mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 22:34:00 +00:00
445 lines
12 KiB
Plaintext
445 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-04-10T14:49:51.025756600Z",
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"start_time": "2024-04-10T14:49:51.021739700Z"
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}
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},
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"source": [],
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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" import cv2\n",
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" import numpy as np\n",
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" import matplotlib.pyplot as plt\n",
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" image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\TopImage_32.jpg')\n",
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" r_channel = image[:, :, 2]\n",
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" b_channel = image[:, :, 0]\n",
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" gray_image = r_channel - b_channel\n",
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" gray_image = np.clip(gray_image, 0, 255)\n",
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" gray_image = np.uint8(gray_image)\n",
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"\n",
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" # 显示新生成的灰度图像\n",
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" plt.imshow(gray_image, cmap='gray')\n",
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" plt.axis('off')\n",
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" plt.show()\n",
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" "
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T14:53:49.016762Z",
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"start_time": "2024-04-10T14:53:48.426912500Z"
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}
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},
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"id": "206810a054630770",
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"# 读取RGB图像\n",
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"img = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\TopImage_32.jpg')\n",
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"\n",
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"\n",
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"# 获取图像的高度和宽度\n",
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"height, width, _ = img.shape\n",
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"\n",
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"# 创建一个新的灰度图像\n",
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"gray_img = np.zeros((height, width), dtype=np.uint8)\n",
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"\n",
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"# 遍历每个像素,计算R-B作为新的灰度值\n",
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"for y in range(height):\n",
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" for x in range(width):\n",
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" r, g, b = img[y, x]\n",
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" gray_value = r - b\n",
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" if gray_value < 0:\n",
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" gray_value = 0\n",
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" elif gray_value > 255:\n",
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" gray_value = 255\n",
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" gray_img[y, x] = int(gray_value)\n",
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"\n",
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"\n",
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"plt.imshow(gray_image, cmap='gray')\n",
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"plt.axis('off')\n",
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"plt.show()\n",
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" \n",
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"# 保存灰度图像\n",
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"# cv2.imwrite('output_image.jpg', gray_img)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T15:04:30.607530400Z",
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"start_time": "2024-04-10T15:03:55.344343200Z"
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}
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},
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"id": "ba82f5c2fd049821",
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"execution_count": 9,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import torch\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"def extract_r_minus_b(image):\n",
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" img_tensor = torch.tensor(image, dtype=torch.float32).to('cuda')\n",
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"\n",
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" r_channel = img_tensor[:, :, 0]\n",
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" b_channel = img_tensor[:, :, 2]\n",
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"\n",
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" # 检查是否存在 NoneType 值，并将其替换为 0\n",
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" r_channel = torch.nan_to_num(r_channel, nan=0)\n",
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" b_channel = torch.nan_to_num(b_channel, nan=0)\n",
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"\n",
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" r_minus_b = torch.clamp(r_channel - b_channel, 0, 255).to('cpu').numpy().astype(np.uint8)\n",
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"\n",
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" return r_minus_b\n",
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"\n",
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"# 测试图像路径\n",
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"image_path = '20240410tomatoRGBtest2/data/39.bmp'\n",
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"\n",
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"# 读取图像\n",
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"image = cv2.imread(image_path)\n",
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"\n",
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"# 测试 extract_r_minus_b() 函数\n",
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"r_minus_b = extract_r_minus_b(image)\n",
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"\n",
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"# 显示结果\n",
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"plt.imshow(r_minus_b, cmap='gray')\n",
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"plt.title('R-B Channel Difference')\n",
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"plt.axis('off')\n",
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"plt.show()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T16:01:24.857246200Z",
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"start_time": "2024-04-10T16:01:24.805385100Z"
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}
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},
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"id": "7f09896cca12ec80",
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"execution_count": 15,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### 自适应阈值分割\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 读取R通道减去B通道的灰度图\n",
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"gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# 自适应阈值分割\n",
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"adaptive_threshold = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)\n",
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"\n",
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"# 显示结果\n",
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"plt.imshow(adaptive_threshold, cmap='gray')\n",
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"plt.axis('off')\n",
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"plt.show()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T14:49:51.023762Z",
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"start_time": "2024-04-10T14:49:47.876441200Z"
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}
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},
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"id": "b9a2db758d89860e",
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"execution_count": 1,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### 带滑动条调节的阈值分割\n",
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"\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import ipywidgets as widgets\n",
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"from IPython.display import display\n",
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"\n",
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"# 读取R通道减去B通道的灰度图\n",
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"gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# 定义回调函数\n",
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"def threshold_callback(threshold):\n",
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" _, thresholded_image = cv2.threshold(gray_image, threshold, 255, cv2.THRESH_BINARY)\n",
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" plt.imshow(thresholded_image, cmap='gray')\n",
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" plt.axis('off')\n",
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" plt.show()\n",
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"\n",
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"# 创建滑动条\n",
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"threshold_slider = widgets.IntSlider(min=0, max=255, step=1, value=128, description='Threshold:')\n",
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"display(threshold_slider)\n",
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"\n",
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"# 调用回调函数\n",
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"threshold_callback(threshold_slider.value)\n",
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"\n",
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"# 连接滑动条和回调函数\n",
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"widgets.interactive(threshold_callback, threshold=threshold_slider)\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T09:49:57.666748700Z",
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"start_time": "2024-04-10T09:49:56.140521900Z"
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}
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},
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"id": "6ff8e4e22d034f53",
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### Otsu阈值分割\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 读取R通道减去B通道的灰度图\n",
