zjsh_yolov11/angle_base_seg/test_seg_angle_f2.py

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

# ------------------ 配置 ------------------
model_path = '/home/hx/yolo/ultralytics_yolo11-main/runs/train/seg/exp3/weights/best.pt'
img_folder = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/seg/dataset2/test'
output_mask_dir = 'output_masks1'
os.makedirs(output_mask_dir, exist_ok=True)

SUPPORTED_FORMATS = ('.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff')

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

def get_long_edge_vector(rect):
    """
    从 minAreaRect 中提取长边的方向向量（单位向量）
    rect: cv2.minAreaRect 返回的 (center, (w, h), angle)
    """
    center, (width, height), angle = rect

    # OpenCV 的 angle 范围是 [-90, 0)
    # 我们要的是长边的方向
    if width >= height:
        rad = np.radians(angle)        # 长边方向
    else:
        rad = np.radians(angle + 90)   # 短边变长边

    dx = np.cos(rad)
    dy = np.sin(rad)
    direction = np.array([dx, dy])
    norm = np.linalg.norm(direction)
    return direction if norm < 1e-8 else direction / norm

def get_contour_center(contour):
    """计算轮廓质心"""
    M = cv2.moments(contour)
    if M["m00"] == 0:
        return np.array([0, 0])
    return np.array([int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])])

def calculate_jaw_angle(jaw1, jaw2):
    """
    计算两个夹具之间的开合角度（取最小内角）
    返回: (angle, dir1, dir2)
    """
    # 获取长边方向
    dir1_orig = get_long_edge_vector(jaw1['rect'])
    dir2_orig = get_long_edge_vector(jaw2['rect'])

    # 计算夹角
    cos_angle = np.clip(np.dot(dir1_orig, dir2_orig), -1.0, 1.0)
    angle = np.degrees(np.arccos(cos_angle))

    # 取最小内角（0 ~ 90°）
    opening_angle = min(angle, 180 - angle)

    # --- 可选：让方向指向夹具中心（箭头朝内）---
    center1 = get_contour_center(jaw1['contour'])
    center2 = get_contour_center(jaw2['contour'])
    fixture_center = (center1 + center2) / 2.0

    to_center1 = fixture_center - center1
    if np.linalg.norm(to_center1) > 1e-6:
        to_center1 = to_center1 / np.linalg.norm(to_center1)
        if np.dot(dir1_orig, to_center1) < 0:
            dir1_orig = -dir1_orig

    to_center2 = fixture_center - center2
    if np.linalg.norm(to_center2) > 1e-6:
        to_center2 = to_center2 / np.linalg.norm(to_center2)
        if np.dot(dir2_orig, to_center2) < 0:
            dir2_orig = -dir2_orig

    return opening_angle, dir1_orig, dir2_orig

def process_image(img_path, output_dir):
    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}")

    # 创建单通道掩码
    composite_mask = np.zeros((h, w), dtype=np.uint8)
    results = model(img_path, imgsz=1280, conf=0.5)

    jaws = []  # 存储检测到的夹具

    for r in results:
        if r.masks is not None:
            masks = r.masks.data.cpu().numpy()
            boxes = r.boxes.xyxy.cpu().numpy()

            for i, mask in enumerate(masks):
                x1, y1, x2, y2 = map(int, boxes[i])
                x1, y1 = max(0, x1), max(0, y1)
                x2, y2 = min(w, x2), min(h, y2)

                obj_mask = np.zeros((h, w), dtype=np.uint8)
                mask_resized = cv2.resize(mask, (w, h))
                obj_mask[y1:y2, x1:x2] = (mask_resized[y1:y2, x1:x2] * 255).astype(np.uint8)

                contours, _ = cv2.findContours(obj_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
                if len(contours) == 0:
                    continue

                largest_contour = max(contours, key=cv2.contourArea)
                area = cv2.contourArea(largest_contour)
                if area < 100:
                    continue

                rect = cv2.minAreaRect(largest_contour)
                jaws.append({
                    'contour': largest_contour,
                    'rect': rect,
                    'area': area
                })

                composite_mask = np.maximum(composite_mask, obj_mask)

    # 创建三通道可视化图
    vis_mask = np.stack([composite_mask] * 3, axis=-1)
    vis_mask[composite_mask > 0] = [255, 255, 255]

    if len(jaws) < 2:
        print(f"⚠️ 检测到的夹具少于2个（共{len(jaws)}个）")
        cv2.imwrite(os.path.join(output_dir, f'mask_{name_only}.png'), composite_mask)
        return

    # 按面积排序，取最大的两个
    jaws.sort(key=lambda x: x['area'], reverse=True)
    jaw1, jaw2 = jaws[0], jaws[1]

    # === 计算夹角和方向 ===
    opening_angle, dir1, dir2 = calculate_jaw_angle(jaw1, jaw2)
    print(f"✅ 夹具开合角度: {opening_angle:.2f}°")

    # === 可视化 ===
    center1 = get_contour_center(jaw1['contour'])
    center2 = get_contour_center(jaw2['contour'])
    fixture_center = ((center1[0] + center2[0]) // 2, (center1[1] + center2[1]) // 2)

    # 绘制最小外接矩形
    box1 = cv2.boxPoints(jaw1['rect']).astype(int)
    box2 = cv2.boxPoints(jaw2['rect']).astype(int)
    cv2.drawContours(vis_mask, [box1], 0, (0, 0, 255), 2)  # jaw1: 红色
    cv2.drawContours(vis_mask, [box2], 0, (255, 0, 0), 2)  # jaw2: 蓝色

    # 绘制主方向箭头（绿色，长度可调）
    scale = 60
    end1 = center1 + scale * dir1
    end2 = center2 + scale * dir2
    cv2.arrowedLine(vis_mask, tuple(center1), tuple(end1.astype(int)), (0, 255, 0), 2, tipLength=0.3)
    cv2.arrowedLine(vis_mask, tuple(center2), tuple(end2.astype(int)), (0, 255, 0), 2, tipLength=0.3)

    # 标注中心点和角度
    cv2.circle(vis_mask, fixture_center, 5, (255, 255, 0), -1)
    cv2.putText(vis_mask, "Center", (fixture_center[0] + 10, fixture_center[1]),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
    cv2.putText(vis_mask, f"Angle: {opening_angle:.2f}°", (20, 50),
                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)

    # 保存结果
    save_path = os.path.join(output_dir, f'mask_with_angle_{name_only}.png')
    cv2.imwrite(save_path, vis_mask)
    print(f"✅ 结果已保存: {save_path}")

# ------------------ 主程序 ------------------
if __name__ == '__main__':
    if not os.path.exists(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"⚠️ 未找到支持的图像文件")
        exit()

    print(f"✅ 发现 {len(image_files)} 张图像，开始处理...")
    for file in image_files:
        process_image(os.path.join(img_folder, file), output_mask_dir)

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