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 origin_anno in annos:
tiles = preprocess_image(deepcopy(origin_anno), H)
for I, anno in tiles:
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)
yield {"image": I, "boxes": boxes, "flags": flags}
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 = cv2.imread(anno.imageName)
cv2.cvtColor(I, cv2.COLOR_BGR2RGB)
if len(I.shape) < 3:
I = cv2.cvtColor(I, cv2.COLOR_GRAY2RGB)
else:
if len(I.shape) < 3:
continue
I = cv2.imread(anno.imageName)
cv2.cvtColor(I, cv2.COLOR_BGR2RGB)
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 = cv2.resize(I, (H["image_width"], H["image_height"]),
interpolation=cv2.INTER_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)
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:
I = imread(anno.imageName)
if I.shape[2] == 4:
I = I[:, :, :3]
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}
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,
root_dir=H['data']['root_dir'] if 'root_dir' in H['data'] else './')
augmenter = Augmentation()
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')
labels = np.array([[r.x1, r.y1, r.x2, r.y2]
for r in anno.rects])
if len(labels) == 0:
labels = np.zeros((0, 4))
I, labels, _ = augmenter(I, labels, np.zeros((len(labels), 1)))
new_rects = []
for box in labels:
r = al.AnnoRect()
r.x1, r.y1, r.x2, r.y2 = box
new_rects.append(r)
new_anno = al.Annotation()
new_anno.imageName = anno.imageName
new_anno.imagePath = anno.imagePath
new_anno.rects = new_rects
anno = new_anno
# img = I[:, :, (2,1,0)].copy()
# for r in anno.rects:
# cv2.rectangle(img, tuple(map(int, (r.x1, r.y1))), tuple(map(int, (r.x2, r.y2))), (0,0,255))
# cv2.imwrite('test.jpg', img)
# pdb.set_trace()
if I.shape[0] != H["image_height"] or I.shape[1] != H[
"image_width"]:
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)