u001B[0m: OpenCV(4.9.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\highgui\\src\\window_w32.cpp:2561: error: (-27:Null pointer) NULL window: 'HSV Thresholding' in function 'cvGetTrackbarPos'\n" + ] + } + ], + "source": [ + "#实现利用hsv空间进行阈值分割，要求hsv三个值做成滑动轴可拖动的实时显示当前阈值分割效果，并且显示当前hsv三个的具体值，\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "\n", + "def nothing(x):\n", + " pass\n", + "\n", + "# 创建一个窗口\n", + "cv2.namedWindow('HSV Thresholding')\n", + "\n", + "# 创建滑动条，用于调整HSV阈值\n", + "cv2.createTrackbar('H Lower', 'HSV Thresholding', 0, 179, nothing)\n", + "cv2.createTrackbar('H Upper', 'HSV Thresholding', 179, 179, nothing)\n", + "cv2.createTrackbar('S Lower', 'HSV Thresholding', 0, 255, nothing)\n", + "cv2.createTrackbar('S Upper', 'HSV Thresholding', 255, 255, nothing)\n", + "cv2.createTrackbar('V Lower', 'HSV Thresholding', 0, 255, nothing)\n", + "cv2.createTrackbar('V Upper', 'HSV Thresholding', 255, 255, nothing)\n", + "\n", + "# 读取图像\n", + "image = cv2.imread('enhanced_image.png')\n", + "\n", + "# 将BGR图像转换为HSV\n", + "hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n", + "\n", + "while True:\n", + " # 获取滑动条的当前位置\n", + " h_lower = cv2.getTrackbarPos('H Lower', 'HSV Thresholding')\n", + " h_upper = cv2.getTrackbarPos('H Upper', 'HSV Thresholding')\n", + " s_lower = cv2.getTrackbarPos('S Lower', 'HSV Thresholding')\n", + " s_upper = cv2.getTrackbarPos('S Upper', 'HSV Thresholding')\n", + " v_lower = cv2.getTrackbarPos('V Lower', 'HSV Thresholding')\n", + " v_upper = cv2.getTrackbarPos('V Upper', 'HSV Thresholding')\n", + "\n", + " # 创建HSV阈值掩膜\n", + " lower_bound = np.array([h_lower, s_lower, v_lower])\n", + " upper_bound = np.array([h_upper, s_upper, v_upper])\n", + " mask = cv2.inRange(hsv, lower_bound, upper_bound)\n", + "\n", + " # 对图像应用掩膜以获取结果\n", + " result = cv2.bitwise_and(image, image, mask=mask)\n", + "\n", + " # 显示图像\n", + " cv2.imshow('HSV Thresholding', result)\n", + "\n", + " # 打印当前HSV阈值\n", + " print(f\"H: ({h_lower}, {h_upper}), S: ({s_lower}, {s_upper}), V: ({v_lower}, {v_upper})\")\n", + "\n", + " # 按'q'键退出循环\n", + " if cv2.waitKey(1) & 0xFF == ord('q'):\n", + " break\n", + "\n", + "# 销毁所有窗口\n", + "cv2.destroyAllWindows()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T15:39:44.463493Z", + "start_time": "2024-03-18T15:39:40.005484Z" + } + }, + "id": "a42269b65f848794", + "execution_count": 70 + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [], + "source": [ + "##循环提取mask\n", + "\n", + "# 指定文件夹路径\n", + "folder_path = 'tcimage' # 替换成你的文件夹路径\n", + "\n", + "# 获取文件夹中的所有文件\n", + "files = os.listdir(folder_path)\n", + "\n", + "for file_name in files:\n", + " file_path = os.path.join(folder_path,file_name)\n", + "\n", + " img = cv2.imread(file_path)\n", + " \n", + " hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", + "\n", + " # cv2.imshow(\"img\", img)\n", + "\n", + " # HSV 的下界限\n", + " lower_red = np.array([160,70,15])\n", + " # HSV 的上界限\n", + " upper_red = np.array([179,255,255])\n", + "\n", + " # 通过上下限提取范围内的掩模mask\n", + " mask = cv2.inRange(hsv, lower_red, upper_red)\n", + " # cv2.imshow(\"mask\", mask)\n", + " \n", + " # 腐蚀与膨胀处理\n", + " \n", + " kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(3, 3))#定义结构元素的形状和大小\n", + " openingmask = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernel) # 闭运算\n", + " openingmask1 = cv2.morphologyEx(openingmask, cv2.MORPH_OPEN,kernel) # 开运算\n", + " \n", + " # cv2.imshow('mask',openingmask)\n", + " # cv2.imshow('mask1',openingmask1)\n", + " # \n", + " # cv2.waitKey(0)\n", + " # cv2.destroyAllWindows()\n", + "\n", + " Img_Name = \"./mask/\" + file_name \n", + " cv2.imwrite(Img_Name, openingmask1)\n", + "\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T08:14:58.545492Z", + "start_time": "2024-03-18T08:14:58.534491Z" + } + }, + "id": "3665c0b573adb358" + }, + { + "cell_type": "code", + "execution_count": 57, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "处理完成，掩膜图像已保存到指定文件夹。