colorimg.py

# to run the code run the following command
# python colorimg.py --image images/img1.jpg --prototxt models/colorization_deploy_v2.prototxt --model models/colorization_release_v2.caffemodel --points models/pts_in_hull.npy
# python colorimg.py --image images/img2.jpg --prototxt models/colorization_deploy_v2.prototxt --model models/colorization_release_v2.caffemodel --points models/pts_in_hull.npy
# python colorimg.py --image images/img3.jpg --prototxt models/colorization_deploy_v2.prototxt --model models/colorization_release_v2.caffemodel --points models/pts_in_hull.npy
# python colorimg.py --image images/img4.jpg --prototxt models/colorization_deploy_v2.prototxt --model models/colorization_release_v2.caffemodel --points models/pts_in_hull.npy

# importing the necessary packages
import numpy as np
import argparse
import cv2

# constructing argument parser and parsing the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str, required=True,
                help="path to input black and white image")
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")
args = vars(ap.parse_args())

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

# adding 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")]

# loading image, scaling px intensity range to [0, 1], converting from BGR to Lab 
image = cv2.imread(args["image"])
scaled = image.astype("float32") / 255.0
lab = cv2.cvtColor(scaled, cv2.COLOR_BGR2LAB)

# resize the lab image 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

# passing the L channel to predict the 'a' and 'b' channel values
'print("[INFO] colorizing image...")'
net.setInput(cv2.dnn.blobFromImage(L))
ab = net.forward()[0, :, :, :].transpose((1, 2, 0))

# resizing predicted 'ab' values to fit same dimensions as input images
ab = cv2.resize(ab, (image.shape[1], image.shape[0]))

# grab unresized 'l' and concat it with predicted 'ab' channels
L = cv2.split(lab)[0]
colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)

# convert the image from Lab to RGB and clip the values outside[0, 1]
colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2BGR)
colorized = np.clip(colorized, 0, 1)

# the colorized image is converted from a float in range [0, 1] to unsigned 8bit int in range [0, 255]
colorized = (255 * colorized).astype("uint8")

# show the original and output colorized images
cv2.imshow("Black and White", image)
cv2.imshow("Colorized", colorized)
cv2.imwrite("results/colorized.jpg", colorized)
cv2.waitKey(0)