更新 main.py

main.py

@@ -2,6 +2,7 @@ import sys
 import os
 import cv2
 import numpy as np
+from collections import defaultdict, deque
 from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, \
     QFileDialog, QFrame, QScrollArea, QComboBox
 from PyQt5.QtCore import QTimer, Qt, pyqtSignal, QThread
@@ -18,6 +19,190 @@ from yolopart.detector import LicensePlateYOLO
 # from CRNN_part.crnn_interface import LPRNmodel_predict
 # from CRNN_part.crnn_interface import LPRNinitialize_model
 
+class PlateStabilizer:
+    """车牌识别结果稳定器"""
+    
+    def __init__(self, history_size=10, confidence_threshold=0.6, stability_frames=5):
+        self.history_size = history_size  # 历史帧数量
+        self.confidence_threshold = confidence_threshold  # 置信度阈值
+        self.stability_frames = stability_frames  # 稳定帧数要求
+        
+        # 存储每个车牌的历史识别结果
+        self.plate_histories = defaultdict(lambda: deque(maxlen=history_size))
+        # 存储当前稳定的车牌结果
+        self.stable_results = {}
+        # 车牌ID计数器
+        self.plate_id_counter = 0
+        # 车牌位置追踪
+        self.plate_positions = {}
+        
+    def calculate_plate_distance(self, pos1, pos2):
+        """计算两个车牌位置的距离"""
+        if pos1 is None or pos2 is None:
+            return float('inf')
+        
+        # 计算中心点距离
+        center1 = ((pos1[0] + pos1[2]) / 2, (pos1[1] + pos1[3]) / 2)
+        center2 = ((pos2[0] + pos2[2]) / 2, (pos2[1] + pos2[3]) / 2)
+        
+        return np.sqrt((center1[0] - center2[0])**2 + (center1[1] - center2[1])**2)
+    
+    def match_plates_to_history(self, current_detections):
+        """将当前检测结果匹配到历史记录"""
+        matched_plates = {}
+        used_ids = set()
+        
+        for detection in current_detections:
+            bbox = detection.get('bbox', [0, 0, 0, 0])
+            best_match_id = None
+            min_distance = float('inf')
+            
+            # 寻找最佳匹配的历史车牌
+            for plate_id, last_pos in self.plate_positions.items():
+                if plate_id in used_ids:
+                    continue
+                    
+                distance = self.calculate_plate_distance(bbox, last_pos)
+                if distance < min_distance and distance < 100:  # 距离阈值
+                    min_distance = distance
+                    best_match_id = plate_id
+            
+            if best_match_id is not None:
+                matched_plates[best_match_id] = detection
+                used_ids.add(best_match_id)
+                self.plate_positions[best_match_id] = bbox
+            else:
+                # 创建新的车牌ID
+                new_id = f"plate_{self.plate_id_counter}"
+                self.plate_id_counter += 1
+                matched_plates[new_id] = detection
+                self.plate_positions[new_id] = bbox
+        
+        return matched_plates
+    
+    def calculate_confidence(self, plate_text, detection_quality=1.0):
+        """计算识别结果的置信度"""
+        if not plate_text or plate_text == "识别失败":
+            return 0.0
+        
+        # 基础置信度基于文本长度和字符类型
+        base_confidence = 0.5
+        
+        # 长度合理性检查
+        if 7 <= len(plate_text) <= 8:
+            base_confidence += 0.2
+        
+        # 字符类型检查（中文+字母+数字的组合）
+        has_chinese = any('\u4e00' <= char <= '\u9fff' for char in plate_text)
+        has_letter = any(char.isalpha() for char in plate_text)
+        has_digit = any(char.isdigit() for char in plate_text)
+        
+        if has_chinese and has_letter and has_digit:
+            base_confidence += 0.2
+        
+        # 检测质量影响
+        confidence = base_confidence * detection_quality
+        
+        return min(confidence, 1.0)
+    
+    def update_and_get_stable_result(self, current_detections, corrected_images, plate_texts):
+        """更新历史记录并返回稳定的识别结果"""
+        if not current_detections:
+            return []
+        
