f4334277
 Hu Chunming
提交3rdparty
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
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)
|
