SiamMask/data/coco/par_crop.py

# --------------------------------------------------------
# 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))