325 lines
10 KiB
Python
325 lines
10 KiB
Python
import numpy as np
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import time
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import math
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import os
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import pyproj
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from middleware.entity.camera_para import Camera_Para
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from touying.ImageReproject_python.img_types import Point, CamParas
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from touying.ImageReproject_python.image_reproject import ir_target_positioning, ir_red_line_reproject, \
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ir_img_reproject, ir_target_positioning_new_list
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os.environ['GDAL_DISABLE_EXCEPTIONS'] = 'NO' # 显式启用异常处理
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# 定义常量
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pi = math.pi
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def target_positioning(u_pixel, v_pixel, gimbal_yawm, gimbal_pitch, gimbal_roll, height):
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"""测试目标定位功能"""
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# 输入参数
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u = 1520 # 示例图像u坐标
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v = 2512 # 示例图像v坐标
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interval = 0.05 # 高程迭代间隔
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gimbal_yaw = 66.40 * pi / 180 # 示例云台偏航角
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gimbal_pitch = -90 * pi / 180 # 示例云台俯仰角
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gimbal_roll = 0 * pi / 180 # 示例云台横滚角
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h = 590.834 # 示例相机高度
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l = (103 + 59 / 60.0 + 26.07 / 3600.0) * pi / 180 # 示例相机经度
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b = (30 + 45 / 60.0 + 46.48 / 3600.0) * pi / 180 # 示例相机纬度
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img_width = 4032 # 示例图像宽度
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img_height = 3024 # 示例图像高度
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# 相机参数
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cam_paras = CamParas(
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fx=2795.68899,
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fy=2795.68899
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)
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# DEM文件路径
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# dem_file = "/home/GW/ImageReproject_python/dem.tif" # 确保此文件存在
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dem_file = r"D:\project\test-touying\ImageReproject_python\dem.tif" # 确保此文件存在
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# 测量执行时间
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start_time = time.time()
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# 执行目标定位
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x, y, z = ir_target_positioning(
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u, v, interval, gimbal_pitch, gimbal_yaw, gimbal_roll,
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h, l, b, cam_paras, img_width, img_height, dem_file
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)
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# 计算执行时间
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elapsed_time = (time.time() - start_time) * 1000 # 转换为毫秒
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# 输出结果
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print(f"目标定位耗时: {elapsed_time:.2f} ms")
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print(f"目标位置: 经度={x}°, 纬度={y}°, 高度={z:.2f}m")
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def read_txt(filename):
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"""从文本文件读取点坐标"""
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points = []
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try:
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with open(filename, 'r') as f:
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for line in f:
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parts = line.strip().split()
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if len(parts) >= 4: # 标签 x y z
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label = parts[0]
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x = float(parts[1])
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y = float(parts[2])
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z = float(parts[3])
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point = Point(x, y, z)
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points.append(point)
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except Exception as e:
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print(f"读取文件失败: {filename}, 错误: {e}")
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return points
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def red_line_reproject(gimbal_yaw, gimbal_pitch, gimbal_roll, height, cam_longtitude, cam_altitude, img_width,
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img_height, points,camera_para:Camera_Para):
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"""测试红线重投影功能"""
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# 输入参数
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# print(f"gimbal_yaw {gimbal_yaw}")
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gimbal_yaw = gimbal_yaw * pi / 180 # 示例云台偏航角
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# print(f"gimbal_pitch {gimbal_pitch}")
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gimbal_pitch = gimbal_pitch * pi / 180 # 示例云台俯仰角
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# print(f"gimbal_roll {gimbal_roll}")
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gimbal_roll = gimbal_roll * pi / 180 # 示例云台横滚角
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# print(f"height {height}")
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h = height # 示例相机高度
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l = cam_longtitude * pi / 180
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b = cam_altitude * pi / 180
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# img_width = 4032 # 示例图像宽度
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# img_height = 3024 # 示例图像高度
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# # # 相机参数；校友之家 4D
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# cam_paras = CamParas(
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# fx=3659.69045,
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# fy=3659.69045,
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# cx=5280/2-40,
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# cy=3956/2-20,
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# )
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# # # 相机参数；哈密4TD
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# cam_paras = CamParas(
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# fx=2795.68899,