+        # 匹配当前检测到历史记录
+        matched_plates = self.match_plates_to_history(current_detections)
+        
+        stable_results = []
+        
+        for plate_id, detection in matched_plates.items():
+            # 获取对应的矫正图像和识别文本
+            detection_idx = current_detections.index(detection)
+            corrected_image = corrected_images[detection_idx] if detection_idx < len(corrected_images) else None
+            plate_text = plate_texts[detection_idx] if detection_idx < len(plate_texts) else "识别失败"
+            
+            # 计算置信度
+            confidence = self.calculate_confidence(plate_text)
+            
+            # 添加到历史记录
+            history_entry = {
+                'text': plate_text,
+                'confidence': confidence,
+                'detection': detection,
+                'corrected_image': corrected_image
+            }
+            self.plate_histories[plate_id].append(history_entry)
+            
+            # 计算稳定结果
+            stable_text = self.get_stable_text(plate_id)
+            
+            if stable_text and stable_text != "识别失败":
+                stable_results.append({
+                    'id': plate_id,
+                    'class_name': detection['class_name'],
+                    'corrected_image': corrected_image,
+                    'plate_number': stable_text,
+                    'detection': detection
+                })
+        
+        return stable_results
+    
+    def get_stable_text(self, plate_id):
+        """获取指定车牌的稳定识别结果"""
+        history = self.plate_histories[plate_id]
+        
+        if len(history) < 3:  # 历史记录太少，返回最新结果
+            return history[-1]['text'] if history else "识别失败"
+        
+        # 统计各种识别结果的加权投票
+        text_votes = defaultdict(float)
+        total_confidence = 0
+        
+        for entry in history:
+            text = entry['text']
+            confidence = entry['confidence']
+            
+            if text != "识别失败" and confidence > 0.3:
+                text_votes[text] += confidence
+                total_confidence += confidence
+        
+        if not text_votes:
+            return "识别失败"
+        
+        # 找到得票最高的结果
+        best_text = max(text_votes.items(), key=lambda x: x[1])
+        
+        # 检查是否足够稳定（得票率超过阈值）
+        vote_ratio = best_text[1] / total_confidence if total_confidence > 0 else 0
+        
+        if vote_ratio >= self.confidence_threshold:
+            return best_text[0]
+        else:
+            # 不够稳定，返回最近的高置信度结果
+            recent_high_conf = [entry for entry in list(history)[-5:] 
+                              if entry['confidence'] > 0.5 and entry['text'] != "识别失败"]
+            
+            if recent_high_conf:
+                return recent_high_conf[-1]['text']
+            else:
+                return history[-1]['text']
+    
+    def clear_old_plates(self, current_plate_ids):
+        """清理不再出现的车牌历史记录"""
+        # 移除超过一定时间未更新的车牌
+        plates_to_remove = []
+        for plate_id in self.plate_histories.keys():
+            if plate_id not in current_plate_ids:
+                plates_to_remove.append(plate_id)
+        
+        for plate_id in plates_to_remove:
+            if plate_id in self.plate_histories:
+                del self.plate_histories[plate_id]
+            if plate_id in self.plate_positions:
+                del self.plate_positions[plate_id]
+            if plate_id in self.stable_results:
+                del self.stable_results[plate_id]
+
 class CameraThread(QThread):
     """摄像头线程类"""
     frame_ready = pyqtSignal(np.ndarray)
@@ -118,6 +303,7 @@ class LicensePlateWidget(QWidget):
     def init_ui(self, class_name, corrected_image, plate_number):
         layout = QHBoxLayout()
         layout.setContentsMargins(10, 5, 10, 5)
+        layout.setSpacing(8)  # 设置组件间距
         
