323 lines
12 KiB
Python
323 lines
12 KiB
Python
import cv2
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import numpy as np
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from ultralytics import YOLO
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import os
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from PIL import Image, ImageDraw, ImageFont
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class LicensePlateYOLO:
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"""
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车牌YOLO检测器类
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负责加载YOLO pose模型并进行车牌检测和角点提取
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"""
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def __init__(self, model_path=None):
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"""
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初始化YOLO检测器
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参数:
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model_path: 模型文件路径，如果为None则使用默认路径
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"""
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self.model = None
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self.model_path = model_path or self._get_default_model_path()
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self.class_names = {0: '蓝牌', 1: '绿牌'}
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self.load_model()
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def _get_default_model_path(self):
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"""获取默认模型路径"""
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current_dir = os.path.dirname(__file__)
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return os.path.join(current_dir, "yolo11s-pose42.pt")
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def load_model(self):
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"""
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加载YOLO pose模型
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返回:
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bool: 加载是否成功
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"""
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try:
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if os.path.exists(self.model_path):
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self.model = YOLO(self.model_path)
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print(f"YOLO模型加载成功: {self.model_path}")
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return True
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else:
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print(f"模型文件不存在: {self.model_path}")
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return False
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except Exception as e:
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print(f"YOLO模型加载失败: {e}")
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return False
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def detect_license_plates(self, image, conf_threshold=0.5):
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"""
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检测图像中的车牌
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参数:
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image: 输入图像 (numpy数组)
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conf_threshold: 置信度阈值
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返回:
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list: 检测结果列表，每个元素包含:
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- box: 边界框坐标 [x1, y1, x2, y2]
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- keypoints: 四个角点坐标 [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
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- confidence: 置信度
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- class_id: 类别ID (0=蓝牌, 1=绿牌)
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- class_name: 类别名称
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"""
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if self.model is None:
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print("模型未加载")
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return []
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try:
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# 进行推理
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results = self.model(image, conf=conf_threshold, verbose=False)
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detections = []
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for result in results:
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# 检查是否有检测结果
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if result.boxes is None or result.keypoints is None:
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continue
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# 提取检测信息
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boxes = result.boxes.xyxy.cpu().numpy() # 边界框
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keypoints = result.keypoints.xy.cpu().numpy() # 关键点
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confidences = result.boxes.conf.cpu().numpy() # 置信度
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classes = result.boxes.cls.cpu().numpy() # 类别
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# 处理每个检测结果
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for i in range(len(boxes)):
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# 检查关键点数量是否为4个
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if len(keypoints[i]) == 4:
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class_id = int(classes[i])
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detection = {
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'box': boxes[i],
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'keypoints': keypoints[i],
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'confidence': confidences[i],
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'class_id': class_id,
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'class_name': self.class_names.get(class_id, '未知')
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}
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detections.append(detection)
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else:
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# 关键点不足4个，记录但标记为不完整
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class_id = int(classes[i])
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detection = {
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'box': boxes[i],
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'keypoints': keypoints[i] if len(keypoints[i]) > 0 else [],
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'confidence': confidences[i],
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'class_id': class_id,
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'class_name': self.class_names.get(class_id, '未知'),
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'incomplete': True # 标记为不完整
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}
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detections.append(detection)
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return detections
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except Exception as e:
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print(f"检测过程中出错: {e}")
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return []
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def draw_detections(self, image, detections, plate_numbers=None):
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"""
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在图像上绘制检测结果
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参数:
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image: 输入图像
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detections: 检测结果列表
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plate_numbers: 车牌号列表，与detections对应
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返回:
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numpy.ndarray: 绘制了检测结果的图像
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"""
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draw_image = image.copy()
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# 转换为PIL图像以支持中文字符
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pil_image = Image.fromarray(cv2.cvtColor(draw_image, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(pil_image)
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# 尝试加载中文字体
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try:
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# Windows系统常见的中文字体
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font_path = "C:/Windows/Fonts/simhei.ttf" # 黑体
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if not os.path.exists(font_path):
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font_path = "C:/Windows/Fonts/msyh.ttc" # 微软雅黑
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if not os.path.exists(font_path):
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font_path = "C:/Windows/Fonts/simsun.ttc" # 宋体
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font = ImageFont.truetype(font_path, 20)
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except:
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# 如果无法加载字体，使用默认字体
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font = ImageFont.load_default()
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for i, detection in enumerate(detections):
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box = detection['box']
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keypoints = detection['keypoints']
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class_name = detection['class_name']
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confidence = detection['confidence']
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incomplete = detection.get('incomplete', False)
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# 获取对应的车牌号
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plate_number = ""
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if plate_numbers and i < len(plate_numbers):
