def iterate(self):
for image_id in self._get_record_names():
if self._stop_iteration():
# Finish iteration.
return
if self._should_skip(image_id):
continue
try:
annotation_path = self._get_image_annotation(image_id)
image_path = self._get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
except tf.errors.NotFoundError:
tf.logging.debug(
'Error reading image or annotation for "{}".'.format(
image_id))
self.errors += 1
continue
gt_boxes = []
for b in annotation["object"]:
try:
label_id = self.classes.index(b["name"])
except ValueError:
continue
gt_boxes.append({
"label": label_id,
"xmin": b["bndbox"]["xmin"],
"ymin": b["bndbox"]["ymin"],
"xmax": b["bndbox"]["xmax"],
"ymax": b["bndbox"]["ymax"],
})
if len(gt_boxes) == 0:
continue
record = {
"width": annotation["size"]["width"],
"height": annotation["size"]["height"],
"depth": annotation["size"]["depth"],
"filename": annotation["filename"],
"image_raw": image,
"gt_boxes": gt_boxes,
}
self._will_add_record(record)
self.yielded_records += 1
yield record
def iterate(self):
for image_id in self._get_record_names():
if self._stop_iteration():
# Finish iteration.
return
if self._should_skip(image_id):
continue
try:
annotation_path = self._get_image_annotation(image_id)
image_path = self._get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
except tf.errors.NotFoundError:
tf.logging.debug(
'Error reading image or annotation for "{}".'.format(
image_id))
self.errors += 1
continue
gt_boxes = []
for b in annotation['object']:
try:
label_id = self.classes.index(b['name'])
except ValueError:
continue
gt_boxes.append({
'label': label_id,
'xmin': b['bndbox']['xmin'],
'ymin': b['bndbox']['ymin'],
'xmax': b['bndbox']['xmax'],
'ymax': b['bndbox']['ymax'],
})
if len(gt_boxes) == 0:
continue
record = {
'width': annotation['size']['width'],
'height': annotation['size']['height'],
'depth': annotation['size']['depth'],
'filename': annotation['filename'],
'image_raw': image,
'gt_boxes': gt_boxes,
}
self._will_add_record(record)
self.yielded_records += 1
yield record
def iterate(self):
for image_id in self._get_record_names():
if self.yielded_records == self.total:
# Finish iteration based on predefined total.
return
if self._only_filename and image_id != self._only_filename:
# Ignore image when using `only_filename` and it doesn't match
continue
try:
annotation_path = self._get_image_annotation(image_id)
image_path = self._get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
except tf.errors.NotFoundError:
tf.logging.debug(
'Error reading image or annotation for "{}".'.format(
image_id))
self.errors += 1
continue
gt_boxes = []
for b in annotation['object']:
try:
label_id = self.classes.index(b['name'])
except ValueError:
continue
gt_boxes.append({
'label': label_id,
'xmin': b['bndbox']['xmin'],
'ymin': b['bndbox']['ymin'],
'xmax': b['bndbox']['xmax'],
'ymax': b['bndbox']['ymax'],
})
if len(gt_boxes) == 0:
continue
self.yielded_records += 1
yield {
'width': annotation['size']['width'],
'height': annotation['size']['height'],
'depth': annotation['size']['depth'],
'filename': annotation['filename'],
'image_raw': image,
'gt_boxes': gt_boxes,
}
def iterate(self):
for image_id in self._get_record_names():
if self._stop_iteration():
# Finish iteration.
return
if not self._is_valid(image_id):
# Ignore image when using image_id is not valid.
continue
try:
annotation_path = self._get_image_annotation(image_id)
image_path = self._get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
except tf.errors.NotFoundError:
tf.logging.debug(
'Error reading image or annotation for "{}".'.format(
image_id))
self.errors += 1
continue
gt_boxes = []
for b in annotation['object']:
try:
label_id = self.classes.index(b['name'])
except ValueError:
continue
gt_boxes.append({
'label': label_id,
'xmin': b['bndbox']['xmin'],
'ymin': b['bndbox']['ymin'],
'xmax': b['bndbox']['xmax'],
'ymax': b['bndbox']['ymax'],
})
if len(gt_boxes) == 0:
continue
self.yielded_records += 1
yield {
'width': annotation['size']['width'],
'height': annotation['size']['height'],
'depth': annotation['size']['depth'],
'filename': annotation['filename'],
'image_raw': image,
'gt_boxes': gt_boxes,
}
def iterate(self):
for image_id in self._get_record_names():
if self._stop_iteration():
return
try:
annotation_path = f"datasets/annotations/{image_id}.xml"
annotation = read_xml(annotation_path)
image = read_image(annotation["path"])
except tf.errors.NotFoundError:
tf.logging.debug(f"Error reading image or annotation for '{image_id}'.")
