# Mostly based on the code written by Clement Godard:
# https://github.com/mrharicot/monodepth/blob/master/utils/evaluation_utils.py
import numpy as np
# import pandas as pd
from path import Path
from scipy.misc import imread
from tqdm import tqdm
class test_framework_KITTI(object):
def __init__(self, root, sequence_set, seq_length=3, step=1):
self.root = root
self.img_files, self.poses, self.sample_indices = read_scene_data(self.root, sequence_set, seq_length, step)
def generator(self):
for img_list, pose_list, sample_list in zip(self.img_files, self.poses, self.sample_indices):
for snippet_indices in sample_list:
imgs = [imread(img_list[i]).astype(np.float32) for i in snippet_indices]
poses = np.stack(pose_list[i] for i in snippet_indices)
first_pose = poses[0]
poses[:,:,-1] -= first_pose[:,-1]
compensated_poses = np.linalg.inv(first_pose[:,:3]) @ poses
yield {'imgs': imgs,
'path': img_list[0],
'poses': compensated_poses
}
def __iter__(self):
return self.generator()
def __len__(self):
return sum(len(imgs) for imgs in self.img_files)
def read_scene_data(data_root, sequence_set, seq_length=3, step=1):
data_root = Path(data_root)
im_sequences = []
poses_sequences = []
indices_sequences = []
demi_length = (seq_length - 1) // 2
shift_range = np.array([step*i for i in range(-demi_length, demi_length + 1)]).reshape(1, -1)
sequences = set()
for seq in sequence_set:
corresponding_dirs = set((data_root/'sequences').dirs(seq))
sequences = sequences | corresponding_dirs
print('getting test metadata for theses sequences : {}'.format(sequences))
for sequence in tqdm(sequences):
poses = np.genfromtxt(data_root/'poses'/'{}.txt'.format(sequence.name)).astype(np.float64).reshape(-1, 3, 4)
imgs = sorted((sequence/'image_2').files('*.png'))
# construct 5-snippet sequences
tgt_indices = np.arange(demi_length, len(imgs) - demi_length).reshape(-1, 1)
snippet_indices = shift_range + tgt_indices
im_sequences.append(imgs)
poses_sequences.append(poses)
indices_sequences.append(snippet_indices)
return im_sequences, poses_sequences, indices_sequences
