image_rows = restored_image.shape[0]
image_cols = restored_image.shape[1]
pixel_size = sensor_width / image_cols # unit: mm/px
pixel_size = pixel_size / 1000 # unit: m/px
end_time = time.time()
print("--- %s seconds ---" % (time.time() - start_time))
read_time = end_time - start_time
else:
print('Read EOP - ' + file)
print('Latitude | Longitude | Height | Omega | Phi | Kappa')
eo = readEO(file_path)
eo = convertCoordinateSystem(eo)
R = Rot3D(eo)
# 4. Extract a projected boundary of the image
bbox = boundary(restored_image, eo, R, ground_height, pixel_size, focal_length)
print("--- %s seconds ---" % (time.time() - start_time))
# 5. Compute GSD & Boundary size
# GSD
gsd = (pixel_size * (eo[2] - ground_height)) / focal_length # unit: m/px
# Boundary size
boundary_cols = int((bbox[1, 0] - bbox[0, 0]) / gsd)
boundary_rows = int((bbox[3, 0] - bbox[2, 0]) / gsd)
# 6. Compute coordinates of the projected boundary
import numpy as np
from EoData import readEO, Rot3D, latlon2tmcentral, tmcentral2latlon
from Boundary import pcs2ccs, projection
bbox_px = np.array([[79, 159, 159, 79],
[2719, 2719, 2639, 2639]])
eo_path = '../Data/DJI_0386.txt'
rows = 3000
cols = 4000
sensor_width = 6.3 # unit: mm
pixel_size = sensor_width / cols # mm/px
focal_length = 4.7 # mm
ground_height = 65 # unit: m
eo = readEO(eo_path)
eo = latlon2tmcentral(eo)
R_GC = Rot3D(eo)
R_CG = R_GC.transpose()
# Convert pixel coordinate system to camera coordinate system
bbox_camera = pcs2ccs(bbox_px, rows, cols,
pixel_size, focal_length) # shape: 3 x bbox_point
# Project camera coordinates to ground coordinates
proj_coordinates = projection(bbox_camera, eo, R_CG, ground_height)
bbox_ground1 = tmcentral2latlon(proj_coordinates[:, 0])
bbox_ground2 = tmcentral2latlon(proj_coordinates[:, 1])
bbox_ground3 = tmcentral2latlon(proj_coordinates[:, 2])
bbox_ground4 = tmcentral2latlon(proj_coordinates[:, 3])