\n" + ] + } + ], + "source": [ + " #先增强图像亮度，然后循环提取并保存mask（20240318版无锡透射百香果）ps:目前参数适用于old文件夹内图像，new文件夹内图像需要调整参数\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "import os\n", + "from PIL import Image, ImageEnhance\n", + "\n", + "# 消除边缘区域条状白色色带，实现仅包含中心果子区域为白色目标区域\n", + "# 边缘去除函数\n", + "def remove_edge_bands(mask, edge_threshold=8):\n", + " h, w = mask.shape[:2]\n", + "\n", + " # 去除上下边缘区域\n", + " mask[:edge_threshold+6, :] = 0\n", + " mask[h-edge_threshold:, :] = 0\n", + "\n", + " return mask\n", + "\n", + "\n", + "\n", + "\n", + "# 设定输入和输出文件夹路径\n", + "input_folder_path = 'data/new/green' # 替换为tiff图像的文件夹路径\n", + "output_folder_path = 'mask/new/green' # 替换为保存mask的文件夹路径\n", + "\n", + "# 检查输出文件夹是否存在，如果不存在就创建它\n", + "if not os.path.exists(output_folder_path):\n", + " os.makedirs(output_folder_path)\n", + "\n", + "# 遍历指定文件夹内的所有tiff文件\n", + "for file_name in os.listdir(input_folder_path):\n", + " if file_name.endswith('.tiff'):\n", + " # 构建完整的文件路径\n", + " file_path = os.path.join(input_folder_path, file_name)\n", + "\n", + " # 打开并增强图像亮度\n", + " image = Image.open(file_path)\n", + " enhancer = ImageEnhance.Brightness(image)\n", + " enhanced_image = enhancer.enhance(3.5) # 增强亮度\n", + " \n", + " # 将PIL图像转换为opencv图像\n", + " img = np.array(enhanced_image)\n", + " img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n", + "\n", + " # 转换到HSV空间\n", + " hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", + "\n", + " # 设置HSV阈值\n", + " lower_red = np.array([133, 40, 43])\n", + " upper_red = np.array([179, 255, 255])\n", + "\n", + " # 提取掩膜\n", + " mask = cv2.inRange(hsv, lower_red, upper_red)\n", + "\n", + " # 腐蚀与膨胀处理\n", + " kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (2, 2))\n", + " openingmask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # 闭运算\n", + " openingmask1 = cv2.morphologyEx(openingmask, cv2.MORPH_OPEN, kernel) # 开运算\n", + "\n", + " # 连通区域处理\n", + " num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(openingmask1, 4, cv2.CV_32S)\n", + " area_threshold = 30\n", + " for i in range(1, num_labels): # 标签0是背景\n", + " if stats[i, cv2.CC_STAT_AREA] < area_threshold:\n", + " openingmask1[labels == i] = 0\n", + " \n", + " # 应用边缘去除\n", + " cleaned_mask = remove_edge_bands(openingmask1)\n", + " \n", + " # 生成输出文件名和路径\n", + " output_file_name = 'mask_' + os.path.splitext(file_name)[0] + '.png'\n", + " output_file_path = os.path.join(output_folder_path, output_file_name)\n", + "\n", + " # 保存处理后的掩膜\n", + " cv2.imwrite(output_file_path, cleaned_mask)\n", + "\n", + "print(\"处理完成，掩膜图像已保存到指定文件夹。\")\n", + "\n", + "###原条光黄果p110顶端少1像素宽度图像，原因在于后续图像过窄，目前参数已是最优解，选择手动替换p110" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T12:15:14.697171Z", + "start_time": "2024-03-18T12:15:14.164074Z" + } + }, + "id": "ab7f303dfa7d8a27" + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "处理完成，掩膜图像已保存到指定文件夹。\n" + ] + } + ], + "source": [ + " #先增强图像亮度，然后循环提取并保存mask（20240318版无锡透射百香果）ps:new文件夹内\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "import os\n", + "from PIL import Image, ImageEnhance\n", + "\n", + "# 消除边缘区域条状白色色带，实现仅包含中心果子区域为白色目标区域\n", + "# 边缘去除函数\n", + "def remove_edge_bands(mask, edge_threshold=2):\n", + " h, w = mask.shape[:2]\n", + "\n", + " # 去除上下边缘区域\n", + " mask[:edge_threshold+18, :] = 0\n", + " mask[h-edge_threshold:, :] = 0\n", + "\n", + " return mask\n", + "\n", + "\n", + "\n", + "\n", + "# 设定输入和输出文件夹路径\n", + "input_folder_path = 'data/new/yellow' # 替换为tiff图像的文件夹路径\n", + "output_folder_path = 'mask/new/yellow' # 替换为保存mask的文件夹路径\n", + "\n", + "# 检查输出文件夹是否存在，如果不存在就创建它\n", + "if not os.path.exists(output_folder_path):\n", + " os.makedirs(output_folder_path)\n", + "\n", + "# 遍历指定文件夹内的所有tiff文件\n", + "for file_name in os.listdir(input_folder_path):\n", + " if file_name.endswith('.tiff'):\n", + " # 构建完整的文件路径\n", + " file_path = os.path.join(input_folder_path, file_name)\n", + "\n", + " # 打开并增强图像亮度\n", + " image = Image.open(file_path)\n", + " enhancer = ImageEnhance.Brightness(image)\n", + " enhanced_image = enhancer.enhance(3) # 增强亮度\n", + " \n", + " # 将PIL图像转换为opencv图像\n", + " img = np.array(enhanced_image)\n", + " img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n", + "\n", + " # 转换到HSV空间\n", + " hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", + "\n", + " # 设置HSV阈值\n", + " lower_red = np.array([133, 50, 50])\n", + " upper_red = np.array([179, 255, 255])\n", + "\n", + " # 提取掩膜\n", + " mask = cv2.inRange(hsv, lower_red, upper_red)\n", + "\n", + " # 腐蚀与膨胀处理\n", + " kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (2, 2))\n", + " openingmask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # 闭运算\n", + " openingmask1 = cv2.morphologyEx(openingmask, cv2.MORPH_OPEN, kernel) # 开运算\n", + "\n", + " # 连通区域处理\n", + " num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(openingmask1, 4, cv2.CV_32S)\n", + " area_threshold = 30\n", + " for i in range(1, num_labels): # 标签0是背景\n", + " if stats[i, cv2.CC_STAT_AREA] < area_threshold:\n", + " openingmask1[labels == i] = 0\n", + " \n", + " # 应用边缘去除\n", + " cleaned_mask = remove_edge_bands(openingmask1)\n", + " \n", + " # 生成输出文件名和路径\n", + " output_file_name = 'mask_' + os.path.splitext(file_name)[0] + '.png'\n", + " output_file_path = os.path.join(output_folder_path, output_file_name)\n", + "\n", + " # 保存处理后的掩膜\n", + " cv2.imwrite(output_file_path, cleaned_mask)\n", + "\n", + "print(\"处理完成，掩膜图像已保存到指定文件夹。