64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
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import cv2
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import numpy as np
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import os
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# 输入：已保存的黑白掩码图像路径
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mask_path = 'composite_mask.png'
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# 输出：可视化结果保存路径
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output_dir = 'output_masks'
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os.makedirs(output_dir, exist_ok=True)
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result_image_path = os.path.join(output_dir, 'mask_with_rotated_rects.jpg')
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# 读取掩码图像（灰度图）
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mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
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if mask is None:
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raise FileNotFoundError(f"❌ 无法读取掩码图像: {mask_path}")
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# 二值化（确保是 0 和 255）
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_, binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)
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# 查找轮廓
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 创建三通道图像用于可视化（白色背景，黑色轮廓）
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vis_image = np.ones((*mask.shape, 3), dtype=np.uint8) * 255 # 白底
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# 存储所有旋转矩形结果
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rotated_rects = []
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for i, cnt in enumerate(contours):
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if len(cnt) < 2:
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continue # 至少需要两个点才能拟合
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# 计算最小外接旋
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rect = cv2.minAreaRect(cnt) # (center, (width, height), angle)
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rotated_rects.append(rect)
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# 获取四个角
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box = cv2.boxPoints(rect)
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box = np.float16(box)
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# 在可视化图像上绘制旋转矩形（绿色）
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cv2.drawContours(vis_image, [box], 0, (0, 180, 0), 2)
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# 可选：标注序号
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cx, cy = int(rect[0][0]), int(rect[0][1])
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cv2.putText(vis_image, str(i), (cx, cy), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 1)
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# 打印信息
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center = rect[0]
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size = rect[1]
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angle = rect[2]
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print(f"物体 {i}:")
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print(f" 中心点: ({center[0]:.1f}, {center[1]:.1f})")
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print(f" 尺寸: 宽={size[0]:.1f}, 高={size[1]:.1f}")
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print(f" 旋转角度: {angle:.2f}°")
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print(f" 四个角点: {box.tolist()}")
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print("-" * 40)
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# 保存可视化结果
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cv2.imwrite(result_image_path, vis_image)
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print(f"✅ 旋转矩形可视化已保存至: {result_image_path}")
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