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Update crop_image.py #21

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21 changes: 12 additions & 9 deletions train/dataset/crop_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import json
import cv2
import time
from tqdm import tqdm

parse = argparse.ArgumentParser(description='Generate training data (cropped) for DCFNet_pytorch')
parse.add_argument('-v', '--visual', dest='visual', action='store_true', help='whether visualise crop')
Expand All @@ -13,7 +14,7 @@

args = parse.parse_args()

print args
print (args)


def crop_hwc(image, bbox, out_sz, padding=(0, 0, 0)):
Expand Down Expand Up @@ -47,12 +48,14 @@ def cxy_wh_2_bbox(cxy, wh):

count = 0
begin_time = time.time()
for snap in snaps:
for snap in tqdm(snaps):
frames = snap['frame']
n_frames = len(frames)
for f, frame in enumerate(frames):
for f, frame in enumerate(tqdm(frames,leave=False)):
img_path = join(snap['base_path'], frame['img_path'])
im = cv2.imread(img_path)
#print (img_path)
#print(im.shape)
avg_chans = np.mean(im, axis=(0, 1))
bbox = frame['obj']['bbox']

Expand All @@ -67,17 +70,17 @@ def cxy_wh_2_bbox(cxy, wh):
lmdb['down_index'][count] = f
lmdb['up_index'][count] = n_frames - f
count += 1
if count % 100 == 0:
elapsed = time.time() - begin_time
print("Processed {} images in {:.2f} seconds. "
"{:.2f} images/second.".format(count, elapsed, count / elapsed))
#if count % 100 == 0:
# elapsed = time.time() - begin_time
# print("Processed {} images in {:.2f} seconds. "
# "{:.2f} images/second.".format(count, elapsed, count / elapsed))

template_id = np.where(lmdb['up_index'] > 1)[0] # NEVER use the last frame as template! I do not like bidirectional.
rand_split = np.random.choice(len(template_id), len(template_id))
lmdb['train_set'] = template_id[rand_split[:(len(template_id)-num_val)]]
lmdb['val_set'] = template_id[rand_split[(len(template_id)-num_val):]]
print len(lmdb['train_set'])
print len(lmdb['val_set'])
print ("train set: %i"%len(lmdb['train_set']))
print ("val set: %i"%len(lmdb['val_set']))

# to list for json
lmdb['train_set'] = lmdb['train_set'].tolist()
Expand Down