| import numpy as np | |
| import cv2 | |
| from cv2 import dnn | |
| print("loading models.....") | |
| net = cv2.dnn.readNetFromCaffe('colorization_deploy_v2.prototxt','colorization_release_v2.caffemodel') | |
| pts = np.load('pts_in_hull.npy') | |
| 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')] | |
| image = cv2.imread('nnl.jpg') | |
| scaled = image.astype("float32")/255.0 | |
| lab = cv2.cvtColor(scaled,cv2.COLOR_BGR2LAB) | |
| resized = cv2.resize(lab,(224,224)) | |
| L = cv2.split(resized)[0] | |
| L -= 50 | |
| net.setInput(cv2.dnn.blobFromImage(L)) | |
| ab = net.forward()[0, :, :, :].transpose((1,2,0)) | |
| ab = cv2.resize(ab, (image.shape[1],image.shape[0])) | |
| L = cv2.split(lab)[0] | |
| colorized = np.concatenate((L[:,:,np.newaxis], ab), axis=2) | |
| colorized = cv2.cvtColor(colorized,cv2.COLOR_LAB2BGR) | |
| colorized = np.clip(colorized,0,1) | |
| colorized = (255 * colorized).astype("uint8") | |
| cv2.imshow("Original",image) | |
| cv2.imshow("Colorized",colorized) | |
| cv2.waitKey(0) |