import xml.etree.cElementTree as ET
from Misc import getParamDict
import glob
root_dir = 'C:/Datasets'
actor_id = 4
start_id = 0
end_id = 59
params = getParamDict()
actors = params['mot_actors']
sequences = params['mot_sequences']
actor = actors[actor_id]
n_frames_list = []
for seq_id in xrange(start_id, end_id + 1):
seq_name = sequences[actor][seq_id]
out_fname = '{:s}/{:s}/Annotations/{:s}.txt'.format(
root_dir, actor, seq_name)
out_fid = open(out_fname, 'w')
fname = '{:s}/{:s}/Annotations/{:s}.xml'.format(root_dir, actor, seq_name)
tree = ET.parse(fname)
root = tree.getroot()
img_dir = '{:s}/{:s}/Images/{:s}'.format(root_dir, actor, seq_name)
n_frames = len(glob.glob('{:s}/*.jpg'.format(img_dir)))
n_frames_list.append(n_frames)
print 'Processing sequence {:d} :: {:s}'.format(seq_id, seq_name)
for frame_obj in tree.iter('frame'):
tsrget_list = frame_obj.find('target_list')
frame_id = int(frame_obj.attrib['num'])
from Misc import getParamDict
from Misc import readDistGridParams
import sys
import cv2
import numpy as np
import os
if __name__ == '__main__':
params_dict = getParamDict()
param_ids = readDistGridParams()
pause_seq = 0
gt_col = (0, 0, 255)
gt_thickness = 1
sequences = params_dict['sequences']
db_root_dir = '../Datasets'
# img_name_fmt='img%03d.jpg'
img_name_fmt = 'frame%05d.jpg'
opt_gt_ssm = '0'
use_opt_gt = 0
seq_id = param_ids['seq_id']
actor = 'VTB'
sequences = sequences[actor]
n_seq = 100
for seq_id in xrange(n_seq):
seq_name = sequences[seq_id]
# seq_name = 'nl_mugII_s1'
def getBasicParams(trackers, xv_input_found, extended_db):
video_pipeline = ['OpenCV']
cv_sources = ['jpeg', 'mpeg', 'usb camera']
sources = [cv_sources]
if xv_input_found:
video_pipeline.append('XVision')
xv_sources = ['mpeg', 'usb camera', 'firewire camera']
sources.append(xv_sources)
initialization = ['manual', 'ground_truth']
color_spaces = ['Grayscale', 'RGB', 'HSV', 'YCrCb', 'HLS', 'Lab']
filters = [
'none', 'gabor', 'laplacian', 'sobel', 'scharr', 'canny', 'LoG', 'DoG'
]
features = ['none', 'hoc']
smoothing = ['none', 'box', 'bilateral', 'gauss', 'median']
smoothing_kernel = map(str, range(3, 26, 2))
params = [
video_pipeline, sources, initialization, color_spaces, filters,
features, smoothing, smoothing_kernel, trackers
]
labels = [
'pipeline', 'source', 'initialization', 'color_space', 'filter',
'feature', 'smoothing', 'smoothing_kernel', 'tracker'
]
default_id = [
video_pipeline.index('OpenCV'),
sources[video_pipeline.index('OpenCV')].index('jpeg'),
initialization.index('ground_truth'),
color_spaces.index('Grayscale'),
filters.index('none'),
features.index('none'),
smoothing.index('gauss'),
smoothing_kernel.index(str(5)),
trackers.index('rkl')
]
if extended_db:
params_dict = getParamDict()
task_type = [
params_dict['actors'][id]
for id in xrange(len(params_dict['actors']))
]
tasks = [params_dict['sequences'][actor] for actor in task_type]
tasks = [seq.values() for seq in tasks]
params.extend([task_type, tasks])
labels.extend(['type', 'task'])
default_id.extend([0, 3])
else:
task_type = ['simple', 'complex']
light_conditions = ['nl', 'dl']
speeds = ['s1', 's2', 's3', 's4', 's5', 'si']
complex_tasks = ['bus', 'highlighting', 'letter', 'newspaper']
simple_tasks = [
'bookI', 'bookII', 'bookIII', 'cereal', 'juice', 'mugI', 'mugII',
'mugIII'
]
robot_tasks = [
'robot_bookI', 'robot_bookII', 'robot_bookIII', 'robot_cereal',
'robot_juice', 'robot_mugI', 'robot_mugII', 'robot_mugIII'
]
tasks = [simple_tasks, complex_tasks, robot_tasks]
params.extend([task_type, light_conditions, speeds, tasks])
labels.extend(['type', 'light', 'speed', 'task'])
default_id.extend([
task_type.index('simple'),
light_conditions.index('nl'),
speeds.index('s3'), tasks[task_type.index('simple')].index('juice')
])
return params, labels, default_id
