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test_kitti_pose.py
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test_kitti_pose.py
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from __future__ import division
import os
import math
import scipy.misc
import tensorflow as tf
import numpy as np
from glob import glob
from SfMLearner import SfMLearner
from kitti_eval.pose_evaluation_utils import dump_pose_seq_TUM
flags = tf.app.flags
flags.DEFINE_integer("batch_size", 1, "The size of of a sample batch")
flags.DEFINE_integer("img_height", 128, "Image height")
flags.DEFINE_integer("img_width", 416, "Image width")
flags.DEFINE_integer("seq_length", 5, "Sequence length for each example")
flags.DEFINE_integer("test_seq", 9, "Sequence id to test")
flags.DEFINE_string("dataset_dir", None, "Dataset directory")
flags.DEFINE_string("output_dir", None, "Output directory")
flags.DEFINE_string("ckpt_file", None, "checkpoint file")
FLAGS = flags.FLAGS
def load_image_sequence(dataset_dir,
frames,
tgt_idx,
seq_length,
img_height,
img_width):
half_offset = int((seq_length - 1)/2)
for o in range(-half_offset, half_offset+1):
curr_idx = tgt_idx + o
curr_drive, curr_frame_id = frames[curr_idx].split(' ')
img_file = os.path.join(
dataset_dir, 'sequences', '%s/image_2/%s.png' % (curr_drive, curr_frame_id))
curr_img = scipy.misc.imread(img_file)
curr_img = scipy.misc.imresize(curr_img, (img_height, img_width))
if o == -half_offset:
image_seq = curr_img
else:
image_seq = np.hstack((image_seq, curr_img))
return image_seq
def is_valid_sample(frames, tgt_idx, seq_length):
N = len(frames)
tgt_drive, _ = frames[tgt_idx].split(' ')
max_src_offset = int((seq_length - 1)/2)
min_src_idx = tgt_idx - max_src_offset
max_src_idx = tgt_idx + max_src_offset
if min_src_idx < 0 or max_src_idx >= N:
return False
# TODO: unnecessary to check if the drives match
min_src_drive, _ = frames[min_src_idx].split(' ')
max_src_drive, _ = frames[max_src_idx].split(' ')
if tgt_drive == min_src_drive and tgt_drive == max_src_drive:
return True
return False
def main():
sfm = SfMLearner()
sfm.setup_inference(FLAGS.img_height,
FLAGS.img_width,
'pose',
FLAGS.seq_length)
saver = tf.train.Saver([var for var in tf.trainable_variables()])
if not os.path.isdir(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
seq_dir = os.path.join(FLAGS.dataset_dir, 'sequences', '%.2d' % FLAGS.test_seq)
img_dir = os.path.join(seq_dir, 'image_2')
N = len(glob(img_dir + '/*.png'))
test_frames = ['%.2d %.6d' % (FLAGS.test_seq, n) for n in range(N)]
with open(FLAGS.dataset_dir + 'sequences/%.2d/times.txt' % FLAGS.test_seq, 'r') as f:
times = f.readlines()
times = np.array([float(s[:-1]) for s in times])
max_src_offset = (FLAGS.seq_length - 1)//2
with tf.Session() as sess:
saver.restore(sess, FLAGS.ckpt_file)
for tgt_idx in range(N):
if not is_valid_sample(test_frames, tgt_idx, FLAGS.seq_length):
continue
if tgt_idx % 100 == 0:
print('Progress: %d/%d' % (tgt_idx, N))
# TODO: currently assuming batch_size = 1
image_seq = load_image_sequence(FLAGS.dataset_dir,
test_frames,
tgt_idx,
FLAGS.seq_length,
FLAGS.img_height,
FLAGS.img_width)
pred = sfm.inference(image_seq[None, :, :, :], sess, mode='pose')
pred_poses = pred['pose'][0]
# Insert the target pose [0, 0, 0, 0, 0, 0]
pred_poses = np.insert(pred_poses, max_src_offset, np.zeros((1,6)), axis=0)
curr_times = times[tgt_idx - max_src_offset:tgt_idx + max_src_offset + 1]
out_file = FLAGS.output_dir + '%.6d.txt' % (tgt_idx - max_src_offset)
dump_pose_seq_TUM(out_file, pred_poses, curr_times)
main()