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import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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from scipy.ndimage import label
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import config
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def point_cloud_to_2d_image(points, resolution=1.0, x_range=(17, 275), y_range=(-129, -1227)):
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"""
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将点云转换为 2D 图像（X-Y 平面），每个像素值表示对应位置的 Z 值平均值
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"""
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if not isinstance(points, np.ndarray):
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points = np.array(points)
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if len(points) == 0:
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raise ValueError("点云为空")
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x_min, x_max = x_range
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y_min, y_max = y_range
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width = int((x_max - x_min) / resolution) + 1
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height = int((y_max - y_min) / resolution) + 1
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image = np.zeros((height, width), dtype=np.float32)
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count_map = np.zeros((height, width), dtype=np.int32)
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for x, y, z in points:
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xi = int((x - x_min) / resolution)
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yi = int((y - y_min) / resolution)
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if 0 <= xi < width and 0 <= yi < height:
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image[yi, xi] += z
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count_map[yi, xi] += 1
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# 防止除零错误
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count_map[count_map == 0] = 1
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image /= count_map
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return image, (x_min, y_min)
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def detect_holes_by_density(density_map, density_threshold_ratio=0.5, min_area=100):
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"""
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基于点云密度图识别空洞区域
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:param density_map: 2D numpy array, 每个像素表示该位置点云密度
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:param density_threshold_ratio: 密度低于均值的 ratio 倍时视为空洞候选
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:param min_area: 最小空洞面积（像素数）
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:return: list of ((cx, cy), area)，空洞中心和面积（图像坐标）
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"""
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# 计算邻域平均密度（3x3窗口）
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avg_density = np.zeros_like(density_map)
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for i in range(density_map.shape[0]):
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for j in range(density_map.shape[1]):
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# 取 3x3 邻域
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neighbors = density_map[
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max(0, i - 1):min(i + 2, density_map.shape[0]),
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max(0, j - 1):min(j + 2, density_map.shape[1])
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]
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avg_density[i, j] = np.mean(neighbors)
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# 构建空洞候选区：密度低于邻域平均值的 50%
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binary_map = (density_map < avg_density * density_threshold_ratio).astype(np.uint8)
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# 连通域分析
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structure = np.array([[1, 1, 1],
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[1, 1, 1],
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[1, 1, 1]])
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labeled_map, num_features = label(binary_map, structure=structure)
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holes = []
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for label_id in range(1, num_features + 1):
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coords = np.where(labeled_map == label_id)
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hole_pixel_count = len(coords[0])
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if hole_pixel_count >= min_area:
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cx = np.mean(coords[1]) # x 坐标（列）
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cy = np.mean(coords[0]) # y 坐标（行）
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area = hole_pixel_count
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holes.append(((cx, cy), area))
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return holes
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def detect_black_regions(binary_mask, min_area=10):
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"""
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检测图像中的黑色连通区域（值为 0 的区域）
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:param binary_mask: 2D numpy array, 二值图（0 表示黑色区域）
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:param min_area: int, 最小面积阈值，小于该值的区域会被忽略
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:return: list of dict, 包含每个区域的信息:
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[
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{
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'id': int,
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'center': (cx, cy),
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'area': int,
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'coordinates': [(x1,y1), (x2,y2), ...]
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},
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...
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]
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"""
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# 确保输入是二值图像（0 是黑）
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binary = (binary_mask == 0).astype(np.uint8)
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# 使用连通域分析
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structure = np.array([[1, 1, 1],
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[1, 1, 1],
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[1, 1, 1]])
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labeled_map, num_features = label(binary, structure=structure)
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regions = []
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for label_id in range(1, num_features + 1):
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coords = np.where(labeled_map == label_id)
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area = len(coords[0])
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if area >= min_area:
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cx = np.mean(coords[1]) # x 坐标（列）
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cy = np.mean(coords[0]) # y 坐标（行）
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regions.append(((cx, cy),area))
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return regions
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def convert_image_holes_to_real(holes, offset, resolution):
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real_holes = []
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x_min, y_min = offset
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for ((cx, cy), area) in holes:
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real_x = x_min + cx * resolution
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real_y = y_min + cy * resolution
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real_holes.append(((real_x, real_y), area * resolution ** 2))
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return real_holes
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def visualize_holes_on_image(image, holes, output_path=None):
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"""
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在图像上画出检测到的空洞中心和轮廓
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"""
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# 彩色化灰度图用于可视化
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color_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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for (cx, cy), _ in holes:
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# 绘制圆形标注空洞中心
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cv2.circle(color_image, (int(cx), int(cy)), radius=5, color=(0, 0, 255), thickness=-1)
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# 显示图像
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# plt.figure(figsize=(10, 8))
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plt.imshow(cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB))
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# plt.title("Detected Holes")
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plt.axis("off")
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output_path = config.save_path("image", "_holes.png")
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if output_path:
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plt.savefig(output_path, bbox_inches='tight', dpi=200)
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print(f"Saved visualization to {output_path}")
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# plt.show()
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def read_pcd_points(pcd_path):
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"""
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从 .pcd 文件中提取点云坐标 (x, y, z)
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:param pcd_path: str, PCD 文件路径
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:return: list of [x, y, z], 点云坐标列表
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"""
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points = []
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data_started = False
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with open(pcd_path, 'r') as f:
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for line in f:
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if line.startswith("DATA"):
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data_started = True
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continue
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if data_started:
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parts = line.strip().split()
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if len(parts) >= 3:
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try:
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x = float(parts[0])
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y = float(parts[1])
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z = float(parts[2])
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points.append([x, y, z])
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except ValueError:
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continue
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return points
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def detect_large_holes(points,sn, x_max):
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# 2. 生成 2D 图像
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x_range = (config.CUT_CONFIG_MAP[sn]["min_pt"][0],x_max) # 手动指定 X 范围
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y_range = (config.CUT_CONFIG_MAP[sn]["min_pt"][1], config.CUT_CONFIG_MAP[sn]["max_pt"][1]) # 注意：这里要保证 y_min < y_max，否则反转一下
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resolution = config.CAMERA_CONFIG_MAP[sn].get("resolution")
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image, offset = point_cloud_to_2d_image(points, resolution=resolution, x_range=x_range, y_range=y_range)
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# 3. 图像归一化用于可视化
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normalized_image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
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# 4. 检测空洞
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holes = detect_black_regions(normalized_image, min_area=20)
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# 5. 可视化空洞
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visualize_holes_on_image(normalized_image, holes)
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# 6. 输出真实世界坐标
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real_holes = convert_image_holes_to_real(holes, offset, resolution)
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return real_holes
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if __name__ == '__main__':
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points = read_pcd_points("D:/PycharmProjects/Hik3D/image/2025-06-25/pcd/182109899_00DA6823936_merged.pcd")
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sn = "00DA6823936"
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x_max = 326
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detect_large_holes(points,sn, x_max)
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