528 lines
22 KiB
Python
528 lines
22 KiB
Python
#
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# import json
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# import os
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# import time
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#
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# import cv2
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# import rasterio
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# import numpy as np
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# from pyproj import Transformer, CRS
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#
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#
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# def convert_to_wgs84(x, y, src_crs):
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# """将投影坐标转换为WGS84经纬度，增加错误处理"""
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# try:
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# if not (isinstance(x, (int, float)) and isinstance(y, (int, float))):
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# print(f"警告: 坐标值无效 (x={x}, y={y})")
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# return None, None
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#
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# transformer = Transformer.from_crs(src_crs, "EPSG:4326", always_xy=True)
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# lon, lat = transformer.transform(x, y)
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#
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# if not np.isfinite(lon) or not np.isfinite(lat):
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# print(f"警告: 坐标转换结果无效 (lon={lon}, lat={lat})")
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# return None, None
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#
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# return lon, lat
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# except Exception as e:
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# print(f"坐标转换异常: {str(e)}")
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# return None, None
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#
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#
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# def visualize_pil_segmentation_mask_opencv(mask_path, tif_path, output_path=None, colormap=cv2.COLORMAP_VIRIDIS, save=True):
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# """
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# 使用OpenCV实现掩码可视化+边界提取（避免Matplotlib后端问题）
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# 返回: (src_crs, json_result_path, vis_output_path, raw_json_path)
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# """
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# # 初始化返回值
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# src_crs = None
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# json_result_path = None
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# vis_output_path = None
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# raw_json_path = None
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#
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# try:
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# # 1. 读取并验证掩码文件
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# if not os.path.exists(mask_path):
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# raise FileNotFoundError(f"掩码文件不存在: {mask_path}")
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#
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# mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
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# if mask is None:
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# raise ValueError(f"无法读取掩码文件(可能已损坏): {mask_path}")
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#
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# # 2. 可视化处理
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# mask_vis = cv2.applyColorMap(mask, colormap)
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# contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# cv2.drawContours(mask_vis, contours, -1, (0, 0, 255), 1)
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#
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# # 3. 设置输出路径
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# if output_path is None:
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# vis_output_path = os.path.splitext(mask_path)[0] + '_vis_cv2.png'
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# else:
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# vis_output_path = output_path
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#
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# # 4. 保存可视化结果
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# if save:
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# if not cv2.imwrite(vis_output_path, mask_vis):
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# raise IOError(f"无法保存可视化结果到: {vis_output_path}")
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# print(f"可视化结果已保存到: {vis_output_path}")
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#
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# # 5. 处理轮廓数据
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# instances = []
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# for i, contour in enumerate(contours):
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# if len(contour) < 1:
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# print(f"警告: 轮廓 {i} 为空，跳过")
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# continue
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#
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# # 简化轮廓（可选，减少点数）
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# epsilon = 0.001 * cv2.arcLength(contour, True)
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# approx_contour = cv2.approxPolyDP(contour, epsilon, True)
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#
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# # 转换为列表格式（JSON 兼容）
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# contour_list = []
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# for point in approx_contour.squeeze():
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# if isinstance(point, np.ndarray) and point.size >= 2:
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# contour_list.append(point.tolist())
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# elif isinstance(point, (list, tuple)) and len(point) >= 2:
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# contour_list.append(list(map(int, point[:2]))) # 确保坐标是整数
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# else:
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# print(f"警告: 无效的轮廓点格式: {point}")
