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test_vot.py
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test_vot.py
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# Copyright (c) SenseTime. All Rights Reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import math
import pdb
import cv2
import torch
import numpy as np
#here musr be false ,else Runtime error 11
torch.backends.cudnn.benchmark = False
from os.path import join
from DFAT.core.config import cfg
from DFAT.models.model_builder import ModelBuilder
from DFAT.tracker.tracker_builder import build_tracker
from DFAT.utils.bbox import get_axis_aligned_bbox
from DFAT.utils.model_load import load_pretrain
from toolkit.datasets import DatasetFactory
from toolkit.utils.region import vot_overlap, vot_float2str
import matplotlib.pyplot as plt
from tensorboardX import SummaryWriter
import torch.nn as nn
parser = argparse.ArgumentParser(description='siamrpn tracking')
# parser.add_argument('--dataset', default='OTB100', type=str,
# help='datasets')
parser.add_argument('--config', default='experiments/siam_base/config_refine.yaml', type=str,
help='config file')
# parser.add_argument('--snapshot', default='experiments/siam_base/snapshot_refine/checkpoint_e13.pth', type=str,
# help='snapshot of models to eval')
# parser.add_argument('--video', default='Basketball', type=str,
# help='eval one special video')
parser.add_argument('--vis', default='True',action='store_true',
help='whether visualzie result')
args = parser.parse_args()
torch.set_num_threads(1)
# load video information
# dataset_root = '/data/Disk_B/zhangyong/VOT2020/rgbt20/sequences'
dataset_root = '/data/Disk_D/zhangyong/votrgbt2019/sequences'
#video_name = 'humans_corridor_occ_2_A'#
list_path = os.path.join(dataset_root, "list.txt")
sequence_list_file = open(list_path, "r")
sequence =sequence_list_file.readlines()
sequence_list_file.close()
#video_path = os.path.join(dataset_root, video_name)
#anno_path = video_path + '/groundtruth.txt'
#rgb_imgs_path = video_path + '/color/'
#depth_imgs_path = video_path + '/depth/'
#rgb_imgs_files = [f for f in os.listdir(rgb_imgs_path)]
#depth_imgs_files = [f for f in os.listdir(depth_imgs_path)]
num_sequences = len(sequence)
cfg.merge_from_file(args.config)
cur_dir = os.path.dirname(os.path.realpath(__file__))
# create model
model = ModelBuilder()
used = [0]
dist_model = nn.DataParallel(model, used).cuda()
# model_name = args.snapshot.split('/')[-1].split('.')[0]
# load model
project_root = "/data/Disk_D/zhangyong/FusionFromPurpose"
cfg_root = join(project_root, "experiments/siam_base")
version = 'v87'
checkpoint_num = 'checkpoint_e95'
# model_file = join('/data/Disk_C/tzy_space/Trackers/FusionFromPurpose-train2', version, checkpoint_num + '.pth')
#model_file = join('/data/Disk_Z/zhangyong_space/snapshot/ffp', version, checkpoint_num + '.pth')
# model_file = join('J:/Untitled Folder/ffp', version, checkpoint_num + '.pth')
model_file = join("/data/Disk_A/zhangyong/DFAT-19-1/experiments/siam_base", 'snapshot_refine/checkpoint_refine_e18.pth')
model = load_pretrain(model, model_file).cuda().eval()
# model_tir = load_pretrain(model, model_file_tir).cuda().eval()
# build tracker
tracker = build_tracker(model)
sequence_name = 'afterrain'# baby, biketwo, diamond, man4, balancebike, courch, mandrivecar, child1, greyman, mandrivecar
sequence_path = os.path.join(dataset_root, sequence_name)
anno_path = sequence_path + '/groundtruth.txt'
rgb_imgs_path = sequence_path + '/color'
ir_imgs_path = sequence_path + '/ir'
rgb_imgs_files = sorted([f for f in os.listdir(rgb_imgs_path)])
ir_imgs_files = sorted([f for f in os.listdir(ir_imgs_path)])
# writer = SummaryWriter(join('/data/Disk_C/tzy_space/Trackers/FusionFromPurpose/results', version, checkpoint_num, \
# sequence_name, 'tir'))
writer = None
