57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
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import cv2
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import numpy as np
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from pathlib import Path
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from val_labels import load_yolo_polygon_labels,draw_mask_on_image
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# 之前的load_yolo_polygon_labels和draw_mask_on_image函数保持不变
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def visualize_labels_in_folder(src_img_dir, src_label_dir, dst_dir, alpha=0.5):
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"""
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遍历源文件夹中的所有图片和对应标签，并在图片上绘制标签后保存至目标文件夹。
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:param src_img_dir: 源图像文件夹路径
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:param src_label_dir: 源标签文件夹路径
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:param dst_dir: 目标文件夹路径，用于保存可视化结果
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:param alpha: 叠加mask的透明度
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"""
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# 确保目标文件夹存在
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Path(dst_dir).mkdir(parents=True, exist_ok=True)
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# 获取所有的图片文件
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img_paths = list(Path(src_img_dir).glob('*.jpg')) + list(Path(src_img_dir).glob('*.png'))
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for img_path in img_paths:
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txt_path = Path(src_label_dir) / f"{img_path.stem}.txt"
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if not txt_path.exists():
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print(f"⚠️ 跳过 {img_path.name}，未找到对应的标签文件")
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continue
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# 读取图像
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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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h, w = img.shape[:2]
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print(f"🖼️ 正在处理图像: {img_path.name}, 大小: {w}x{h}")
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# 加载标签
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labels = load_yolo_polygon_labels(txt_path, img.shape)
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if len(labels) == 0:
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print(f"🟡 未找到有效标签: {txt_path}")
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continue
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# 绘制叠加图
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result_img = draw_mask_on_image(img, labels, alpha)
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# 保存结果
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output_path = Path(dst_dir) / img_path.name
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cv2.imwrite(str(output_path), result_img)
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print(f"✅ 已保存可视化结果: {output_path}")
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if __name__ == "__main__":
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SRC_IMG_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/seg/resize1" # 修改为你的源图像文件夹路径
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SRC_LABEL_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/seg/resize1" # 修改为你的源标签文件夹路径
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DST_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/seg/resize_seg5" # 修改为目标文件夹路径
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visualize_labels_in_folder(SRC_IMG_DIR, SRC_LABEL_DIR, DST_DIR)
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