Merge pull request '图片与视频' (#6) from main-v2 into main

Reviewed-on: #6

main.py

@@ -3,7 +3,7 @@ import cv2
 import numpy as np
 from PyQt5.QtWidgets import (
     QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout,
-    QLabel, QPushButton, QScrollArea, QFrame, QSizePolicy
+    QLabel, QPushButton, QScrollArea, QFrame, QSizePolicy,QFileDialog
 )
 from PyQt5.QtCore import QTimer, Qt, pyqtSignal, QThread
 from PyQt5.QtGui import QImage, QPixmap, QFont, QPainter, QPen, QColor
@@ -206,7 +206,12 @@ class MainWindow(QMainWindow):
         self.start_button.clicked.connect(self.start_camera)
         self.stop_button.clicked.connect(self.stop_camera)
         self.stop_button.setEnabled(False)
-        
+        self.btn_image = QPushButton('选择图片')
+        self.btn_video = QPushButton('选择视频')
+        self.btn_image.clicked.connect(self.open_image_file)
+        self.btn_video.clicked.connect(self.open_video_file)
+        button_layout.addWidget(self.btn_image)
+        button_layout.addWidget(self.btn_video)
         button_layout.addWidget(self.start_button)
         button_layout.addWidget(self.stop_button)
         button_layout.addStretch()
@@ -425,6 +430,42 @@ class MainWindow(QMainWindow):
         if self.camera_thread:
             self.camera_thread.stop_camera()
         event.accept()
+        
+    def open_image_file(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, '选择图片', '', '图片文件 (*.jpg *.png)')
+        if file_path:
+            image = cv2.imread(file_path)
+            self.process_image(image)
+    
+    def open_video_file(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, '选择视频', '', '视频文件 (*.mp4 *.avi)')
+        if file_path:
+            self.cap = cv2.VideoCapture(file_path)
+            self.video_timer = QTimer()
+            self.video_timer.timeout.connect(self.process_video_frame)
+            self.video_timer.start(30)
+    
+    def process_image(self, image):
+        self.detections = self.detector.detect_license_plates(image)
+        display_image = self.draw_detections(image.copy())
+        self.display_static_image(display_image)
+        self.update_results_display()
+    
+    def process_video_frame(self):
+        ret, frame = self.cap.read()
+        if ret:
+            self.process_image(frame)
+        else:
+            self.video_timer.stop()
+            self.cap.release()
+    
+    def display_static_image(self, image):
+        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        h, w, ch = rgb_image.shape
+        bytes_per_line = ch * w
+        qt_image = QImage(rgb_image.data, w, h, bytes_per_line, QImage.Format_RGB888)
+        pixmap = QPixmap.fromImage(qt_image)
+        self.camera_label.setPixmap(pixmap.scaled(self.camera_label.size(), Qt.KeepAspectRatio))
 
 def main():
     app = QApplication(sys.argv)

test_lpr_real_images.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+LPRNet接口真实图片测试脚本
+测试LPRNET_part目录下的真实车牌图片
+"""
+
+import cv2
+import numpy as np
+import os
+from LPRNET_part.lpr_interface import LPRNinitialize_model, LPRNmodel_predict
+
+def test_real_images():
+    """
+    测试LPRNET_part目录下的真实车牌图片
+    """
+    print("=== LPRNet真实图片测试 ===")
+    
+    # 初始化模型
+    print("1. 初始化LPRNet模型...")
+    success = LPRNinitialize_model()
+    if not success:
+        print("模型初始化失败！")
+        return
+    
+    # 获取LPRNET_part目录下的图片文件
+    lprnet_dir = "LPRNET_part"
+    image_files = []
+    
+    if os.path.exists(lprnet_dir):
+        for file in os.listdir(lprnet_dir):
+            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
+                image_files.append(os.path.join(lprnet_dir, file))
+    
+    if not image_files:
+        print("未找到图片文件！")
+        return
+    
+    print(f"2. 找到 {len(image_files)} 个图片文件")
+    
+    # 测试每个图片
+    for i, image_path in enumerate(image_files, 1):
+        print(f"\n--- 测试图片 {i}: {os.path.basename(image_path)} ---")
+        
+        try:
+            # 使用支持中文路径的方式读取图片
+            image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+            
+            if image is None:
+                print(f"无法读取图片: {image_path}")
+                continue
+            
+            print(f"图片尺寸: {image.shape}")
+            
+            # 进行预测
+            result = LPRNmodel_predict(image)
+            print(f"识别结果: {result}")
+            print(f"识别车牌号: {''.join(result)}")
+            
+        except Exception as e:
+            print(f"处理图片 {image_path} 时出错: {e}")
+            import traceback
+            traceback.print_exc()
+    
+    print("\n=== 测试完成 ===")
+
+def test_image_loading():
+    """
+    测试图片加载方式
+    """
+    print("\n=== 图片加载测试 ===")
+    
+    lprnet_dir = "LPRNET_part"
+    
+    if os.path.exists(lprnet_dir):
+        for file in os.listdir(lprnet_dir):
+            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
+                image_path = os.path.join(lprnet_dir, file)
+                print(f"\n测试文件: {file}")
+                
+                # 方法1: 普通cv2.imread
+                img1 = cv2.imread(image_path)
+                print(f"cv2.imread结果: {img1 is not None}")
+                
+                # 方法2: 支持中文路径的方式
+                try:
+                    img2 = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+                    print(f"cv2.imdecode结果: {img2 is not None}")
+                    if img2 is not None:
+                        print(f"图片尺寸: {img2.shape}")
+                except Exception as e:
+                    print(f"cv2.imdecode失败: {e}")
+
+if __name__ == "__main__":
+    # 首先测试图片加载
+    test_image_loading()
+    
+    # 然后测试完整的识别流程
+    test_real_images()