image = Image.open('path_to_image.jpg') gray_image = image.convert('L') gray_image.save('gray_image.jpg') 3、使用Scikit-Image库 Scikit-Image是另一个强大的图像处理库,它提供了丰富的图像处理功能。您可以使用Scikit-Image将RGB图像转换为灰度图像: from skimage import io, color image = io.imr...
binary_data=[]# 用于存储每个灰度值对应的二进制数据# 遍历图像的每一个像素点foryinrange(height):forxinrange(width):pixel_value=gray_image.getpixel((x,y))# 获取当前像素的灰度值binary_value=format(pixel_value,'08b')# 转换为8位二进制字符串binary_data.append(binary_value)# 将二进制值添加到...
最后,我们需要显示处理后的图像,并将其保存: plt.imshow(binary_image)plt.axis('off')# 关闭坐标轴显示plt.show()# 显示图像cv2.imwrite('path/to/output_binary_image.jpg',binary_image)# 保存图像 1. 2. 3. 4. 代码解释: plt.imshow用于显示图像。 cv2.imwrite用于保存处理后的图像到指定路径。 Gantt...
当然,以下是如何使用Python的OpenCV库(cv2)将RGB彩色图像转换为灰度图像,再将灰度图像转换为二值图像的步骤和代码示例: 1. 使用cv2读取RGB彩色图像 首先,我们需要使用OpenCV的cv2.imread函数来读取一张RGB彩色图像。 python import cv2 # 读取RGB彩色图像 image = cv2.imread('path_to_your_image.jpg') 请将'...
ax1.imshow(binary_img) ax1.set_title("Hue-thresholded image") ax1.axis('off') fig.tight_layout()### We finally perform an additional thresholding on the Value channel to partly# remove the shadow of the cup:fig, ax0 = plt.subplots(figsize=(4,3)) value_threshold =0.10binary_img ...
# Convert to grayscale (1 channel) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Merge channels to create color image (3 channels) gray_three = cv2.merge([gray,gray,gray]) # Fill a contour on both the single channel and three channel image ...
pip install opencv-python-headless numpy 2. 读取RGB和IR图像 接下来,使用OpenCV读取RGB和IR图像。假设您已经有两张对应的图像,分别是RGB图像和IR图像: import cv2 import numpy as np 读取RGB和IR图像 rgb_image = cv2.imread('path_to_rgb_image.jpg') ...
/* Bin Image */ gGrayImTemp = imgGray; threshold(gGrayImTemp, imgBin, gBinGrayThreshold, 255, THRESH_BINARY); imshow("Bin Gray Channel", imgBin); cv::createTrackbar("Threshold", "Bin Gray Channel", &gBinGrayThreshold, BIN...
/* Bin Image */ gHImTemp = channelH; threshold(gHImTemp, imgBin, gBinHSVThreshold, 255, THRESH_BINARY); imshow("HSV Bin Channel", imgBin); cv::createTrackbar("H", "imgOri", &gHScale, HSV_MAX, scaleHCallBack); cv::createTrackbar("S", "imgOri", &gSScale, HSV_MAX, scaleSCallBack...
python def Triangle(img): # Otsu 二值化之前先对图像进行高斯滤波处理,平滑图像,去除噪声 #(5,5)为高斯核大小,0为标准差 blur = cv2.GaussianBlur(img, (5, 5), 0) re3, th_img = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_TRIANGLE) return th_img 可以看到左图使用triangle...