210 lines
7.6 KiB
Python
210 lines
7.6 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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# ================================
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# 配置区（所有固定参数放在这里）
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# ================================
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# --- 模型与类别 ---
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CLASS_NAMES = ['ban', 'bag'] # 类别名称
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WEIGHT_PATH = r"/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb_ailai2/weights/best.pt"
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IMAGE_PATH = r"/home/hx/ailai_image_obb/ailai_obb/camera01/1.jpg"
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# --- 结果保存路径 ---
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RESULT_DIR = "./inference_results"
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os.makedirs(RESULT_DIR, exist_ok=True) # 确保目录存在
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RESULT_IMAGE_PATH = os.path.join(RESULT_DIR, os.path.basename(IMAGE_PATH)) # 保存路径
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FIXED_BAG_CENTER_FILE = os.path.join(RESULT_DIR, "bag_center.txt") # 固定参考点，只读'
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# --- ban 的实际物理尺寸（单位：毫米 mm）---
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BAN_REAL_WIDTH_MM = 890 # 实际宽度（例如：800 mm）
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BAN_REAL_HEIGHT_MM = 1990 # 实际高度（例如：1990 mm）
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# --- ban 在图像中的平均像素尺寸（由模型检测得到，稳定值）---
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BAN_PIXEL_WIDTH_PX = 536.35 # 图像中检测到的平均宽度（像素）
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BAN_PIXEL_HEIGHT_PX = 242.83 # 图像中检测到的平均高度（像素）
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# --- 计算缩放因子（mm/px）---
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SCALE_X = BAN_REAL_WIDTH_MM / BAN_PIXEL_WIDTH_PX # mm/px
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SCALE_Y = BAN_REAL_HEIGHT_MM / BAN_PIXEL_HEIGHT_PX # mm/px
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print(f"标定信息 1 像素 = {SCALE_X:.3f} mm (水平) {SCALE_Y:.3f} mm (垂直)")
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# --- 固定 bag 角度（单位：度）---
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FIXED_BAG_ANGLE_DEG = 0.0 # 固定参考角度
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# ================================
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# 工具函数
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# ================================
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def pixels_to_physical(dx_px, dy_px):
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"""
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将像素偏移量转换为物理偏移量（毫米）
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"""
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dx_mm = dx_px * SCALE_X
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dy_mm = dy_px * SCALE_Y
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return dx_mm, dy_mm
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def load_center_from_txt(txt_path):
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"""加载文本中的中心点 (cx, cy)"""
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if not os.path.exists(txt_path):
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print(f"文件不存在 {txt_path}")
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return None
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try:
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with open(txt_path, "r") as f:
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line = f.readline().strip()
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if not line:
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return None
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x, y = map(float, line.split())
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return (x, y)
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except Exception as e:
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print(f"读取文件失败 {txt_path} {e}")
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return None
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def get_angles_and_centers(image_path, weight_path):
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"""获取每个类别的角度和中心点"""
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img = cv2.imread(image_path)
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if img is None:
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print(f"无法读取图像 {image_path}")
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return {cls: None for cls in CLASS_NAMES}, None
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model = YOLO(weight_path)
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results = model(img, imgsz=640, conf=0.15, task='obb')
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result = results[0]
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boxes = result.obb
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if boxes is None or len(boxes) == 0:
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print("未检测到任何目标")
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return {cls: None for cls in CLASS_NAMES}, img
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xywhr = boxes.xywhr.cpu().numpy()
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confs = boxes.conf.cpu().numpy()
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class_ids = boxes.cls.cpu().numpy().astype(int)
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angles_and_centers = {}
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for class_id, class_name in enumerate(CLASS_NAMES):
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mask = (class_ids == class_id)
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if not np.any(mask):
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print(f"未检测到类别 {class_name}")
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angles_and_centers[class_name] = None
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continue
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idx_in_class = np.argmax(confs[mask])
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global_idx = np.where(mask)[0][idx_in_class]
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cx, cy, w, h, r = xywhr[global_idx]
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angle_deg = np.degrees(r)
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angles_and_centers[class_name] = {
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'angle': angle_deg,
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'center': (float(cx), float(cy)),
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'width': float(w),
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'height': float(h),
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'box_points': cv2.boxPoints(((cx, cy), (w, h), angle_deg)) # 获取四个角点
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}
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return angles_and_centers, img
