213 lines
7.9 KiB
Python
213 lines
7.9 KiB
Python
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 = '/home/hx/yolo/ultralytics_yolo11-main/runs/train/seg/exp3/weights/best.pt'
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img_folder = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/seg/dataset2/test'
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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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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 加速（如有）
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def get_orientation_vector(rect):
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"""从 minAreaRect 中提取主方向向量（单位向量）"""
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center, (width, height), angle = rect
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if width >= height:
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rad = np.radians(angle)
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else:
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rad = np.radians(angle + 90)
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direction = np.array([np.cos(rad), np.sin(rad)])
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return direction / (np.linalg.norm(direction) + 1e-8)
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def get_contour_center(contour):
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"""计算轮廓质心"""
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M = cv2.moments(contour)
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if M["m00"] == 0:
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return np.array([0, 0])
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return np.array([int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])])
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def calculate_jaw_opening_angle(jaw1, jaw2):
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"""
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计算夹具开合角度，并返回修正后的方向向量用于可视化
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返回: (angle, dir1_final, dir2_final)
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"""
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center1 = get_contour_center(jaw1['contour'])
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center2 = get_contour_center(jaw2['contour'])
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fixture_center = np.array([(center1[0] + center2[0]) / 2.0, (center1[1] + center2[1]) / 2.0])
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# 获取原始主方向
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dir1_orig = get_orientation_vector(jaw1['rect'])
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dir2_orig = get_orientation_vector(jaw2['rect'])
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def correct_and_compute(d1, d2):
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"""校正方向并计算夹角"""
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# 校正：确保方向指向夹具中心
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to_center1 = fixture_center - center1
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if np.linalg.norm(to_center1) > 1e-6:
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to_center1 = to_center1 / np.linalg.norm(to_center1)
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if np.dot(d1, to_center1) < 0:
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d1 = -d1
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to_center2 = fixture_center - center2
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if np.linalg.norm(to_center2) > 1e-6:
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to_center2 = to_center2 / np.linalg.norm(to_center2)
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if np.dot(d2, to_center2) < 0:
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d2 = -d2
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# 计算夹角
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cos_angle = np.clip(np.dot(d1, d2), -1.0, 1.0)
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angle = np.degrees(np.arccos(cos_angle))
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return angle, d1, d2
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# --- 第一步：原始方向计算 ---
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angle_raw, dir1_raw, dir2_raw = correct_and_compute(dir1_orig, dir2_orig)
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if angle_raw <= 170.0:
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print(f"✅ 角度正常: {angle_raw:.2f}°")
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return angle_raw, dir1_raw, dir2_raw
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print(f"⚠️ 初始角度过大: {angle_raw:.2f}°，尝试翻转 jaw2 主方向...")
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# --- 第二步：jaw2 主方向取反后重新计算 ---
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angle_corrected, dir1_corr, dir2_corr = correct_and_compute(dir1_orig, -dir2_orig)
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print(f"🔄 方向修正后: {angle_corrected:.2f}°")
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# --- 第三步：数值兜底修正（角度仍过大）---
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if angle_corrected > 170.0:
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final_angle = 180.0 - angle_corrected
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print(f"🔧 数值修正: {angle_corrected:.2f}° → {final_angle:.2f}°")
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return final_angle, dir1_corr, dir2_corr
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return angle_corrected, dir1_corr, dir2_corr
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def process_image(img_path, output_dir):
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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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# 初始化合并掩码
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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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rotated_rects = []
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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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boxes = r.boxes.xyxy.cpu().numpy()
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for i, mask in enumerate(masks):
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x1, y1, x2, y2 = map(int, boxes[i])
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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# 调整掩码大小并赋值
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obj_mask = np.zeros((h, w), dtype=np.uint8)
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mask_resized = cv2.resize(mask, (w, h))
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obj_mask[y1:y2, x1:x2] = (mask_resized[y1:y2, x1:x2] * 255).astype(np.uint8)
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# 提取轮廓
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contours, _ = cv2.findContours(obj_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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if len(contours) == 0:
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continue
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largest_contour = max(contours, key=cv2.contourArea)
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area = cv2.contourArea(largest_contour)
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if area < 100: # 过滤小区域
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continue
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rect = cv2.minAreaRect(largest_contour)
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rotated_rects.append({
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'rect': rect,
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'contour': largest_contour,
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'area': area
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})
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composite_mask = np.maximum(composite_mask, obj_mask)
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# 保存合并掩码
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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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if len(rotated_rects) < 2:
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print(f"⚠️ 检测到的对象少于2个（共{len(rotated_rects)}个）: {filename}")
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return
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# 按面积排序，取前两个
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rotated_rects.sort(key=lambda x: x['area'], reverse=True)
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jaw1, jaw2 = rotated_rects[0], rotated_rects[1]
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# ------------------ 计算角度 + 获取修正后的方向 ------------------
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opening_angle, dir1_final, dir2_final = calculate_jaw_opening_angle(jaw1, jaw2)
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print(f"✅ 最终夹具开合角度: {opening_angle:.2f}°")
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# ------------------ 可视化 ------------------
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vis_img = img.copy()
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# 绘制最小外接矩形
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box1 = cv2.boxPoints(jaw1['rect'])
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box1 = np.int32(box1)
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cv2.drawContours(vis_img, [box1], 0, (0, 0, 255), 2) # jaw1: 红色
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box2 = cv2.boxPoints(jaw2['rect'])
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box2 = np.int32(box2)
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cv2.drawContours(vis_img, [box2], 0, (255, 0, 0), 2) # jaw2: 蓝色
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# 计算中心点
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center1 = get_contour_center(jaw1['contour'])
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center2 = get_contour_center(jaw2['contour'])
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fixture_center = ((center1[0] + center2[0]) // 2, (center1[1] + center2[1]) // 2)
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# ✅ 使用修正后的方向向量绘制箭头
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scale = 50
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end1 = center1 + scale * dir1_final
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end2 = center2 + scale * dir2_final
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cv2.arrowedLine(vis_img, tuple(center1), tuple(end1.astype(int)), (0, 255, 0), 2, tipLength=0.3) # 绿色箭头
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cv2.arrowedLine(vis_img, tuple(center2), tuple(end2.astype(int)), (0, 255, 0), 2, tipLength=0.3)
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# 标注夹具中心
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cv2.circle(vis_img, fixture_center, 5, (255, 255, 0), -1)
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cv2.putText(vis_img, "Fixture Center", (fixture_center[0] + 10, fixture_center[1]),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
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# 标注角度
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cv2.putText(vis_img, f"Open Angle: {opening_angle:.2f}°", (20, 50),
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cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
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# 保存可视化图像
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vis_save_path = os.path.join(output_dir, f'angle_{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 ------------------
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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 = [f for f in os.listdir(img_folder) if f.lower().endswith(SUPPORTED_FORMATS)]
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if len(image_files) == 0:
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print(f"⚠️ 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}") |