         # 车牌类型标签
         type_label = QLabel(class_name)
@@ -155,7 +341,6 @@ class LicensePlateWidget(QWidget):
         
         # 矫正后的车牌图像
         image_label = QLabel()
-        image_label.setFixedSize(120, 40)
         image_label.setStyleSheet("border: 1px solid #ddd; background-color: white;")
         
         if corrected_image is not None:
@@ -169,16 +354,44 @@ class LicensePlateWidget(QWidget):
                 q_image = QImage(corrected_image.data, w, h, bytes_per_line, QImage.Format_Grayscale8)
             
             pixmap = QPixmap.fromImage(q_image)
-            scaled_pixmap = pixmap.scaled(120, 40, Qt.KeepAspectRatio, Qt.SmoothTransformation)
+            
+            # 动态计算显示尺寸，保持车牌的宽高比
+            original_width = pixmap.width()
+            original_height = pixmap.height()
+            
+            # 设置最大显示尺寸限制
+            max_width = 150
+            max_height = 60
+            
+            # 计算缩放比例，确保图像完整显示
+            width_ratio = max_width / original_width if original_width > 0 else 1
+            height_ratio = max_height / original_height if original_height > 0 else 1
+            scale_ratio = min(width_ratio, height_ratio, 1.0)  # 不放大，只缩小
+            
+            # 计算实际显示尺寸
+            display_width = int(original_width * scale_ratio)
+            display_height = int(original_height * scale_ratio)
+            
+            # 确保最小显示尺寸
+            display_width = max(display_width, 80)
+            display_height = max(display_height, 25)
+            
+            # 设置标签尺寸并缩放图像
+            image_label.setFixedSize(display_width, display_height)
+            scaled_pixmap = pixmap.scaled(display_width, display_height, Qt.KeepAspectRatio, Qt.SmoothTransformation)
             image_label.setPixmap(scaled_pixmap)
+            image_label.setAlignment(Qt.AlignCenter)
         else:
+            # 当没有图像时，设置固定尺寸显示提示信息
+            image_label.setFixedSize(120, 40)
             image_label.setText("车牌未完全\n进入摄像头")
             image_label.setAlignment(Qt.AlignCenter)
             image_label.setStyleSheet("border: 1px solid #ddd; background-color: #f5f5f5; color: #666;")
         
-        # 车牌号标签
+        # 车牌号标签 - 使用自适应宽度
         number_label = QLabel(plate_number)
-        number_label.setFixedWidth(150)
+        number_label.setMinimumWidth(120)  # 设置最小宽度
+        number_label.setMaximumWidth(200)  # 设置最大宽度
         number_label.setAlignment(Qt.AlignCenter)
         number_label.setStyleSheet(
             "QLabel { "
@@ -190,6 +403,11 @@ class LicensePlateWidget(QWidget):
             "font-weight: bold; "
             "}"
         )
+        # 根据文本长度调整宽度
+        font_metrics = number_label.fontMetrics()
+        text_width = font_metrics.boundingRect(plate_number).width()
+        optimal_width = max(120, min(200, text_width + 20))  # 加20像素的边距
+        number_label.setFixedWidth(optimal_width)
         
         layout.addWidget(type_label)
         layout.addWidget(image_label)
@@ -197,6 +415,9 @@ class LicensePlateWidget(QWidget):
         layout.addStretch()
         
         self.setLayout(layout)
+        # 调整整体组件的最小高度以适应动态图像尺寸
+        min_height = max(60, image_label.height() + 20)  # 至少60像素高度
+        self.setMinimumHeight(min_height)
         self.setStyleSheet(
             "QWidget { "
             "background-color: white; "
@@ -221,6 +442,13 @@ class MainWindow(QMainWindow):
         self.last_plate_results = []  # 存储上一次的车牌识别结果
         self.current_recognition_method = "CRNN"  # 当前识别方法
         