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# continue
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#
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# if len(contour_list)> 3:
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# contour_list.append(contour_list[0]) #多加一个点，构成封闭平面
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# instances.append({
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# "instance_id": i + 1,
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# "contour": contour_list,
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# "area": int(cv2.contourArea(contour))
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# })
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#
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# if not instances:
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# print("警告: 未检测到任何有效轮廓")
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# return None, None, vis_output_path, None
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#
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# # 6. 保存原始JSON
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# raw_json_path = os.path.splitext(mask_path)[0] + '.json'
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# try:
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# with open(raw_json_path, 'w') as f:
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# json.dump(instances, f, indent=2)
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# print(f"基础轮廓数据已保存到: {raw_json_path}")
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# except Exception as e:
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# print(f"警告: 无法保存基础JSON文件 - {str(e)}")
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# raw_json_path = None
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# # start = time.perf_counter()
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# # count=0
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# # 7. 处理TIFF坐标转换（仅当提供有效tif_path时）
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# if tif_path and isinstance(tif_path, str) and tif_path.lower().endswith(('.tif', '.tiff')):
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# try:
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# with rasterio.open(tif_path) as src:
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# src_crs = src.crs
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# if not src_crs:
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# print("警告: TIFF文件缺少坐标系信息，跳过坐标转换")
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# json_result_path = raw_json_path
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# return src_crs, json_result_path, vis_output_path, raw_json_path
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#
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# transform = src.transform
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# band_data = src.read(1)
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# height, width = band_data.shape
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#
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# for instance in instances:
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# coord = []
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# for point in instance["contour"]:
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# try:
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# col, row = point[:2] # 提取坐标
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# if not (0 <= col < width and 0 <= row < height):
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# print(f"警告: 坐标({col}, {row})超出图像范围 ({width}x{height})")
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# coord.append(None)
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# continue
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# # count=count+1
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# # 像素坐标 → 投影坐标
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# x, y = transform * (col, row)
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# lon, lat = convert_to_wgs84(x, y, src_crs)
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#
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# # 获取像素值
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# z = float(band_data[int(row), int(col)])
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#
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# if lon is not None and lat is not None:
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# coord.append([lon, lat, z])
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# else:
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# coord.append([None, None, z])
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# except Exception as e:
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# print(f"警告: 处理轮廓点 {point} 时出错 - {str(e)}")
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# coord.append(None)
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#
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# instance["coord"] = [[c for c in coord if c is not None]] # 过滤无效坐标
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#
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# # 保存带坐标的结果
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# json_result_path = os.path.join(
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# os.path.dirname(raw_json_path),
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# "result_" + os.path.basename(raw_json_path)
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# )
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#
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# try:
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# with open(json_result_path, 'w') as f:
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# json.dump(instances, f, indent=2, default=lambda obj: float(obj) if isinstance(obj, np.generic) else str(obj))
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# print(f"完整结果(含坐标)已保存到: {json_result_path}")
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# except Exception as e:
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# print(f"错误: 无法保存结果JSON文件 - {str(e)}")
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# json_result_path = raw_json_path # 回退到原始JSON
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#
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# except Exception as e:
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# print(f"处理TIFF坐标时发生错误: {str(e)}")
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# json_result_path = raw_json_path # 回退到原始JSON
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# else:
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# json_result_path = raw_json_path # 非TIFF文件直接返回原始JSON
