python实现灰度图像彩色化

# 图像彩色化 import cv2 import numpy as np if __name__ == "__main__": # 设定好三个已经训练好的模型 prototxt = "colorization_deploy_v2.prototxt" model = "colorization_release_v1.caffemodel" points = "pts_in_hull.npy" image_path = "grey-color.jpg" print("[INFO] loading model...") net = cv2.dnn.readNetFromCaffe(prototxt, model) pts = np.load(points) # 读取npy文件 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(image_path) scaled = image.astype("float32") / 255.0 lab = cv2.cvtColor(scaled, cv2.COLOR_BGR2LAB) # 将所有训练图像从RGB颜色空间转换为Lab颜色空间 resized = cv2.resize(lab, (224, 224)) L = cv2.split(resized)[0] L -= 50 # 使用L通道作为网络的输入并训练网络预测ab通道 print("[INFO] colorizing image...") net.setInput(cv2.dnn.blobFromImage(L)) ab = net.forward()[0, :, :, :].transpose((1, 2, 0)) # 将输入L通道与预测的ab通道组合 ab = cv2.resize(ab, (image.shape[1], image.shape[0])) L = cv2.split(lab)[0] colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2) # 将Lab图像转换回RGB colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2BGR) colorized = np.clip(colorized, 0, 1) colorized = (255 * colorized).astype("uint8") cv2.imshow("original", image) cv2.imshow("result", colorized) cv2.waitKey(0) cv2.destroyAllWindows()