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def draw_results_and_save(image, angles_and_centers, fixed_bag_center, save_path):
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"""绘制旋转框、中心点、标签、偏移向量，并保存图像"""
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vis_img = image.copy()
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# 颜色定义
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colors = {
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'ban': (0, 255, 0), # 绿色
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'bag': (255, 0, 0) # 蓝色
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}
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# 绘制每个检测结果
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for cls_name, info in angles_and_centers.items():
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if info is None:
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continue
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# 绘制旋转框
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box_pts = np.int32(info['box_points'])
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cv2.drawContours(vis_img, [box_pts], 0, colors[cls_name], 2)
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# 绘制中心点
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cx, cy = int(info['center'][0]), int(info['center'][1])
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cv2.circle(vis_img, (cx, cy), 5, (0, 0, 255), -1) # 红色实心圆
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# 添加标签
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label = f"{cls_name} {info['angle']:.1f}°"
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cv2.putText(vis_img, label, (cx - 30, cy - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[cls_name], 2)
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# 绘制 fixed_bag_center 和偏移向量
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current_bag = angles_and_centers.get('bag')
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if fixed_bag_center and current_bag:
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fx, fy = int(fixed_bag_center[0]), int(fixed_bag_center[1])
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cx, cy = int(current_bag['center'][0]), int(current_bag['center'][1])
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# 绘制 fixed center（空心圆）
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cv2.circle(vis_img, (fx, fy), 7, (255, 255, 0), 2) # 青色
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# 绘制从 fixed 到 current 的箭头（偏移向量）
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cv2.arrowedLine(vis_img, (fx, fy), (cx, cy), (0, 255, 255), 2, tipLength=0.05)
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# 添加偏移文字
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dx_px = cx - fx
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dy_px = cy - fy
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dx_mm, dy_mm = pixels_to_physical(dx_px, dy_px)
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offset_text = f"Δx={dx_mm:.1f}mm Δy={dy_mm:.1f}mm"
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cv2.putText(vis_img, offset_text, (fx + 10, fy + 20),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
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# 保存图像
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cv2.imwrite(save_path, vis_img)
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print(f"\n✅ 可视化结果已保存至: {save_path}")
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# ===============================
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# 主程序
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# ===============================
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if __name__ == "__main__":
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# 1. 获取当前检测结果和图像
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angles_and_centers, img = get_angles_and_centers(IMAGE_PATH, WEIGHT_PATH)
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if img is None:
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exit(1)
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print("\n各类别旋转角度 中心点")
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for cls_name, info in angles_and_centers.items():
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if info is not None:
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print(f" {cls_name} 角度 {info['angle']:.2f} 中心点 {info['center'][0]:.2f} {info['center'][1]:.2f} 尺寸 {info['width']:.2f} {info['height']:.2f} px")
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else:
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print(f" {cls_name} 未检测到")
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# 2. 获取固定参考 bag 中心点
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fixed_bag_center = load_center_from_txt(FIXED_BAG_CENTER_FILE)
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print(f"\n固定参考 bag 中心点 {fixed_bag_center}")
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# 3. 计算偏移量（像素）
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current_bag = angles_and_centers.get('bag')
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if current_bag and current_bag['center'] and fixed_bag_center:
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dx_px = current_bag['center'][0] - fixed_bag_center[0]
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dy_px = current_bag['center'][1] - fixed_bag_center[1]
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print(f"\n像素偏移量")
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print(f" Δx {dx_px:.2f} px Δy {dy_px:.2f} px")
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# 转换为物理偏移量
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dx_mm, dy_mm = pixels_to_physical(dx_px, dy_px)
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print(f"\n物理偏移量 基于 ban 尺寸标定")
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print(f" ΔX {dx_mm:.2f} mm ΔY {dy_mm:.2f} mm")
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# 计算角度偏移量
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current_bag_angle = current_bag['angle']
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angle_diff = current_bag_angle - FIXED_BAG_ANGLE_DEG
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print(f"\n角度偏移量")
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print(f" 当前 bag 角度 {current_bag_angle:.2f}")
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print(f" 固定 bag 角度 {FIXED_BAG_ANGLE_DEG}")
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print(f" 角度差 {angle_diff:.2f}")
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else:
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print(f"\n偏移量计算失败 数据缺失")
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if not current_bag:
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print(" 未检测到 bag")
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if not fixed_bag_center:
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print(" 固定参考点文件为空或不存在")
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# 4. 绘制并保存可视化结果
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draw_results_and_save(img, angles_and_centers, fixed_bag_center, RESULT_IMAGE_PATH)
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