hik3d-python/image.py

import numpy as np
import cv2

import threading
from thread_pool import submit_task
from scipy.ndimage import label

import config

from logConfig import get_logger

# 使用示例
logger = get_logger()
#
# def point_cloud_to_2d_image(points, resolution=1.0, x_range=(17, 275), y_range=(-129, -1227)):
#     """
#     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
#     """
#     if not isinstance(points, np.ndarray):
#         points = np.array(points)
#
#     if len(points) == 0:
#         raise ValueError("点云为空")
#
#     x_min, x_max = x_range
#     y_min, y_max = y_range
#
#     width = int((x_max - x_min) / resolution) + 1
#     height = int((y_max - y_min) / resolution) + 1
#
#     image = np.zeros((height, width), dtype=np.float32)
#     count_map = np.zeros((height, width), dtype=np.int32)
#
#     for x, y, z in points:
#         xi = int((x - x_min) / resolution)
#         yi = int((y - y_min) / resolution)
#
#         if 0 <= xi < width and 0 <= yi < height:
#             image[yi, xi] += z
#             count_map[yi, xi] += 1
#
#     # 防止除零错误
#     count_map[count_map == 0] = 1
#     image /= count_map
#
#     return image, (x_min, y_min)
# 优化 point_cloud_to_2d_image 函数
def point_cloud_to_2d_image(points, resolution=1.0, x_range=(17, 275), y_range=(-129, -1227)):
    """
    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
    """
    if not isinstance(points, np.ndarray):
        points = np.array(points)

    if len(points) == 0:
        raise ValueError("点云为空")

    x_min, x_max = x_range
    y_min, y_max = y_range

    width = int((x_max - x_min) / resolution) + 1
    height = int((y_max - y_min) / resolution) + 1

    # 使用向量化操作替代循环
    x_coords = points[:, 0]
    y_coords = points[:, 1]
    z_coords = points[:, 2]

    # 计算像素坐标
    xi = ((x_coords - x_min) / resolution).astype(int)
    yi = ((y_coords - y_min) / resolution).astype(int)

    # 筛选有效坐标
    valid_mask = (xi >= 0) & (xi < width) & (yi >= 0) & (yi < height)
    xi = xi[valid_mask]
    yi = yi[valid_mask]
    z_coords = z_coords[valid_mask]

    # 使用直方图统计替代循环累加
    image = np.zeros((height, width), dtype=np.float32)
    count_map = np.zeros((height, width), dtype=np.int32)

    # 使用 bincount 进行快速统计
    indices = yi * width + xi
    z_sums = np.bincount(indices, weights=z_coords, minlength=height * width)
    counts = np.bincount(indices, minlength=height * width)

    image.flat[:] = z_sums
    count_map.flat[:] = counts

    # 防止除零错误
    count_map[count_map == 0] = 1
    image /= count_map

    return image, (x_min, y_min)


def stitch(imagePath1, imagePath2):
    # 读取两张图像
    image1 = cv2.imread(imagePath1)
    image2 = cv2.imread(imagePath2)

    # 创建 Stitcher 对象
    stitcher = cv2.Stitcher_create()

    # 拼接图像
    (status, stitched) = stitcher.stitch((image1, image2))

    # 检查拼接结果
    if status == cv2.Stitcher_OK:
        cv2.imwrite('stitched_output.jpg', stitched)
        print("图像拼接成功！")
    else:
        print(f"图像拼接失败，错误代码: {status}")
if __name__ == '__main__':
    stitch('D:/git/test/hik3d-python/image/2025-07-01/color/105601193_-_Color.bmp'
           , 'D:/git/test/hik3d-python/image/2025-07-01/color/164646720_-_Color.bmp')



def detect_black_regions(binary_mask, min_area=10,box_area=10):
    """
    检测图像中的黑色连通区域（值为 0 的区域）

    :param binary_mask: 2D numpy array, 二值图（0 表示黑色区域）
    :param min_area: int, 最小面积阈值，小于该值的区域会被忽略
    :return: list of dict, 包含每个区域的信息:
             [
                 {
                     'id': int,
                     'center': (cx, cy),
                     'area': int,
                     'coordinates': [(x1,y1), (x2,y2), ...]
                 },
                 ...
             ]
    """
    # 确保输入是二值图像（0 是黑）
    binary = (binary_mask == 0).astype(np.uint8)

