File size: 5,757 Bytes
d4b77ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
# --------------------------------------------------------
# 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))
|