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plate_number = plate_numbers[i]
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# 绘制边界框
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x1, y1, x2, y2 = map(int, box)
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# 根据车牌类型选择颜色
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if class_name == '绿牌':
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box_color = (0, 255, 0) # 绿色
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elif class_name == '蓝牌':
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box_color = (0, 0, 255) # 蓝色
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else:
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box_color = (128, 128, 128) # 灰色
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# 在PIL图像上绘制边界框
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draw.rectangle([(x1, y1), (x2, y2)], outline=box_color, width=2)
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# 构建标签文本
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if plate_number:
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label = f"{class_name} {plate_number} {confidence:.2f}"
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else:
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label = f"{class_name} {confidence:.2f}"
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if incomplete:
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label += " (不完整)"
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# 计算文本大小
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bbox = draw.textbbox((0, 0), label, font=font)
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text_width = bbox[2] - bbox[0]
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text_height = bbox[3] - bbox[1]
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# 绘制文本背景
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draw.rectangle([(x1, y1 - text_height - 10), (x1 + text_width, y1)],
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fill=box_color)
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# 绘制文本
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draw.text((x1, y1 - text_height - 5), label, fill=(255, 255, 255), font=font)
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# 转换回OpenCV格式
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draw_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
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# 绘制关键点和连线（使用OpenCV）
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for i, detection in enumerate(detections):
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box = detection['box']
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keypoints = detection['keypoints']
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incomplete = detection.get('incomplete', False)
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x1, y1, x2, y2 = map(int, box)
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# 根据车牌类型选择颜色
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class_name = detection['class_name']
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if class_name == '绿牌':
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box_color = (0, 255, 0) # 绿色
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elif class_name == '蓝牌':
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box_color = (0, 0, 255) # 蓝色
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else:
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box_color = (128, 128, 128) # 灰色
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# 绘制关键点和连线
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if len(keypoints) >= 4 and not incomplete:
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# 四个角点完整，用黄色连线
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points = [(int(kp[0]), int(kp[1])) for kp in keypoints[:4]]
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# 绘制关键点
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for point in points:
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cv2.circle(draw_image, point, 5, (0, 255, 255), -1)
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# 连接关键点形成四边形（按顺序连接）
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# 假设关键点顺序为: right_bottom, left_bottom, left_top, right_top
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for j in range(4):
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cv2.line(draw_image, points[j], points[(j+1)%4], (0, 255, 255), 2)
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elif len(keypoints) > 0:
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# 关键点不完整，用红色标记现有点
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for kp in keypoints:
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point = (int(kp[0]), int(kp[1]))
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cv2.circle(draw_image, point, 5, (0, 0, 255), -1)
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return draw_image
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def correct_license_plate(self, image, keypoints, target_size=(240, 80)):
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"""
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使用四个角点对车牌进行透视变换矫正
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参数:
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image: 原始图像
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keypoints: 四个角点坐标
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target_size: 目标尺寸 (width, height)
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返回:
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numpy.ndarray: 矫正后的车牌图像，如果失败返回None
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"""
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if len(keypoints) != 4:
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return None
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try:
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# 将关键点转换为numpy数组
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src_points = np.array(keypoints, dtype=np.float32)
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# 定义目标矩形的四个角点
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# 假设关键点顺序为: right_bottom, left_bottom, left_top, right_top
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# 重新排序为标准顺序: left_top, right_top, right_bottom, left_bottom
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width, height = target_size
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dst_points = np.array([
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[0, 0], # left_top
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[width, 0], # right_top
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[width, height], # right_bottom
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[0, height] # left_bottom
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], dtype=np.float32)
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# 重新排序源点以匹配目标点
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# 原顺序: right_bottom, left_bottom, left_top, right_top
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# 目标顺序: left_top, right_top, right_bottom, left_bottom
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reordered_src = np.array([
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src_points[2], # left_top
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src_points[3], # right_top
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src_points[0], # right_bottom
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src_points[1] # left_bottom
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], dtype=np.float32)
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# 计算透视变换矩阵
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matrix = cv2.getPerspectiveTransform(reordered_src, dst_points)
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# 应用透视变换
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corrected = cv2.warpPerspective(image, matrix, target_size)
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return corrected
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except Exception as e:
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print(f"车牌矫正失败: {e}")
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return None
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def get_model_info(self):
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"""
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获取模型信息
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返回:
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dict: 模型信息字典
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"""
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if self.model is None:
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return {"status": "未加载", "path": self.model_path}
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return {
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"status": "已加载",
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"path": self.model_path,
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"model_type": "YOLO11 Pose",
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"classes": self.class_names
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}
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def initialize_yolo_detector(model_path=None):
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"""
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初始化YOLO检测器的便捷函数
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参数:
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model_path: 模型文件路径
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返回:
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LicensePlateYOLO: 初始化后的检测器实例
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"""
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detector = LicensePlateYOLO(model_path)
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return detector
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if __name__ == "__main__":
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# 测试代码
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detector = initialize_yolo_detector()
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print("检测器信息:", detector.get_model_info()) |