VideoColorizer.py

# USAGE
# python bw2color_video.py --prototxt model/colorization_deploy_v2.prototxt --model model/colorization_release_v2.caffemodel --points model/pts_in_hull.npy

# import the necessary packages
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", type=str,
	help="path to optional input video (webcam will be used otherwise)")
ap.add_argument("-p", "--prototxt", type=str, required=True,
	help="path to Caffe prototxt file")
ap.add_argument("-m", "--model", type=str, required=True,
	help="path to Caffe pre-trained model")
ap.add_argument("-c", "--points", type=str, required=True,
	help="path to cluster center points")
ap.add_argument("-w", "--width", type=int, default=500,
	help="input width dimension of frame")
args = vars(ap.parse_args())


print("[INFO] opening video file...")
vs = cv2.VideoCapture('video//GrayscaleVideo.mp4')

# load our serialized black and white colorizer model and cluster
# center points from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
pts = np.load(args["points"])

# add the cluster centers as 1x1 convolutions to the model
class8 = net.getLayerId("class8_ab")
conv8 = net.getLayerId("conv8_313_rh")
pts = pts.transpose().reshape(2, 313, 1, 1)
net.getLayer(class8).blobs = [pts.astype("float32")]
net.getLayer(conv8).blobs = [np.full([1, 313], 2.606, dtype="float32")]

# loop over frames from the video stream
count = 1
while True:
    # grab the next frame and handle if we are reading from either
    # VideoCapture or VideoStream
    frame = vs.read()
    frame = frame[1]

    # if we are viewing a video and we did not grab a frame then we
    # have reached the end of the video
    # resize the input frame, scale the pixel intensities to the
    # range [0, 1], and then convert the frame from the BGR to Lab
    # color space
    frame = imutils.resize(frame, width=args["width"])
    scaled = frame.astype("float32") / 255.0
    lab = cv2.cvtColor(scaled, cv2.COLOR_BGR2LAB)

    # resize the Lab frame to 224x224 (the dimensions the colorization
    # network accepts), split channels, extract the 'L' channel, and
    # then perform mean centering
    resized = cv2.resize(lab, (224, 224))
    L = cv2.split(resized)[0]
    L -= 50

    # pass the L channel through the network which will *predict* the
    # 'a' and 'b' channel values
    net.setInput(cv2.dnn.blobFromImage(L))
    ab = net.forward()[0, :, :, :].transpose((1, 2, 0))

    # resize the predicted 'ab' volume to the same dimensions as our
    # input frame, then grab the 'L' channel from the *original* input
    # frame (not the resized one) and concatenate the original 'L'
    # channel with the predicted 'ab' channels
    ab = cv2.resize(ab, (frame.shape[1], frame.shape[0]))
    L = cv2.split(lab)[0]
    colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)

    # convert the output frame from the Lab color space to RGB, clip
    # any values that fall outside the range [0, 1], and then convert
    # to an 8-bit unsigned integer ([0, 255] range)
    colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2BGR)
    colorized = np.clip(colorized, 0, 1)
    colorized = (255 * colorized).astype("uint8")

    # show the original and final colorized frames
    cv2.imwrite("video//images//image%d.png" % count, colorized)
    key = cv2.waitKey(1) & 0xFF
    count += 1
    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break
    
# close any open windows
#cv2.destroyAllWindows()