add training data

training_dataset/coco/gen_json.py

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+from pycocotools.coco import COCO
+from os.path import join
+import json
+
+
+dataDir = '.'
+for dataType in ['val2017', 'train2017']:
+    dataset = dict()
+    annFile = '{}/annotations/instances_{}.json'.format(dataDir,dataType)
+    coco = COCO(annFile)
+    n_imgs = len(coco.imgs)
+    for n, img_id in enumerate(coco.imgs):
+        print('subset: {} image id: {:04d} / {:04d}'.format(dataType, n, n_imgs))
+        img = coco.loadImgs(img_id)[0]
+        annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)
+        anns = coco.loadAnns(annIds)
+        video_crop_base_path = join(dataType, img['file_name'].split('/')[-1].split('.')[0])
+
+        if len(anns) > 0:
+            dataset[video_crop_base_path] = dict()        
+
+        for trackid, ann in enumerate(anns):
+            rect = ann['bbox']
+            c = ann['category_id']
+            bbox = [rect[0], rect[1], rect[0]+rect[2], rect[1]+rect[3]]
+            if rect[2] <= 0 or rect[3] <= 0:  # lead nan error in cls.
+                continue
+            dataset[video_crop_base_path]['{:02d}'.format(trackid)] = {'000000': bbox}
+
+    print('save json (dataset), please wait 20 seconds~')
+    json.dump(dataset, open('{}.json'.format(dataType), 'w'), indent=4, sort_keys=True)
+    print('done!')
+

training_dataset/coco/par_crop.py

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+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
+
+
+# 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_img(img, anns, set_crop_base_path, set_img_base_path, instanc_size=511):
+    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 trackid, ann in enumerate(anns):
+        rect = ann['bbox']
+        bbox = [rect[0], rect[1], rect[0] + rect[2], rect[1] + rect[3]]
+        if rect[2] <= 0 or rect[3] <=0:
+            continue
+        z, x = crop_like_SiamFC(im, bbox, instanc_size=instanc_size, padding=avg_chans)
+        cv2.imwrite(join(frame_crop_base_path, '{:06d}.{:02d}.z.jpg'.format(0, trackid)), z)
+        cv2.imwrite(join(frame_crop_base_path, '{:06d}.{:02d}.x.jpg'.format(0, trackid)), x)
+
+
+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 ['val2017', 'train2017']:
+        set_crop_base_path = join(crop_path, dataType)
+        set_img_base_path = join(dataDir, dataType)
+
+        annFile = '{}/annotations/instances_{}.json'.format(dataDir,dataType)
+        coco = COCO(annFile)
+        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, instanc_size) for id in coco.imgs]
+            for i, f in enumerate(futures.as_completed(fs)):
+                # Write progress to error so that it can be seen
+                printProgress(i, n_imgs, 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))

training_dataset/coco/pycocotools/Makefile

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+all:
+    # install pycocotools locally
+	python setup.py build_ext --inplace
+	rm -rf build
+
+install:
+	# install pycocotools to the Python site-packages
+	python setup.py build_ext install
+	rm -rf build
+clean:
+	rm _mask.c _mask.cpython-36m-x86_64-linux-gnu.so

training_dataset/coco/pycocotools/__init__.py

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+__author__ = 'tylin'