Initial commit

angle2.py

@@ -0,0 +1,182 @@
+import cv2
+import numpy as np
+import itertools
+import os
+import time
+
+# 设置输入输出文件夹路径
+input_folder = "output10"  # 输入文件夹
+output_folder = "output12"  # 输出文件夹
+
+# 创建输出文件夹（如果不存在）
+os.makedirs(output_folder, exist_ok=True)
+start_time = time.time()
+
+def order_points(points):
+    """
+    将四边形的四个点按顺时针顺序排列。
+    :param points: 四边形的四个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)]
+    :return: 排序后的点
+    """
+    # 计算质心
+    center = np.mean(points, axis=0)
+
+    # 计算每个点相对于质心的角度
+    angles = [np.arctan2(point[1] - center[1], point[0] - center[0]) for point in points]
+
+    # 按角度排序
+    sorted_points = [point for _, point in sorted(zip(angles, points))]
+    return sorted_points
+
+def order_counters(contours):
+    counterclockwise_contours = []
+    for contour in contours:
+        # 计算轮廓的签名面积
+        area = cv2.contourArea(contour, oriented=True)
+
+        # 如果面积是负值，说明轮廓是逆时针
+        if area > 0:
+            # 将轮廓反转为逆时针
+            contour = contour[::-1]
+
+        # 添加到新的列表中
+        counterclockwise_contours.append(contour)
+    return counterclockwise_contours
+
+def calculate_weight(points, non_transparent_area):
+    """
+    计算四边形的权重，用于选择最优四边形。
+    :param points: 四边形的四个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)]
+    :param non_transparent_area: 非透明区域的面积
+    :return: 四边形的权重（-1 表示无效四边形）
+    """
+    contour = np.array(points, dtype=np.int32)
+    area = cv2.contourArea(contour)
+
+    # 筛选条件 1: 四边形的面积比例
+    if area / non_transparent_area < 0.8:
+        return -1
+
+    # 计算边长
+    edges = [np.linalg.norm(np.array(points[i]) - np.array(points[(i + 1) % 4])) for i in range(4)]
+    max_edge, min_edge = max(edges), min(edges)
+
+    # 筛选条件 2: 边长相似性
+    if max_edge - min_edge > max_edge / 7:
+        return -1
+
+    edge_similarity = 1 - (max_edge - min_edge) / max_edge
+
+    # 计算角度
+    angles = []
+    for i in range(4):
+        v1 = np.array(points[(i + 1) % 4]) - np.array(points[i])
+        v2 = np.array(points[(i + 3) % 4]) - np.array(points[i])
+        cosine_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
+        angle = np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0)))
+        angles.append(angle)
+
+    # 筛选条件 3: 角度相似性
+    if any(angle < 80 or angle > 100 for angle in angles):
+        return -1
+
+    angle_similarity = 1 - sum(abs(angle - 90) for angle in angles) / 360
+
+    # 计算最终权重
+    square_similarity = edge_similarity * angle_similarity
+    return square_similarity * 0.5 + (area / non_transparent_area) * 0.5  # 使用非透明面积归一化
+
+def find_best_quadrilateral(corner_points, non_transparent_area):
+    """
+    从角点中筛选最优四边形。
+    :param corner_points: 检测到的角点 [(x1, y1), (x2, y2), ...]
+    :param non_transparent_area: 非透明区域的面积
+    :return: 最优四边形的点集和最大权重
+    """
+    quadrilaterals = list(itertools.combinations(corner_points, 4))
+    best_quad = None
+    max_weight = -1
+
+    for quad in quadrilaterals:
+        # 对四边形的点进行顺时针排序
+        quad = order_points(quad)
+
+        weight = calculate_weight(quad, non_transparent_area)
+        if weight > max_weight:
+            max_weight = weight
+            best_quad = quad
+
+    return best_quad, max_weight
+
+def process_image(image_path, save_path):
+    # 加载图片
+    image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
+    if image is None:
+        print(f"无法加载图片: {image_path}")
+        return
+
+    img_height, img_width = image.shape[:2]
+    alpha_channel = image[:, :, 3]
+
+    non_transparent_area = np.count_nonzero(alpha_channel > 0)
+    if non_transparent_area == 0:  # 如果图片完全透明，跳过
+        print(f"图片完全透明: {image_path}")
+        return
+
+    # 找到边框上的轮廓
+    contours, _ = cv2.findContours(alpha_channel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # 创建一个空白的边框图片
+    border_image = np.zeros_like(alpha_channel)
+    cv2.drawContours(border_image, contours, -1, color=255, thickness=1)
+
+    # 角点检测 (最多 16 个角点)
+    max_corners = 14
+    corners = cv2.goodFeaturesToTrack(border_image, maxCorners=max_corners, qualityLevel=0.01, minDistance=15, blockSize=5)
+    if corners is not None:
+        corners = np.intp(corners)
+        corner_points = [tuple(corner.ravel()) for corner in corners]
+
+        # 使用独立函数筛选最优四边形
+        best_quad, max_weight = find_best_quadrilateral(corner_points, non_transparent_area)
+
+        if best_quad is None:
+            print(f"未找到有效四边形: {image_path}")
+
+            output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB)
+            for point in corner_points:
+                cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1)
+
+            # 用蓝色标记边框
+            cv2.drawContours(output_image, contours, -1, color=(0, 0, 255), thickness=1)  # 蓝色 (BGR: 255, 0, 0)
+
+            cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR))
+            return
+
+        area = cv2.contourArea(np.array(best_quad, dtype=np.int32))
+        print(image_path+"\t\t"+str(area / non_transparent_area))
+
+        # 绘制最优四边形和角点
+        output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB)
+        for point in corner_points:
+            cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1)
+
+        cv2.polylines(output_image, [np.array(best_quad, dtype=np.int32)], isClosed=True, color=(0, 255, 0), thickness=2)
+
+        # 用蓝色标记边框
+        cv2.drawContours(output_image, contours, -1, color=(0, 0, 255), thickness=1)  # 蓝色 (BGR: 255, 0, 0)
+
+        cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR))
+    else:
+        print(f"未检测到角点: {image_path}")
+
+# 遍历输入文件夹中的图片
+for filename in os.listdir(input_folder):
+    if filename.lower().endswith((".png", ".jpg", ".jpeg")):  # 支持常见图片格式
+        input_path = os.path.join(input_folder, filename)
+        output_path = os.path.join(output_folder, filename)
+        process_image(input_path, output_path)
+
+end_time = time.time()
+print(f"处理图片的时间: {end_time - start_time:.4f} 秒")
+print(f"所有图片已处理完成，结果保存在: {output_folder}")