def pad_image_data(list_of_blobs):
max_shape = blob_utils.get_max_shape([blobs['data'].shape[1:] for blobs in list_of_blobs])
output_list = []
for blobs in list_of_blobs:
data_padded = np.zeros((3, max_shape[0], max_shape[1]), dtype=np.float32)
_, h, w = blobs['data'].shape
data_padded[:, :h, :w] = blobs['data']
blobs['data'] = data_padded
output_list.append(blobs)
return output_list
def _flo_to_blob(flo, target_size):
"""
"""
flo, flo_scale = blob_utils.prep_im_for_blob(flo, (0, 0), [target_size], cfg.TRAIN.MAX_SIZE)
flo = flo[0]*flo_scale # scale the value.
flo_scale = flo_scale[0]
max_shape = blob_utils.get_max_shape([flo.shape[:2]])
blob = np.zeros((1, max_shape[0], max_shape[1], 2), dtype=np.float32)
blob[0, 0:flo.shape[0], 0:flo.shape[1], :] = flo
channel_swap = (0, 3, 1, 2)
blob = blob.transpose(channel_swap)
return blob
def pad_image_data(list_of_blobs):
max_shape = blob_utils.get_max_shape(
[blobs['data'].shape[-2:] for blobs in list_of_blobs])
output_list = []
for blobs in list_of_blobs:
c, h, w = blobs['data'].shape[-3:]
if cfg.MODEL.LR_VIEW_ON or cfg.MODEL.GIF_ON or cfg.MODEL.LRASY_MAHA_ON:
data_padded = np.zeros((2, 3, max_shape[0], max_shape[1]),
dtype=np.float32)
data_padded[:, :, :h, :w] = blobs['data']
else:
data_padded = np.zeros((c, max_shape[0], max_shape[1]),
dtype=np.float32)
data_padded[:c, :h, :w] = blobs['data']
blobs['data'] = data_padded
output_list.append(blobs)
return output_list
