115 lines
4.6 KiB
Python
115 lines
4.6 KiB
Python
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import os
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import cv2
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from ultralytics import YOLO
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class ObjectDetector:
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"""封装 YOLO 目标检测模型，检测图像中的缺陷"""
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def __init__(self, model_path):
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"模型文件不存在: {model_path}")
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self.model = YOLO(model_path)
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print(f"[INFO] 成功加载 YOLO 目标检测模型: {model_path}")
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def detect(self, img_np, conf_threshold=0.5):
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# 注意：这里先不设 conf 阈值，以便后续按类别筛选最高分
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results = self.model.predict(img_np, conf=0.0, verbose=False) # 获取所有预测
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detections = []
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for result in results:
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boxes = result.boxes.cpu().numpy()
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for box in boxes:
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if box.conf.item() >= conf_threshold: # 在 Python 层过滤
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detection_info = {
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'bbox': box.xyxy[0],
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'confidence': box.conf.item(),
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'class_id': int(box.cls.item())
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}
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detections.append(detection_info)
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return detections
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def detect_quexian(img_path="1.png", model_path="/home/hx/yolo/ultralytics_yolo11-main/runs/train/cls/exp_xiantiao_cls/weights/best.pt",
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conf_threshold=0.5, debug=False):
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"""
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检测木条图像中的孔洞/裂缝缺陷，并返回是否为良品。
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每个类别仅保留置信度最高的一个框。
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Args:
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img_path (str): 输入图像路径
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model_path (str): YOLO 检测模型路径（必须是 detect 任务训练的）
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conf_threshold (float): 置信度阈值
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debug (bool): 是否启用调试模式（打印详细信息）
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Returns:
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bool: True 表示无缺陷（良品），False 表示有缺陷（不良品）
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"""
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# 1. 加载图像
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if not os.path.exists(img_path):
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raise FileNotFoundError(f"图片不存在: {img_path}")
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img_np = cv2.imread(img_path)
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if img_np is None:
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raise ValueError("图像加载失败，可能路径错误或图像文件损坏。")
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# 2. 加载模型并检测（获取所有 ≥ conf_threshold 的框）
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detector = ObjectDetector(model_path)
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all_detections = detector.detect(img_np, conf_threshold=conf_threshold)
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# 3. 按类别分组，取每个类别中置信度最高的框
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best_per_class = {}
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for det in all_detections:
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cls_id = det['class_id']
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if cls_id not in best_per_class or det['confidence'] > best_per_class[cls_id]['confidence']:
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best_per_class[cls_id] = det
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# 转为列表（用于后续处理）
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top_detections = list(best_per_class.values())
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# 4. 判定是否有缺陷：只要有一个类别有框，就算有缺陷
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has_defect = len(top_detections) > 0
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is_good = not has_defect
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# 5. 可视化：只绘制每个类别的最高置信度框
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label_map = {0: "hole", 1: "crack"}
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vis_img = img_np.copy()
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for det in top_detections:
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x1, y1, x2, y2 = map(int, det['bbox'])
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conf = det['confidence']
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cls_id = det['class_id']
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label = f"{label_map.get(cls_id, '未知')} {conf:.2f}"
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cv2.rectangle(vis_img, (x1, y1), (x2, y2), (0, 0, 255), 2)
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cv2.putText(vis_img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
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# 6. 调试信息（仅在 debug=True 时输出）
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if debug:
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print(f"\n===== 缺陷检测结果 (DEBUG 模式) =====")
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print(f"置信度阈值: {conf_threshold}")
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print(f"有效类别数量: {len(top_detections)}")
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for i, det in enumerate(top_detections):
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cls_name = label_map.get(det['class_id'], '未知')
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bbox_int = det['bbox'].astype(int).tolist()
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print(f" - 类别 '{cls_name}' 最高置信度框: 置信度={det['confidence']:.3f}, bbox={bbox_int}")
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# 7. 显示结果图像
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cv2.imshow('Detection Results', vis_img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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return is_good
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if __name__ == "__main__":
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# 示例：启用 debug 模式
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is_good_product = detect_quexian(
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#img_path="/home/hx/开发/ML_xiantiao/class_xiantiao_pc/test_image/val/1.jpg",
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img_path="/home/hx/开发/ML_xiantiao/class_xiantiao_pc/test_image/train/微信图片_20251216095823_227.jpg",
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model_path="/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_detect/weights/best.pt",
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conf_threshold=0.5,
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debug=True # 改为 False 即静默模式
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)
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# 主程序最终输出（简洁版）
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if is_good_product:
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print("产品合格")
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else:
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print("产品存在缺陷")
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