1# Script is based on https://github.com/richzhang/colorization/blob/master/colorization/colorize.py 2# To download the caffemodel and the prototxt, see: https://github.com/richzhang/colorization/tree/master/colorization/models 3# To download pts_in_hull.npy, see: https://github.com/richzhang/colorization/blob/master/colorization/resources/pts_in_hull.npy 4import numpy as np 5import argparse 6import cv2 as cv 7 8def parse_args(): 9 parser = argparse.ArgumentParser(description='iColor: deep interactive colorization') 10 parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera') 11 parser.add_argument('--prototxt', help='Path to colorization_deploy_v2.prototxt', required=True) 12 parser.add_argument('--caffemodel', help='Path to colorization_release_v2.caffemodel', required=True) 13 parser.add_argument('--kernel', help='Path to pts_in_hull.npy', required=True) 14 15 args = parser.parse_args() 16 return args 17 18if __name__ == '__main__': 19 W_in = 224 20 H_in = 224 21 imshowSize = (640, 480) 22 23 args = parse_args() 24 25 # Select desired model 26 net = cv.dnn.readNetFromCaffe(args.prototxt, args.caffemodel) 27 28 pts_in_hull = np.load(args.kernel) # load cluster centers 29 30 # populate cluster centers as 1x1 convolution kernel 31 pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1) 32 net.getLayer(net.getLayerId('class8_ab')).blobs = [pts_in_hull.astype(np.float32)] 33 net.getLayer(net.getLayerId('conv8_313_rh')).blobs = [np.full([1, 313], 2.606, np.float32)] 34 35 if args.input: 36 cap = cv.VideoCapture(args.input) 37 else: 38 cap = cv.VideoCapture(0) 39 40 while cv.waitKey(1) < 0: 41 hasFrame, frame = cap.read() 42 if not hasFrame: 43 cv.waitKey() 44 break 45 46 img_rgb = (frame[:,:,[2, 1, 0]] * 1.0 / 255).astype(np.float32) 47 48 img_lab = cv.cvtColor(img_rgb, cv.COLOR_RGB2Lab) 49 img_l = img_lab[:,:,0] # pull out L channel 50 (H_orig,W_orig) = img_rgb.shape[:2] # original image size 51 52 # resize image to network input size 53 img_rs = cv.resize(img_rgb, (W_in, H_in)) # resize image to network input size 54 img_lab_rs = cv.cvtColor(img_rs, cv.COLOR_RGB2Lab) 55 img_l_rs = img_lab_rs[:,:,0] 56 img_l_rs -= 50 # subtract 50 for mean-centering 57 58 net.setInput(cv.dnn.blobFromImage(img_l_rs)) 59 ab_dec = net.forward()[0,:,:,:].transpose((1,2,0)) # this is our result 60 61 (H_out,W_out) = ab_dec.shape[:2] 62 ab_dec_us = cv.resize(ab_dec, (W_orig, H_orig)) 63 img_lab_out = np.concatenate((img_l[:,:,np.newaxis],ab_dec_us),axis=2) # concatenate with original image L 64 img_bgr_out = np.clip(cv.cvtColor(img_lab_out, cv.COLOR_Lab2BGR), 0, 1) 65 66 frame = cv.resize(frame, imshowSize) 67 cv.imshow('origin', frame) 68 cv.imshow('gray', cv.cvtColor(frame, cv.COLOR_RGB2GRAY)) 69 cv.imshow('colorized', cv.resize(img_bgr_out, imshowSize)) 70