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# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
import cv2
import numpy as np
from os.path import join, isdir
from os import mkdir, makedirs
from concurrent import futures
import sys
import time
import json
import glob
# 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(video, v, crop_path, data_path, instanc_size):
video_crop_base_path = join(crop_path, video)
if not isdir(video_crop_base_path): makedirs(video_crop_base_path)
anno_base_path = join(data_path, 'Annotations')
img_base_path = join(data_path, 'JPEGImages')
for trackid, o in enumerate(list(v)):
obj = v[o]
for frame in obj:
file_name = frame['file_name']
ann_path = join(anno_base_path, file_name+'.png')
img_path = join(img_base_path, file_name+'.jpg')
im = cv2.imread(img_path)
label = cv2.imread(ann_path, 0)
avg_chans = np.mean(im, axis=(0, 1))
bbox = frame['bbox']
bbox[2] += bbox[0]
bbox[3] += bbox[1]
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(file_name.split('/')[-1]), trackid)), x)
mask = crop_like_SiamFCx((label==int(o)).astype(np.float32), bbox, instanc_size=instanc_size, padding=0)
mask = ((mask > 0.2)*255).astype(np.uint8)
x[:,:,0] = mask + (mask == 0)*x[:,:,0]
# cv2.imshow('maskonx', x)
# cv2.waitKey(0)
cv2.imwrite(join(video_crop_base_path, '{:06d}.{:02d}.m.png'.format(int(file_name.split('/')[-1]), trackid)), mask)
def main(instanc_size=511, num_threads=12):
dataDir = '.'
crop_path = './crop{:d}'.format(instanc_size)
if not isdir(crop_path): mkdir(crop_path)
for dataType in ['train']:
set_crop_base_path = join(crop_path, dataType)
set_img_base_path = join(dataDir, dataType)
annFile = '{}/instances_{}.json'.format(dataDir, dataType)
ytb_vos = json.load(open(annFile,'r'))
n_video = len(ytb_vos)
with futures.ProcessPoolExecutor(max_workers=num_threads) as executor:
fs = [executor.submit(crop_video, k, v, set_crop_base_path, set_img_base_path, instanc_size)
for k,v in ytb_vos.items()]
for i, f in enumerate(futures.as_completed(fs)):
# Write progress to error so that it can be seen
printProgress(i, n_video, prefix=dataType, suffix='Done ', barLength=40)
print('done')
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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