\")\n", + "\n", + "###new当前参数黄果p62顶部白色区域过大，当前参数下其他图像效果较好，选择手动替换p62，针对new下的yellow和green此时边缘参数宽度为5，上边缘+7，下边缘+5\n", + "###对于new下empty内的yellow，边缘参数宽度为2，上边缘+3，下边缘+2,P1和p30效果不好，p1上边缘没有去除干净，p30上边缘去除过多，选择手动替换" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T16:14:54.187480Z", + "start_time": "2024-03-18T16:14:53.736931Z" + } + }, + "id": "6afa206f04690497", + "execution_count": 101 + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "bc021fb45388a2b3" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spectrum/04average.ipynb b/spectrum/04average.ipynb new file mode 100644 index 0000000..d6377f5 --- /dev/null +++ b/spectrum/04average.ipynb @@ -0,0 +1,46873 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-03-18T12:34:28.037458Z", + "start_time": "2024-03-18T12:34:27.089388Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import cv2\n", + "import copy\n", + "import pandas as pd\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "name": 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1873.8487471526196, 1850.5854864952814, 1831.7062154246664, 1813.8609827530101]\n" + ] + } + ], + "source": [ + "\n", + "# 指定文件夹路径\n", + "folder_path = 'data/new/green' \n", + "mask_folder_path = 'mask/new/green' \n", + "output_path = 'result/new/green/green.xlsx'\n", + "\n", + "# 获取文件夹中的所有文件\n", + "files = os.listdir(folder_path)\n", + "files1 = os.listdir(mask_folder_path)\n", + "\n", + "number = 104\n", + "\n", + "D =[]\n", + "for i in range(number):\n", + " with open(folder_path + '/' + str(i+1) + '.hdr', 'r') as hdr_file:\n", + " lines = hdr_file.readlines()\n", + " for line in lines:\n", + " if line.startswith('lines'):\n", + " height = int(line.split()[-1])\n", + " elif line.startswith('samples'):\n", + " width = int(line.split()[-1])\n", + " elif line.startswith('bands'):\n", + " bands = int(line.split()[-1])\n", + " raw_image = np.fromfile(folder_path + '/' + str(i+1) + '.raw', dtype='uint16')\n", + " mask = cv2.imread(mask_folder_path + '/' + 'mask_'+ str(i+1) + '.png', cv2.IMREAD_GRAYSCALE)\n", + " formatImage = np.zeros((height, width, bands))\n", + " for row in range(0, height):\n", + " for dim in range(0, bands):\n", + " formatImage[row,:,dim] = raw_image[(dim + row*bands) * width:(dim + 1 + row*bands)* width]\n", + " # 创建一个和光谱图像形状相同的全零数组，用于存储目标区域的光谱图像\n", + " spectrum_image = np.zeros_like(formatImage)\n", + " # 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作\n", + " mask = np.stack([mask]*bands, axis=2)\n", + " # 使用掩码提取目标区域的光谱图像\n", + " spectrum_image = np.where(mask == 255, formatImage, 0)\n", + " # print(tomato_spectrum_image.shape)\n", + " # cv2.imshow('', tomato_spectrum_image[:, :, 145])\n", + " # cv2.waitKey(0)\n", + " data_save = []\n", + " for j in range(bands):\n", + " data = copy.deepcopy(spectrum_image[:, :, j])\n", + " data[data>0] = 1\n", + " num = np.sum(data)\n", + " print(num)\n", + " average_values_tomato = np.sum(spectrum_image[:, :, j])/num\n", + " print(str(i)+\"目标区域\"+\"波段\"+str(j+1)+\"的平均值:\", average_values_tomato)\n", + " data_save.append(average_values_tomato)\n", + " print(data_save)\n", + " D.append(data_save)\n", + "# 创建一个DataFrame来存储结果\n", + "# D_array = np.array(D).reshape(224, 120)\n", + "df = pd.DataFrame(D)\n", + "#转置\n", + "df = df.transpose()\n", + "\n", + "# 将DataFrame导出为Excel文件\n", + "df.to_excel(output_path, index=False)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-03-18T16:23:28.577436Z", + "start_time": "2024-03-18T16:22:00.872543Z" + } + }, + "id": "7aa5b23fed589342" + }, + { + "cell_type": "code", + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "f0f198bb2d8d797c" + }, + { + "cell_type": "code", + "execution_count": 48, + "outputs": [], + "source": [ + "## 数据拼接\n", + "# 指定Excel文件的文件夹路径\n", + "folder_path = 'average_result1124' # 替换成你的文件夹路径\n", + "# 获取文件夹下所有Excel文件的文件名\n", + "excel_files = [file for file in os.listdir(folder_path) if file.endswith('.xlsx') or file.endswith('.xls')]\n", + "\n", + "\n", + "# 创建一个空列表，用于存储每个Excel文件的数据框\n", + "dfs = []\n", + "for i in range(1,40):\n", + " df = pd.read_excel(folder_path + '/'+str(i) +'.xlsx')\n", + " # 