# writer = join('/data/Disk_A/tzy_space/Trackers/FusionFromPurpose/results', version, checkpoint_num, \
# sequence_name, 'tir')
writer = 0
pred_bboxes = []
# pre_r = []
# pre_i = []
lost_number = 0
toc = 0
with open(anno_path) as f:
ground_truth_list = [i.split('\n')[0].split(',') for i in f.readlines()]
frame_counter = 0
restart = 1
flag = 0
for frame in range(len(ground_truth_list)) :
tic = cv2.getTickCount()
#read image
im_rgb = cv2.imread(os.path.join(rgb_imgs_path, rgb_imgs_files[frame]))
im_tir = cv2.imread(os.path.join(ir_imgs_path, ir_imgs_files[frame]))
#get gt
#groundtruth = [ground_truth_list[frame][]]
#pdb.set_trace()
cx, cy, w, h = get_axis_aligned_bbox(np.array(ground_truth_list[frame], dtype='float32'))
gt_bbox = [cx - (w - 1) / 2, cy - (h - 1) / 2, w, h]
# im = []
im = [im_rgb, im_tir]
if frame == 0 or restart == 1:
gt_init = gt_bbox
tracker.init(im, gt_bbox, writer, frame_counter+1)
pred_bbox = gt_bbox
pred_bboxes.append(1)
restart = 0
flag = 2
print('%s : frame %d initialized!' % (sequence_name, frame_counter+1))
elif frame > 0:
# outputs = tracker.track(im)
frame_counter = frame_counter + 1
outputs = tracker.track(im, frame_counter+1, writer)
flag = flag + 1
pred_bbox = outputs['bbox']
pred_bbox =pred_bbox
# print('%s : frame %d tracked!' % (sequence_name, frame_counter))
draw_bb = [int(pred_bbox[i]) for i in range(len(pred_bbox))]
draw_bb[2] = draw_bb[2] + draw_bb[0]
draw_bb[3] = draw_bb[3] + draw_bb[1]
# writer.add_image_with_boxes('result_image_'+ str(frame_counter), im_rgb, draw_bb, dataformats='HWC')
# pre_r = outputs['bb_r']
# pre_i = outputs['bb_i']
# if frame % 10 == 0 :
# tracker.update(im_rgb, im_ir, pred_bbox)
# writer.add_image('a', make_grid())
overlap = vot_overlap(pred_bbox, gt_bbox, (im_rgb.shape[1], im_rgb.shape[0]))
print('Predited cx: %f, cy: %f , w: %f, H:%f'%(pred_bbox[0], pred_bbox[1], pred_bbox[2], pred_bbox[3]))
if overlap > 0:
# not lost
pred_bboxes.append(pred_bbox)
print('%s : frame %d tracked! idx: %d' % (sequence_name, frame_counter, outputs['idx']))
if frame_counter > len(ground_truth_list):
print('%s : finished!' % (sequence_name))
break
# cv2.rectangle(im_rgb, (int(pred_bbox[0]), int(pred_bbox[1])),
# (int(pred_bbox[0] + pred_bbox[2]), int(pred_bbox[1] + pred_bbox[3])), (0, 255, 0), 3)#yellow
# cv2.rectangle(im_rgb, (int(gt_bbox[0]), int(gt_bbox[1])),
# (int(gt_bbox[0] + gt_bbox[2]), int(gt_bbox[1] + gt_bbox[3])), (0, 0, 255),3) # yellow
# cv2.putText(im_rgb, str(frame), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 3)
# cv2.imshow(sequence_name, im_rgb)
# cv2.waitKey(1)
else:
# lost object
pred_bboxes.append(2)
# frame_counter = frame_counter + 1
print('%s : frame %d failed!' % (sequence_name, frame_counter))
frame_counter = frame_counter + 4 # skip 5 frames
lost_number += 1
restart = 1
if frame_counter > len(ground_truth_list):
print('%s : finished!' % (sequence_name))
break
else:
pred_bboxes.append(0)
print('Totally lost %d times'%(lost_number))
# toc += cv2.getTickCount() - tic
# if frame == 0:
# cv2.destroyAllWindows()
# if args.vis and frame > 0:
# gt_bbox = list(map(int, gt_bbox))
# pred_bbox = list(map(int, pred_bbox))
# cv2.rectangle(im_rgb, (gt_bbox[0], gt_bbox[1]),
# (gt_bbox[0] + gt_bbox[2], gt_bbox[1] + gt_bbox[3]), (0, 255, 0), 3)#yellow
# cv2.rectangle(im_rgb, (pred_bbox[0], pred_bbox[1]),
# (pred_bbox[0] + pred_bbox[2], pred_bbox[1] + pred_bbox[3]), (0, 255, 255), 3)#blue
# # cv2.rectangle(im_rgb, (pre_r[0], pre_r[1]),
# # (pre_r[0] + pre_r[2], pre_r[1] + pre_r[3]), (255, 0, 0), 3) # red
# # cv2.rectangle(im_rgb, (pre_i[0], pre_i[1]),
# # (pre_i[0] + pre_i[2], pre_i[1] + pre_i[3]), (0, 0, 255), 3) # blue
#
# cv2.putText(im_rgb, str(frame), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
# cv2.imshow(sequence_name, im_rgb)
# cv2.waitKey(1)
# toc /= cv2.getTickFrequency()
#for i in range(num_sequences) :
