基于OpenCV+DNN的灰度图像自动彩色化代码（含Python代码+模型文件）

# -*- coding: utf-8 -*- """ @Time ： 2022/8/1 18:53 @Auth ： 思绪无限 Bilibili：思绪亦无限 公众号：AI技术研究与分享 @File ：main.py @IDE ：PyCharm 功能： """ import os import cv2 # opencv 3.4.2+ required import numpy as np proto = './models/models_colorization_deploy_v2.prototxt' weights = './models/colorization_release_v2.caffemodel' pts_in_hull = np.load('./models/pts_in_hull.npy') pts_in_hull = pts_in_hull.transpose().reshape(2, 313, 1, 1).astype(np.float32) # load model net = cv2.dnn.readNetFromCaffe(proto, weights) # populate cluster centers as 1x1 convolution kernel net.getLayer(net.getLayerId('class8_ab')).blobs = [pts_in_hull] net.getLayer(net.getLayerId('conv8_313_rh')).blobs = [np.full((1, 313), 2.606, np.float32)] # 读取图片 img_path = 'img/g5.png' img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) img_input = img.copy() # BGR转为RGB img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # 转换通道 img_rgb = img.copy() # 归一化输入图像 img_rgb = (img_rgb / 255.).astype(np.float32) # convert RGB to LAB img_lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2Lab) # # only L channel to be used img_l = img_lab[:, :, 0] input_img = cv2.resize(img_l, (224, 224)) input_img -= 50 # subtract 50 for mean-centering # 运行推理模型 net.setInput(cv2.dnn.blobFromImage(input_img)) # 输入网络 pred = net.forward()[0, :, :, :].transpose((1, 2, 0)) # 运行推理预测 # 转换为原始尺寸 pred_resize = cv2.resize(pred, (img.shape[1], img.shape[0])) pred_lab = np.concatenate([img_l[:, :, np.newaxis], pred_resize], axis=2) # LAB转RGB pred_rgb = cv2.cvtColor(pred_lab, cv2.COLOR_Lab2RGB) pred_rgb = np.clip(pred_rgb, 0, 1) * 255 pred_rgb = pred_rgb.astype(np.uint8) # 保存结果图片 filename, ext = os.path.splitext(img_path) input_filename = '%s_input%s' % (filename, ext) output_filename = '%s_output%s' % (filename, ext) pred_rgb_output = cv2.cvtColor(pred_rgb, cv2.COLOR_RGB2BGR) cv2.imwrite(input_filename, img_input) cv2.imwrite(output_filename, np.concatenate([img, pred_rgb_output], axis=1))