Generation_depth_Img.py

import numpy as np
import time
import os
import utilities
import cv2
from tqdm import tqdm
import math
import tifffile


"""
    ** Code for generation of depth image
"""


# Global varianbles
path_reference = "/run/user/1001/gvfs/smb-share:server=141.58.125.9,share=s-platte/ShuFangwen/results/lvl4_nadir/validation_set/4_colorlabel"  # where provide a list of visible images

path_Imgs = "/run/user/1001/gvfs/smb-share:server=141.58.125.9,share=s-platte/ShuFangwen/data/Nadir_level3_level4_level5/ImgTexture/Level_4/"
path_Ori = "/run/user/1001/gvfs/smb-share:server=141.58.125.9,share=s-platte/ShuFangwen/data/Nadir_level3_level4_level5/Ori/Level_4/"

file_XYZ = "/run/user/1001/gvfs/smb-share:server=141.58.125.9,share=s-platte/ShuFangwen/data/Data_11_1_19_5cm/val_xyz_y.txt"

save_path = "/data/fangwen/depth_img_lvl4_nadir/validation_set"
utilities.make_if_not_exists(save_path)


# Reading data
print("read points cloud from txt file...")
start_time = time.time()
pt_xyz = np.loadtxt(file_XYZ)[:,:3]
duration = time.time() - start_time
print("which needs {0}s\n".format(duration))

# Processing
img_list = os.listdir(path_reference)  # length = 416, as for 5cm LiDAR pointcloud

for img_name in img_list:

    ori_name = img_name.replace("tif", "ori")
    # img_name = "CF014332.tif"
    # ori_name = "CF014332.ori"

    # get interior and exterior orientations
    f, pixel_size, img_width, img_height, K, R, Xc, Yc, Zc = utilities.get_INTER_and_EXTER_Orientations(os.path.join(path_Ori, ori_name))

    # without frustum culling and hidden-point-removal
    px, py = utilities.pointcloud2pixelcoord(R, K, Xc, Yc, Zc, pt_xyz)

    # generation depth image, without interpolation
    img_depth = np.zeros((img_height, img_width, 1), dtype=np.float32)  # 32 bit

    for i in tqdm(range(0, px.shape[1])):
        if img_width > px[0, i] > 0 and img_height > py[0, i] > 0:

            depth = math.sqrt( (Xc - pt_xyz[i, 0])**2 + (Yc - pt_xyz[i, 1])**2 + (Zc - pt_xyz[i, 2])**2 )

            if img_depth[int(py[0, i]), int(px[0, i])] == 0:
                img_depth[int(py[0, i]), int(px[0, i])] = depth

            elif img_depth[int(py[0, i]), int(px[0, i])] != 0 and \
                    depth <= img_depth[int(py[0, i]), int(px[0, i])]:  # only save the closest distance in that pixel
                img_depth[int(py[0, i]), int(px[0, i])] = depth

    # set "nan" to pixel where no point projected
    # mask = (img_depth == 0)
    # img_depth[mask[:, :] == True] = np.nan
    #
    # where_are_NaNs = np.isnan(img_depth)
    # img_depth[where_are_NaNs] = 0.0

    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15))
    closing = cv2.morphologyEx(img_depth, cv2.MORPH_CLOSE, kernel)

    tifffile.imsave(os.path.join(save_path, img_name), closing)

    # test = tifffile.imread(os.path.join(save_path, img_name+'_dist.tif'))