将数据框添加到列表中\n", + " dfs.append(df)\n", + "\n", + "# 使用pandas.concat函数将列表中的所有数据框按列合并\n", + "result = pd.concat(dfs, axis=1)\n", + "\n", + "# 将结果保存为新的Excel文件\n", + "result.to_excel('merged1124.xlsx', index=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-24T11:44:27.355022100Z", + "start_time": "2023-11-24T11:44:25.300526600Z" + } + }, + "id": "1cb1985738259578" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "8799ab899f51c21c" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spectrum/PLS.py b/spectrum/PLS.py new file mode 100644 index 0000000..7eb0360 --- /dev/null +++ b/spectrum/PLS.py @@ -0,0 +1,80 @@ +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.decomposition import PCA +from sklearn.cross_decomposition import PLSRegression +from sklearn.metrics import r2_score, mean_squared_error +import copy +from math import * + +#载入数据 +data_path = './/merged1124.xlsx' #数据 +label_path = './/label.xlsx' #标签（反射率） + +D = pd.read_excel(data_path) +L = pd.read_excel(label_path) +data = np.array(D) +label = np.array(L) +print(label.shape) + +# 绘制原始后图片 +plt.figure(500) +x_col = data[:,0] #数组逆序 +y_col = data[:, 1:40] +plt.plot(x_col, y_col) +plt.xlabel("Wavenumber(nm)") +plt.ylabel("Absorbance") +plt.title("The spectrum of the tomato dataset",fontweight= "semibold",fontsize='x-large') +# plt.savefig('.//Result//MSC.png') +plt.show() + +#随机划分数据集 +x_data = np.transpose(data[:, 1:40]) +print(x_data.shape) +y_data = copy.deepcopy(label) + +print(y_data.shape) + +test_ratio = 0.2 +X_train,X_test,y_train,y_test = train_test_split(x_data,y_data,test_size=test_ratio,shuffle=True,random_state=2) + +#载入数据 +#PCA降维到10个维度,测试该数据最好 +pca=PCA(n_components=28) #只保留2个特征 +pca.fit(X_train) +X_train_reduction = pca.transform(X_train) +X_test_reduction = pca.transform(X_test) + +# PCA降维后图片绘制 +plt.figure(100) +plt.scatter(X_train_reduction[:, 0], X_train_reduction[:, 1],marker='o') +plt.xlabel("Wavenumber(nm)") +plt.ylabel("Absorbance") +plt.title("The PCA for tomato dataset",fontweight= "semibold",fontsize='large') +# plt.savefig('.//Result//PCA.png') +plt.show() + +#pls预测 +pls2 = PLSRegression(n_components=5) +pls2.fit(X_train_reduction, y_train) + +train_pred = pls2.predict(X_train_reduction) +pred = pls2.predict(X_test_reduction) + +#计算R2 +train_R2 = r2_score(train_pred,y_train) +R2 = r2_score(y_test,pred) #Y_true, Pred +print('训练R2:{}'.format(train_R2)) +print('测试R2:{}'.format(R2)) +#计算MSE +print('********************') +x_MSE = mean_squared_error(train_pred,y_train) +t_MSE = mean_squared_error(y_test,pred) +print('训练MSE:{}'.format(x_MSE)) +print('测试MSE:{}'.format(t_MSE)) + +#计算RMSE +print('********************') +print('训练RMSE:{}'.format(sqrt(x_MSE))) +print('测试RMSE:{}'.format(sqrt(t_MSE))) diff --git a/spectrum/pinjie.py b/spectrum/pinjie.py new file mode 100644 index 0000000..a64028a --- /dev/null +++ b/spectrum/pinjie.py @@ -0,0 +1,22 @@ +import pandas as pd +import os +# 指定Excel文件的文件夹路径 +folder_path = 'average_result' # 替换成你的文件夹路径 +# 获取文件夹下所有Excel文件的文件名 +excel_files = [file for file in os.listdir(folder_path) if file.endswith('.xlsx') or file.endswith('.xls')] +# 按照文件名排序，确保按照表格序号顺序读取 +# 使用int函数将文件名转换为整数，然后进行排序 +# excel_files.sort(key=lambda x: int(x.split('.')[0])) + +# 创建一个空列表，用于存储每个Excel文件的数据框 +dfs = [] +for i in range(1,40): + df = pd.read_excel(folder_path + '/'+'test'+str(i) +'.xlsx') + # 将数据框添加到列表中 + dfs.append(df) + +# 使用pandas.concat函数将列表中的所有数据框按列合并 +result = pd.concat(dfs, axis=1) + +# 将结果保存为新的Excel文件 +result.to_excel('merged.xlsx', index=False) \ No newline at end of file diff --git a/spectrum/test.py b/spectrum/test.py new file mode 100644 index 0000000..53b071a --- /dev/null +++ b/spectrum/test.py @@ -0,0 +1,112 @@ +import numpy as np +import cv2 +import copy +import pandas as pd +import os + +# 指定文件夹路径 +folder_path = 'data' # 替换成你的文件夹路径 +mask_folder_path = 'result' # 替换成你的掩码图像文件夹路径 + +# 获取文件夹中的所有文件 +files = os.listdir(folder_path) +files1 = os.listdir(mask_folder_path) + +D =[] +for i in range(39, 40): + with open(folder_path + '/' + str(i) + '_ref.hdr', 'r') as hdr_file: + lines = hdr_file.readlines() + for line in lines: + if line.startswith('lines'): + height = int(line.split()[-1]) + elif line.startswith('samples'): + width = int(line.split()[-1]) + elif line.startswith('bands'): + bands = int(line.split()[-1]) + raw_image = np.fromfile(folder_path + '/' + str(i) + '_ref.raw', dtype='float32') + mask = cv2.imread(mask_folder_path + '/' + str(i) + '.tiff', cv2.IMREAD_GRAYSCALE) + formatImage = np.zeros((height, width, bands)) + for row in range(0, height): + for dim in range(0, bands): + formatImage[row,:,dim] = raw_image[(dim + row*bands) * width:(dim + 1 + row*bands)* width] + # 创建一个和光谱图像形状相同的全零数组，用于存储西红柿区域的光谱图像 + tomato_spectrum_image = np.zeros_like(formatImage) + # 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作 + mask = np.stack([mask]*bands, axis=2) + # 使用掩码提取西红柿区域的光谱图像 + tomato_spectrum_image = np.where(mask == 255, formatImage, 0) + # print(tomato_spectrum_image.shape) + # cv2.imshow('', tomato_spectrum_image[:, :, 145]) + # cv2.waitKey(0) + data_save = [] + for i in range(0, 224): + data = copy.deepcopy(tomato_spectrum_image[:, :, i]) + data[data>0] = 1 + num = np.sum(data) + print(num) + average_values_tomato = np.sum(tomato_spectrum_image[:, :, i])/num + print("西红柿区域各波段的平均值:", average_values_tomato) + data_save.append(average_values_tomato) + print(data_save) + D.append(data_save) +# 创建一个DataFrame来存储结果 +D_array = np.array(D).reshape(224, 1) +df = pd.DataFrame(D_array) + +# 将DataFrame导出为Excel文件 +df.to_excel('test39.xlsx', index=False) + + + +#选择通道数为12/46/96的三个通道相当于rgb三通道的图片 +# imgR = formatImage[:,:,12] +# imgG = formatImage[:,:,46] +# imgB = formatImage[:,:,96] +# +# rgbImg = cv2.merge([imgR, imgG, imgB]) +# cv2.imshow('test', rgbImg) +# cv2.waitKey() + +# +# # 使用图像的宽度、高度和波段数来重塑raw光谱图像的形状 +# height = 756 # 替换成你的图像高度 +# width = 1200 # 替换成你的图像宽度 +# bands = 224 # 替换成你的图像波段数 +# spectrum_image = raw_image.reshape((height, width, bands)) +# # 读取mask掩码图像，假设掩码图像是一个二值图像，西红柿的区域为255，其他区域为0 +# mask = cv2.imread(mask_file_path, cv2.IMREAD_GRAYSCALE) +# # 创建一个和光谱图像形状相同的全零数组，用于存储西红柿区域的光谱图像 +# tomato_spectrum_image = np.zeros_like(spectrum_image) +# +# # 将掩码图像扩展到和光谱图像形状相同，以便进行按位与操作 +# mask = np.stack([mask]*bands, axis=2) +# +# # 使用掩码提取西红柿区域的光谱图像 +# tomato_spectrum_image = np.where(mask == 255, spectrum_image, 0) +# print(tomato_spectrum_image.shape) +# # data_save = [] +# # for i in range(0, 224): +# # data = copy.deepcopy(tomato_spectrum_image[:, :, i]) +# # data[data>0] = 1 +# # num = np.sum(data) +# # print(num) +# # average_values_tomato = np.sum(tomato_spectrum_image[:, :, i])/num +# # print("西红柿区域各波段的平均值:", average_values_tomato) +# # data_save.append(average_values_tomato) +# # print(data_save) +# # 计算每个谱段上西红柿的光谱信息均值 +# # average_values_tomato = np.mean(tomato_spectrum_image, axis=(0, 1)) +# +# # print(tomato_spectrum_image[:, :, 145]) +# +# # cv2.imshow('', tomato_spectrum_image[:, :, 145]) +# # cv2.waitKey(0) +# # 打印结果 +# # print("西红柿区域各波段的平均值:", average_values_tomato) +# # import pandas as pd +# # +# # # 创建一个DataFrame来存储结果 +# # df = pd.DataFrame(average_values_tomato, columns=['Average Spectral Values']) + +# # 将DataFrame导出为Excel文件 +# df.to_excel('39.xlsx', index=False) \ No newline at end of file diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..4954707 --- /dev/null +++ b/utils.py @@ -0,0 +1,516 @@ + +import cv2 + +import numpy as np +from genetic_selection import GeneticSelectionCV +from sklearn.metrics import confusion_matrix, classification_report +from sklearn.model_selection import train_test_split +from sklearn.tree import DecisionTreeClassifier +import logging +import os +import shutil +import time +import socket + + + +def read_envi_ascii(file_name, save_xy=False, hdr_file_name=None): + """ + Read envi ascii file. Use ENVI ROI Tool -> File -> output ROIs to ASCII... + + :param file_name: file name of ENVI ascii file + :param hdr_file_name: hdr file name for a "BANDS" vector in the output + :param save_xy: save the x, y position on the first two cols of the result vector + :return: dict {class_name: vector, ...} + """ + number_line_start_with = "; Number of ROIs: " + roi_name_start_with, roi_npts_start_with = "; ROI name: ", "; ROI npts:" + data_start_with, data_start_with2, data_start_with3 = "; ID", "; ID", "; ID" + class_num, class_names, class_nums, vectors = 0, [], [], [] + with open(file_name, 'r') as f: + for line_text in f: + if line_text.startswith(number_line_start_with): + class_num = int(line_text[len(number_line_start_with):]) + elif line_text.startswith(roi_name_start_with): + class_names.append(line_text[len(roi_name_start_with):-1]) + elif line_text.startswith(roi_npts_start_with): + class_nums.append(int(line_text[len(roi_name_start_with):-1])) + elif line_text.startswith(data_start_with) or line_text.startswith(data_start_with2) or line_text.startswith(data_start_with3): + col_list = list(filter(None, line_text[1:].split(" "))) + assert (len(class_names) == class_num) and (len(class_names) == len(class_nums)) + break + elif line_text.startswith(";"): + continue + for vector_rows in class_nums: + vector_str = '' + for i in range(vector_rows): + vector_str += f.readline() + vector = np.fromstring(vector_str, dtype=float, sep=" ").reshape(-1, len(col_list)) + assert vector.shape[0] == vector_rows + vector = vector[:, 3:] if not save_xy else vector[:, 1:] + vectors.append(vector) + f.readline() # suppose to read a blank line + if hdr_file_name is not None: + import re + with open(hdr_file_name, 'r') as f: + hdr_info = f.read() + bands = re.findall(r"wavelength = {[^{}]+}", hdr_info, flags=re.IGNORECASE | re.MULTILINE) + bands_num = re.findall(r"bands\s*=\s*(\d+)", hdr_info, flags=re.I) + if (len(bands) == 0) or len(bands_num) == 0: + Warning("The given hdr file is invalid, can't find bands = ? or wavelength = {?}.") + else: + bands = re.findall(r'{[^{}]+}', bands[0], flags=re.MULTILINE)[0][3:-2] + bands = bands.split(',\n') + bands = np.asarray(bands, dtype=float) + bands_num = int(bands_num[0]) + if bands_num == bands.shape[0]: + bands = np.array(bands, dtype=float) + vectors.append(bands) + class_names.append("BANDS") + else: + Warning("The given hdr file is invalid, bands num is not equal to wavelength.") + return dict(zip(class_names, vectors)) + + +def ga_feature_extraction(data_x, data_y): + ''' + 使用遗传算法进行特征提取 + :param data_x: 特征 + :param data_y: 类别 + ''' + Xtrain, Xtest, Ytrain, Ytest = train_test_split(data_x, data_y, test_size=0.3) + clf = DecisionTreeClassifier(random_state=3) + selector = GeneticSelectionCV(clf, cv=30, + verbose=1, + scoring="accuracy", + max_features=10, + n_population=500, + crossover_proba=0.6, + mutation_proba=0.3, + n_generations=300, + crossover_independent_proba=0.6, + mutation_independent_proba=0.1, + tournament_size=10, + n_gen_no_change=10, + caching=True, + n_jobs=-1) + selector = selector.fit(Xtrain, Ytrain) + Xtrain_ga, Xtest_ga = Xtrain[:, selector.support_], Xtest[:, selector.support_] + clf = clf.fit(Xtrain_ga, Ytrain) + print(np.where(selector.support_ == True)) + y_pred = clf.predict(Xtest_ga) + print(classification_report(Ytest, y_pred)) + print(confusion_matrix(Ytest, y_pred)) + + +def read_raw(file_name, shape=None, setect_bands=None, cut_shape=None): + ''' + 读取raw文件 + :param file_name: 文件名 + :param setect_bands: 选择的波段 + :return: 波段数据 + ''' + if shape is None: + shape = (692, 272, 384) + with open(file_name, 'rb') as f: + data = np.frombuffer(f.read(), dtype=np.float32).reshape(shape).transpose(0, 2, 1) + if setect_bands is not None: + data = data[:, :, setect_bands] + if cut_shape is not None: + data = data[: cut_shape[0], : cut_shape[1], :] + return data + + +def save_raw(file_name, data): + ''' + 保存raw文件 + :param file_name: 文件名 + :param data: 数据 + ''' + data = data.transpose(0, 2, 1) + # 将data转换为一维数组 + data = data.reshape(-1) + with open(file_name, 'wb') as f: + f.write(data.astype(np.float32).tobytes()) + + +def read_rgb(file_name): + ''' + 读取rgb文件 + :param file_name: 文件名 + :return: rgb数据 + ''' + data = cv2.imread(file_name) + data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB) + #给一个颜色对应的字典，用于将rgb转换为类别，白色对应0，黄色对应1，青色对应2，红色对应3，绿色对应4，蓝色对应5 + color_dict = {(255, 255, 255): 0, (255, 255, 0): 1, (0, 255, 255): 2, (255, 0, 0): 3, (0, 255, 0): 4, (0, 0, 255): 5} + # 保存图片的形状，用于将一维数组转换为三维数组 + shape = data.shape + # 将rgb转换为类别 + data = data.reshape(-1, 3).tolist() + # 将rgb转换为类别 + mapped_data = [] + + for i, color in enumerate(data): + mapped_value = color_dict.get(tuple(color)) + if mapped_value is None: + print("No mapping found for color", color, "at index", i) + else: + mapped_data.append(mapped_value) + # 将一维数组转换为三维数组 + data = np.array(mapped_data).reshape(shape[0], shape[1]) + return data + + +def read_data(raw_path, rgb_path, shape=None, setect_bands=None, blk_size=4, cut_shape=None, dp=False): + ''' + 读取数据 + :param raw_path: raw文件路径 + :param rgb_path: rgb文件路径 + :param setect_bands: 选择的波段 + :return: 波段数据，rgb数据 + ''' + if shape is None: + shape = (692, 272, 384) + with open(raw_path, 'rb') as f: + raw = np.frombuffer(f.read(), dtype=np.float32).reshape(shape).transpose(0, 2, 1) + if setect_bands is not None: + raw = raw[:, :, setect_bands] + color_dict = {(255, 255, 255): 0, (255, 255, 0): 1, (0, 255, 255): 2, (255, 0, 0): 3, (0, 255, 0): 4, + (0, 0, 255): 5} + rgb = cv2.imread(rgb_path) + rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB) + if