zjsh_yolov11/angle_base_seg/test_line_angle_f.py

from ultralytics import YOLO
import cv2
import numpy as np
import os

# ------------------ 配置 ------------------
model_path = '../ultralytics_yolo11-main/runs/train/exp4/weights/best.pt'
img_folder = '/home/hx/yolo/test_image'  # 你的图片文件夹路径
output_mask_dir = 'output_masks1'
os.makedirs(output_mask_dir, exist_ok=True)

# 支持的图像格式
SUPPORTED_FORMATS = ('.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff')

# ------------------ 加载模型 ------------------
model = YOLO(model_path)
model.to('cuda')  # 使用 GPU


def process_image(img_path, output_dir):
    """处理单张图像：提取最长4条边，找出最接近垂直的2条"""
    img = cv2.imread(img_path)
    if img is None:
        print(f"❌ 无法读取图像: {img_path}")
        return

    h, w = img.shape[:2]
    filename = os.path.basename(img_path)
    name_only = os.path.splitext(filename)[0]

    print(f"\n🔄 正在处理: {filename}")

    # ------------------ 生成合成掩码 ------------------
    composite_mask = np.zeros((h, w), dtype=np.uint8)
    results = model(img_path, imgsz=1280, conf=0.5)

    for r in results:
        if r.masks is not None:
            masks = r.masks.data.cpu().numpy()
            for mask in masks:
                mask_resized = cv2.resize(mask, (w, h))
                mask_img = (mask_resized * 255).astype(np.uint8)
                composite_mask = np.maximum(composite_mask, mask_img)

    # 保存掩码
    mask_save_path = os.path.join(output_dir, f'mask_{name_only}.png')
    cv2.imwrite(mask_save_path, composite_mask)
    print(f"✅ 掩码已保存: {mask_save_path}")

    # ------------------ 提取轮廓 ------------------
    contours, _ = cv2.findContours(composite_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    if len(contours) == 0:
        print(f"⚠️ 未检测到轮廓: {filename}")
        return

    all_contours = np.vstack(contours)
    epsilon = 0.005 * cv2.arcLength(all_contours, True)
    approx = cv2.approxPolyDP(all_contours, epsilon, True)
    pts = [p[0] for p in approx]  # 展平为 (x, y)

    # ------------------ 提取边段 ------------------
    line_segments = []
    n = len(pts)
    for i in range(n):
        p1 = np.array(pts[i])
        p2 = np.array(pts[(i + 1) % n])
        dx = p2[0] - p1[0]
        dy = p2[1] - p1[1]
        length = np.linalg.norm([dx, dy])

        if length < 20:
            continue  # 忽略太短的边

        # 计算“垂直相似度”：|dx| / |dy|，越小越接近垂直
        vertical_score = float('inf') if abs(dy) < 1e-6 else abs(dx) / abs(dy)
        # 计算斜率
        slope = float('inf') if abs(dx) < 1e-6 else dy / dx

        line_segments.append({
            'start': p1,
            'end': p2,
            'length': length,
            'dx': dx,
            'dy': dy,
            'slope': slope,
            'vertical_score': vertical_score  # 越小越接近垂直
        })

    # 按长度排序，取最长的4条边
    line_segments.sort(key=lambda x: x['length'], reverse=True)
    top_4_segments = line_segments[:4]

    if len(top_4_segments) < 2:
        print(f"⚠️ 不足2条有效边（共{len(top_4_segments)}条）: {filename}")
        return

    # ------------------ 找出最接近垂直的2条边 ------------------
    # 按 vertical_score 升序排序：值越小，越接近垂直
    top_4_segments.sort(key=lambda x: x['vertical_score'])
    vertical_candidates = top_4_segments[:2]  # 最接近垂直的两条边

    # ------------------ 打印信息 ------------------
    print(f"✅ 提取最长的4条边:")
    for i, seg in enumerate(top_4_segments):
        slope_val = seg['slope'] if isinstance(seg['slope'], float) else (
            'inf' if seg['slope'] == float('inf') else '-inf')
        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}")

    print(f"✅ 最接近垂直的两条边（垂直评分最小）:")
    for i, seg in enumerate(vertical_candidates):
        slope_val = seg['slope'] if isinstance(seg['slope'], float) else (
            'inf' if seg['slope'] == float('inf') else '-inf')
        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}")

    # ------------------ 可视化 ------------------
    vis_img = img.copy()
    colors = [(0, 0, 255), (255, 0, 0)]  # 红色, 蓝色

    for i, seg in enumerate(vertical_candidates):
        p1, p2 = seg['start'], seg['end']
        color = colors[i]
        cv2.line(vis_img, tuple(p1), tuple(p2), color, 3, cv2.LINE_AA)
        mid_x = (p1[0] + p2[0]) // 2
        mid_y = (p1[1] + p2[1]) // 2
        slope_val = 'inf' if not isinstance(seg['slope'], float) else f"{seg['slope']:.1f}"
        cv2.putText(vis_img, f"{seg['length']:.0f}, k={slope_val}",
                    (mid_x, mid_y - 10 + i * 25),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)

    # 添加图例说明
    cv2.putText(vis_img, "Red: 1st closest to vertical", (20, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)
    cv2.putText(vis_img, "Blue: 2nd closest to vertical", (20, 60),
                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 1)

    # 保存可视化图像
    vis_save_path = os.path.join(output_dir, f'vertical_{name_only}.jpg')
    cv2.imwrite(vis_save_path, vis_img)
    print(f"✅ 可视化图像已保存: {vis_save_path}")


# ------------------ 主程序：遍历文件夹 ------------------
if __name__ == '__main__':
    if not os.path.isdir(img_folder):
        print(f"❌ 图像文件夹不存在: {img_folder}")
        exit()

    image_files = [
        f for f in os.listdir(img_folder)
        if f.lower().endswith(SUPPORTED_FORMATS)
    ]

    if len(image_files) == 0:
        print(f"⚠️ 在 {img_folder} 中未找到支持的图像文件")
        exit()

    print(f"✅ 发现 {len(image_files)} 张图像，开始批量处理...")

    for image_file in image_files:
        image_path = os.path.join(img_folder, image_file)
        process_image(image_path, output_mask_dir)

    print(f"\n🎉 所有图像处理完成！结果保存在: {output_mask_dir}")