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