163 lines
6.1 KiB
Python
163 lines
6.1 KiB
Python
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from ultralytics import YOLO
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import cv2
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import numpy as np
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import os
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# ------------------ 配置 ------------------
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model_path = 'ultralytics_yolo11-main/runs/train/exp4/weights/best.pt'
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img_folder = '/home/hx/yolo/ultralytics_yolo11-main/dataset1/test' # 你的图片文件夹路径
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output_mask_dir = 'output_masks1'
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os.makedirs(output_mask_dir, exist_ok=True)
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# 支持的图像格式
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SUPPORTED_FORMATS = ('.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff')
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# ------------------ 加载模型 ------------------
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model = YOLO(model_path)
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model.to('cuda') # 使用 GPU
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def process_image(img_path, output_dir):
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"""处理单张图像：提取最长4条边，找出最接近垂直的2条"""
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img = cv2.imread(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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return
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h, w = img.shape[:2]
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filename = os.path.basename(img_path)
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name_only = os.path.splitext(filename)[0]
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print(f"\n🔄 正在处理: {filename}")
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# ------------------ 生成合成掩码 ------------------
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composite_mask = np.zeros((h, w), dtype=np.uint8)
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results = model(img_path, imgsz=1280, conf=0.5)
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for r in results:
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if r.masks is not None:
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masks = r.masks.data.cpu().numpy()
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for mask in masks:
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mask_resized = cv2.resize(mask, (w, h))
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mask_img = (mask_resized * 255).astype(np.uint8)
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composite_mask = np.maximum(composite_mask, mask_img)
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# 保存掩码
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mask_save_path = os.path.join(output_dir, f'mask_{name_only}.png')
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cv2.imwrite(mask_save_path, composite_mask)
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print(f"✅ 掩码已保存: {mask_save_path}")
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# ------------------ 提取轮廓 ------------------
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contours, _ = cv2.findContours(composite_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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if len(contours) == 0:
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print(f"⚠️ 未检测到轮廓: {filename}")
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return
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all_contours = np.vstack(contours)
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epsilon = 0.005 * cv2.arcLength(all_contours, True)
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approx = cv2.approxPolyDP(all_contours, epsilon, True)
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pts = [p[0] for p in approx] # 展平为 (x, y)
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# ------------------ 提取边段 ------------------
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line_segments = []
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n = len(pts)
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for i in range(n):
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p1 = np.array(pts[i])
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p2 = np.array(pts[(i + 1) % n])
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dx = p2[0] - p1[0]
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dy = p2[1] - p1[1]
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length = np.linalg.norm([dx, dy])
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if length < 20:
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continue # 忽略太短的边
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# 计算“垂直相似度”：|dx| / |dy|，越小越接近垂直
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vertical_score = float('inf') if abs(dy) < 1e-6 else abs(dx) / abs(dy)
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# 计算斜率
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slope = float('inf') if abs(dx) < 1e-6 else dy / dx
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line_segments.append({
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'start': p1,
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'end': p2,
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'length': length,
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'dx': dx,
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'dy': dy,
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'slope': slope,
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'vertical_score': vertical_score # 越小越接近垂直
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})
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# 按长度排序，取最长的4条边
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line_segments.sort(key=lambda x: x['length'], reverse=True)
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top_4_segments = line_segments[:4]
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if len(top_4_segments) < 2:
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print(f"⚠️ 不足2条有效边（共{len(top_4_segments)}条）: {filename}")
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return
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# ------------------ 找出最接近垂直的2条边 ------------------
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# 按 vertical_score 升序排序：值越小，越接近垂直
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top_4_segments.sort(key=lambda x: x['vertical_score'])
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vertical_candidates = top_4_segments[:2] # 最接近垂直的两条边
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# ------------------ 打印信息 ------------------
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print(f"✅ 提取最长的4条边:")
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for i, seg in enumerate(top_4_segments):
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slope_val = seg['slope'] if isinstance(seg['slope'], float) else (
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'inf' if seg['slope'] == float('inf') else '-inf')
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print(f" 第{i + 1}长边: 长度={seg['length']:.1f}, 斜率={slope_val:.3f}, 垂直评分={seg['vertical_score']:.3f}")
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print(f"✅ 最接近垂直的两条边（垂直评分最小）:")
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for i, seg in enumerate(vertical_candidates):
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slope_val = seg['slope'] if isinstance(seg['slope'], float) else (
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'inf' if seg['slope'] == float('inf') else '-inf')
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print(f" 垂直边{i + 1}: 长度={seg['length']:.1f}, 斜率={slope_val:.3f}, 垂直评分={seg['vertical_score']:.3f}")
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# ------------------ 可视化 ------------------
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vis_img = img.copy()
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colors = [(0, 0, 255), (255, 0, 0)] # 红色, 蓝色
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for i, seg in enumerate(vertical_candidates):
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p1, p2 = seg['start'], seg['end']
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color = colors[i]
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cv2.line(vis_img, tuple(p1), tuple(p2), color, 3, cv2.LINE_AA)
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mid_x = (p1[0] + p2[0]) // 2
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mid_y = (p1[1] + p2[1]) // 2
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slope_val = 'inf' if not isinstance(seg['slope'], float) else f"{seg['slope']:.1f}"
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cv2.putText(vis_img, f"{seg['length']:.0f}, k={slope_val}",
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(mid_x, mid_y - 10 + i * 25),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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# 添加图例说明
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cv2.putText(vis_img, "Red: 1st closest to vertical", (20, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)
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cv2.putText(vis_img, "Blue: 2nd closest to vertical", (20, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 1)
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# 保存可视化图像
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vis_save_path = os.path.join(output_dir, f'vertical_{name_only}.jpg')
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cv2.imwrite(vis_save_path, vis_img)
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print(f"✅ 可视化图像已保存: {vis_save_path}")
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# ------------------ 主程序：遍历文件夹 ------------------
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if __name__ == '__main__':
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if not os.path.isdir(img_folder):
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print(f"❌ 图像文件夹不存在: {img_folder}")
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exit()
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image_files = [
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f for f in os.listdir(img_folder)
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if f.lower().endswith(SUPPORTED_FORMATS)
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]
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if len(image_files) == 0:
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print(f"⚠️ 在 {img_folder} 中未找到支持的图像文件")
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exit()
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print(f"✅ 发现 {len(image_files)} 张图像，开始批量处理...")
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for image_file in image_files:
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image_path = os.path.join(img_folder, image_file)
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process_image(image_path, output_mask_dir)
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print(f"\n🎉 所有图像处理完成！结果保存在: {output_mask_dir}")
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