diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ea549ea --- /dev/null +++ b/.gitignore @@ -0,0 +1,84 @@ +### JetBrains template +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# AWS User-specific +.idea/**/aws.xml + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# SonarLint plugin +.idea/sonarlint/ + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + +!/20240529RGBtest3/data/ +!/20240529RGBtest3/data/ +!/20240410RGBtest1/super-tomato/defect_big.bmp +!/20240410RGBtest1/super-tomato/defect_mask.bmp +!/20240410RGBtest1/defect_big.bmp diff --git a/.idea/.name b/.idea/.name new file mode 100644 index 0000000..721f4b2 --- /dev/null +++ b/.idea/.name @@ -0,0 +1 @@ +04average.ipynb \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml index 2c0499a..f6889bb 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,4 +1,7 @@ + + \ No newline at end of file diff --git a/.idea/other.xml b/.idea/other.xml new file mode 100644 index 0000000..2e75c2e --- /dev/null +++ b/.idea/other.xml @@ -0,0 +1,6 @@ + + + + + \ No newline at end of file diff --git a/.idea/supermachine--tomato-passion_fruit.iml b/.idea/supermachine--tomato-passion_fruit.iml index d0876a7..2368678 100644 --- a/.idea/supermachine--tomato-passion_fruit.iml +++ b/.idea/supermachine--tomato-passion_fruit.iml @@ -1,7 +1,9 @@ - + + + diff --git a/20240410RGBtest1/image_preprocessing.py b/20240410RGBtest1/image_preprocessing.py index edfd7b4..a246029 100644 --- a/20240410RGBtest1/image_preprocessing.py +++ b/20240410RGBtest1/image_preprocessing.py @@ -214,7 +214,7 @@ def extract_max_connected_area(image, lower_hsv, upper_hsv): def main(): parser = argparse.ArgumentParser(description='Process some integers.') - parser.add_argument('--dir_path', type=str, default=r'D:\project\Tomato\20240410RGBtest2\datatest', + parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240419RGBtest2\data', help='the directory path of images') parser.add_argument('--threshold_s_l', type=int, default=180, help='the threshold for s_l') @@ -224,7 +224,7 @@ def main(): args = parser.parse_args() for img_file in os.listdir(args.dir_path): - if img_file.endswith('.png'): + 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) diff --git a/20240410RGBtest1/super-tomato/image_preprocessing.py b/20240410RGBtest1/super-tomato/image_preprocessing.py index 11cd38d..b39a5cb 100644 --- a/20240410RGBtest1/super-tomato/image_preprocessing.py +++ b/20240410RGBtest1/super-tomato/image_preprocessing.py @@ -211,7 +211,7 @@ def extract_max_connected_area(image_path, lower_hsv, upper_hsv): def main(): parser = argparse.ArgumentParser(description='Process some integers.') - parser.add_argument('--dir_path', type=str, default=r'D:\project\Tomato\20240410RGBtest2\data', + parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240419RGBtest2\data', help='the directory path of images') parser.add_argument('--threshold_s_l', type=int, default=180, help='the threshold for s_l') 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 index af31774..6e6147f 100644 --- 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 @@ -20,7 +20,10 @@ ASCII字符，比如`类型1`为' '(空格)，`类型2`为' '(空格)，`类型3 - **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： + $$ + i-4=rows \times cols \times 3 + $$ + 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) | | :--------: | :-------: | :--------: | :-------: | :-------: | :--: | :-----------: | @@ -28,7 +31,11 @@ ASCII字符，比如`类型1`为' '(空格)，`类型2`为' '(空格)，`类型3 **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： + +$$ +i-14=rows \times cols \times 3 +$$ +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) | | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | @@ -36,7 +43,10 @@ ASCII字符，比如`类型1`为' '(空格)，`类型2`为' '(空格)，`类型3 - **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： + $$ + i-6=rows \times cols \times bands \times 4 + $$ + 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) | | :--------: | :-------: | :--------: | :-------: | :---------: | :--------: | :-------: | :--: | :-----------: | diff --git a/20240529RGBtest3/classifer.py b/20240529RGBtest3/classifer.py new file mode 100644 index 0000000..ad2492a --- /dev/null +++ b/20240529RGBtest3/classifer.py @@ -0,0 +1,224 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/6/4 21:34 +# @Author : GG +# @File : classifer.py +# @Software: PyCharm + + +import cv2 +import numpy as np + +class Tomato: + def __init__(self): + ''' 初始化 Tomato 类。''' + pass + + def extract_s_l(self, image): + ''' + 提取图像的 S 通道（饱和度）和 L 通道（亮度），并将两者相加。 + :param image: 输入的 BGR 图像 + :return: S 通道和 L 通道相加的结果 + ''' + 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(self, image, threshold=190): + ''' + 通过阈值处理识别图像中的反射区域。 + :param image: 输入的单通道图像 + :param threshold: 用于二值化的阈值 + :return: 二值化后的图像，高于阈值的部分为白色，其余为黑色 + ''' + _, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) + return reflection + + def otsu_threshold(self, image): + ''' + 使用 Otsu 大津法自动计算并应用阈值，进行图像的二值化处理。 + :param image: 输入的单通道图像 + :return: 二值化后的图像 + ''' + _, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + return binary + + def extract_g_r(self, image): + ''' + 提取图像中的 G 通道（绿色），放大并减去 R 通道（红色）。 + :param image: 输入的 BGR 图像 + :return: G 通道乘以 1.5 后减去 R 通道的结果 + ''' + g_channel = image[:, :, 1] + r_channel = image[:, :, 2] + result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel) + return result + + def extract_r_b(self, image): + ''' + 提取图像中的 R 通道（红色）和 B 通道（蓝色），并进行相减。 + :param image: 输入的 BGR 图像 + :return: R 通道减去 B 通道的结果 + ''' + r_channel = image[:, :, 2] + b_channel = image[:, :, 0] + result = cv2.subtract(r_channel, b_channel) + return result + + def extract_r_g(self, image): + ''' + 提取图像中的 R 通道（红色）和 G 通道（绿色），并进行相减。 + :param image: 输入的 BGR 图像 + :return: R 通道减去 G 通道的结果 + ''' + r_channel = image[:, :, 2] + g_channel = image[:, :, 1] + result = cv2.subtract(r_channel, g_channel) + return result + + def threshold_segmentation(self, image, threshold, color=255): + ''' + 对图像进行阈值分割，高于阈值的部分设置为指定的颜色。 + :param image: 输入的单通道图像 + :param threshold: 阈值 + :param color: 设置的颜色值 + :return: 分割后的二值化图像 + ''' + _, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY) + return result + + def bitwise_operation(self, image1, image2, operation='and'): + ''' + 对两幅图像执行位运算（与或运算）。 + :param image1: 第一幅图像 + :param image2: 第二幅图像 + :param operation: 执行的操作类型（'and' 或 'or'） + :return: 位运算后的结果 + ''' + 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(self, bin_img): + ''' + 提取二值图像中的最大连通区域。 + :param bin_img: 输入的二值图像 + :return: 只包含最大连通区域的二值图像 + ''' + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8) + if num_labels <= 1: + return np.zeros_like(bin_img) + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + new_bin_img = np.zeros_like(bin_img) + new_bin_img[labels == largest_label] = 255 + return new_bin_img + + def close_operation(self, bin_img, kernel_size=(5, 5)): + ''' + 对二值图像进行闭运算，用于消除内部小孔和连接接近的对象。 + :param bin_img: 输入的二值图像 + :param kernel_size: 核的大小 + :return: 进行闭运算后的图像 + ''' + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) + closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel) + return closed_img + + def open_operation(self, bin_img, kernel_size=(5, 5)): + ''' + 对二值图像进行开运算，用于去除小的噪点。 + :param bin_img: 输入的二值图像 + :param kernel_size: 核的大小 + :return: 进行开运算后的图像 + ''' + 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(self, original_img, bin_img): + ''' + 在原始图像上绘制最大西红柿轮廓的近似多边形。 + :param original_img: 原始 BGR 图像 + :param bin_img: 西红柿的二值图像 + :return: 带有绘制边缘的原始图像和边缘掩码 + ''' + bin_img_processed = self.close_operation(bin_img, kernel_size=(15, 15)) + contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return original_img, np.zeros_like(bin_img) + max_contour = max(contours, key=cv2.contourArea) + epsilon = 0.0006 * cv2.arcLength(max_contour, True) + approx = cv2.approxPolyDP(max_contour, epsilon, True) + cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3) + mask = np.zeros_like(bin_img) + cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED) + return original_img, mask + + def draw_tomato_edge_convex_hull(self, original_img, bin_img): + ''' + 在原始图像上绘制最大西红柿轮廓的凸包。 + :param original_img: 原始 BGR 图像 + :param bin_img: 西红柿的二值图像 + :return: 带有绘制凸包的原始图像 + ''' + 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 + + def fill_holes(self, bin_img): + ''' + 使用 floodFill 算法填充图像中的孔洞。 + :param bin_img: 输入的二值图像 + :return: 填充后的图像 + ''' + img_filled = bin_img.copy() + height, width = bin_img.shape + mask = np.zeros((height + 2, width + 2), np.uint8) + cv2.floodFill(img_filled, mask, (0, 0), 255) + img_filled_inv = cv2.bitwise_not(img_filled) + img_filled = cv2.bitwise_or(bin_img, img_filled_inv) + return img_filled + + def bitwise_and_rgb_with_binary(self, rgb_img, bin_img): + ''' + 将 RGB 图像与二值图像进行按位与操作，用于将二值区域应用于原始图像。 + :param rgb_img: 原始 RGB 图像 + :param bin_img: 二值图像 + :return: 按位与后的结果图像 + ''' + bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR) + result = cv2.bitwise_and(rgb_img, bin_img_3channel) + return result + + def extract_max_connected_area(self, image, lower_hsv, upper_hsv): + ''' + 提取图像中满足 HSV 范围条件的最大连通区域，并填充孔洞。 + :param image: 输入的 BGR 图像 + :param lower_hsv: HSV 范围的下限 + :param upper_hsv: HSV 范围的上限 + :return: 处理后的图像 + ''' + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + mask = cv2.inRange(hsv, lower_hsv, upper_hsv) + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) + largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) + new_bin_img = np.zeros_like(mask) + new_bin_img[labels == largest_label] = 255 + img_filled = new_bin_img.copy() + height, width = new_bin_img.shape + mask = np.zeros((height + 2, width + 2), np.uint8) + cv2.floodFill(img_filled, mask, (0, 0), 255) + img_filled_inv = cv2.bitwise_not(img_filled) + img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv) + return img_filled diff --git a/20240529RGBtest3/main.py b/20240529RGBtest3/main.py new file mode 100644 index 0000000..6cef35a --- /dev/null +++ b/20240529RGBtest3/main.py @@ -0,0 +1,125 @@ +# -*- 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 create_pipes, receive_rgb_data, send_data, receive_spec_data, analyze_tomato +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 + """ + # if cmd == 'IM': + + long_axis, short_axis, number_defects, total_pixels, rp = analyze_tomato(img) + 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) + + + + diff --git a/20240529RGBtest3/picture.ipynb b/20240529RGBtest3/picture.ipynb new file mode 100644 index 0000000..6772688 --- /dev/null +++ b/20240529RGBtest3/picture.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-06-03T08:44:12.688855Z", + "start_time": "2024-06-03T08:44:07.661963Z" + } + }, + "source": [ + "import cv2\n", + "import numpy as np\n", + "\n", + "# 读取彩色图像，使用原始字符串处理文件路径\n", + "image = cv2.imread(r\"D:\\project\\supermachine--tomato-passion_fruit\\20240529RGBtest3\\data\\bad\\36.bmp\")\n", + "\n", + "# 将RGB图像转换到HSV颜色空间\n", + "hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n", + "\n", + "# 将RGB图像转换到Lab颜色空间\n", + "lab_image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)\n", + "\n", + "# 提取S分量\n", + "s_channel = hsv_image[:,:,1]\n", + "\n", + "# 提取L分量\n", + "l_channel = lab_image[:,:,0]\n", + "\n", + "# 计算S+L图像\n", + "sl_image = cv2.addWeighted(s_channel, 0.5, l_channel, 0.5, 0)\n", + "\n", + "# 使用Otsu阈值分割\n", + "_, otsu_segmentation = cv2.threshold(sl_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n", + "\n", + "# 显示原始图像和分割结果\n", + "cv2.imshow(\"Original Image\", image)\n", + "cv2.imshow(\"Otsu Segmentation\", otsu_segmentation)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()\n", + "#存图\n", + "# cv2.imwrite(r\"D:\\project\\supermachine--tomato-passion_fruit\\20240529RGBtest3\\33_otsu.bmp\", otsu_segmentation)\n" + ], + "outputs": [], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "29d27b11f43683db" + } + ], + "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/20240529RGBtest3/test.py b/20240529RGBtest3/test.py new file mode 100644 index 0000000..fbd8f6c --- /dev/null +++ b/20240529RGBtest3/test.py @@ -0,0 +1,292 @@ +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\supermachine--tomato-passion_fruit\20240529RGBtest3\data\broken', + 