|
|
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)
|
|
|
|