    # 使用连通域分析
    structure = np.array([[1, 1, 1],
                          [1, 1, 1],
                          [1, 1, 1]])
    labeled_map, num_features = label(binary, structure=structure)

    regions = []
    count = 0

    for label_id in range(1, num_features + 1):
        coords = np.where(labeled_map == label_id)
        area = len(coords[0])
        if area >= min_area:
            cx = np.mean(coords[1])  # x 坐标（列）
            cy = np.mean(coords[0])  # y 坐标（行）
            logger.info(f"区域: {label_id} 中心: {cx, cy} 面积: {area}")
            regions.append(((cx, cy),area))
            count += ( area/box_area)
    return regions,count
def convert_image_holes_to_real(holes, offset, resolution):
    real_holes = []
    x_min, y_min = offset
    for ((cx, cy), area) in holes:
        real_x = x_min + cx * resolution
        real_y = y_min + cy * resolution
        real_holes.append(((real_x, real_y), area * resolution ** 2))
    return real_holes


def visualize_holes_on_image(image, holes, output_path=None):
    """
    在图像上画出检测到的空洞中心和轮廓
    """
    try:
        # 确保图像数据有效
        if image is None or len(image) == 0:
            print("Warning: Empty image data for visualization")
            return

        # 彩色化灰度图用于可视化
        if len(image.shape) == 2:  # 灰度图
            color_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
        else:
            color_image = image.copy()

        # 绘制空洞标记
        if holes:
            for (cx, cy), _ in holes:
                # 确保坐标有效
                if 0 <= int(cx) < color_image.shape[1] and 0 <= int(cy) < color_image.shape[0]:
                    cv2.circle(color_image, (int(cx), int(cy)), radius=5, color=(0, 0, 255), thickness=-1)

        # 生成输出路径
        if output_path is None:
            output_path = config.save_path("image", "_holes.png")

        # 保存图像
        success = cv2.imwrite(output_path, color_image)
        if success:
            print(f"Saved visualization to {output_path}")
        else:
            print(f"Failed to save visualization to {output_path}")

    except Exception as e:
        print(f"Error in visualize_holes_on_image: {e}")


def read_pcd_points(pcd_path):
    """
    从 .pcd 文件中提取点云坐标 (x, y, z)

    :param pcd_path: str, PCD 文件路径
    :return: list of [x, y, z], 点云坐标列表
    """
    points = []
    data_started = False

    with open(pcd_path, 'r') as f:
        for line in f:
            if line.startswith("DATA"):
                data_started = True
                continue
            if data_started:
                parts = line.strip().split()
                if len(parts) >= 3:
                    try:
                        x = float(parts[0])
                        y = float(parts[1])
                        z = float(parts[2])
                        points.append([x, y, z])
                    except ValueError:
                        continue
    return points


def detect_large_holes(points, sn, type):
    # 获取裁剪配置
    cat_map = config.CUT_CONFIG_MAP.get(sn + "_" + type, None) or config.CUT_CONFIG_MAP.get(sn)
    template_map = config.TEMPLATE_CONFIG_MAP[type]
    camera_map = config.CAMERA_CONFIG_MAP[sn]

    # 2. 生成 2D 图像
    x_range = (cat_map["min_pt"][0], cat_map["max_pt"][0])
    y_range = (cat_map["min_pt"][1], cat_map["max_pt"][1])
    resolution = config.CAMERA_CONFIG_MAP[sn].get("resolution")
    image, offset = point_cloud_to_2d_image(points, resolution=resolution, x_range=x_range, y_range=y_range)

    # 3. 图像归一化用于可视化
    normalized_image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)

    min_area = min(template_map["min_area"], (template_map["width"] * template_map["height"]) /(2*camera_map["resolution"]*camera_map["resolution"]))
    # 4. 检测空洞
    holes, count = detect_black_regions(normalized_image, min_area, (template_map["width"] * template_map["height"]) / 2)

    if config.CAMERA_CONFIG_MAP[sn].get("save_image"):
        # 创建数据副本确保异步执行时数据完整性
        image_copy = normalized_image.copy()
        holes_copy = list(holes)  # 创建holes的副本

        # 预先生成输出路径
        output_path = config.save_path("image", f"_{sn}_holes.png")

        # 使用线程池异步执行可视化操作
        submit_task(visualize_holes_on_image, image_copy, holes_copy, output_path)

    # 6. 输出真实世界坐标
    real_holes = convert_image_holes_to_real(holes, offset, resolution)
    return real_holes, count

# if __name__ == '__main__':
#     points = read_pcd_points("D:/PycharmProjects/Hik3D/image/2025-06-25/pcd/182109899_00DA6823936_merged.pcd")
#     sn = "00DA6823936"
#     x_max = 326
#     detect_large_holes(points,sn, x_max)