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) + + + + # 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/20240529RGBtest3/utils.py b/20240529RGBtest3/utils.py new file mode 100644 index 0000000..5a73307 --- /dev/null +++ b/20240529RGBtest3/utils.py @@ -0,0 +1,280 @@ +# -*- coding: utf-8 -*- +# @Time : 2024/4/20 18:24 +# @Author : TG +# @File : utils.py +# @Software: PyCharm + +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 +from classifer import Tomato + +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 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) + continue + +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) + + + + +def get_tomato_dimensions(edge_img): + """ + 根据边缘二值化轮廓图,计算果子的长径、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): 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 analyze_tomato(img): + """ + 分析给定图像，提取和返回西红柿的长径、短径、缺陷数量和缺陷总面积，并返回处理后的图像。 + 使用 Tomoto 类的图像处理方法，以及自定义的尺寸和缺陷信息获取函数。 + 参数: + img (numpy.ndarray): 输入的 BGR 图像 + 返回: + tuple: (长径, 短径, 缺陷区域个数, 缺陷区域总像素, 处理后的图像) + """ + tomato = Tomato() # 创建 Tomato 类的实例 + # 设置 S-L 通道阈值并处理图像 + threshold_s_l = 180 + s_l = tomato.extract_s_l(img) + thresholded_s_l = tomato.threshold_segmentation(s_l, threshold_s_l) + new_bin_img = tomato.largest_connected_component(thresholded_s_l) + # 绘制西红柿边缘并获取缺陷信息 + edge, mask = tomato.draw_tomato_edge(img, new_bin_img) + org_defect = tomato.bitwise_and_rgb_with_binary(edge, new_bin_img) + # 获取西红柿的尺寸信息 + long_axis, short_axis = get_tomato_dimensions(mask) + # 获取缺陷信息 + number_defects, total_pixels = get_defect_info(new_bin_img) + # 将处理后的图像转换为 RGB 格式 + rp = cv2.cvtColor(org_defect, cv2.COLOR_BGR2RGB) + return long_axis, short_axis, number_defects, total_pixels, rp diff --git a/20240529RGBtest3/xs/dimensionality_reduction.py b/20240529RGBtest3/xs/dimensionality_reduction.py new file mode 100644 index 0000000..b9e266f --- /dev/null +++ b/20240529RGBtest3/xs/dimensionality_reduction.py @@ -0,0 +1,64 @@ +import numpy as np +import matplotlib.pyplot as plt +from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor +from sklearn.svm import SVR +from sklearn.neighbors import KNeighborsRegressor +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +from spec_read import all_spectral_data + +def prepare_data(data): + """Reshape data and select specified spectral bands.""" + reshaped_data = data.reshape(100, -1) + selected_bands = [1, 2, 3, 58, 59, 60, 106, 107, 108, 112, 113, 114, 142, 146, 200, 201, 202] + return reshaped_data[:, selected_bands] + +def split_data(X, y, test_size=0.20, random_state=1): + """Split data into training and test sets.""" + return train_test_split(X, y, test_size=test_size, random_state=random_state) + +def evaluate_model(model, X_test, y_test): + """Evaluate the model and return MSE and predictions.""" + y_pred = model.predict(X_test) + mse = mean_squared_error(y_test, y_pred) + return mse, y_pred + +def print_predictions(y_test, y_pred, model_name): + """Print actual and predicted values.""" + print(f"Test Set Predictions for {model_name}:") + for i, (real, pred) in enumerate(zip(y_test, y_pred)): + print(f"Sample {i + 1}: True Value = {real:.2f}, Predicted Value = {pred:.2f}") + +def main(): + sweetness_acidity = np.array([ + 16.2, 16.1, 17, 16.9, 16.8, 17.8, 18.1, 17.2, 17, 17.2, 17.1, 17.2, + 17.2, 17.2, 18.1, 17, 17.6, 17.4, 17.1, 17.1, 16.9, 17.6, 17.3, 16.3, + 16.5, 18.7, 17.6, 16.2, 16.8, 17.2, 16.8, 17.3, 16, 16.6, 16.7, 16.7, + 17.3, 16.3, 16.8, 17.4, 17.3, 16.3, 16.1, 17.2, 18.6, 16.8, 16.1, 17.2, + 18.3, 16.5, 16.6, 17, 17, 17.8, 16.4, 18, 17.7, 17, 18.3, 16.8, 17.5, + 17.7, 18.5, 18, 17.7, 17, 18.3, 18.1, 17.4, 17.7, 17.8, 16.3, 17.1, 16.8, + 17.2, 17.5, 16.6, 17.7, 17.1, 17.7, 19.4, 20.3, 17.3, 15.8, 18, 17.7, + 17.2, 15.2, 18, 18.4, 18.3, 15.7, 17.2, 18.6, 15.6, 17, 16.9, 17.4, 17.8, + 16.5 + ]) + + X = prepare_data(all_spectral_data) + X_train, X_test, y_train, y_test = split_data(X, sweetness_acidity) + + models = { + "RandomForest": RandomForestRegressor(n_estimators=100), + "GradientBoosting": GradientBoostingRegressor(n_estimators=100), + "SVR": SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1), + "KNeighbors": KNeighborsRegressor(n_neighbors=5) + } + + for model_name, model in models.items(): + model.fit(X_train, y_train) + mse, y_pred = evaluate_model(model, X_test, y_test) + print(f"Model: {model_name}") + print(f"Mean Squared Error on the test set: {mse}") + print_predictions(y_test, y_pred, model_name) + print("\n" + "-"*50 + "\n") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/20240529RGBtest3/xs/hsv.py b/20240529RGBtest3/xs/hsv.py new file mode 100644 index 0000000..f06a7ad --- /dev/null +++ b/20240529RGBtest3/xs/hsv.py @@ -0,0 +1,89 @@ +import cv2 +import numpy as np +import matplotlib.pyplot as plt +import os + +def create_mask(hsv_image, hue_value, hue_delta, value_target, value_delta): + # 创建H通道阈值掩码 + lower_hue = np.array([hue_value - hue_delta, 0, 0]) + upper_hue = np.array([hue_value + hue_delta, 255, 255]) + hue_mask = cv2.inRange(hsv_image, lower_hue, upper_hue) + + # 创建V通道排除中心值的掩码 + lower_value_1 = np.array([0, 0, 0]) + upper_value_1 = np.array([180, 255, value_target - value_delta]) + lower_value_2 = np.array([0, 0, value_target + value_delta]) + upper_value_2 = np.array([180, 255, 255]) + + value_mask_1 = cv2.inRange(hsv_image, lower_value_1, upper_value_1) + value_mask_1 = cv2.bitwise_not(value_mask_1) + cv2.imshow('value_mask_1', value_mask_1) + value_mask_2 = cv2.inRange(hsv_image, lower_value_2, upper_value_2) + cv2.imshow('value_mask_2', value_mask_2) + value_mask = cv2.bitwise_and(value_mask_1, value_mask_2) + cv2.imshow('value_mask', value_mask) + # 等待用户按下任意键 + cv2.waitKey(0) + + # 关闭所有窗口 + cv2.destroyAllWindows() + + # 合并H通道和V通道掩码 + return cv2.bitwise_and(hue_mask, value_mask) + +def apply_morphology(mask): + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + return cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) + +def find_largest_component(mask): + num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask, 4, cv2.CV_32S) + if num_labels < 2: + return None # No significant components found + max_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # Skip background + return (labels == max_label).astype(np.uint8) * 255 + +def process_image(image_path, hue_value=37, hue_delta=10, value_target=25, value_delta=10): + image = cv2.imread(image_path) + if image is None: + print(f"Error: Image at {image_path} could not be read.") + return None + + hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + combined_mask = create_mask(hsv_image, hue_value, hue_delta, value_target, value_delta) + combined_mask = apply_morphology(combined_mask) + max_mask = find_largest_component(combined_mask) + cv2.imshow('max_mask', max_mask) + # 等待用户按下任意键 + cv2.waitKey(0) + # 关闭所有窗口 + cv2.destroyAllWindows() + + if max_mask is None: + print(f"No significant components found in {image_path}.") + return None + + result_image = cv2.bitwise_and(image, image, mask=max_mask) + result_image[max_mask == 0] = [255, 255, 255] # Set background to white + return result_image + +def save_image(image, output_path): + cv2.imwrite(output_path, image) + +def process_images_in_folder(input_folder, output_folder): + if not os.path.exists(output_folder): + os.makedirs(output_folder) + + for filename in os.listdir(input_folder): + if filename.lower().endswith(".bmp"): + image_path = os.path.join(input_folder, filename) + result_image = process_image(image_path) + if result_image is not None: + output_path = os.path.join(output_folder, filename) + save_image(result_image, output_path) + print(f"Processed and saved {filename} to {output_folder}.") + + +# 主函数调用 +input_folder = r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img' # 替换为你的输入文件夹路径 +output_folder = r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\01' # 替换为你的输出文件夹路径 +process_images_in_folder(input_folder, output_folder) \ No newline at end of file diff --git a/20240529RGBtest3/xs/mean.py b/20240529RGBtest3/xs/mean.py new file mode 100644 