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# fy=2795.68899,
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# cx=4032/2,
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# cy=3024/2,
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# )
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cam_paras = CamParas(
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fx=camera_para.fx,
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fy=camera_para.fy,
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cx=camera_para.cx/2,
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cy=camera_para.cy/2,
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)
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# 直接使用摄像头拍照的宽度
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camera_img_width=camera_para.cx
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# 直接使用摄像头拍照的高度
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camera_img_height=camera_para.cy
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# 图像文件路径
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# img_file = r"D:\project\test-touying\ImageReproject_python\test_image.jpeg" # 确保此文件存在
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# img_save_file = r"D:\project\test-touying\ImageReproject_python\test_image_reprojected.jpg"
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# # 读取点坐标
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# points = [] # 如果有点坐标文件，可以使用read_txt函数读取
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# points = read_txt(r"D:\project\test-touying\ImageReproject_python\points.txt")
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# 测量执行时间
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start_time = time.time()
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# # 输入参数
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# # gimbal_yaw = 66.40 * pi / 180 # 示例云台偏航角
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# gimbal_pitch = -90 * pi / 180 # 示例云台俯仰角
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# gimbal_roll = 0 * pi / 180 # 示例云台横滚角
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# h = 590.834 # 示例相机高度
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# l = (103 + 59 / 60.0 + 26.07 / 3600.0) * pi / 180 # 示例相机经度
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# b = (30 + 45 / 60.0 + 46.48 / 3600.0) * pi / 180 # 示例相机纬度
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# img_width = 4032 # 示例图像宽度
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# img_height = 3024 # 示例图像高度
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# 执行红线重投影
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# print(f"gimbal_pitch {gimbal_pitch}")
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# print(f"gimbal_yaw {gimbal_yaw}")
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# print(f"gimbal_roll {gimbal_roll}")
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# print(f"h {h}")
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# print(f"l {l}")
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# print(f"b {b}")
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# print(f"cam_paras {cam_paras}")
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# print(f"img_width {img_width}")
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# print(f"img_height {img_height}")
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# print(f"points {points}")
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# utm_points = []
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utm_points=convert_points_to_utm(points)
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pixel_list = ir_red_line_reproject(
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gimbal_pitch, gimbal_yaw, gimbal_roll, h, l, b,
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cam_paras, img_width, img_height,camera_img_width,camera_img_height, utm_points
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)
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# 计算执行时间
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elapsed_time = (time.time() - start_time) * 1000 # 转换为毫秒
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# 输出结果
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# print(f"红线重投影耗时: {elapsed_time:.2f} ms")
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return pixel_list
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#
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# def convert_points_to_utm(points, utm_points):
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# # 创建UTM投影
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# wgs84 = pyproj.CRS('EPSG:4326')
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#
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# # 计算所有点的中心点
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# sum_lat = 0.0
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# sum_lon = 0.0
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# count = len(points)
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#
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# for point in points:
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# sum_lat += point.X # 纬度
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# sum_lon += point.Y # 经度
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#
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# if count > 0:
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# center_lat = sum_lat / count
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# center_lon = sum_lon / count
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#
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# # 计算中心点对应的UTM带号
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# utm_zone = int(center_lon / 6) + 31
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#
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# # 使用计算出的带号创建UTM投影
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# utm_proj = pyproj.CRS(f'EPSG:{32600 + utm_zone}')
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# else:
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# # 如果没有点，使用默认带号
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# utm_proj = pyproj.CRS(f'EPSG:{32600 + 48}')
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#
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# transformer = pyproj.Transformer.from_crs(wgs84, utm_proj, always_xy=True)
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# # 将点坐标转换为UTM坐标
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# for point in points:
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# # 将经纬度转换为UTM坐标
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# x, y = transformer.transform(point.Y, point.X)
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# utm_point = Point(x, y, point.Z,point.id)
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# utm_points.append(utm_point)
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def convert_points_to_utm(points):
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# 创建UTM投影
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wgs84 = pyproj.CRS('EPSG:4326')
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# 计算所有点的中心点
