opencv/samples/dnn/edge_detection.py

import cv2 as cv
import argparse

parser = argparse.ArgumentParser(
        description='This sample shows how to define custom OpenCV deep learning layers in Python. '
                    'Holistically-Nested Edge Detection (https://arxiv.org/abs/1504.06375) neural network '
                    'is used as an example model. Find a pre-trained model at https://github.com/s9xie/hed.')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--prototxt', help='Path to deploy.prototxt', required=True)
parser.add_argument('--caffemodel', help='Path to hed_pretrained_bsds.caffemodel', required=True)
parser.add_argument('--width', help='Resize input image to a specific width', default=500, type=int)
parser.add_argument('--height', help='Resize input image to a specific height', default=500, type=int)
args = parser.parse_args()

#! [CropLayer]
class CropLayer(object):
    def __init__(self, params, blobs):
        self.xstart = 0
        self.xend = 0
        self.ystart = 0
        self.yend = 0

    # Our layer receives two inputs. We need to crop the first input blob
    # to match a shape of the second one (keeping batch size and number of channels)
    def getMemoryShapes(self, inputs):
        inputShape, targetShape = inputs[0], inputs[1]
        batchSize, numChannels = inputShape[0], inputShape[1]
        height, width = targetShape[2], targetShape[3]

        self.ystart = (inputShape[2] - targetShape[2]) // 2
        self.xstart = (inputShape[3] - targetShape[3]) // 2
        self.yend = self.ystart + height
        self.xend = self.xstart + width

        return [[batchSize, numChannels, height, width]]

    def forward(self, inputs):
        return [inputs[0][:,:,self.ystart:self.yend,self.xstart:self.xend]]
#! [CropLayer]

#! [Register]
cv.dnn_registerLayer('Crop', CropLayer)
#! [Register]

# Load the model.
net = cv.dnn.readNet(cv.samples.findFile(args.prototxt), cv.samples.findFile(args.caffemodel))

kWinName = 'Holistically-Nested Edge Detection'
cv.namedWindow('Input', cv.WINDOW_NORMAL)
cv.namedWindow(kWinName, cv.WINDOW_NORMAL)

cap = cv.VideoCapture(args.input if args.input else 0)
while cv.waitKey(1) < 0:
    hasFrame, frame = cap.read()
    if not hasFrame:
        cv.waitKey()
        break

    cv.imshow('Input', frame)

    inp = cv.dnn.blobFromImage(frame, scalefactor=1.0, size=(args.width, args.height),
                               mean=(104.00698793, 116.66876762, 122.67891434),
                               swapRB=False, crop=False)
    net.setInput(inp)

    out = net.forward()
    out = out[0, 0]
    out = cv.resize(out, (frame.shape[1], frame.shape[0]))
    cv.imshow(kWinName, out)