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Fix DNN samples for compatibility with Python 3.
Add PyInt_Check in pyopencv_dnn.hpp.
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@ -16,6 +16,11 @@ bool pyopencv_to(PyObject *o, dnn::DictValue &dv, const char *name)
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dv = dnn::DictValue((int64)PyLong_AsLongLong(o));
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return true;
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}
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else if (PyInt_Check(o))
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{
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dv = dnn::DictValue((int64)PyInt_AS_LONG(o));
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return true;
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}
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else if (PyFloat_Check(o))
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{
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dv = dnn::DictValue(PyFloat_AS_DOUBLE(o));
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@ -1,4 +1,6 @@
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# Script is based on https://github.com/richzhang/colorization/colorize.py
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# Script is based on https://github.com/richzhang/colorization/blob/master/colorize.py
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# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/master/models
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# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/blob/master/resources/pts_in_hull.npy
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import numpy as np
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import argparse
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import cv2 as cv
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@ -27,8 +29,8 @@ if __name__ == '__main__':
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# populate cluster centers as 1x1 convolution kernel
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pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1)
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net.getLayer(long(net.getLayerId('class8_ab'))).blobs = [pts_in_hull.astype(np.float32)]
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net.getLayer(long(net.getLayerId('conv8_313_rh'))).blobs = [np.full([1, 313], 2.606, np.float32)]
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net.getLayer(net.getLayerId('class8_ab')).blobs = [pts_in_hull.astype(np.float32)]
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net.getLayer(net.getLayerId('conv8_313_rh')).blobs = [np.full([1, 313], 2.606, np.float32)]
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if args.input:
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cap = cv.VideoCapture(args.input)
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@ -95,9 +95,9 @@ if __name__ == "__main__":
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else:
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cropSize = (cols, int(cols / WHRatio))
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y1 = (rows - cropSize[1]) / 2
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y1 = int((rows - cropSize[1]) / 2)
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y2 = y1 + cropSize[1]
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x1 = (cols - cropSize[0]) / 2
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x1 = int((cols - cropSize[0]) / 2)
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x2 = x1 + cropSize[0]
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frame = frame[y1:y2, x1:x2]
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@ -1,8 +1,5 @@
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import numpy as np
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import argparse
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import os
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import sys
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sys.path.append('/home/arrybn/build/opencv/lib')
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import cv2 as cv
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try:
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import cv2 as cv
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