+        # 添加车牌稳定器
+        self.plate_stabilizer = PlateStabilizer(
+            history_size=15,  # 保存15帧历史
+            confidence_threshold=0.7,  # 70%置信度阈值
+            stability_frames=5  # 需要5帧稳定
+        )
+        
         self.init_ui()
         self.init_detector()
         self.init_camera()
@@ -292,7 +520,7 @@ class MainWindow(QMainWindow):
         # 右侧结果显示区域
         right_frame = QFrame()
         right_frame.setFrameStyle(QFrame.StyledPanel)
-        right_frame.setFixedWidth(400)
+        right_frame.setFixedWidth(460)
         right_frame.setStyleSheet("QFrame { background-color: #fafafa; border: 2px solid #ddd; }")
         right_layout = QVBoxLayout(right_frame)
         
@@ -388,6 +616,32 @@ class MainWindow(QMainWindow):
         model_path = os.path.join(os.path.dirname(__file__), "yolopart", "yolo11s-pose42.pt")
         self.detector = LicensePlateYOLO(model_path)
     
+    def reset_processing_state(self):
+        """重置处理状态和清理界面"""
+        # 重置处理标志
+        self.is_processing = False
+        
+        # 清空当前帧和检测结果
+        self.current_frame = None
+        self.detections = []
+        
+        # 重置车牌稳定器
+        self.plate_stabilizer = PlateStabilizer(
+            history_size=15,
+            confidence_threshold=0.7,
+            stability_frames=5
+        )
+        
+        # 清空右侧结果显示
+        self.count_label.setText("识别到的车牌数量: 0")
+        for i in reversed(range(self.results_layout.count())):
+            child = self.results_layout.itemAt(i).widget()
+            if child:
+                child.setParent(None)
+        self.last_plate_results = []
+        
+        print("处理状态已重置，界面已清理")
+    
     def init_camera(self):
         """初始化摄像头线程"""
         self.camera_thread = CameraThread()
@@ -401,7 +655,11 @@ class MainWindow(QMainWindow):
     
     def start_camera(self):
         """启动摄像头"""
+        # 重置处理状态和清理界面
+        self.reset_processing_state()
+        
         if self.camera_thread.start_camera():
+            self.current_mode = "camera"
             self.start_button.setEnabled(False)
             self.stop_button.setEnabled(True)
             self.camera_label.setText("摄像头启动中...")
@@ -435,6 +693,9 @@ class MainWindow(QMainWindow):
         elif self.current_mode == "video" and self.video_thread and self.video_thread.running:
             self.stop_video()
         
+        # 重置处理状态和清理界面
+        self.reset_processing_state()
+        
         # 选择视频文件
         video_path, _ = QFileDialog.getOpenFileName(self, "选择视频文件", "", "视频文件 (*.mp4 *.avi *.mov *.mkv)")
         
@@ -483,6 +744,9 @@ class MainWindow(QMainWindow):
         elif self.current_mode == "video" and self.video_thread and self.video_thread.running:
             self.stop_video()
         
+        # 重置处理状态和清理界面
+        self.reset_processing_state()
+        
         # 选择图片文件
         image_path, _ = QFileDialog.getOpenFileName(self, "选择图片文件", "", "图片文件 (*.jpg *.jpeg *.png *.bmp)")
         
@@ -538,6 +802,9 @@ class MainWindow(QMainWindow):
     
     def process_frame(self, frame):
         """处理摄像头帧"""
+        if frame is None:
+            return
+            
         self.current_frame = frame.copy()
         
         # 先显示原始帧，保证视频流畅播放
@@ -686,48 +953,47 @@ class MainWindow(QMainWindow):
             self.camera_label.setText(f"显示错误: {str(e)}")
     
     def update_results_display(self):
-        """更新右侧结果显示"""
+        """更新右侧结果显示（使用稳定化结果）"""
-        # 更新车牌数量
+        print(f"开始更新结果显示，当前模式: {self.current_mode}, 检测数量: {len(self.detections) if self.detections else 0}")
-        count = len(self.detections)
-        self.count_label.setText(f"识别到的车牌数量: {count}")
         
