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1!pip install opencv-python
2!pip install Pillow
3
4import numpy as np
5import cv2
6from cv2 import dnn
7from PIL import Image
8
9#--------Model file paths--------#
10proto_file = 'colorization_deploy_v2.prototxt'
11model_file = 'colorization_release_v2.caffemodel'
12hull_pts = 'pts_in_hull.npy'
13img_path = 'img1.jpg'
14#--------------#--------------#
15 
16#--------Reading the model params--------#
17# net = dnn.readNetFromCaffe(proto_file,model_file)
18net = dnn.readNetFromCaffe(proto_file,model_file)
19
20dnn.readNetFromCaffe
21
22kernel = np.load(hull_pts)
23#-----------------------------------#---------------------#
24
25
26#-----Reading and preprocessing image--------#
27img = cv2.imread(img_path)
28scaled = img.astype("float32") / 255.0
29lab_img = cv2.cvtColor(scaled, cv2.COLOR_BGR2LAB)
30#-----------------------------------#---------------------#
31
32# add the cluster centers as 1x1 convolutions to the model
33class8 = net.getLayerId("class8_ab")
34conv8 = net.getLayerId("conv8_313_rh")
35pts = kernel.transpose().reshape(2, 313, 1, 1)
36net.getLayer(class8).blobs = [pts.astype("float32")]
37net.getLayer(conv8).blobs = [np.full([1, 313], 2.606, dtype="float32")]
38#-----------------------------------#---------------------#
39
40# we'll resize the image for the network
41resized = cv2.resize(lab_img, (224, 224))
42# split the L channel
43L = cv2.split(resized)[0]
44# mean subtraction
45L -= 50
46#-----------------------------------#---------------------#
47
48# predicting the ab channels from the input L channel
49
50net.setInput(cv2.dnn.blobFromImage(L))
51ab_channel = net.forward()[0, :, :, :].transpose((1, 2, 0))
52# resize the predicted 'ab' volume to the same dimensions as our
53# input image
54ab_channel = cv2.resize(ab_channel, (img.shape[1], img.shape[0]))
55
56
57# Take the L channel from the image
58L = cv2.split(lab_img)[0]
59# Join the L channel with predicted ab channel
60colorized = np.concatenate((L[:, :, np.newaxis], ab_channel), axis=2)
61
62# Then convert the image from Lab to BGR
63colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2BGR)
64colorized = np.clip(colorized, 0, 1)
65
66# change the image to 0-255 range and convert it from float32 to int
67colorized = (255 * colorized).astype("uint8")
68
69# Let's resize the images and show them together
70img = cv2.resize(img,(640,640))
71colorized = cv2.resize(colorized,(640,640))
72
73result = cv2.hconcat([img,colorized])
74cv2.imshow("Grayscale -> Colour", result)
75cv2.waitKey(0)
76
77color_coverted = cv2.cvtColor(colorized, cv2.COLOR_BGR2RGB)
78
79img = Image.fromarray(color_coverted, 'RGB')
80img.save('img1_converted.jpg')
81img