index 0000000..d3d1505 --- /dev/null +++ b/20240529RGBtest3/xs/mean.py @@ -0,0 +1,71 @@ +import numpy as np +import matplotlib.pyplot as plt +from sklearn.ensemble import RandomForestRegressor +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +from spec_read import all_spectral_data + +def prepare_data(data): + """Calculate the average spectral values for each fruit across all pixels.""" + return np.mean(data, axis=(1, 2)) + +def train_model(X, y): + """Train a RandomForest model.""" + rf = RandomForestRegressor(n_estimators=100) + rf.fit(X, y) + return rf + +def split_data(X, y, test_size=0.20, random_state=42): + """Split data into training and test sets.""" + return train_test_split(X, y, test_size=test_size, random_state=random_state) + +def evaluate_model(model, X_test, y_test): + """Evaluate the model and return MSE and predictions.""" + y_pred = model.predict(X_test) + mse = mean_squared_error(y_test, y_pred) + return mse, y_pred + +def print_predictions(y_test, y_pred): + """Print actual and predicted values.""" + print("Test Set Predictions:") + for i, (real, pred) in enumerate(zip(y_test, y_pred)): + print(f"Sample {i + 1}: True Value = {real:.2f}, Predicted Value = {pred:.2f}") + +def plot_spectra(X, y): + """Plot the average spectra for all samples and annotate with sweetness_acidity values.""" + plt.figure(figsize=(10, 6)) + for i in range(X.shape[0]): + plt.plot(X[i], label=f'Sample {i+1}') + plt.annotate(f'{y[i]:.1f}', xy=(len(X[i])-1, X[i][-1]), xytext=(5, 0), + textcoords='offset points', ha='left', va='center') + plt.xlabel('Wavelength Index') + plt.ylabel('Average Spectral Value') + plt.title('Average Spectral Curves for All Samples') + plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.05)) + plt.show() + +def main(): + sweetness_acidity = np.array([ + 16.2, 16.1, 17, 16.9, 16.8, 17.8, 18.1, 17.2, 17, 17.2, 17.1, 17.2, + 17.2, 17.2, 18.1, 17, 17.6, 17.4, 17.1, 17.1, 16.9, 17.6, 17.3, 16.3, + 16.5, 18.7, 17.6, 16.2, 16.8, 17.2, 16.8, 17.3, 16, 16.6, 16.7, 16.7, + 17.3, 16.3, 16.8, 17.4, 17.3, 16.3, 16.1, 17.2, 18.6, 16.8, 16.1, 17.2, + 18.3, 16.5, 16.6, 17, 17, 17.8, 16.4, 18, 17.7, 17, 18.3, 16.8, 17.5, + 17.7, 18.5, 18, 17.7, 17, 18.3, 18.1, 17.4, 17.7, 17.8, 16.3, 17.1, 16.8, + 17.2, 17.5, 16.6, 17.7, 17.1, 17.7, 19.4, 20.3, 17.3, 15.8, 18, 17.7, + 17.2, 15.2, 18, 18.4, 18.3, 15.7, 17.2, 18.6, 15.6, 17, 16.9, 17.4, 17.8, + 16.5 + ]) + + X = prepare_data(all_spectral_data) + plot_spectra(X, sweetness_acidity) # 绘制光谱曲线并添加标注 + X_train, X_test, y_train, y_test = split_data(X, sweetness_acidity) + rf_model = train_model(X_train, y_train) + mse, y_pred = evaluate_model(rf_model, X_test, y_test) + + print("Transformed data shape:", X_train.shape) + print("Mean Squared Error on the test set:", mse) + print_predictions(y_test, y_pred) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/20240529RGBtest3/xs/rgb.py b/20240529RGBtest3/xs/rgb.py new file mode 100644 index 0000000..8aa3af1 --- /dev/null +++ b/20240529RGBtest3/xs/rgb.py @@ -0,0 +1,70 @@ +import cv2 +import numpy as np +import matplotlib.pyplot as plt + +def dual_threshold_and_max_component(image_path, hue_value=37, hue_delta=10, value_target=30, value_delta=10): + # 读取图像 + image = cv2.imread(image_path) + + # 检查图像是否读取成功 + if image is None: + print("Error: Image could not be read.") + return + + # 将图像从BGR转换到HSV色彩空间 + hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # 创建H通道阈值掩码 + lower_hue = np.array([hue_value - hue_delta, 0, 0]) + upper_hue = np.array([hue_value + hue_delta, 255, 255]) + hue_mask = cv2.inRange(hsv_image, lower_hue, upper_hue) + + # 创建V通道排除中心值的掩码 + lower_value_1 = np.array([0, 0, 0]) + upper_value_1 = np.array([180, 255, value_target - value_delta]) + lower_value_2 = np.array([0, 0, value_target + value_delta]) + upper_value_2 = np.array([180, 255, 255]) + + value_mask_1 = cv2.inRange(hsv_image, lower_value_1, upper_value_1) + value_mask_2 = cv2.inRange(hsv_image, lower_value_2, upper_value_2) + value_mask = cv2.bitwise_or(value_mask_1, value_mask_2) + + # 