mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
70 lines
2.6 KiB
70 lines
2.6 KiB
7 years ago
|
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(args.prototxt, 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)
|