cut_shape is not None: + raw = raw[ :cut_shape[0], :cut_shape[1], :] + rgb = rgb[ :cut_shape[0], :cut_shape[1], :] + data_x = [] + data_y = [] + for i in range(0, rgb.shape[0], blk_size): + for j in range(0, rgb.shape[1], blk_size): + x = raw[i:i + blk_size, j:j + blk_size, :] + y = rgb[i:i + blk_size, j:j + blk_size] + # # 取y的第三行第三列的像素值，判断该像素值是否在color_dict中，如果在则将x和y添加到data_x和data_y中 + # y = tuple(y[2, 2, :]) + # if y in color_dict.keys(): + # data_x.append(x) + # data_y.append(color_dict[y]) + # 取y的中心点像素值，判断该像素值是否在color_dict中，如果在则将x和y添加到data_x和data_y中 + y = tuple(y[blk_size//2, blk_size//2, :]) + if y in color_dict.keys(): + data_x.append(x) + data_y.append(color_dict[y]) + data_x = np.array(data_x) + data_y = np.array(data_y).astype(np.uint8) + return data_x, data_y + + +def try_connect(connect_ip: str, port_number: int, is_repeat: bool = False, max_reconnect_times: int = 50) -> ( + bool, socket.socket): + """ + 尝试连接. + + :param is_repeat: 是否是重新连接 + :param max_reconnect_times:最大重连次数 + :return: (连接状态True为成功, Socket / None) + """ + reconnect_time = 0 + while reconnect_time < max_reconnect_times: + logging.warning(f'尝试{"重新" if is_repeat else ""}发起第{reconnect_time + 1}次连接...') + try: + connected_sock = PreSocket(socket.AF_INET, socket.SOCK_STREAM) + connected_sock.connect((connect_ip, port_number)) + except Exception as e: + reconnect_time += 1 + logging.error(f'第{reconnect_time}次连接失败... 5秒后重新连接...\n {e}') + time.sleep(5) + continue + logging.warning(f'{"重新" if is_repeat else ""}连接成功') + return True, connected_sock + return False, None + + +class PreSocket(socket.socket): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.pre_pack = b'' + self.settimeout(5) + + def receive(self, *args, **kwargs): + if self.pre_pack == b'': + return self.recv(*args, **kwargs) + else: + data_len = args[0] + required, left = self.pre_pack[:data_len], self.pre_pack[data_len:] + self.pre_pack = left + return required + + def set_prepack(self, pre_pack: bytes): + temp = self.pre_pack + self.pre_pack = temp + pre_pack + + +class DualSock(PreSocket): + def __init__(self, connect_ip='127.0.0.1', recv_port: int = 21122, send_port: int = 21123): + super().__init__() + received_status, self.received_sock = try_connect(connect_ip=connect_ip, port_number=recv_port) + send_status, self.send_sock = try_connect(connect_ip=connect_ip, port_number=send_port) + self.status = received_status and send_status + + def send(self, *args, **kwargs) -> int: + return self.send_sock.send(*args, **kwargs) + + def receive(self, *args, **kwargs) -> bytes: + return self.received_sock.receive(*args, **kwargs) + + def set_prepack(self, pre_pack: bytes): + self.received_sock.set_prepack(pre_pack) + + def reconnect(self, connect_ip='127.0.0.1', recv_port:int = 21122, send_port: int = 21123): + received_status, self.received_sock = try_connect(connect_ip=connect_ip, port_number=recv_port) + send_status, self.send_sock = try_connect(connect_ip=connect_ip, port_number=send_port) + return received_status and send_status + + +def receive_sock(recv_sock: PreSocket, pre_pack: bytes = b'', time_out: float = -1.0, time_out_single=5e20) -> ( +bytes, bytes): + """ + 从指定的socket中读取数据.自动阻塞，如果返回的数据为空则说明连接出现问题，需要重新连接。 + + :param recv_sock: 指定sock + :param pre_pack: 上一包的粘包内容 + :param time_out: 每隔time_out至少要发来一次指令,否则认为出现问题进行重连，小于0则为一直等 + :param time_out_single: 单次指令超时时间，单位是秒 + :return: data, next_pack + """ + recv_sock.set_prepack(pre_pack) + # 开头校验 + time_start_recv = time.time() + while True: + if time_out > 0: + if (time.time() - time_start_recv) > time_out: + logging.error(f'指令接收超时') + return b'', b'' + try: + temp = recv_sock.receive(1) + except ConnectionError as e: + logging.error(f'连接出错, 错误代码:\n{e}') + return b'', b'' + except TimeoutError as e: + # logging.error(f'超时了，错误代码: \n{e}') + logging.info('运行中,等待指令..') + continue + except socket.timeout as e: + logging.info('运行中,等待指令..') + continue + except Exception as e: + logging.error(f'遇见未知错误，错误代码: \n{e}') + return b'', b'' + if temp == b'\xaa': + break + + # 接收开头后，开始进行时间记录 + time_start_recv = time.time() + + # 获取报文长度 + temp = b'' + while len(temp) < 4: + if (time.time() - time_start_recv) > time_out_single: + logging.error(f'单次指令接收超时') + return b'', b'' + try: + temp += recv_sock.receive(1) + except Exception as e: + logging.error(f'接收报文的长度不正确, 错误代码: \n{e}') + return b'', b'' + try: + data_len = int.from_bytes(temp, byteorder='big') + except Exception as e: + logging.error(f'转换失败,错误代码 \n{e}') + return b'', b'' + + # 读取报文内容 + temp = b'' + while len(temp) < data_len: + if (time.time() - time_start_recv) > time_out_single: + logging.error(f'单次指令接收超时') + return b'', b'' + try: + temp += recv_sock.receive(data_len) + except Exception as e: + logging.error(f'接收报文内容失败, 错误代码: \n{e}') + return b'', b'' + data, next_pack = temp[:data_len], temp[data_len:] + recv_sock.set_prepack(next_pack) + next_pack = b'' + + # 进行数据校验 + temp = b'' + while len(temp) < 3: + if (time.time() - time_start_recv) > time_out_single: + logging.error(f'单次指令接收超时') + return b'', b'' + try: + temp += recv_sock.receive(1) + except Exception as e: + logging.error(f'接收报文校验失败, 错误代码: \n{e}') + return b'', b'' + if temp == b'\xff\xff\xbb': + return data, next_pack + else: + logging.error(f"接收了一个完美的只错了校验位的报文") + return b'', b'' + + +def parse_protocol(data: bytes) -> (str, any): + ''' + 指令转换 + :param data: 接收到的报文 + :return: 指令类型，指令内容 + ''' + try: + assert len(data) > 4 + except AssertionError: + logging.error('指令转换失败，长度不足5') + return '', None + cmd, data = data[:4], data[4:] + cmd = cmd.decode('ascii').strip().upper() + if cmd == 'IM': + n_rows, n_cols, img = data[:2], data[2:4], data[4:] + try: + n_rows, n_cols = [int.from_bytes(x, byteorder='big') for x in [n_rows, n_cols]] + except Exception as e: + logging.error(f'长宽转换失败, 错误代码{e}, 报文大小: n_rows:{n_rows}, n_cols: {n_cols}') + return '', None + try: + assert n_rows * n_cols * 3 == len(img) + except AssertionError: + logging.error('图像指令IM转换失败，数据长度错误') + return '', None + img = np.frombuffer(img, dtype=np.uint8).reshape((n_rows, n_cols, -1)) + return cmd, img + + +def ack_sock(send_sock: socket.socket, cmd_type: str) -> bool: + ''' + 发送应答 + :param cmd_type:指令类型 + :param send_sock:指定sock + :return:是否发送成功 + ''' + msg = b'\xaa\x00\x00\x00\x05' + (' A' + cmd_type).upper().encode('ascii') + b'\xff\xff\xff\xbb' + try: + send_sock.send(msg) + except Exception as e: + logging.error(f'发送应答失败，错误类型：{e}') + return False + return True + + +def done_sock(send_sock: socket.socket, cmd_type: str, result = '') -> bool: + ''' + 发送任务完成指令 + :param send_sock: 指定sock + :param cmd_type: 指令类型 + :param result: 数据 + :return: 是否发送成功 + ''' + cmd = cmd_type.strip().upper() + if cmd_type == 'IM': + result = result.encode() + # 指令4位 + length = len(result) + 4 + length = length.to_bytes(4, byteorder='big') + # msg = b'\xaa' + length + (' D' + cmd).upper().encode('ascii') + result + b'\xff\xff\xbb' + msg = result + try: + send_sock.send(msg) + except Exception as e: + logging.error(f'发送完成指令失败，错误类型：{e}') + return False + return True + +def simple_sock(send_sock: socket.socket, cmd_type: str, result) -> bool: + ''' + 发送任务完成指令 + :param cmd_type:指令类型 + :param send_sock:指定sock + :param result:数据 + :return:是否发送成功 + ''' + cmd_type = cmd_type.strip().upper() + if cmd_type == 'IM': + if result == 0: + msg = b'S' + elif result == 1: + msg = b'Z' + elif result == 2: + msg = b'Q' + elif cmd_type == 'TR': + msg = b'A' + elif cmd_type == 'MD': + msg = b'D' + elif cmd_type == 'KM': + msg = b'K' + result = result.encode('ascii') + result = b',' + result + length = len(result) + msg = msg + length.to_bytes(4, 'big') + result + try: + send_sock.send(msg) + except Exception as e: + logging.error(f'发送完成指令失败，错误类型：{e}') + return False + return True + +def mkdir_if_not_exist(dir_name, is_delete=False): + """ + 创建文件夹 + :param dir_name: 文件夹 + :param is_delete: 是否删除 + :return: 是否成功 + """ + try: + if is_delete: + if os.path.exists(dir_name): + shutil.rmtree(dir_name) + print('[Info] 文件夹 "%s" 存在, 删除文件夹.' % dir_name) + + if not os.path.exists(dir_name): + os.makedirs(dir_name) + print('[Info] 文件夹 "%s" 不存在, 创建文件夹.' % dir_name) + return True + except Exception as e: + print('[Exception] %s' % e) + return False + +def create_file(file_name): + """ + 创建文件 + :param file_name: 文件名 + :return: None + """ + if os.path.exists(file_name): + print("文件存在：%s" % file_name) + return False + # os.remove(file_name) # 删除已有文件 + if not os.path.exists(file_name): + print("文件不存在，创建文件：%s" % file_name) + open(file_name, 'a').close() + return True + + +class Logger(object): + def __init__(self, is_to_file=False, path=None): + self.is_to_file = is_to_file + if path is None: + path = "Astragalins.log" + self.path = path + create_file(path) + + def log(self, content): + if self.is_to_file: + with open(self.path, "a") as f: + print(time.strftime("[%Y-%m-%d_%H-%M-%S]:"), file=f) + print(content, file=f) + else: + print(content) \ No newline at end of file