#sequence_name = sequence[i].split('\n')[0]
# sequence_name = 'aftertree'
# sequence_path = os.path.join(dataset_root, sequence_name)
# anno_path = sequence_path + '/groundtruth.txt'
# rgb_imgs_path = sequence_path + '/color'
# ir_imgs_path = sequence_path + '/ir'
# rgb_imgs_files = [f for f in os.listdir(rgb_imgs_path)]
# ir_imgs_files = [f for f in os.listdir(ir_imgs_path)]
#
# pred_bboxes = []
# pre_r = []
# pre_i = []
# lost_number = 0
# toc = 0
#
# with open(anno_path) as f:
# ground_truth_list = [i.split('\n')[0].split(',') for i in f.readlines()]
#
#
# for frame in range(len(ground_truth_list)) :
# tic = cv2.getTickCount()
# #read image
# im_rgb = cv2.imread(os.path.join(rgb_imgs_path, rgb_imgs_files[frame]))
# im_ir = cv2.imread(os.path.join(ir_imgs_path, ir_imgs_files[frame]))
# #get gt
# #groundtruth = [ground_truth_list[frame][]]
# cx, cy, w, h = get_axis_aligned_bbox(np.array(ground_truth_list[frame], dtype='float32'))
# gt_bbox = [cx - (w - 1) / 2, cy - (h - 1) / 2, w, h]
# if frame == 0 :
# gt_init = gt_bbox
# tracker.init(im_rgb, im_ir, gt_bbox)
# pred_bbox = gt_bbox
# pred_bboxes.append(1)
# elif frame > 0:
# outputs = tracker.track(im_rgb, im_ir)
# pred_bbox = outputs['bbox']
# # pre_r = outputs['bb_r']
# # pre_i = outputs['bb_i']
# # if frame % 10 == 0 :
# # tracker.update(im_rgb, im_ir, pred_bbox)
#
#
# overlap = vot_overlap(pred_bbox, gt_bbox, (im_rgb.shape[1], im_rgb.shape[0]))
# if overlap > 0:
# # not lost
# pred_bboxes.append(pred_bbox)
# else:
# # lost object
# pred_bboxes.append(2)
# frame_counter = frame + 5 # skip 5 frames
# lost_number += 1
# else:
# pred_bboxes.append(0)
# toc += cv2.getTickCount() - tic
# if frame == 0:
# cv2.destroyAllWindows()
# if args.vis and frame > 0:
# '''
# cv2.polylines(im_rgb, [np.array(gt_bbox, np.int).reshape((-1, 1, 2))],
# True, (0, 255, 0), 3)
# if cfg.MASK.MASK:
# mask = outputs['mask'] > cfg.TRACK.MASK_THERSHOLD
# im_rgb[:, :, 2] = mask * 255 + (1 - mask) * im_rgb[:, :, 2]
# location_int = np.int0(pred_bbox)
# cv2.polylines(im_rgb, [location_int.reshape((-1, 1, 2))], True, (0, 255, 255), 3)
# # cv2.polylines(img, [np.array(pred_bbox, np.int).reshape((-1, 1, 2))],
# # True, (0, 255, 255), 3)
# else:
# bbox = list(map(int, pred_bbox))
# cv2.rectangle(im_rgb, (bbox[0], bbox[1]),
# (bbox[0] + bbox[2], bbox[1] + bbox[3]), (0, 255, 255), 3)
# cv2.putText(im_rgb, str(frame), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
# cv2.putText(im_rgb, str(lost_number), (40, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# asr = min(600 / im_rgb.shape[0], 800 / im_rgb.shape[1])
# img = cv2.resize(im_rgb, (int(im_rgb.shape[0] * asr), int(im_rgb.shape[1] * asr)))
# cv2.imshow(sequence_name, im_rgb)
# cv2.waitKey(1)
# '''
#
#
# gt_bbox = list(map(int, gt_bbox))
# pred_bbox = list(map(int, pred_bbox))
# cv2.rectangle(im_rgb, (gt_bbox[0], gt_bbox[1]),
# (gt_bbox[0] + gt_bbox[2], gt_bbox[1] + gt_bbox[3]), (0, 255, 0), 3)#yellow
# cv2.rectangle(im_rgb, (pred_bbox[0], pred_bbox[1]),
# (pred_bbox[0] + pred_bbox[2], pred_bbox[1] + pred_bbox[3]), (0, 255, 255), 3)#blue
# # cv2.rectangle(im_rgb, (pre_r[0], pre_r[1]),
# # (pre_r[0] + pre_r[2], pre_r[1] + pre_r[3]), (255, 0, 0), 3) # red
# # cv2.rectangle(im_rgb, (pre_i[0], pre_i[1]),
# # (pre_i[0] + pre_i[2], pre_i[1] + pre_i[3]), (0, 0, 255), 3) # blue
#
# cv2.putText(im_rgb, str(frame), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
# cv2.imshow(sequence_name, im_rgb)
# cv2.waitKey(1)
# toc /= cv2.getTickFrequency()
# print('({:3d}) Video: {:12s} Time: {:5.1f}s Speed: {:3.1f}fps'.format(
# # i + 1, sequence_name, toc, (i + 1) / toc))