自学教程：使用Python中OpenCV和深度学习进行全面嵌套边缘检测

# USAGE# python detect_edges_image.py --edge-detector hed_model --image images/bird.jpg# 导入必要的包import argparseimport cv2import osimport imutils# 构建命令行参数及解析# --edge-detector Holistically-Nested Edge Detection检测器模型路径# --image 图片路径ap = argparse.ArgumentParser()ap.add_argument("-d", "--edge-detector", type=str, required=True,                help="path to OpenCV's deep learning edge detector")ap.add_argument("-i", "--image", type=str, required=True,                help="path to input image")args = vars(ap.parse_args())class CropLayer(object):    def __init__(self, params, blobs):        # 初始化剪切区域开始和结束点的坐标        self.xstart = 0        self.ystart = 0        self.xend = 0        self.yend = 0    # 计算输入图像的体积    def getMemoryShapes(self, inputs):        # 剪切类将接收俩个参数        # 剪切第一个输入blob以匹配第二个blob，保持批次和通道数        # 输出输入容积的形状及目标形状        # 提取批量大小及通道数        # 分别提取目标形状的高和宽        (inputShape, targetShape) = (inputs[0], inputs[1])        (batchSize, numChannels) = (inputShape[0], inputShape[1])        (H, W) = (targetShape[2], targetShape[3])        # 计算开始和结束剪切坐标的值        self.xstart = int((inputShape[3] - targetShape[3]) // 2)        self.ystart = int((inputShape[2] - targetShape[2]) // 2)        self.xend = self.xstart + W        self.yend = self.ystart + H        # 返回体积，接下来进行实际裁剪        return [[batchSize, numChannels, H, W]]    def forward(self, inputs):        return [inputs[0][:, :, self.ystart:self.yend, self.xstart:self.xend]]# 从磁盘加载序列化的边缘检测器模型print("[INFO] loading edge detector...")protoPath = os.path.sep.join([args["edge_detector"],                              "deploy.prototxt"])modelPath = os.path.sep.join([args["edge_detector"],                              "hed_pretrained_bsds.caffemodel"])net = cv2.dnn.readNetFromCaffe(protoPath, modelPath)# 绑定剪裁类到模型cv2.dnn_registerLayer("Crop", CropLayer)# 加载输入图像，获取其维度image = cv2.imread(args["image"])image = imutils.resize(image, width=400)(H, W) = image.shape[:2]# 转换图像为灰度图，高斯平滑，执行Canny边缘检测print("[INFO] performing Canny edge detection...")gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)blurred = cv2.GaussianBlur(gray, (5, 5), 0)canny = cv2.Canny(blurred, 30, 150)# 根据输入图像为全面的嵌套边缘检测器（Holistically-Nested Edge Detector）构建一个输出blobblob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(W, H),                             mean=(104.00698793, 116.66876762, 122.67891434),                             swapRB=False, crop=False)# # 设置blob作为网络的输入并执行算法以计算边缘图print("[INFO] performing holistically-nested edge detection...")net.setInput(blob)hed = net.forward()# 调整输出为原始图像尺寸的大小hed = cv2.resize(hed[0, 0], (W, H))# 将图像像素缩回到范围[0,255]并确保类型为“UINT8”hed = (255 * hed).astype("uint8")# 展示HED边缘检测的结果及Canny边缘检测的结果cv2.imshow("Input", image)cv2.imshow("Canny", canny)cv2.imshow("HED", hed)cv2.waitKey(0)

# USAGE 默认使用电脑自带的摄像头# python detect_edges_video.py --edge-detector hed_model# 使用视频文件流# python detect_edges_video.py --edge-detector hed_model --input xl.mp4# 导入必要的包from imutils.video import VideoStreamimport argparseimport imutilsimport time  # 此模块允许放置睡眠命令以允许视频流建立和“热身”。import cv2import os# 构建命令行参数及解析# --edge-detector Holistically-Nested Edge Detection检测器模型路径# --input 视频源：网络摄像头，视频文件或其他源。