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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)) | |