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uav123.py
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uav123.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright © 2019 WR Tan National Tsing Hua University
#
# Distributed under terms of the MIT license.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import matplotlib
matplotlib.use('Agg')
from got10k.trackers import Tracker
from got10k.experiments import ExperimentUAV123
import logging
import os
import sys
import time
import cv2
import numpy as np
import tensorflow as tf
# Code root absolute path
CODE_ROOT = '/home/william/SiamFC-TensorFlow'
# Checkpoint for evaluation
CHECKPOINT = '/home/william/iSiam-TF/Logs/SiamFC/track_model_checkpoints/SiamFC-iSiam'
sys.path.insert(0, CODE_ROOT)
from utils.misc_utils import load_cfgs
from inference import inference_wrapper_uav as inference_wrapper
from inference.tracker_uav import ISiamTrackerTF
from utils.infer_utils import Rectangle
# Set GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
tf.logging.set_verbosity(tf.logging.DEBUG)
class ISiamTracker(Tracker):
def __init__(self):
super(ISiamTracker, self).__init__(name='ISiamTracker')
checkpoint_path = CHECKPOINT
logging.info('Evaluating {}...'.format(checkpoint_path))
# Read configurations from json
self.model_config, _, self.track_config = load_cfgs(checkpoint_path)
self.track_config['log_level'] = 1 # Skip verbose logging for speed
self.track_config['scale_step'] = 1.025 # 1.025
self.track_config['scale_damp'] = 1.
self.track_config['window_influence'] = 0.2 # 0.176
# Build the inference graph.
g = tf.Graph()
with g.as_default():
self.model = inference_wrapper.InferenceWrapper()
restore_fn = self.model.build_graph_from_config(self.model_config, self.track_config, checkpoint_path)
g.finalize()
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
self.sess = tf.Session(graph=g, config=sess_config)
# Load the model from checkpoint.
restore_fn(self.sess)
self.tracker = ISiamTrackerTF(self.model, self.model_config, self.track_config)
def init(self, image, box):
vid = image.filename.split('/')[-2]
im = np.array(image)
self.dir_name = os.path.join('/home/william/uav123_benchmark/results/samples', vid)
if not os.path.exists(self.dir_name):
os.mkdir(self.dir_name)
return self.tracker.init(self.sess, im, box, self.dir_name)
def update(self, image):
im = np.array(image)
return self.tracker.track(self.sess, im, self.dir_name)
if __name__ == '__main__':
# setup tracker
tracker = ISiamTracker()
# run experiments
experiment = ExperimentUAV123('/home/william/dataset/uav123/UAV123', version='UAV123')
experiment.run(tracker, visualize=False)
# report performance
experiment.report([tracker.name,
#'ISiamTracker_base',
#'ISiamTracker_nodft',
#'ISiamTracker_nofbgs_nodft',
#'ISiamTracker_noibgs_nodft',
#'ISiamTracker_nobgs_nodft',
#'ISiamTracker_dense_nobgs_nodft',
])