ap = argparse.ArgumentParser()ap.add_argument("-d", "--edge-detector", type=str, required=True,                help="path to OpenCV's deep learning edge detector")ap.add_argument("-i", "--input", type=str,                help="path to optional input video (webcam will be used otherwise)")args = vars(ap.parse_args())class CropLayer(object):    def __init__(self, params, blobs):        # 初始化剪切区域开始和结束点的坐标        self.xstart = 0        self.ystart = 0        self.xend = 0        self.yend = 0    # 计算输入图像的体积    def getMemoryShapes(self, inputs):        # 剪切类将接收俩个参数        # 剪切第一个输入blob以匹配第二个blob，保持批次和通道数        # 输出输入容积的形状及目标形状        # 提取批量大小及通道数        # 分别提取目标形状的高和宽        (inputShape, targetShape) = (inputs[0], inputs[1])        (batchSize, numChannels) = (inputShape[0], inputShape[1])        (H, W) = (targetShape[2], targetShape[3])        # 计算开始和结束剪切坐标的值        self.xstart = int((inputShape[3] - targetShape[3]) // 2)        self.ystart = int((inputShape[2] - targetShape[2]) // 2)        self.xend = self.xstart + W        self.yend = self.ystart + H        # 返回体积，接下来进行实际裁剪        return [[batchSize, numChannels, H, W]]    def forward(self, inputs):        # 使用派生（x，y）-oordinate来执行裁剪        return [inputs[0][:, :, self.ystart:self.yend, self.xstart:self.xend]]# 初始化视频流，脚本将动态选取使用视频文件流还是网络摄像头流webcam = not args.get("input", False)# 如果未提供视频文件路径，则使用电脑自带摄像头if webcam:    print("[INFO] starting video stream...")    vs = VideoStream(src=0).start()    time.sleep(2.0)# 否则，获取视频文件流指针else:    print("[INFO] opening video file...")    vs = cv2.VideoCapture(args["input"])# 从磁盘加载序列化的HED检测器模型print("[INFO] loading edge detector...")protoPath = os.path.sep.join([args["edge_detector"],                              "deploy.prototxt"])modelPath = os.path.sep.join([args["edge_detector"],                              "hed_pretrained_bsds.caffemodel"])net = cv2.dnn.readNetFromCaffe(protoPath, modelPath)# 将剪裁类注册到模型cv2.dnn_registerLayer("Crop", CropLayer)# 遍历视频流的帧while True:    # 获取每一帧，如果使用网络摄像头，获取下一帧    frame = vs.read()    frame = frame if webcam else frame[1]    # 如果在处理视频文件流，没有获取到帧则代表已经到了文件尾部，则跳出循环    if not webcam and frame is None:        break    # 等比例缩放帧为宽度500，并获取其维度    frame = imutils.resize(frame, width=300)    (H, W) = frame.shape[:2]    # 转换灰度图，高斯模糊并执行Canny边缘检测    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    blurred = cv2.GaussianBlur(gray, (5, 5), 0)    canny = cv2.Canny(blurred, 30, 150)    # 为HED边缘检测器构建输入帧的blob，设置blob，并执行检测以计算边缘图    blob = cv2.dnn.blobFromImage(frame, scalefactor=1.0, size=(W, H),                                 mean=(104.00698793, 116.66876762, 122.67891434),                                 swapRB=False, crop=False)    net.setInput(blob)    hed = net.forward()    hed = cv2.resize(hed[0, 0], (W, H))    hed = (255 * hed).astype("uint8")    # 展示Canny、HED的检测结果    cv2.imshow("Frame", frame)    cv2.imshow("Canny", canny)    cv2.imshow("HED", hed)    key = cv2.waitKey(1) & 0xFF    # 按下‘q'键表示退出循环    if key == ord("q"):        break# 如果在使用网络摄像头流，则终止相机视频流if webcam:    vs.stop()# 否则，释放视频文件流指针else:    vs.release()# 关闭所有打开的windowcv2.destroyAllWindows()