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# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
from os.path import join, isdir
from os import listdir, mkdir, makedirs
import cv2
import numpy as np
import glob
import xml.etree.ElementTree as ET
from concurrent import futures
import sys
import time
VID_base_path = './ILSVRC2015'
ann_base_path = join(VID_base_path, 'Annotations/VID/train/')
sub_sets= sorted({'ILSVRC2015_VID_train_0000', 'ILSVRC2015_VID_train_0001', 'ILSVRC2015_VID_train_0002', 'ILSVRC2015_VID_train_0003', 'val'})
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix='', suffix='', decimals=1, barLength=100):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required : total iterations (Int)
prefix - Optional : prefix string (Str)
suffix - Optional : suffix string (Str)
decimals - Optional : positive number of decimals in percent complete (Int)
barLength - Optional : character length of bar (Int)
"""
formatStr = "{0:." + str(decimals) + "f}"
percents = formatStr.format(100 * (iteration / float(total)))
filledLength = int(round(barLength * iteration / float(total)))
bar = '' * filledLength + '-' * (barLength - filledLength)
sys.stdout.write('\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),
if iteration == total:
sys.stdout.write('\x1b[2K\r')
sys.stdout.flush()
def crop_hwc(image, bbox, out_sz, padding=(0, 0, 0)):
a = (out_sz-1) / (bbox[2]-bbox[0])
b = (out_sz-1) / (bbox[3]-bbox[1])
c = -a * bbox[0]
d = -b * bbox[1]
mapping = np.array([[a, 0, c],
[0, b, d]]).astype(np.float)
crop = cv2.warpAffine(image, mapping, (out_sz, out_sz), borderMode=cv2.BORDER_CONSTANT, borderValue=padding)
return crop
def pos_s_2_bbox(pos, s):
return [pos[0]-s/2, pos[1]-s/2, pos[0]+s/2, pos[1]+s/2]
def crop_like_SiamFC(image, bbox, context_amount=0.5, exemplar_size=127, instanc_size=255, padding=(0, 0, 0)):
target_pos = [(bbox[2]+bbox[0])/2., (bbox[3]+bbox[1])/2.]
target_size = [bbox[2]-bbox[0], bbox[3]-bbox[1]]
wc_z = target_size[1] + context_amount * sum(target_size)
hc_z = target_size[0] + context_amount * sum(target_size)
s_z = np.sqrt(wc_z * hc_z)
scale_z = exemplar_size / s_z
d_search = (instanc_size - exemplar_size) / 2
pad = d_search / scale_z
s_x = s_z + 2 * pad
z = crop_hwc(image, pos_s_2_bbox(target_pos, s_z), exemplar_size, padding)
x = crop_hwc(image, pos_s_2_bbox(target_pos, s_x), instanc_size, padding)
return z, x
def crop_like_SiamFCx(image, bbox, context_amount=0.5, exemplar_size=127, instanc_size=255, padding=(0, 0, 0)):
target_pos = [(bbox[2]+bbox[0])/2., (bbox[3]+bbox[1])/2.]
target_size = [bbox[2]-bbox[0], bbox[3]-bbox[1]]
wc_z = target_size[1] + context_amount * sum(target_size)
hc_z = target_size[0] + context_amount * sum(target_size)
s_z = np.sqrt(wc_z * hc_z)
scale_z = exemplar_size / s_z
d_search = (instanc_size - exemplar_size) / 2
pad = d_search / scale_z
s_x = s_z + 2 * pad
x = crop_hwc(image, pos_s_2_bbox(target_pos, s_x), instanc_size, padding)
return x
def crop_video(sub_set, video, crop_path, instanc_size):
video_crop_base_path = join(crop_path, sub_set, video)
if not isdir(video_crop_base_path): makedirs(video_crop_base_path)
sub_set_base_path = join(ann_base_path, sub_set)
xmls = sorted(glob.glob(join(sub_set_base_path, video, '*.xml')))
for xml in xmls:
xmltree = ET.parse(xml)
# size = xmltree.findall('size')[0]
# frame_sz = [int(it.text) for it in size]
objects = xmltree.findall('object')
objs = []
filename = xmltree.findall('filename')[0].text
im = cv2.imread(xml.replace('xml', 'JPEG').replace('Annotations', 'Data'))
avg_chans = np.mean(im, axis=(0, 1))
for object_iter in objects:
trackid = int(object_iter.find('trackid').text)
# name = (object_iter.find('name')).text
bndbox = object_iter.find('bndbox')
# occluded = int(object_iter.find('occluded').text)
bbox = [int(bndbox.find('xmin').text), int(bndbox.find('ymin').text),
int(bndbox.find('xmax').text), int(bndbox.find('ymax').text)]
# z, x = crop_like_SiamFC(im, bbox, instanc_size=instanc_size, padding=avg_chans)
# cv2.imwrite(join(video_crop_base_path, '{:06d}.{:02d}.z.jpg'.format(int(filename), trackid)), z)
# cv2.imwrite(join(video_crop_base_path, '{:06d}.{:02d}.x.jpg'.format(int(filename), trackid)), x)
x = crop_like_SiamFCx(im, bbox, instanc_size=instanc_size, padding=avg_chans)
cv2.imwrite(join(video_crop_base_path, '{:06d}.{:02d}.x.jpg'.format(int(filename), trackid)), x)
def main(instanc_size=511, num_threads=24):
crop_path = './crop{:d}'.format(instanc_size)
if not isdir(crop_path): mkdir(crop_path)
for sub_set in sub_sets:
sub_set_base_path = join(ann_base_path, sub_set)
videos = sorted(listdir(sub_set_base_path))
n_videos = len(videos)
with futures.ProcessPoolExecutor(max_workers=num_threads) as executor:
fs = [executor.submit(crop_video, sub_set, video, crop_path, instanc_size) for video in videos]
for i, f in enumerate(futures.as_completed(fs)):
# Write progress to error so that it can be seen
printProgress(i, n_videos, prefix=sub_set, suffix='Done ', barLength=40)
if __name__ == '__main__':
since = time.time()
main(int(sys.argv[1]), int(sys.argv[2]))
time_elapsed = time.time() - since
print('Total complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
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