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@@ -91,127 +91,82 @@ def draw_rotated_bounding_boxes(image_path, inner_box_file, outer_box_file, outp
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plt.show()
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-def analyze_centering_rotated(inner_points: Union[str, List], outer_points: Union[str, List], ):
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+def analyze_centering_rect(inner_points: Union[str, List], outer_points: Union[str, List]):
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"""
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- 使用旋转矩形进行最精确的分析, 包括定向边距和精确的浮点数比值。
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+ 使用横平竖直的矩形 (Axis-Aligned Bounding Box) 进行居中分析。
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+ 适用于前端已经修正过的矩形数据。
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"""
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if isinstance(inner_points, str):
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- inner_points = get_points_from_file(inner_points)
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+ inner_points = get_rect_from_file(inner_points)
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if isinstance(outer_points, str):
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- outer_points = get_points_from_file(outer_points)
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+ outer_points = get_rect_from_file(outer_points)
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- # 将点列表转换为OpenCV需要的NumPy数组格式
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- inner_contour = np.array(inner_points, dtype=np.int32)
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- outer_contour = np.array(outer_points, dtype=np.int32)
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+ # 1. 转换为 float32 数组，保留计算精度
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+ inner_contour = np.array(inner_points, dtype=np.float32)
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+ outer_contour = np.array(outer_points, dtype=np.float32)
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- # 计算最小面积的旋转矩形
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- # 结果格式: ((中心点x, 中心点y), (宽度, 高度), 旋转角度)
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- inner_rect = cv2.minAreaRect(inner_contour)
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- outer_rect = cv2.minAreaRect(outer_contour)
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+ # --- 修改点 1: 在计算 min/max 后立即转为 Python float ---
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+ def get_float_bounding_rect(points):
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+ # np.min 返回的是 numpy.float32，必须转为 python float
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+ x_min = float(np.min(points[:, 0]))
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+ y_min = float(np.min(points[:, 1]))
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+ x_max = float(np.max(points[:, 0]))
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+ y_max = float(np.max(points[:, 1]))
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- # 获取矩形的4个角点, 并转化为坐标
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- # outer_box_corners = cv2.boxPoints(outer_rect)
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- # outer_box_corners_int = np.intp(outer_box_corners).tolist()
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- #
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- # inner_box_corners = cv2.boxPoints(inner_rect)
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- # inner_box_corners_int = np.intp(inner_box_corners).tolist()
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- def get_rect(contour):
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- # 获取矩形框
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- x, y, w, h = cv2.boundingRect(contour)
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- # 2. 构造 4 个顶点坐标
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- box_corners_int = np.array([
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- [x, y], # 左上
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- [x + w, y], # 右上
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- [x + w, y + h], # 右下
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- [x, y + h] # 左下
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- ], dtype=np.int32).tolist()
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- return box_corners_int
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-
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- inner_box_corners_int = get_rect(inner_contour)
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- outer_box_corners_int = get_rect(outer_contour)
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-
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- # --- 1. 标准化矩形尺寸，确保宽度总是长边 ---
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- def standardize_rect(rect):
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- (cx, cy), (w, h), angle = rect
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- if w < h:
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- # 如果宽度小于高度，交换它们，并调整角度
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- w, h = h, w
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- angle += 90
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- return (cx, cy), (w, h), angle
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-
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- inner_center, inner_dims, inner_angle = standardize_rect(inner_rect)
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- outer_center, outer_dims, outer_angle = standardize_rect(outer_rect)
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-
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- print("\n--- 基于旋转矩形的精确分析 ---")
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- print(
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- f"内框 (标准化后): 中心=({inner_center[0]:.1f}, {inner_center[1]:.1f}), 尺寸=({inner_dims[0]:.1f}, {inner_dims[1]:.1f}), 角度={inner_angle:.1f}°")
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- print(
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- f"外框 (标准化后): 中心=({outer_center[0]:.1f}, {outer_center[1]:.1f}), 尺寸=({outer_dims[0]:.1f}, {outer_dims[1]:.1f}), 角度={outer_angle:.1f}°")
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-
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- # --- 2. 评估居中度 (基于中心点) ---
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- offset_x_abs = abs(inner_center[0] - outer_center[0])
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- offset_y_abs = abs(inner_center[1] - outer_center[1])
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- print(f"\n[居中度] 图像坐标系绝对中心偏移: 水平={offset_x_abs:.2f} px | 垂直={offset_y_abs:.2f} px")
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-
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- # --- 3. 评估平行度 ---
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- angle_diff = abs(inner_angle - outer_angle)
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- # 处理角度环绕问题 (例如 -89° 和 89° 其实只差 2°)
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- angle_diff = min(angle_diff, 180 - angle_diff)
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- print(f"[平行度] 角度差异: {angle_diff:.2f}° (值越小，内外框越平行)")
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-
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- # --- 4. 计算定向边距和比值 (核心部分) ---
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- # 计算从外框中心指向内框中心的向量
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- center_delta_vec = (inner_center[0] - outer_center[0], inner_center[1] - outer_center[1])
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-
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- # 将外框的旋转角度转换为弧度，用于三角函数计算
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- angle_rad = math.radians(outer_angle)
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-
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- # 计算外框自身的坐标轴单位向量（相对于图像坐标系）
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- # local_x_axis: 沿着外框宽度的方向向量
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- # local_y_axis: 沿着外框高度的方向向量