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sum_lat = 0.0
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sum_lon = 0.0
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count = len(points)
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for point in points:
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sum_lat += point.X # 纬度
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sum_lon += point.Y # 经度
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if count > 0:
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center_lat = sum_lat / count
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center_lon = sum_lon / count
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# 计算中心点对应的UTM带号
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utm_zone = int(center_lon / 6) + 31
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# 使用计算出的带号创建UTM投影
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utm_proj = pyproj.CRS(f'EPSG:{32600 + utm_zone}')
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else:
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# 如果没有点，使用默认带号
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utm_proj = pyproj.CRS(f'EPSG:{32600 + 48}')
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transformer = pyproj.Transformer.from_crs(wgs84, utm_proj, always_xy=True)
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utm_points = []
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# 将点坐标转换为UTM坐标
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for point in points:
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# 将经纬度转换为UTM坐标
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x, y = transformer.transform(point.Y, point.X)
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utm_point = Point(x, y, point.Z,point.id) ############
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utm_points.append(utm_point)
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return utm_points
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def img_reproject():
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"""测试图像重投影功能"""
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# 输入参数
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obj_longitude = 103.990109 # 目标点经度(度)
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obj_latitude = 30.762915 # 目标点纬度(度)
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obj_height = 481.64 # 目标点高度
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gimbal_yaw = 66.40 * pi / 180 # 示例云台偏航角
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gimbal_pitch = -90 * pi / 180 # 示例云台俯仰角
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gimbal_roll = 0 * pi / 180 # 示例云台横滚角
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h = 590.834 # 示例相机高度
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l = (103 + 59 / 60.0 + 26.07 / 3600.0) * pi / 180 # 示例相机经度
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b = (30 + 45 / 60.0 + 46.48 / 3600.0) * pi / 180 # 示例相机纬度
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img_width = 4032 # 示例图像宽度
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img_height = 3024 # 示例图像高度
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# 相机参数
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cam_paras = CamParas(
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fx=2795.68899,
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fy=2795.68899
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)
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# 测量执行时间
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start_time = time.time()
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# 执行图像重投影
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pixel_x, pixel_y = ir_img_reproject(
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gimbal_pitch, gimbal_yaw, gimbal_roll, h, l, b,
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cam_paras, img_width, img_height, obj_longitude, obj_latitude, obj_height
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)
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# 计算执行时间
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elapsed_time = (time.time() - start_time) * 1000 # 转换为毫秒
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# 输出结果
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print(f"图像重投影耗时: {elapsed_time:.2f} ms")
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print(f"目标点在图像上的坐标: x={pixel_x:.2f}, y={pixel_y:.2f}")
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# 通过相机姿态、经纬度、相机目标中心点高程、图片目标坐标数组，计算目标经纬度
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def cal_canv_location_by_osd( us, vs, gimbal_pitch, gimbal_yaw, gimbal_roll,
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height,l, b, img_width, img_height, heights):
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"""测试目标定位功能"""
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# 输入参数
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# u = 1520 # 示例图像u坐标
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# v = 2512 # 示例图像v坐标
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# interval = 0.05 # 高程迭代间隔
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gimbal_yaw = gimbal_yaw * pi / 180 # 示例云台偏航角
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gimbal_pitch = gimbal_pitch * pi / 180 # 示例云台俯仰角
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gimbal_roll = gimbal_roll * pi / 180 # 示例云台横滚角
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l = l * pi / 180 # 示例相机经度
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b = b * pi / 180 # 示例相机纬度
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img_width = img_width # 示例图像宽度
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img_height = img_height # 示例图像高度
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# 相机参数；哈密4TD
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cam_paras = CamParas(
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fx=2795.68899,
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fy=2795.68899,
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cx=4032/2-40,
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cy=3024/2-20,
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)
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# 测量执行时间
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start_time = time.time()
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# 执行目标定位
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results = ir_target_positioning_new_list(
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us, vs, gimbal_pitch, gimbal_yaw, gimbal_roll,
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height,l, b, cam_paras, img_width, img_height, heights
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)
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# 计算执行时间
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elapsed_time = (time.time() - start_time) * 1000 # 转换为毫秒
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# 输出结果
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print(f"像素坐标反算经纬度耗时: {elapsed_time:.2f} ms")
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return results #坐标集合，应该是二维数组
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