def load(self, add_gt=True):
with timed_operation("Load groundtruth boxes from {}".format(
self.tfrecords_pattern)):
examples = self.get_examples(self.tfrecords_pattern)
result = []
skipped = 0
for i, example in enumerate(examples):
image_path = example_to_str(example, 'image_path')
old_path = example_to_str(example, 'path')
w = example_to_int(example, 'width')
h = example_to_int(example, 'height')
bboxes = example_to_numpy(example, 'bboxes', np.float32,
(-1, 4))
if bboxes.shape[0] == 0:
skipped += 1
assert len(bboxes.shape) == 2
assert bboxes.shape[1] == 4
lbl = example_to_int(example, 'label') + 1 # BACKGROUND is 0
labels = np.asarray(list(repeat(lbl, len(bboxes))))
is_crowd = np.zeros(len(bboxes))
result.append({
'height': h,
'width': w,
'id': old_path,
'file_name': image_path,
'boxes': bboxes,
'class': labels,
'is_crowd': is_crowd,
'label': lbl - 1
})
logger.info('{} images have zero boxes.'.format(skipped))
return result
def create_new_example(example,
inbreast_corrections=None,
suffix='',
flip_label_gen=False):
# Save image as png
image = get_image(example, 'image', suffix=suffix)
old_path = example_to_str(example, 'path')
old_path_with_suffix = old_path + suffix
new_path = os.path.join(img_dir, old_path_with_suffix.replace(
'/', '_')) + '.png'
to_png(image, new_path)
mask = get_image(example, 'mask', suffix)
old_mask_path = example_to_str(example, 'path') + suffix + '_mask'
new_mask_path = os.path.join(img_dir, old_mask_path.replace('/',
'_')) + '.png'
to_png(mask, new_mask_path)
# Convert mask to bboxes
bboxes = mask_to_boxes(mask)
lbl = example_to_int(example, 'label')
lbl = inbreast_label(old_path, lbl, inbreast_corrections)
if suffix and flip_label_gen:
print("Swapping label for image: {}".format(old_path_with_suffix))
lbl = int(not bool(lbl)) # Swap the label
# Save it all into Example proto
features = feature_dict(img_path=new_path,
old_path=old_path_with_suffix,
bboxes=bboxes,
width=np.int64(image.shape[1]),
height=np.int64(image.shape[0]),
label=np.int64(lbl))
return to_example(features)
def parse_example_no_session(proto):
def get_image(example, feature_name, suffix=''):
w = features.example_to_int(example, 'width')
h = features.example_to_int(example, 'height')
return features.example_to_numpy(example, feature_name + suffix, np.float32, (h, w))
path = features.example_to_str(proto, 'path')
image = get_image(proto, 'image')
mask = get_image(proto, 'mask')
imgs = [normalize_image_np(img, img.shape) for img in [image, mask]]
concat = np.concatenate(imgs, axis=-1)
return concat, image, mask, features.example_to_int(proto, 'label'), path
def convert(examples, img_dir):
os.makedirs(img_dir, exist_ok=True)
for example in examples:
# Save image as png
image = get_image(example, 'image')
old_path = example_to_str(example, 'path')
new_path = os.path.join(img_dir, old_path.replace('/', '_'))
to_png(image, new_path)
# Convert mask to bboxes
mask = get_image(example, 'mask')
bboxes = mask_to_boxes(mask)
# Save it all into Example proto
features = feature_dict(
img_path=new_path,
old_path=old_path,
bboxes=bboxes,
width=np.int64(image.shape[1]),
height=np.int64(image.shape[0]),
label=np.int64(example_to_int(example, 'label')))
yield to_example(features)
def augment_example(example):
image = get_image(example, 'image')
img_path = example_to_str(example, 'path')
mask = get_image(example, 'mask')
aug_image, aug_mask = augment_image(image, mask)
lbl = example_to_int(example, 'label')
# Save it all into Example proto
assert image.shape[0] == height
assert image.shape[1] == width
features = to_feature_dict(img_path=img_path,
img=image,
mask=mask,
width=np.int64(image.shape[1]),
height=np.int64(image.shape[0]),
label=np.int64(lbl))
features.update(
to_feature_dict(img_path=img_path,
img=aug_image,
mask=aug_mask,
width=np.int64(image.shape[1]),
height=np.int64(image.shape[0]),
label=np.int64(lbl),
suffix='_gen'))
return to_example(features)
def get_image(example, feature_name, suffix=''):
w = features.example_to_int(example, 'width')
h = features.example_to_int(example, 'height')
return features.example_to_numpy(example, feature_name + suffix, np.float32, (h, w))