import glob
import os
import sys
import cv2
import matplotlib.pyplot as plt
import numpy as np
def main():
input_depth_dir = os.path.expanduser(
'~/Kitti/depth/val_selection_cropped/velodyne_raw')
images_to_use = sorted(glob.glob(input_depth_dir + '/*'))
# Process depth images
num_images = len(images_to_use)
all_sparsities = np.zeros(num_images)
for i in range(num_images):
# Print progress
sys.stdout.write('\rProcessing index {} / {}'.format(i, num_images - 1))
sys.stdout.flush()
depth_image_path = images_to_use[i]
# Load depth from image
depth_image = cv2.imread(depth_image_path, cv2.IMREAD_ANYDEPTH)
# Divide by 256
depth_map = depth_image / 256.0
num_valid_pixels = len(np.where(depth_map > 0.0)[0])
num_pixels = depth_image.shape[0] * depth_image.shape[1]
sparsity = num_valid_pixels / (num_pixels * 2/3)
all_sparsities[i] = sparsity
print('')
print('Sparsity')
print('Min: ', np.amin(all_sparsities))
print('Max: ', np.amax(all_sparsities))
print('Mean: ', np.mean(all_sparsities))
print('Median: ', np.median(all_sparsities))
plt.hist(all_sparsities, bins=20)
plt.show()
if __name__ == '__main__':
main()
