89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
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import cv2
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from ultralytics import YOLO
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# ---------------------------
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# 类别映射（必须与训练时 data.yaml 一致）
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# ---------------------------
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CLASS_NAMES = {
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0: "未堆料",
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1: "小堆料",
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2: "大堆料",
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3: "未浇筑满",
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4: "浇筑满"
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}
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# ---------------------------
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# 加权判断函数（仅用于 class1/class2）
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# ---------------------------
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def weighted_small_large(pred_probs, threshold=0.4, w1=0.3, w2=0.7):
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p1 = float(pred_probs[1])
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p2 = float(pred_probs[2])
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total = p1 + p2
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if total > 0:
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score = (w1 * p1 + w2 * p2) / total
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else:
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score = 0.0
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final_class = "大堆料" if score >= threshold else "小堆料"
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return final_class, score, p1, p2
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# ---------------------------
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# 单张图片推理主函数
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# ---------------------------
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def classify_single_image(model_path, image_path, threshold=0.5):
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# 加载模型
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print("🚀 加载模型...")
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model = YOLO(model_path)
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# 读取图像
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img = cv2.imread(image_path)
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if img is None:
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raise FileNotFoundError(f"❌ 无法读取图像: {image_path}")
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print(f"📷 推理图像: {image_path}")
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# 整图分类（不裁剪）
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results = model(img)
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pred_probs = results[0].probs.data.cpu().numpy().flatten()
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class_id = int(pred_probs.argmax())
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confidence = float(pred_probs[class_id])
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class_name = CLASS_NAMES.get(class_id, f"未知类别({class_id})")
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# 对 小堆料/大堆料 使用加权逻辑
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if class_id in [1, 2]:
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final_class, score, p1, p2 = weighted_small_large(pred_probs, threshold=threshold)
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print("\n🔍 检测到堆料区域，使用加权判断:")
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print(f" 小堆料概率: {p1:.4f}")
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print(f" 大堆料概率: {p2:.4f}")
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print(f" 加权得分: {score:.4f} (阈值={threshold})")
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else:
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final_class = class_name
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score = confidence
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# 输出最终结果
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print("\n" + "="*40)
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print(f"最终分类结果: {final_class}")
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print(f"置信度/得分: {score:.4f}")
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print("="*40)
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return final_class, score
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# ---------------------------
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# 运行入口（请修改路径）
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# ---------------------------
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if __name__ == "__main__":
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MODEL_PATH = "60best.pt"
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IMAGE_PATH = "class4.png" # 👈 改成你的单张图片路径
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# 可选：调整加权阈值（默认 0.4）
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THRESHOLD = 0.4
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try:
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result_class, result_score = classify_single_image(
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model_path=MODEL_PATH,
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image_path=IMAGE_PATH,
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threshold=THRESHOLD
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)
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except Exception as e:
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print(f"程序出错: {e}")
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