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interpolation.py
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import numpy as np
import os
import glob
import motmetrics as mm
import argparse
from util.evaluation import Evaluator
import pdb
def mkdir_if_missing(d):
if not os.path.exists(d):
os.makedirs(d)
def eval_mota(data_root, txt_path):
accs = []
img_root = os.path.join(data_root, "images/train")
# seqs = sorted([s for s in os.listdir(data_root) if s.endswith('FRCNN')])
seqs = sorted([s for s in os.listdir(img_root)])
for seq in seqs:
video_out_path = os.path.join(txt_path, seq + '.txt')
assert os.path.exists(video_out_path), f"{video_out_path} does not exist!"
evaluator = Evaluator(img_root, seq, 'mot')
accs.append(evaluator.eval_file(video_out_path))
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(
summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names
)
print(strsummary)
def get_mota(data_root, txt_path):
accs = []
img_root = os.path.join(data_root, "images/train")
# seqs = sorted([s for s in os.listdir(data_root) if s.endswith('FRCNN')])
seqs = sorted([s for s in os.listdir(img_root)])
for seq in seqs:
video_out_path = os.path.join(txt_path, seq + '.txt')
evaluator = Evaluator(img_root, seq, 'mot')
accs.append(evaluator.eval_file(video_out_path))
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(
summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names
)
mota = float(strsummary.split(' ')[-6][:-1])
return mota
def write_results_score(filename, results):
save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\n'
with open(filename, 'w') as f:
for i in range(results.shape[0]):
frame_data = results[i]
frame_id = int(frame_data[0])
track_id = int(frame_data[1])
x1, y1, w, h = frame_data[2:6]
score = frame_data[6]
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, w=w, h=h, s=-1)
f.write(line)
def dti(txt_path, save_path, n_min=25, n_dti=20):
seq_txts = sorted(glob.glob(os.path.join(txt_path, '*.txt')))
for seq_txt in seq_txts:
seq_name = seq_txt.split('/')[-1]
seq_data = np.loadtxt(seq_txt, dtype=np.float64, delimiter=',')
min_id = int(np.min(seq_data[:, 1]))
max_id = int(np.max(seq_data[:, 1]))
seq_results = np.zeros((1, 10), dtype=np.float64)
for track_id in range(min_id, max_id + 1):
index = (seq_data[:, 1] == track_id)
tracklet = seq_data[index]
tracklet_dti = tracklet
if tracklet.shape[0] == 0:
continue
n_frame = tracklet.shape[0]
n_conf = np.sum(tracklet[:, 6] > 0.5)
if n_frame > n_min:
frames = tracklet[:, 0]
frames_dti = {}
for i in range(0, n_frame):
right_frame = frames[i]
if i > 0:
left_frame = frames[i - 1]
else:
left_frame = frames[i]
# disconnected track interpolation
if 1 < right_frame - left_frame < n_dti:
num_bi = int(right_frame - left_frame - 1)
right_bbox = tracklet[i, 2:6]
left_bbox = tracklet[i - 1, 2:6]
for j in range(1, num_bi + 1):
curr_frame = j + left_frame
curr_bbox = (curr_frame - left_frame) * (right_bbox - left_bbox) / \
(right_frame - left_frame) + left_bbox
frames_dti[curr_frame] = curr_bbox
num_dti = len(frames_dti.keys())
if num_dti > 0:
data_dti = np.zeros((num_dti, 10), dtype=np.float64)
for n in range(num_dti):
data_dti[n, 0] = list(frames_dti.keys())[n]
data_dti[n, 1] = track_id
data_dti[n, 2:6] = frames_dti[list(frames_dti.keys())[n]]
data_dti[n, 6:] = [1, -1, -1, -1]
tracklet_dti = np.vstack((tracklet, data_dti))
seq_results = np.vstack((seq_results, tracklet_dti))
save_seq_txt = os.path.join(save_path, seq_name)
seq_results = seq_results[1:]
seq_results = seq_results[seq_results[:, 0].argsort()]
write_results_score(save_seq_txt, seq_results)
def get_args_parser():
parser = argparse.ArgumentParser('Interpolate Trained Results', add_help=False)
parser.add_argument('--data_dir', default='./datasets/mot_mix', type=str, help="path to dataset directory")
parser.add_argument('--dataset_name', required=True, type=str, choices=('MOT15', 'MOT17'), help="dataset name")
parser.add_argument('--input_txt_dir', default='/opt/tiger/demo/ByteTrack/YOLOX_outputs/yolox_x_mix_det/track_results', type=str, help="input tracking results")
parser.add_argument('--output_txt_dir', default='/opt/tiger/demo/ByteTrack/YOLOX_outputs/yolox_x_mix_det/track_results_dti', type=str, help="output tracking results")
return parser
if __name__ == '__main__':
parser = argparse.ArgumentParser('Interpolate trained results', parents=[get_args_parser()])
args = parser.parse_args()
save_path = args.output_txt_dir
txt_path = args.input_txt_dir
mkdir_if_missing(save_path)
data_root = args.data_dir
dti(txt_path, save_path, n_min=15, n_dti=10)
if args.dataset_name == "MOT15":
print('Before DTI: ')
eval_mota(data_root, txt_path)
print('After DTI:')
eval_mota(data_root, save_path)
# mota_best = 0.0
# best_n_min = 0
# best_n_dti = 0
# for n_min in range(5, 50, 5):
# for n_dti in range(5, 30, 5):
# dti(txt_path, save_path, n_min, n_dti)
# mota = get_mota(data_root, save_path)
# if mota > mota_best:
# mota_best = mota
# best_n_min = n_min
# best_n_dti = n_dti
# print(mota_best, best_n_min, best_n_dti)
# print(mota_best, best_n_min, best_n_dti)