dv = dnn::DictValue((int64)PyLong_AsLongLong(o));
return true;
}
+ else if (PyInt_Check(o))
+ {
+ dv = dnn::DictValue((int64)PyInt_AS_LONG(o));
+ return true;
+ }
else if (PyFloat_Check(o))
{
dv = dnn::DictValue(PyFloat_AS_DOUBLE(o));
-# Script is based on https://github.com/richzhang/colorization/colorize.py
+# Script is based on https://github.com/richzhang/colorization/blob/master/colorize.py
+# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/master/models
+# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/blob/master/resources/pts_in_hull.npy
import numpy as np
import argparse
import cv2 as cv
# populate cluster centers as 1x1 convolution kernel
pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1)
- net.getLayer(long(net.getLayerId('class8_ab'))).blobs = [pts_in_hull.astype(np.float32)]
- net.getLayer(long(net.getLayerId('conv8_313_rh'))).blobs = [np.full([1, 313], 2.606, np.float32)]
+ net.getLayer(net.getLayerId('class8_ab')).blobs = [pts_in_hull.astype(np.float32)]
+ net.getLayer(net.getLayerId('conv8_313_rh')).blobs = [np.full([1, 313], 2.606, np.float32)]
if args.input:
cap = cv.VideoCapture(args.input)
else:
cropSize = (cols, int(cols / WHRatio))
- y1 = (rows - cropSize[1]) / 2
+ y1 = int((rows - cropSize[1]) / 2)
y2 = y1 + cropSize[1]
- x1 = (cols - cropSize[0]) / 2
+ x1 = int((cols - cropSize[0]) / 2)
x2 = x1 + cropSize[0]
frame = frame[y1:y2, x1:x2]