self.errors += 1
continue
gt_boxes = list()
for obj in annotation["object"]:
try:
label_id = self.classes.index(obj["name"])
except ValueError:
continue
gt_boxes.append({
"label": label_id,
"xmin": obj["bndbox"]["xmin"],
"ymin": obj["bndbox"]["ymin"],
"xmax": obj["bndbox"]["xmax"],
"ymax": obj["bndbox"]["ymax"],
})
if len(gt_boxes) == 0:
continue
self.yielded_records += 1
yield {
"width": annotation["size"]["width"],
"height": annotation["size"]["height"],
"depth": annotation["size"]["depth"],
"filename": annotation["filename"],
"image_raw": image,
"gt_boxes": gt_boxes,
}
def iterate(self):
for image_id in self._get_record_names():
if self._stop_iteration():
return
if self._should_skip(image_id):
continue
try:
annotation_path = self._get_image_annotation(image_id)
image_path = self._get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
except tf.errors.NotFoundError:
tf.logging.debug(
'Error reading image or annotation for "{}".'.format(
image_id))
self.errors += 1
continue
objects = annotation.get("object")
if objects is None:
# If there's no bounding boxes, we don't want it.
continue
image_pil = Image.open(six.BytesIO(image))
width = image_pil.width
height = image_pil.height
gt_boxes = []
for b in annotation["object"]:
try:
label_id = self.classes.index(self._wnids[b["name"]])
except ValueError:
continue
(xmin, ymin, xmax, ymax) = self._adjust_bbox(
xmin=int(b["bndbox"]["xmin"]),
ymin=int(b["bndbox"]["ymin"]),
xmax=int(b["bndbox"]["xmax"]),
ymax=int(b["bndbox"]["ymax"]),
old_width=int(annotation["size"]["width"]),
old_height=int(annotation["size"]["height"]),
new_width=width,
new_height=height,
)
gt_boxes.append({
"label": label_id,
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
})
if len(gt_boxes) == 0:
continue
record = {
"width": width,
"height": height,
"depth": 3,
"filename": annotation["filename"],
"image_raw": image,
"gt_boxes": gt_boxes,
}
self._will_add_record(record)
self.yielded_records += 1
yield record
def image_to_example(self, classes, image_id):
annotation_path = self.get_image_annotation(image_id)
image_path = self.get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
# TODO: consider alternatives to using Pillow here.
image_pil = Image.open(image_path)
width = image_pil.width
height = image_pil.height
image_pil.close()
obj_vals = {
'label': [],
'xmin': [],
'ymin': [],
'xmax': [],
'ymax': [],
}
objects = annotation.get('object')
if objects is None:
# If there's no bounding boxes, we don't want it
return
for b in annotation['object']:
try:
label_id = classes.index(self._wnids[b['name']])
except ValueError:
continue
(xmin, ymin, xmax, ymax) = adjust_bbox(
xmin=int(b['bndbox']['xmin']), ymin=int(b['bndbox']['ymin']),
xmax=int(b['bndbox']['xmax']), ymax=int(b['bndbox']['ymax']),
old_width=int(annotation['size']['width']),
old_height=int(annotation['size']['height']),
new_width=width, new_height=height
)
obj_vals['label'].append(to_int64(label_id))
obj_vals['xmin'].append(to_int64(xmin))
obj_vals['ymin'].append(to_int64(ymin))
obj_vals['xmax'].append(to_int64(xmax))
obj_vals['ymax'].append(to_int64(ymax))
if len(obj_vals['label']) == 0:
# No bounding box matches the available classes.
return
object_feature_lists = {
'label': tf.train.FeatureList(feature=obj_vals['label']),
'xmin': tf.train.FeatureList(feature=obj_vals['xmin']),
'ymin': tf.train.FeatureList(feature=obj_vals['ymin']),
'xmax': tf.train.FeatureList(feature=obj_vals['xmax']),
'ymax': tf.train.FeatureList(feature=obj_vals['ymax']),
}
object_features = tf.train.FeatureLists(
feature_list=object_feature_lists
)
sample = {
'width': to_int64(width),
'height': to_int64(height),
'depth': to_int64(3),
'filename': to_string(annotation['filename']),
'image_raw': to_bytes(image),
}
# Now build an `Example` protobuf object and save with the writer.
context = tf.train.Features(feature=sample)
example = tf.train.SequenceExample(
feature_lists=object_features, context=context
)
return example
def iterate(self):
for image_id in self._get_record_names():
if self._stop_iteration():
return
if not self._is_valid(image_id):
continue
try:
annotation_path = self._get_image_annotation(image_id)
image_path = self._get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
except tf.errors.NotFoundError:
tf.logging.debug(
'Error reading image or annotation for "{}".'.format(
image_id))
self.errors += 1
continue
objects = annotation.get('object')
if objects is None:
# If there's no bounding boxes, we don't want it.
continue
image_pil = Image.open(six.BytesIO(image))
width = image_pil.width
height = image_pil.height
gt_boxes = []
for b in annotation['object']:
try:
label_id = self.classes.index(self._wnids[b['name']])
except ValueError:
continue
(xmin, ymin, xmax, ymax) = self._adjust_bbox(
xmin=int(b['bndbox']['xmin']),
ymin=int(b['bndbox']['ymin']),
xmax=int(b['bndbox']['xmax']),
ymax=int(b['bndbox']['ymax']),
old_width=int(annotation['size']['width']),
old_height=int(annotation['size']['height']),
new_width=width, new_height=height
)
gt_boxes.append({
'label': label_id,
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
})
if len(gt_boxes) == 0:
continue
self.yielded_records += 1
yield {
'width': width,
'height': height,
'depth': 3,
'filename': annotation['filename'],
'image_raw': image,
'gt_boxes': gt_boxes,
}
def image_to_example(self, classes, image_id):
annotation_path = self.get_image_annotation(image_id)
image_path = self.get_image_path(image_id)
# Read both the image and the annotation into memory.
annotation = read_xml(annotation_path)
image = read_image(image_path)
object_features_values = {
'label': [],
'xmin': [],
'ymin': [],
'xmax': [],
'ymax': [],
}
for b in annotation['object']:
try:
label_id = classes.index(b['name'])
except ValueError:
continue
object_features_values['label'].append(to_int64(label_id))
object_features_values['xmin'].append(to_int64(
b['bndbox']['xmin']))
object_features_values['ymin'].append(to_int64(
b['bndbox']['ymin']))
object_features_values['xmax'].append(to_int64(
b['bndbox']['xmax']))
object_features_values['ymax'].append(to_int64(
b['bndbox']['ymax']))
if len(object_features_values['label']) == 0:
# No bounding box matches the available classes.
return
object_feature_lists = {
'label':
tf.train.FeatureList(feature=object_features_values['label']),
'xmin':
tf.train.FeatureList(feature=object_features_values['xmin']),
'ymin':
tf.train.FeatureList(feature=object_features_values['ymin']),
'xmax':
tf.train.FeatureList(feature=object_features_values['xmax']),
'ymax':
tf.train.FeatureList(feature=object_features_values['ymax']),
}
object_features = tf.train.FeatureLists(
feature_list=object_feature_lists)
sample = {
'width': to_int64(int(annotation['size']['width'])),
'height': to_int64(int(annotation['size']['height'])),
'depth': to_int64(int(annotation['size']['depth'])),
'filename': to_string(annotation['filename']),
'image_raw': to_bytes(image),
}
# Now build an `Example` protobuf object and save with the writer.
context = tf.train.Features(feature=sample)
example = tf.train.SequenceExample(feature_lists=object_features,
context=context)
return example