# Script is based on https://github.com/richzhang/colorization/colorize.py import numpy as np import argparse import cv2 as cv def parse_args(): parser = argparse.ArgumentParser(description='iColor: deep interactive colorization') parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera') parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', default='./models/colorization_release_v2.prototxt') parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', default='./models/colorization_release_v2.caffemodel') parser.add_argument('--kernel', help='Path to pts_in_hull.npy', default='./resources/pts_in_hull.npy') args = parser.parse_args() return args if __name__ == '__main__': W_in = 224 H_in = 224 imshowSize = (640, 480) args = parse_args() # Select desired model net = cv.dnn.readNetFromCaffe(args.prototxt, args.caffemodel) pts_in_hull = np.load(args.kernel) # load cluster centers # 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)] if args.input: cap = cv.VideoCapture(args.input) else: cap = cv.VideoCapture(0) while cv.waitKey(1) < 0: hasFrame, frame = cap.read() if not hasFrame: cv.waitKey() break img_rgb = (frame[:,:,[2, 1, 0]] * 1.0 / 255).astype(np.float32) img_lab = cv.cvtColor(img_rgb, cv.COLOR_RGB2Lab) img_l = img_lab[:,:,0] # pull out L channel (H_orig,W_orig) = img_rgb.shape[:2] # original image size # resize image to network input size img_rs = cv.resize(img_rgb, (W_in, H_in)) # resize image to network input size img_lab_rs = cv.cvtColor(img_rs, cv.COLOR_RGB2Lab) img_l_rs = img_lab_rs[:,:,0] img_l_rs -= 50 # subtract 50 for mean-centering net.setInput(cv.dnn.blobFromImage(img_l_rs)) ab_dec = net.forward('class8_ab')[0,:,:,:].transpose((1,2,0)) # this is our result (H_out,W_out) = ab_dec.shape[:2] ab_dec_us = cv.resize(ab_dec, (W_orig, H_orig)) img_lab_out = np.concatenate((img_l[:,:,np.newaxis],ab_dec_us),axis=2) # concatenate with original image L img_bgr_out = np.clip(cv.cvtColor(img_lab_out, cv.COLOR_Lab2BGR), 0, 1) frame = cv.resize(frame, imshowSize) cv.imshow('origin', frame) cv.imshow('gray', cv.cvtColor(frame, cv.COLOR_RGB2GRAY)) cv.imshow('colorized', cv.resize(img_bgr_out, imshowSize))