-        # 准备新的车牌结果列表
+        if not self.detections:
-        new_plate_results = []
+            self.count_label.setText("识别到的车牌数量: 0")
-        for i, detection in enumerate(self.detections):
+            # 清除显示
-            # 矫正车牌图像
+            for i in reversed(range(self.results_layout.count())):
+                child = self.results_layout.itemAt(i).widget()
+                if child:
+                    child.setParent(None)
+            self.last_plate_results = []
+            print("无检测结果，已清空界面")
+            return
+        
+        # 获取矫正图像和识别文本
+        corrected_images = []
+        plate_texts = []
+        
+        for detection in self.detections:
             corrected_image = self.correct_license_plate(detection)
+            corrected_images.append(corrected_image)
             
-            # 获取车牌号，传入车牌类型信息
+            if corrected_image is not None:
-            plate_number = self.recognize_plate_number(corrected_image, detection['class_name'])
+                plate_text = self.recognize_plate_number(corrected_image, detection['class_name'])
+            else:
+                plate_text = "识别失败"
+            plate_texts.append(plate_text)
         
-            # 添加到新结果列表
+        # 使用稳定器获取稳定的识别结果
-            new_plate_results.append({
+        stable_results = self.plate_stabilizer.update_and_get_stable_result(
-                'id': i + 1,
+            self.detections, corrected_images, plate_texts
-                'class_name': detection['class_name'],
+        )
-                'corrected_image': corrected_image,
-                'plate_number': plate_number
-            })
         
-        # 比较新旧结果是否相同
+        # 更新车牌数量显示
-        results_changed = False
+        self.count_label.setText(f"识别到的车牌数量: {len(stable_results)}")
-        if len(self.last_plate_results) != len(new_plate_results):
+        print(f"稳定结果数量: {len(stable_results)}")
-            results_changed = True
-        else:
-            for i in range(len(new_plate_results)):
-                if i >= len(self.last_plate_results):
-                    results_changed = True
-                    break
         
-                last_result = self.last_plate_results[i]
+        # 检查结果是否发生变化
-                new_result = new_plate_results[i]
+        results_changed = self.check_results_changed(stable_results)
+        print(f"结果是否变化: {results_changed}")
         
-                # 比较车牌类型和车牌号
-                if (last_result['class_name'] != new_result['class_name'] or 
-                    last_result['plate_number'] != new_result['plate_number']):
-                    results_changed = True
-                    break
-        
-        # 只有当结果发生变化时才更新显示
         if results_changed:
             # 清除之前的结果
             for i in reversed(range(self.results_layout.count())):
@@ -735,18 +1001,42 @@ class MainWindow(QMainWindow):
                 if child:
                     child.setParent(None)
             
-            # 添加新的结果
+            # 添加新的稳定结果
-            for result in new_plate_results:
+            for i, result in enumerate(stable_results):
                 plate_widget = LicensePlateWidget(
-                    result['id'],
+                    i + 1,  # 显示序号
                     result['class_name'],
                     result['corrected_image'],
                     result['plate_number']
                 )
                 self.results_layout.addWidget(plate_widget)
+                print(f"添加车牌widget: {result['plate_number']}")
             
-            # 更新存储的上一次结果
+            # 更新存储的结果
-            self.last_plate_results = new_plate_results
+            self.last_plate_results = stable_results
+        
+        # 清理旧的车牌记录
+        current_plate_ids = [result['id'] for result in stable_results]
+        self.plate_stabilizer.clear_old_plates(current_plate_ids)
+        print("结果显示更新完成")
+    
+    def check_results_changed(self, new_results):
+        """检查识别结果是否发生变化"""
+        if len(self.last_plate_results) != len(new_results):
+            return True
+        
+        for i, new_result in enumerate(new_results):
+            if i >= len(self.last_plate_results):
+                return True
+            
+            old_result = self.last_plate_results[i]
+            
+            # 比较关键字段
+            if (old_result.get('class_name') != new_result.get('class_name') or 
+                old_result.get('plate_number') != new_result.get('plate_number')):
+                return True
+        
+        return False
     
     def correct_license_plate(self, detection):
         """矫正车牌图像"""