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# # end = time.perf_counter()
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# # print(f"总计 {count} 点，总计耗时: {end - start:.6f} 秒")
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#
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# return src_crs, json_result_path, vis_output_path, raw_json_path
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#
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# except Exception as e:
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# print(f"可视化处理过程中发生严重错误: {str(e)}")
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# return None, None, vis_output_path, None # 确保返回路径变量
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import json
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import os
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import numpy as np
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import cv2
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import rasterio
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from pyproj import Transformer, CRS
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from geographiclib.geodesic import Geodesic # 用于精确计算球面多边形面积
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from pyproj import Geod
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def calculate_polygon_area(coords):
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"""计算球面多边形面积（单位：平方米）"""
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if len(coords) < 3:
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return 0.0
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try:
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geod = Geod(ellps="WGS84") # WGS84 椭球体
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# 注意: pyproj.Geod 要求坐标顺序为 (lon, lat)
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lons, lats = zip(*[(lon, lat) for lon, lat in coords])
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area, _ = geod.polygon_area_perimeter(lons, lats)
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return abs(area) # 返回绝对值（平方米）
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except Exception as e:
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print(f"面积计算异常: {str(e)}")
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return 0.0
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def convert_to_wgs84(x, y, src_crs):
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"""将投影坐标转换为WGS84经纬度，增加错误处理"""
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try:
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if not (isinstance(x, (int, float)) and isinstance(y, (int, float))):
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print(f"警告: 坐标值无效 (x={x}, y={y})")
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return None, None
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transformer = Transformer.from_crs(src_crs, "EPSG:4326", always_xy=True)
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lon, lat = transformer.transform(x, y)
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if not np.isfinite(lon) or not np.isfinite(lat):
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print(f"警告: 坐标转换结果无效 (lon={lon}, lat={lat})")
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return None, None
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return lon, lat
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except Exception as e:
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print(f"坐标转换异常: {str(e)}")
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return None, None
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#
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# def visualize_pil_segmentation_mask_opencv(mask_path, tif_path, output_path=None, colormap=cv2.COLORMAP_VIRIDIS, save=True):
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# """
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# 修改版：过滤面积 < 10㎡ 的轮廓，并更新可视化结果
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# """
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# src_crs = None
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# json_result_path = None
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# vis_output_path = None
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# raw_json_path = None
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#
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# try:
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# # 1. 读取掩码文件
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# if not os.path.exists(mask_path):
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# raise FileNotFoundError(f"掩码文件不存在: {mask_path}")
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#
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# mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
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# if mask is None:
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# raise ValueError(f"无法读取掩码文件(可能已损坏): {mask_path}")
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#
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# # 2. 初始化可视化图像（后续会更新）
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# mask_vis = cv2.applyColorMap(mask, colormap)
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# contours, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# cv2.drawContours(mask_vis, contours, -1, (0, 0, 255), 1)
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#
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# # 3. 处理轮廓数据
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# valid_instances = []
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# filtered_contours = [] # 存储过滤后的轮廓（用于可视化）
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#
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# for i, contour in enumerate(contours):
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# if len(contour) < 3: # 至少需要3个点构成多边形
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# print(f"警告: 轮廓 {i} 点数不足，跳过")
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# continue
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#
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# # 简化轮廓
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# epsilon = 0.001 * cv2.arcLength(contour, True)
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# approx_contour = cv2.approxPolyDP(contour, epsilon, True)
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#
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# # 转换为列表格式
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# contour_list = []
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# for point in approx_contour.squeeze():
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# if isinstance(point, np.ndarray) and point.size >= 2:
