基于深度学习的图像增强-zeros-DCE模型源码分享

from datetime import datetime import math import sys from shibie import Ui_MainWindow from PyQt5.QtWidgets import * from PyQt5 import QtCore, QtGui, QtWidgets from PyQt5.QtCore import * from PyQt5.QtGui import * import cv2 as cv import numpy as np import os import time import matplotlib.pyplot as plt  import torch import torch.nn as nn from torchvision import transforms from PIL import Image import model  class ImageProcess(QMainWindow, Ui_MainWindow):     def __init__(self, parent=None):         super(ImageProcess, self).__init__()         self.setupUi(self)         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')          # 3. 加载模型         self.DCE_net = model.enhance_net_nopool().to(self.device)         self.DCE_net.load_state_dict(torch.load('snapshots/Epoch99.pth', map_location=self.device))         self.DCE_net.eval()      def paintEvent(self, event):         painter = QPainter(self)         pixmap = QPixmap("main_background.jpg")  # 加载背景图片         painter.drawPixmap(self.rect(), pixmap)  # 将图片拉伸填充整个窗口                  painter.end()      def cv_imread(self,filePath):         cv_img = cv.imdecode(np.fromfile(filePath, dtype=np.uint8), -1)         return cv_img      def dakaituxiang(self):         filename,  _ = QFileDialog.getOpenFileName(self, '打开图片')         if filename:             # self.captured = cv.imdecode(np.fromfile(filename,dtype=np.uint8),-1)             self.captured = self.cv_imread(str(filename))             self.image_path = filename             # self.captured = cv.imread(str(filename))             # OpenCV图像以BGR通道存储，显示时需要从BGR转到RGB             # self.captured = cv.cvtColor(self.captured, cv.COLOR_BGR2RGB)              rows, cols, channels = self.captured.shape             bytesPerLine = channels * cols             cv_img=cv.cvtColor(self.captured,cv.COLOR_RGB2BGR)             QImg = QImage(cv_img.data, cols, rows,                           bytesPerLine, QImage.Format_RGB888)             self.timecha = 0             self.result_tu = self.captured             self.axes_yuantu.setPixmap(QPixmap.fromImage(QImg).scaled(                 self.axes_yuantu.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))      def btn_save(self):         if hasattr(self, 'result_tu') and self.result_tu is not None:             filename, _ = QFileDialog.getSaveFileName(self, '保存图片', '', 'Images (*.png *.jpg *.bmp)')             if filename:                 cv.imwrite(filename, cv.cvtColor(self.result_tu, cv.COLOR_RGB2BGR))                 QMessageBox.information(self, '提示', '图片保存成功！')         else:             QMessageBox.warning(self, '警告', '没有可保存的图像！')      def btnzero(self):         original_img = Image.open(self.image_path).convert('RGB')         starttime = time.time()         # 2. 图像预处理         transform = transforms.Compose([             transforms.ToTensor(),         ])         img_tensor = transform(original_img).unsqueeze(0).to(self.device)         with torch.no_grad():             _, enhanced_img, _ = self.DCE_net(img_tensor)                  # 后处理         enhanced_img = enhanced_img.squeeze(0).permute(1, 2, 0).cpu().numpy()         enhanced_img = np.clip(enhanced_img * 255, 0, 255).astype(np.uint8)         enhanced_img = Image.fromarray(enhanced_img)          enhanced_img = np.array(enhanced_img)  # 直接转 NumPy 数组         enhanced_img = cv.cvtColor(enhanced_img, cv.COLOR_RGB2BGR)         enhanced_img = cv.cvtColor(enhanced_img, cv.COLOR_BGR2RGB)         endtime = time.time()         timecha = (endtime - starttime) * 1000         timecha = round(timecha, 2)         self.timecha = timecha         self.result_tu = enhanced_img                  # 8. 显示结果         cv_img = enhanced_img         rows, cols, channels = enhanced_img.shape         bytesPerLine = channels * cols         QImg = QImage(cv_img.data, cols, rows,bytesPerLine, QImage.Format_RGB888)                  # 显示在GUI的axes_quzao区域（可根据需要修改为其他QLabel）         self.axes_bianyuan_2.setPixmap(QPixmap.fromImage(QImg).scaled(             self.axes_bianyuan_2.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))                  return  if __name__ == '__main__':     app = QApplication(sys.argv)     window = ImageProcess()     window.show()     sys.exit(app.exec_())