def load_idl_tf(idlfile, H, jitter):
"""Take the idlfile and net configuration and create a generator
that outputs a jittered version of a random image from the annolist
that is mean corrected."""
annolist = al.parse(idlfile)
annos = []
for anno in annolist:
anno.imageName = os.path.join(
os.path.dirname(os.path.realpath(idlfile)), anno.imageName)
annos.append(anno)
random.seed(0)
if H['data']['truncate_data']:
annos = annos[:10]
for epoch in itertools.count():
random.shuffle(annos)
for anno in annos:
try:
if 'grayscale' in H and 'grayscale_prob' in H:
I = imread(anno.imageName, mode = 'RGB' if random.random() < H['grayscale_prob'] else 'L')
if len(I.shape) < 3:
I = cv2.cvtColor(I, cv2.COLOR_GRAY2RGB)
else:
if len(I.shape) < 3:
continue
I = imread(anno.imageName, mode = 'RGB')
if I.shape[0] != H["image_height"] or I.shape[1] != H["image_width"]:
if epoch == 0:
anno = rescale_boxes(I.shape, anno, H["image_height"], H["image_width"])
I = imresize(I, (H["image_height"], H["image_width"]), interp='cubic')
if jitter:
jitter_scale_min=0.9
jitter_scale_max=1.1
jitter_offset=16
I, anno = annotation_jitter(I,
anno, target_width=H["image_width"],
target_height=H["image_height"],
jitter_scale_min=jitter_scale_min,
jitter_scale_max=jitter_scale_max,
jitter_offset=jitter_offset)
boxes, flags = annotation_to_h5(H,
anno,
H["grid_width"],
H["grid_height"],
H["rnn_len"])
yield {"image": I, "boxes": boxes, "flags": flags}
except Exception as exc:
print(exc)
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