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"gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# 使用Otsu阈值分割\n",
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"_, otsu_threshold = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
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"\n",
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"# 显示结果\n",
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"plt.imshow(otsu_threshold, cmap='gray')\n",
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"plt.axis('off')\n",
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"plt.show()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T09:54:09.077152500Z",
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"start_time": "2024-04-10T09:54:08.502123300Z"
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}
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},
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"id": "6207115686820296",
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"execution_count": 8,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### Sobel边缘检测\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 读取R通道减去B通道的灰度图\n",
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"gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# 使用Sobel边缘检测\n",
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"sobel_x = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=5)\n",
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"sobel_y = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=5)\n",
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"edges = np.sqrt(sobel_x**2 + sobel_y**2)\n",
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"\n",
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"# 显示结果\n",
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"plt.imshow(edges, cmap='gray')\n",
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"plt.axis('off')\n",
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"plt.show()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T09:55:13.525305800Z",
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"start_time": "2024-04-10T09:55:12.652295600Z"
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}
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},
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"id": "f3bd38e5e62cbd3c",
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"execution_count": 9,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### 形态学处理\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 读取R通道减去B通道的灰度图\n",
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"gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# 定义结构元素\n",
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"kernel = np.ones((2,2),np.uint8)\n",
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"\n",
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"# 闭运算\n",
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"closing = cv2.morphologyEx(gray_image, cv2.MORPH_CLOSE, kernel)\n",
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"\n",
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"# 显示结果\n",
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"plt.imshow(closing, cmap='gray')\n",
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"plt.axis('off')\n",
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"plt.show()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T09:57:05.878548800Z",
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"start_time": "2024-04-10T09:57:05.060591800Z"
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}
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},
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"id": "7395629910ee8620",
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"execution_count": 11,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### 角点检测\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 读取R通道减去B通道的灰度图\n",
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"gray_image = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\data\\r-b.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# Shi-Tomasi角点检测\n",
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"corners = cv2.goodFeaturesToTrack(gray_image, 100, 0.01, 10)\n",
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"\n",
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"# 绘制角点\n",
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"corners = np.int0(corners)\n",
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"for i in corners:\n",
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" x, y = i.ravel()\n",
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" cv2.circle(gray_image, (x, y), 3, 255, -1)\n",
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"\n",
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"# 显示结果\n",
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"plt.imshow(gray_image, cmap='gray')\n",
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"plt.axis('off')\n",
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"plt.show()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-10T09:59:32.789829500Z",
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"start_time": "2024-04-10T09:59:31.769444500Z"
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}
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},
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"id": "990a0ecebe27d9a0",
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"execution_count": 12,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### 二值化对象\n",
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"\n",
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"import cv2\n",
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"import numpy as np\n",
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"\n",
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"# 读取原始图像\n",
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"img = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\output\\r_minus_b_26.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# 使用Otsu's二值化方法\n",
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"_, binary_img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
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"\n",
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"# # 显示二值化后的图像\n",
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"# cv2.imshow('Binary Image', binary_img)\n",
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"# cv2.waitKey(0)\n",
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"# cv2.destroyAllWindows()\n",
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"# 保存二值化后的图像\n",
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"cv2.imwrite('06.jpg', binary_img)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-04-12T06:34:26.530387300Z",
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"start_time": "2024-04-12T06:34:26.132598800Z"
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}
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},
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"id": "c4a6c0bfd05d3283",
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"execution_count": 1,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
|
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"### 光滑边缘\n",
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"import cv2\n",
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"import numpy as np\n",
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"\n",
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"# 假设您的两张图像已经保存为 image1.png 和 image2.png\n",
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"image1 = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\06.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"image2 = cv2.imread(r'D:\\project\\Tomato\\20240410tomatoRGBtest2\\010.png', cv2.IMREAD_GRAYSCALE)\n",
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"\n",
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"# 应用高斯模糊来平滑边缘\n",
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"smoothed_image1 = cv2.GaussianBlur(image1, (9, 9), 0)\n",
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"smoothed_image2 = cv2.GaussianBlur(image2, (9, 9), 0)\n",
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"\n",
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"# 将处理后的图像转换为二值图像\n",
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"_, binary_image1 = cv2.threshold(smoothed_image1, 128, 255, cv2.THRESH_BINARY)\n",
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"_, binary_image2 = cv2.threshold(smoothed_image2, 128, 255, cv2.THRESH_BINARY)\n",
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"\n",
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"# 保存二值化后的图像\n",
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"cv2.imwrite('smoothed_binary_image06.png', binary_image1)\n",
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"cv2.imwrite('smoothed_binary_image2.png', binary_image2)"
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],
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"metadata": {
|
|
"collapsed": false,
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|
"ExecuteTime": {
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|
"end_time": "2024-04-12T06:35:18.063684900Z",
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"start_time": "2024-04-12T06:35:17.935028900Z"
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}
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},
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"id": "1f6ac60b3150fc9e",
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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|
"collapsed": false
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|
},
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"id": "3749d97ac3719946",
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|
"execution_count": null,
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"outputs": []
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}
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|
],
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"metadata": {
|
|
"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
|
|
"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
|
|
"name": "ipython",
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|
"version": 2
|
|
},
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|
"file_extension": ".py",
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|
"mimetype": "text/x-python",
|
|
"name": "python",
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|
"nbconvert_exporter": "python",
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|
"pygments_lexer": "ipython2",
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|
"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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|
}
|