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- local_x_axis = (math.cos(angle_rad), math.sin(angle_rad))
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- local_y_axis = (-math.sin(angle_rad), math.cos(angle_rad))
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-
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- # 使用点积 (dot product) 将中心偏移向量投影到外框的局部坐标轴上
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- # 这能告诉我们，内框中心相对于外框中心，在“卡片自己的水平和垂直方向上”移动了多少
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- offset_along_width = center_delta_vec[0] * local_x_axis[0] + center_delta_vec[1] * local_x_axis[1]
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- offset_along_height = center_delta_vec[0] * local_y_axis[0] + center_delta_vec[1] * local_y_axis[1]
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-
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- # 计算水平和垂直方向上的总边距
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- total_horizontal_margin = outer_dims[0] - inner_dims[0]
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- total_vertical_margin = outer_dims[1] - inner_dims[1]
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-
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- # 根据投影的偏移量来分配总边距
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- # 理想情况下，偏移为0，左右/上下边距各分一半
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- # 如果有偏移，一侧增加，另一侧减少
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- margin_left = (total_horizontal_margin / 2) - offset_along_width
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- margin_right = (total_horizontal_margin / 2) + offset_along_width
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- margin_top = (total_vertical_margin / 2) - offset_along_height
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- margin_bottom = (total_vertical_margin / 2) + offset_along_height
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-
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- # print("\n[定向边距分析 (沿卡片方向)]")
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- # print(f"水平边距 (像素): 左={margin_left:.1f} | 右={margin_right:.1f}")
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- # print(f"垂直边距 (像素): 上={margin_top:.1f} | 下={margin_bottom:.1f}")
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- #
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- # print("\n[精确边距比值分析]")
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- # # 直接展示浮点数比值
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- # print(f"水平比值 (左:右) = {margin_left:.2f} : {margin_right:.2f}")
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- # print(f"垂直比值 (上:下) = {margin_top:.2f} : {margin_bottom:.2f}")
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-
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- # 为了更直观，也可以计算一个百分比
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- if total_horizontal_margin > 0:
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- left_percent = (margin_left / total_horizontal_margin) * 100
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- right_percent = (margin_right / total_horizontal_margin) * 100
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+ w = x_max - x_min
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+ h = y_max - y_min
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+ return x_min, y_min, w, h
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+
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+ ix, iy, iw, ih = get_float_bounding_rect(inner_contour)
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+ ox, oy, ow, oh = get_float_bounding_rect(outer_contour)
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+
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+ # 构造标准的4点格式返回
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+ # 因为 ix, iy 等已经是 python float 了，所以列表里装的也是 safe 的 float
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+ def get_box_corners(x, y, w, h):
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+ return [
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+ [x, y],
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+ [x + w, y],
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+ [x + w, y + h],
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+ [x, y + h]
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+ ]
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+
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+ inner_box_corners_float = get_box_corners(ix, iy, iw, ih)
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+ outer_box_corners_float = get_box_corners(ox, oy, ow, oh)
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+
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+ print("\n--- 基于修正矩形(横平竖直, Float精度)的分析 ---")
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+ print(f"内框: x={ix:.2f}, y={iy:.2f}, w={iw:.2f}, h={ih:.2f}")
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+
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+ # --- 3. 计算边距 (Margins) ---
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+ margin_left = ix - ox
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+ margin_right = (ox + ow) - (ix + iw)
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+ margin_top = iy - oy
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+ margin_bottom = (oy + oh) - (iy + ih)
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+
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+ # --- 4. 计算百分比 ---
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+ total_horizontal_margin = margin_left + margin_right
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+ total_vertical_margin = margin_top + margin_bottom
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+
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+ left_percent = 50.0
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+ right_percent = 50.0
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+ top_percent = 50.0
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+ bottom_percent = 50.0
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+
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+ # --- 修改点 2: 计算结果也要转为 float (虽然上面的 ix 是 float，但运算可能再次产生 numpy 类型) ---
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+ if total_horizontal_margin > 0.0001:
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+ left_percent = float((margin_left / total_horizontal_margin) * 100)
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+ right_percent = float((margin_right / total_horizontal_margin) * 100)
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print(f"水平边距分布: 左 {left_percent:.1f}% | 右 {right_percent:.1f}%")
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- if total_vertical_margin > 0:
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- top_percent = (margin_top / total_vertical_margin) * 100
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- bottom_percent = (margin_bottom / total_vertical_margin) * 100
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+ if total_vertical_margin > 0.0001:
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+ top_percent = float((margin_top / total_vertical_margin) * 100)
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+ bottom_percent = float((margin_bottom / total_vertical_margin) * 100)
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print(f"垂直边距分布: 上 {top_percent:.1f}% | 下 {bottom_percent:.1f}%")
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+ angle_diff = 0.0
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+
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return ((left_percent, right_percent),
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(top_percent, bottom_percent),
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- angle_diff), inner_box_corners_int, outer_box_corners_int
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+ angle_diff), inner_box_corners_float, outer_box_corners_float
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def analyze_centering_rect(inner_points: Union[str, List], outer_points: Union[str, List]):
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AnlaAnla