import cv2 import numpy as np class FieldTransformer: """用于将视频中的像素坐标转换为场地坐标的工具类""" def __init__(self): # 标准橄榄球场尺寸（单位：米） self.field_length = 100 # 大约100码（不包括端区） self.field_width = 50 # 大约53.3码 # 默认变换矩阵（假设俯视图） self.perspective_matrix = None # 是否已自动检测场地 self.field_detected = False # 场地边界点（图像坐标系） self.field_corners = None def auto_detect_field(self, frame): """尝试自动检测球场区域""" try: # 转换为灰度图 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 应用高斯模糊 blur = cv2.GaussianBlur(gray, (5, 5), 0) # 边缘检测 edges = cv2.Canny(blur, 50, 150) # 膨胀边缘以连接间隙 dilated = cv2.dilate(edges, np.ones((3, 3), np.uint8), iterations=2) # 查找轮廓 contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 按面积排序 contours = sorted(contours, key=cv2.contourArea, reverse=True) # 尝试找到矩形区域（假设场地为矩形） for contour in contours[:5]: # 只检查最大的5个轮廓 # 近似轮廓 epsilon = 0.02 * cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, epsilon, True) # 如果近似为四边形，可能是场地 if len(approx) == 4: # 进一步处理，确认是否为场地 field_area = cv2.contourArea(approx) frame_area = frame.shape[0] * frame.shape[1] # 如果面积占总面积的比例合适，则认为找到了场地 if 0.2 < field_area / frame_area < 0.95: # 存储场地角点 self.field_corners = approx.reshape(4, 2) # 根据场地角点计算透视变换矩阵 self._calculate_perspective_matrix() self.field_detected = True return True # 未找到合适的场地轮廓，使用默认变换 self._setup_default_transform(frame) return False except Exception as e: print(f"场地自动检测失败: {str(e)}") self._setup_default_transform(frame) return False def _setup_default_transform(self, frame): """设置默认变换矩阵（假设俯视图）""" height, width = frame.shape[:2] # 默认场地框（图像四角） self.field_corners = np.array([ [0.1 * width, 0.1 * height], # 左上 [0.9 * width, 0.1 * height], # 右上 [0.9 * width, 0.9 * height], # 右下 [0.1 * width, 0.9 * height] # 左下 ], dtype=np.float32) self._calculate_perspective_matrix() def _calculate_perspective_matrix(self): """计算透视变换矩阵""" # 标准化角点顺序：左上，右上，右下，左下 rect = self._order_points(self.field_corners) # 目标场地坐标（标准场地，单位：米） dst = np.array([ [0, 0], # 左上 [self.field_length, 0], # 右上 [self.field_length, self.field_width], # 右下 [0, self.field_width] # 左下 ], dtype=np.float32) # 计算透视变换矩阵 self.perspective_matrix = cv2.getPerspectiveTransform(rect, dst) def _order_points(self, pts): """对四边形角点进行排序: 左上，右上，右下，左下""" # 初始化结果数组 rect = np.zeros((4, 2), dtype=np.float32) # 计算点的和，左上角的和最小，右下角的和最大 s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] # 左上 rect[2] = pts[np.argmax(s)] # 右下 # 计算差，右上角的差最小，左下角的差最大 diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] # 右上 rect[3] = pts[np.argmax(diff)] # 左下 return rect def transform_to_field(self, tracks, frame=None): """将球员位置从像素坐标转换到场地坐标 Args: tracks: {id: (x, y, w, h)} 形式的字典，x,y是中心点 frame: 可选的原始视频帧，用于自动检测场地 Returns: {id: (field_x, field_y)} 形式的字典 """ # 如果没有初始化透视矩阵且提供了frame if self.perspective_matrix is None and frame is not None: self.auto_detect_field(frame) # 如果仍未初始化，使用默认值 if self.perspective_matrix is None: # 创建一个假想的frame mock_frame = np.zeros((720, 1280, 3), dtype=np.uint8) self._setup_default_transform(mock_frame) # 转换每个轨迹到场地坐标 field_positions = {} for track_id, (x, y, w, h) in tracks.items(): # 转换中心点 pt = np.array([[x, y]], dtype=np.float32) pt = cv2.perspectiveTransform(pt.reshape(-1, 1, 2), self.perspective_matrix) # 存储场地坐标 field_positions[track_id] = (float(pt[0][0][0]), float(pt[0][0][1])) return field_positions def convert_field_to_image(self, field_positions, frame_shape): """将场地坐标转换回图像坐标 Args: field_positions: {id: (field_x, field_y)} 形式的字典 frame_shape: 原始帧的形状(高度, 宽度) Returns: {id: (image_x, image_y)} 形式的字典 """ if self.perspective_matrix is None: # 创建一个假想的frame mock_frame = np.zeros((frame_shape[0], frame_shape[1], 3), dtype=np.uint8) self._setup_default_transform(mock_frame) # 计算逆变换矩阵 inv_matrix = np.linalg.inv(self.perspective_matrix) # 转换每个场地坐标到图像坐标 image_positions = {} for track_id, (field_x, field_y) in field_positions.items(): # 转换点 pt = np.array([[[field_x, field_y]]], dtype=np.float32) pt = cv2.perspectiveTransform(pt, inv_matrix) # 存储图像坐标 image_positions[track_id] = (float(pt[0][0][0]), float(pt[0][0][1])) return image_positions def visualize_field_transform(self, frame): """在帧上可视化场地变换""" if self.field_corners is None: self.auto_detect_field(frame) vis_frame = frame.copy() # 绘制检测到的场地边界 if self.field_corners is not None: # 将角点转换为整数坐标 corners = self.field_corners.astype(np.int32) # 绘制场地边界 cv2.polylines(vis_frame, [corners], True, (0, 255, 0), 2) # 标记角点 for i, (x, y) in enumerate(corners): cv2.circle(vis_frame, (x, y), 5, (0, 0, 255), -1) cv2.putText(vis_frame, str(i), (x-10, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2) # 添加说明文本 cv2.putText(vis_frame, "检测到场地", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) else: cv2.putText(vis_frame, "未检测到场地，使用默认变换", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) return vis_frame