198 lines
6.2 KiB
Python
198 lines
6.2 KiB
Python
import os
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from pathlib import Path
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import cv2
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import numpy as np
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from ultralytics import YOLO
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# ---------------------------
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# 类别映射
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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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# 加载 ROI 列表
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# ---------------------------
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def load_global_rois(txt_path):
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rois = []
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if not os.path.exists(txt_path):
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print(f"❌ ROI 文件不存在: {txt_path}")
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return rois
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with open(txt_path, 'r') as f:
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for line in f:
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s = line.strip()
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if s:
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try:
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x, y, w, h = map(int, s.split(','))
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rois.append((x, y, w, h))
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except Exception as e:
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print(f"⚠️ 无法解析 ROI 行 '{s}': {e}")
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return rois
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# ---------------------------
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# 裁剪并 resize ROI
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# ---------------------------
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def crop_and_resize(img, rois, target_size=640):
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crops = []
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h_img, w_img = img.shape[:2]
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for i, (x, y, w, h) in enumerate(rois):
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# 边界检查
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if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
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print(f"⚠️ ROI 超出图像边界，跳过: ({x}, {y}, {w}, {h})")
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continue
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roi = img[y:y+h, x:x+w]
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roi_resized = cv2.resize(roi, (target_size, target_size), interpolation=cv2.INTER_AREA)
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crops.append((roi_resized, i))
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return crops
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# ---------------------------
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# class1/class2 加权判断
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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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# 单张图片推理函数
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# ---------------------------
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def classify_image_weighted(image, model, threshold=0.5):
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results = model(image)
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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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# 对于 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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else:
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final_class = class_name
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score = confidence
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p1 = float(pred_probs[1])
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p2 = float(pred_probs[2])
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return final_class, score, p1, p2
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# ---------------------------
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# 批量推理主函数（保存原图，不改名）
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# ---------------------------
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def batch_classify_images(model_path, input_folder, output_root, roi_file, target_size=640, 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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# 确保输出根目录存在
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output_root = Path(output_root)
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output_root.mkdir(parents=True, exist_ok=True)
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print(f"📁 输出目录: {output_root}")
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# 为所有类别创建子目录
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class_dirs = {}
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for name in CLASS_NAMES.values():
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d = output_root / name
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d.mkdir(exist_ok=True)
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class_dirs[name] = d
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# 加载 ROI 区域
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rois = load_global_rois(roi_file)
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if not rois:
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print("❌ 没有有效 ROI，退出程序")
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return
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print(f"🎯 已加载 {len(rois)} 个 ROI 区域")
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# 定义缺陷严重性等级（数字越小越严重）
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severity_rank = {
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"未堆料": 0,
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"大堆料": 1,
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"小堆料": 2,
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"未浇筑满": 3,
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"浇筑满": 4
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}
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# 遍历输入文件夹中的所有图片
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input_folder = Path(input_folder)
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image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff'}
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processed_count = 0
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for img_path in input_folder.glob("*.*"):
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if img_path.suffix.lower() not in image_extensions:
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continue
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try:
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print(f"\n📄 处理: {img_path.name}")
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img = cv2.imread(str(img_path))
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if img is None:
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print(f"❌ 无法读取图像: {img_path.name}")
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continue
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# 裁剪并缩放 ROI
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crops = crop_and_resize(img, rois, target_size)
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if not crops:
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print(f"⚠️ 无有效 ROI 裁剪区域: {img_path.name}")
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continue
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detected_classes = []
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# 遍历每个 ROI 进行分类
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for roi_img, roi_idx in crops:
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final_class, score, p1, p2 = classify_image_weighted(roi_img, model, threshold=threshold)
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detected_classes.append(final_class)
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print(f" 🔍 ROI{roi_idx}: {final_class} (score={score:.2f})")
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# 选择最严重的类别
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most_severe_class = min(detected_classes, key=lambda x: severity_rank.get(x, 99))
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# 构造目标路径：保持原文件名
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dst_path = class_dirs[most_severe_class] / img_path.name
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# 保存原图（不修改内容，不重命名）
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cv2.imwrite(str(dst_path), img)
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print(f"✅ 保存 -> [{most_severe_class}] {dst_path}")
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processed_count += 1
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except Exception as e:
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print(f"❌ 处理失败 {img_path.name}: {e}")
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print(f"\n🎉 批量处理完成！共处理 {processed_count} 张图像。")
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# ---------------------------
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# 使用示例（请根据实际情况修改路径）
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# ---------------------------
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if __name__ == "__main__":
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# 🔧 用户配置区
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model_path = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/cls_resize/exp_cls2/weights/best.pt"
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input_folder = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1000"
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output_root = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1000/classified"
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roi_file = "./roi_coordinates/1_rois.txt"
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target_size = 640
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threshold = 0.4 # 小堆料 vs 大堆料的加权阈值
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# 🚀 开始执行
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batch_classify_images(
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model_path=model_path,
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input_folder=input_folder,
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output_root=output_root,
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roi_file=roi_file,
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target_size=target_size,
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threshold=threshold
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) |