107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
import os
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import shutil
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from pathlib import Path
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from ultralytics import YOLO
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import cv2
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# ---------------------------
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# ROI 裁剪函数
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# ---------------------------
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def load_global_rois(txt_path):
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"""加载全局 ROI 坐标"""
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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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line = line.strip()
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if line:
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try:
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x, y, w, h = map(int, line.split(','))
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rois.append((x, y, w, h))
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print(f"📌 加载 ROI: (x={x}, y={y}, w={w}, h={h})")
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except Exception as e:
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print(f"⚠️ 无法解析 ROI 行: {line}, 错误: {e}")
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return rois
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def crop_and_resize(img, rois, target_size=640):
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"""根据 ROI 裁剪并 resize"""
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crops = []
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for i, (x, y, w, h) in enumerate(rois):
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h_img, w_img = img.shape[:2]
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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 越界，跳过: {x},{y},{w},{h}")
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continue
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roi_img = img[y:y+h, x:x+w]
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roi_resized = cv2.resize(roi_img, (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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# 分类函数
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# ---------------------------
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def classify_and_save_images(model_path, input_folder, output_root, roi_file, target_size=640):
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# 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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# 创建类别子文件夹 (class0 到 class4)
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class_dirs = []
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for i in range(5): # 假设有5个类别 (0-4)
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class_dir = output_root / f"class{i}"
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class_dir.mkdir(exist_ok=True)
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class_dirs.append(class_dir)
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# 加载 ROI
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rois = load_global_rois(roi_file)
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if len(rois) == 0:
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print("❌ 没有有效 ROI，退出")
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return
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# 遍历输入文件夹
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for img_path in Path(input_folder).glob("*.*"):
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if img_path.suffix.lower() not in ['.jpg', '.jpeg', '.png', '.bmp', '.tif']:
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continue
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try:
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# 读取原图
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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}")
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continue
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# 根据 ROI 裁剪
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crops = crop_and_resize(img, rois, target_size)
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for roi_img, roi_idx in crops:
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# YOLO 推理
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results = model(roi_img)
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pred = results[0].probs.data # 获取概率分布
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class_id = int(pred.argmax())
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# 保存到对应类别文件夹
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suffix = f"_roi{roi_idx}" if len(crops) > 1 else ""
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dst_path = class_dirs[class_id] / f"{img_path.stem}{suffix}{img_path.suffix}"
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cv2.imwrite(dst_path, roi_img) # 保存裁剪后的 ROI 图像
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print(f"Processed {img_path.name}{suffix} -> Class {class_id}")
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except Exception as e:
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print(f"Error processing {img_path.name}: {str(e)}")
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# ---------------------------
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# 主程序
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# ---------------------------
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if __name__ == "__main__":
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model_path = r"best.pt"
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input_folder = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/f6"
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output_root = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/class44"
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roi_file = "./roi_coordinates/1_rois.txt" # 训练时使用的 ROI 文件
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target_size = 640
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classify_and_save_images(model_path, input_folder, output_root, roi_file, target_size)
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