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# contour_list.append(point.tolist())
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# elif isinstance(point, (list, tuple)) and len(point) >= 2:
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# contour_list.append(list(map(int, point[:2])))
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# else:
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# print(f"警告: 无效的轮廓点格式: {point}")
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# continue
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#
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# if len(contour_list) < 3:
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# print(f"警告: 轮廓 {i} 简化后点数不足，跳过")
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# continue
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#
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# # 临时存储当前轮廓（后续可能被过滤）
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# temp_instance = {
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# "instance_id": i + 1,
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# "contour": contour_list,
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# "area_pixels": int(cv2.contourArea(contour)),
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# "coord": None # 后续填充
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# }
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#
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# # 4. 处理TIFF坐标转换（如果提供）
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# if tif_path and tif_path.lower().endswith(('.tif', '.tiff')):
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# try:
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# with rasterio.open(tif_path) as src:
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# src_crs = src.crs
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# if not src_crs:
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# print("警告: TIFF文件缺少坐标系信息，跳过坐标转换")
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# valid_instances.append(temp_instance)
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# filtered_contours.append(contour)
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# continue
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#
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# transform = src.transform
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# height, width = src.read(1).shape
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# coords = []
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#
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# # 修复后的代码
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# valid_coords = []
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# for point in contour_list:
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# col, row = point[:2]
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# # 检查坐标是否在图像范围内
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# if not (0 <= col < width and 0 <= row < height):
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# print(f"警告: 坐标({col}, {row})超出图像范围")
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# continue # 跳过这个点但不中断整个轮廓
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#
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# try:
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# # 转换为投影坐标
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# x, y = transform * (col, row)
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# # 转换为经纬度
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# lon, lat = convert_to_wgs84(x, y, src_crs)
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# if lon is not None and lat is not None:
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# valid_coords.append([lon, lat])
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# except Exception as e:
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# print(f"坐标处理错误: {str(e)}")
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# continue
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#
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# # 过滤无效坐标
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# valid_coords = [c for c in coords if c is not None]
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# if len(valid_coords) < 3:
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# print(f"警告: 轮廓 {i} 有效坐标不足，跳过")
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# continue
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#
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# # 计算面积（平方米）
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# area_m2 = calculate_polygon_area(valid_coords)
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# temp_instance["coord"] = valid_coords
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# temp_instance["area_m2"] = area_m2
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#
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# # 过滤面积 < 10㎡ 的轮廓
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# if area_m2 >= 10:
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# valid_instances.append(temp_instance)
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# filtered_contours.append(contour) # 保留轮廓用于可视化
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# else:
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# print(f"过滤小面积轮廓: ID={i+1}, 面积={area_m2:.2f}㎡")
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#
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# except Exception as e:
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# print(f"处理TIFF坐标时发生错误: {str(e)}")
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# valid_instances.append(temp_instance) # 保留原始数据
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# filtered_contours.append(contour)
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# else:
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# # 非TIFF文件：直接保留轮廓
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# valid_instances.append(temp_instance)
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# filtered_contours.append(contour)
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#
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# # 5. 更新可视化图像（仅绘制保留的轮廓）
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# mask_vis_filtered = cv2.applyColorMap(mask, colormap)
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# if filtered_contours:
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# cv2.drawContours(mask_vis_filtered, filtered_contours, -1, (0, 0, 255), 1)
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#
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# # 6. 保存可视化结果
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# if output_path is None:
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# vis_output_path = os.path.splitext(mask_path)[0] + '_vis_cv2.png'
