import cv2 import numpy as np img = cv2.imread("Resources/The Legend of Zelda.jpg") kernel = np.ones((5, 5), np.uint8) # 卷积核 # 颜色空间转换,转换成灰度图(注意是BGR而不是RBG) imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 平滑处理,高斯模糊, 高斯核的宽和高只能是奇数 imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 0) # 边缘检测, 实际也是采用了高斯模糊去除噪音并设置梯度阈值进行过滤 imgCanny = cv2.Canny(img, 150, 200) # 膨胀,可以适当增加迭代次数 imgDilated = cv2.dilate(imgCanny, kernel, iterations=1) # 侵蚀 imgEroded = cv2.erode(imgDilated, kernel, iterations=1) # 缩小到0.2倍并拼接 imgStack = stackImages(0.2, [[img, imgGray, imgBlur], [imgCanny, imgDilated, imgEroded]]) cv2.imshow("Image Stack", imgStack) cv2.waitKey(0)
其中stackImage函数的定义为
def stackImages(scale, imgArray): ‘‘‘ 图像堆栈,可缩放,按列表排列,不受颜色通道限制 ‘‘‘ rows = len(imgArray) cols = len(imgArray[0]) rowsAvailable = isinstance(imgArray[0], list) width = imgArray[0][0].shape[1] height = imgArray[0][0].shape[0] if rowsAvailable: for x in range(0, rows): for y in range(0, cols): if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]: imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale) else: imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale) if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv2.cvtColor(imgArray[x][y], cv2.COLOR_GRAY2BGR) imageBlank = np.zeros((height, width, 3), np.uint8) hor = [imageBlank]*rows hor_con = [imageBlank]*rows for x in range(0, rows): hor[x] = np.hstack(imgArray[x]) ver = np.vstack(hor) else: for x in range(0, rows): if imgArray[x].shape[:2] == imgArray[0].shape[:2]: imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale) else: imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale) if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR) hor = np.hstack(imgArray) ver = hor return ver
封装了numpy中的vstack和hstack,方便使用
效果:
更多图像处理可参考官方文档:https://docs.opencv.org/master/d2/d96/tutorial_py_table_of_contents_imgproc.html
原文:https://www.cnblogs.com/yl-xy/p/13379562.html