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| # -------------------------------------------------------- | |
| # SiamMask | |
| # Licensed under The MIT License | |
| # Written by Qiang Wang (wangqiang2015 at ia.ac.cn) | |
| # -------------------------------------------------------- | |
| from pycocotools.coco import COCO | |
| 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 argparse | |
| parser = argparse.ArgumentParser(description='COCO Parallel Preprocessing for SiamMask') | |
| parser.add_argument('--exemplar_size', type=int, default=127, help='size of exemplar') | |
| parser.add_argument('--context_amount', type=float, default=0.5, help='context amount') | |
| parser.add_argument('--search_size', type=int, default=511, help='size of cropped search region') | |
| parser.add_argument('--enable_mask', action='store_true', help='whether crop mask') | |
| parser.add_argument('--num_threads', type=int, default=24, help='number of threads') | |
| args = parser.parse_args() | |
| # 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_SiamFCx(image, bbox, exemplar_size=127, context_amount=0.5, search_size=255, padding=(0, 0, 0)): | |
| target_pos = [(bbox[2]+bbox[0])/2., (bbox[3]+bbox[1])/2.] | |
| target_size = [bbox[2]-bbox[0]+1, bbox[3]-bbox[1]+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 = (search_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), search_size, padding) | |
| return x | |
| def crop_img(img, anns, set_crop_base_path, set_img_base_path, | |
| exemplar_size=127, context_amount=0.5, search_size=511, enable_mask=True): | |
| frame_crop_base_path = join(set_crop_base_path, img['file_name'].split('/')[-1].split('.')[0]) | |
| if not isdir(frame_crop_base_path): makedirs(frame_crop_base_path) | |
| im = cv2.imread('{}/{}'.format(set_img_base_path, img['file_name'])) | |
| avg_chans = np.mean(im, axis=(0, 1)) | |
| for track_id, ann in enumerate(anns): | |
| rect = ann['bbox'] | |
| if rect[2] <= 0 or rect[3] <= 0: | |
| continue | |
| bbox = [rect[0], rect[1], rect[0]+rect[2]-1, rect[1]+rect[3]-1] | |
| x = crop_like_SiamFCx(im, bbox, exemplar_size=exemplar_size, context_amount=context_amount, | |
| search_size=search_size, padding=avg_chans) | |
| cv2.imwrite(join(frame_crop_base_path, '{:06d}.{:02d}.x.jpg'.format(0, track_id)), x) | |
| if enable_mask: | |
| im_mask = coco.annToMask(ann).astype(np.float32) | |
| x = (crop_like_SiamFCx(im_mask, bbox, exemplar_size=exemplar_size, context_amount=context_amount, | |
| search_size=search_size) > 0.5).astype(np.uint8) * 255 | |
| cv2.imwrite(join(frame_crop_base_path, '{:06d}.{:02d}.m.png'.format(0, track_id)), x) | |
| def main(exemplar_size=127, context_amount=0.5, search_size=511, enable_mask=True, num_threads=24): | |
| global coco # will used for generate mask | |
| data_dir = '.' | |
| crop_path = './crop{:d}'.format(search_size) | |
| if not isdir(crop_path): mkdir(crop_path) | |
| for data_subset in ['val2017', 'train2017']: | |
| set_crop_base_path = join(crop_path, data_subset) | |
| set_img_base_path = join(data_dir, data_subset) | |
| anno_file = '{}/annotations/instances_{}.json'.format(data_dir, data_subset) | |
| coco = COCO(anno_file) | |
| n_imgs = len(coco.imgs) | |
| with futures.ProcessPoolExecutor(max_workers=num_threads) as executor: | |
| fs = [executor.submit(crop_img, coco.loadImgs(id)[0], | |
| coco.loadAnns(coco.getAnnIds(imgIds=id, iscrowd=None)), | |
| set_crop_base_path, set_img_base_path, | |
| exemplar_size, context_amount, search_size, | |
| enable_mask) for id in coco.imgs] | |
| for i, f in enumerate(futures.as_completed(fs)): | |
| printProgress(i, n_imgs, prefix=data_subset, suffix='Done ', barLength=40) | |
| print('done') | |
| if __name__ == '__main__': | |
| since = time.time() | |
| main(args.exemplar_size, args.context_amount, args.search_size, args.enable_mask, args.num_threads) | |
| time_elapsed = time.time() - since | |
| print('Total complete in {:.0f}m {:.0f}s'.format( | |
| time_elapsed // 60, time_elapsed % 60)) | |