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# else:
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# vis_output_path = output_path
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#
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# if save:
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# if not cv2.imwrite(vis_output_path, mask_vis_filtered):
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# raise IOError(f"无法保存可视化结果到: {vis_output_path}")
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# print(f"可视化结果已保存到: {vis_output_path}")
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#
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# # 7. 保存JSON数据
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# raw_json_path = os.path.splitext(mask_path)[0] + '.json'
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# with open(raw_json_path, 'w') as f:
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# json.dump(valid_instances, f, indent=2)
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# print(f"基础轮廓数据已保存到: {raw_json_path}")
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#
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# # 8. 返回结果（src_crs 可能为None）
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# return src_crs, raw_json_path, vis_output_path, raw_json_path
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#
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# except Exception as e:
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# print(f"可视化处理过程中发生严重错误: {str(e)}")
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# return None, None, vis_output_path, None
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def visualize_pil_segmentation_mask_opencv(mask_path, tif_path, output_path=None, colormap=cv2.COLORMAP_VIRIDIS, save=True):
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"""
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修改版：过滤面积 < 10㎡ 的轮廓，并更新可视化结果
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"""
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src_crs = None
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json_result_path = None
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vis_output_path = None
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raw_json_path = None
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try:
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# 1. 读取掩码文件（同上）
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if not os.path.exists(mask_path):
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raise FileNotFoundError(f"掩码文件不存在: {mask_path}")
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mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
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if mask is None:
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raise ValueError(f"无法读取掩码文件(可能已损坏): {mask_path}")
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# 2. 初始化可视化图像
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mask_vis = cv2.applyColorMap(mask, colormap)
|
||
contours, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
cv2.drawContours(mask_vis, contours, -1, (0, 0, 255), 1)
|
||
|
||
# 3. 处理轮廓数据
|
||
valid_instances = []
|
||
filtered_contours = []
|
||
|
||
for i, contour in enumerate(contours):
|
||
if len(contour) < 3:
|
||
print(f"警告: 轮廓 {i} 点数不足，跳过")
|
||
continue
|
||
|
||
# 简化轮廓
|
||
epsilon = 0.001 * cv2.arcLength(contour, True)
|
||
approx_contour = cv2.approxPolyDP(contour, epsilon, True)
|
||
|
||
# 转换为列表格式并确保闭合
|
||
contour_list = []
|
||
for point in approx_contour.squeeze():
|
||
if isinstance(point, np.ndarray) and point.size >= 2:
|
||
contour_list.append(point.tolist())
|
||
elif isinstance(point, (list, tuple)) and len(point) >= 2:
|
||
contour_list.append(list(map(float, point[:2]))) # 使用float保持精度
|
||
|
||
if len(contour_list) < 3:
|
||
print(f"警告: 轮廓 {i} 简化后点数不足，跳过")
|
||
continue
|
||
|
||
# 闭合多边形
|
||
# if contour_list[0] != contour_list[-1]:
|
||
# contour_list.append(contour_list[0])
|
||
contour_list.append(contour_list[0])
|
||
|
||
temp_instance = {
|
||
"instance_id": i + 1,
|
||
"contour": contour_list,
|
||
"area_pixels": int(cv2.contourArea(contour)),
|
||
"coord": None
|
||
}
|
||
|
||
# 4. 处理TIFF坐标转换
|
||
if tif_path and tif_path.lower().endswith(('.tif', '.tiff')):
|
||
try:
|
||
with rasterio.open(tif_path) as src:
|
||
src_crs = src.crs
|
||
if not src_crs:
|
||
print("警告: TIFF文件缺少坐标系信息")
|
||
valid_instances.append(temp_instance)
|
||
filtered_contours.append(contour)
|
||
continue
|
||
|
||
transform = src.transform
|
||
height, width = src.read(1).shape
|
||
all_coords = []
|
||
valid_coords = []
|
||
|
||
for point in contour_list[:-1]: # 不处理重复的闭合点
|
||
col, row = point[:2]
|
||
if not (0 <= col < width and 0 <= row < height):
|
||
print(f"警告: 坐标({col}, {row})超出图像范围")
|
||
continue
|
||
|
||
try:
|
||
x, y = transform * (col, row)
|
||
lon, lat = convert_to_wgs84(x, y, src_crs)
|
||
if lon is not None and lat is not None:
|
||
all_coords.append([lon, lat])
|
||
except Exception as e:
|
||
print(f"坐标处理错误: {str(e)}")
|
||
continue
|
||
|
||
# 必须至少有3个有效坐标才能构成多边形
|
||
if len(all_coords) >= 3:
|
||
# 计算面积（平方米）
|
||
area_m2 = calculate_polygon_area(all_coords)
|
||
# # 闭合多边形
|
||
all_coords.append(all_coords[0])
|
||
# 适配geojson的三层格式
|
||
temp_instance["coord"] = [all_coords]
|
||
temp_instance["area_m2"] = area_m2
|
||
|
||
# 过滤面积 < 10㎡ 的轮廓
|
||
if area_m2 >= 10:
|
||
valid_instances.append(temp_instance)
|
||
filtered_contours.append(contour)
|
||
else:
|
||
print(f"过滤小面积轮廓: ID={i+1}, 面积={area_m2:.2f}㎡")
|
||
else:
|
||
print(f"警告: 轮廓 {i} 有效坐标不足3个")
|
||
|
||
except Exception as e:
|
||
print(f"处理TIFF坐标时发生错误: {str(e)}")
|
||
valid_instances.append(temp_instance)
|
||
filtered_contours.append(contour)
|
||
else:
|
||
valid_instances.append(temp_instance)
|
||
filtered_contours.append(contour)
|
||
|
||
# 5. 更新可视化图像
|
||
mask_vis_filtered = cv2.applyColorMap(mask, colormap)
|
||
if filtered_contours:
|
||
cv2.drawContours(mask_vis_filtered, filtered_contours, -1, (0, 0, 255), 1)
|
||
|
||
# ...（剩余的保存和返回逻辑保持不变）
|
||
|
||
# 6. 保存可视化结果
|
||
if output_path is None:
|
||
vis_output_path = os.path.splitext(mask_path)[0] + '_vis_cv2.png'
|
||
else:
|
||
vis_output_path = output_path
|
||
|
||
if save:
|
||
if not cv2.imwrite(vis_output_path, mask_vis_filtered):
|
||
raise IOError(f"无法保存可视化结果到: {vis_output_path}")
|
||
print(f"可视化结果已保存到: {vis_output_path}")
|
||
|
||
# 7. 保存JSON数据
|
||
raw_json_path = os.path.splitext(mask_path)[0] + '.json'
|
||
with open(raw_json_path, 'w') as f:
|
||
json.dump(valid_instances, f, indent=2)
|
||
print(f"基础轮廓数据已保存到: {raw_json_path}")
|
||
|
||
# 8. 返回结果（src_crs 可能为None）
|
||
return src_crs, raw_json_path, vis_output_path, raw_json_path
|
||
|
||
except Exception as e:
|
||
print(f"可视化处理过程中发生严重错误: {str(e)}")
|
||
return None, None, vis_output_path, None |