合并H通道和V通道掩码 + combined_mask = cv2.bitwise_and(hue_mask, value_mask) + + # 形态学操作 - 开运算，去除小的粘连 + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + combined_mask = cv2.morphologyEx(combined_mask, cv2.MORPH_OPEN, kernel) + + # 连通域分析 + num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(combined_mask, 4, cv2.CV_32S) + + # 找出最大的连通区域（除了背景） + max_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # 跳过背景 + max_mask = (labels == max_label).astype(np.uint8) * 255 + + # 使用掩码生成结果图像 + result_image = cv2.bitwise_and(image, image, mask=max_mask) + # 设置背景为白色 + result_image[max_mask == 0] = [255, 255, 255] + + # 将结果图像从BGR转换到RGB以便正确显示 + result_image_rgb = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB) + + # 使用matplotlib显示原始图像和结果图像 + plt.figure(figsize=(10, 5)) + plt.subplot(1, 2, 1) + plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + plt.title('Original Image') + plt.axis('off') + + plt.subplot(1, 2, 2) + plt.imshow(result_image_rgb) + plt.title('Largest Connected Component on White Background') + plt.axis('off') + + plt.show() + +# 使用函数 +image_path = r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img\50.bmp' # 替换为你的图片路径 +dual_threshold_and_max_component(image_path) \ No newline at end of file diff --git a/20240529RGBtest3/xs/spec_read.py b/20240529RGBtest3/xs/spec_read.py new file mode 100644 index 0000000..8dc036b --- /dev/null +++ b/20240529RGBtest3/xs/spec_read.py @@ -0,0 +1,70 @@ +import numpy as np +import os + + +def read_spectral_data(hdr_path, raw_path): + # Read HDR file for image dimensions information + with open(hdr_path, 'r', encoding='latin1') as hdr_file: + lines = hdr_file.readlines() + height = width = bands = 0 + 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]) + + # Read spectral data from RAW file + raw_image = np.fromfile(raw_path, dtype='uint16') + # Initialize the image with the actual read dimensions + formatImage = np.zeros((height, width, bands)) + + for row in range(height): + for dim in range(bands): + formatImage[row, :, dim] = raw_image[(dim + row * bands) * width:(dim + 1 + row * bands) * width] + + # Ensure the image is 30x30x224 by cropping or padding + target_height, target_width, target_bands = 30, 30, 224 + # Crop or pad height + if height > target_height: + formatImage = formatImage[:target_height, :, :] + elif height < target_height: + pad_height = target_height - height + formatImage = np.pad(formatImage, ((0, pad_height), (0, 0), (0, 0)), mode='constant', constant_values=0) + + # Crop or pad width + if width > target_width: + formatImage = formatImage[:, :target_width, :] + elif width < target_width: + pad_width = target_width - width + formatImage = np.pad(formatImage, ((0, 0), (0, pad_width), (0, 0)), mode='constant', constant_values=0) + + # Crop or pad bands if necessary (usually bands should not change) + if bands > target_bands: + formatImage = formatImage[:, :, :target_bands] + elif bands < target_bands: + pad_bands = target_bands - bands + formatImage = np.pad(formatImage, ((0, 0), (0, 0), (0, pad_bands)), mode='constant', constant_values=0) + + return formatImage + + +# Specify the directory containing the HDR and RAW files +directory = '/Users/xs/PycharmProjects/super-tomato/baixiangguo/光谱数据3030/' + +# Initialize a list to hold all the spectral data arrays +all_spectral_data = [] + +# Loop through each data set (assuming there are 40 datasets) +for i in range(1, 101): + hdr_path = os.path.join(directory, f'{i}.HDR') + raw_path = os.path.join(directory, f'{i}') + + # Read data + spectral_data = read_spectral_data(hdr_path, raw_path) + all_spectral_data.append(spectral_data) + +# Stack all data into a single numpy array +all_spectral_data = np.stack(all_spectral_data) +print(all_spectral_data.shape) # This should print (40, 30, 30, 224) \ No newline at end of file diff --git a/spectrum/02picture.ipynb b/spectrum/02picture.ipynb index 403122c..a363289 100644 --- a/spectrum/02picture.ipynb +++ b/spectrum/02picture.ipynb @@ -11,7 +11,6 @@ "start_time": "2024-03-18T12:58:35.489144Z" } }, - "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", @@ -19,73 +18,12 @@ "import numpy as np\n", "import cv2 as cv\n", "import os" - ] + ], + "outputs": [] }, { "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", @@ -126,21 +64,11 @@ "start_time": "2024-03-18T13:06:04.869067Z" } }, - "id": "3443037608cc60f6" + "id": "3443037608cc60f6", + "outputs": [] }, { "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", @@ -192,97 +120,12 @@ } }, "id": "283f370b2401221b", - "execution_count": 5 + "execution_count": 5, + "outputs": [] }, { "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", @@ -357,27 +200,28 @@ "start_time": "2023-11-24T02:50:24.958389400Z" } }, - "id": "15a3889f7bf1917f" + "id": "15a3889f7bf1917f", + "outputs": [] }, { "cell_type": "code", "execution_count": null, - "outputs": [], "source": [], "metadata": { "collapsed": false }, - "id": "17b929b3de3fa2ed" + "id": "17b929b3de3fa2ed", + "outputs": [] }, { "cell_type": "code", "execution_count": null, - "outputs": [], "source": [], "metadata": { "collapsed": false }, - "id": "e00c8e929ee57b60" + "id": "e00c8e929ee57b60", + "outputs": [] } ], "metadata": { diff --git a/spectrum/03mask.ipynb b/spectrum/03mask.ipynb index 356f917..1480e8a 100644 --- a/spectrum/03mask.ipynb +++ b/spectrum/03mask.ipynb @@ -11,26 +11,16 @@ "start_time": "2024-03-18T07:45:52.061770Z" } }, - "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", "import os" - ] + ], + "outputs": [] }, { "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", @@ -49,21 +39,12 @@ "start_time": "2023-11-20T12:33:25.353136100Z" } }, - "id": "eba7a5b467780310" + "id": "eba7a5b467780310", + "outputs": [] }, { "cell_type": "code", "execution_count": 56, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import cv2\n", "import numpy as np\n", @@ -86,20 +67,11 @@ "start_time": "2024-03-18T08:31:54.363728Z" } }, - "id": "14abba9e7d1d9496" + "id": "14abba9e7d1d9496", + "outputs": [] }, { "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", @@ -128,12 +100,12 @@ } }, "id": "4bde773e19fd521e", - "execution_count": 77 + "execution_count": 77, + "outputs": [] }, { "cell_type": "code", "execution_count": 78, - "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", @@ -188,11 +160,11 @@ "start_time": "2024-03-18T15:43:19.273376Z" } }, - "id": "a6e663957af72d33" + "id": "a6e663957af72d33", + "outputs": [] }, { "cell_type": "code", - "outputs": [], "source": [], "metadata": { "collapsed": false, @@ -202,11 +174,11 @@ } }, "id": "7264359547e5d56f", - "execution_count": 25 + "execution_count": 25, + "outputs": [] }, { "cell_type": "code", - "outputs": [], "source": [ "#白色颜色带处理\n", "\n", @@ -250,184 +222,11 @@ } }, "id": "9eedb7f708e43473", - "execution_count": 95 + "execution_count": 95, + "outputs": [] }, { "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: (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", - "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", - "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", - "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", @@ -492,12 +291,12 @@ } }, "id": "a42269b65f848794", - "execution_count": 70 + "execution_count": 70, + "outputs": [] }, { "cell_type": "code", "execution_count": 28, - "outputs": [], "source": [ "##循环提取mask\n", "\n", @@ -548,20 +347,12 @@ "start_time": "2024-03-18T08:14:58.534491Z" } }, - "id": "3665c0b573adb358" + "id": "3665c0b573adb358", + "outputs": [] }, { "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", @@ -650,19 +441,11 @@ "start_time": "2024-03-18T12:15:14.164074Z" } }, - "id": "ab7f303dfa7d8a27" + "id": "ab7f303dfa7d8a27", + "outputs": [] }, { "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", @@ -753,16 +536,18 @@ } }, "id": "6afa206f04690497", - "execution_count": 101 + "execution_count": 101, + "outputs": [] }, { "cell_type": "code", - "outputs": [], "source": [], "metadata": { "collapsed": false }, - "id": "bc021fb45388a2b3" + "id": "bc021fb45388a2b3", + "execution_count": null, + "outputs": [] } ], "metadata": {