def _convert_to_example(image_data, shape, bboxes, labels, labels_text):
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
# pylint: enable=expression-not-assigned
image_format = b'PNG'
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)
}))
return example
def _convert_to_example(image_data, shape, charbb, bbox, label,imname):
nbbox = np.array(bbox)
ymin = list(nbbox[:, 0])
xmin = list(nbbox[:, 1])
ymax = list(nbbox[:, 2])
xmax = list(nbbox[:, 3])
#print 'shape: {}, height:{}, width:{}'.format(shape,shape[0],shape[1])
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/x0': float_feature(charbb[0,0,:].tolist()),
'image/object/bbox/x1': float_feature(charbb[0,1,:].tolist()),
'image/object/bbox/x2': float_feature(charbb[0,2,:].tolist()),
'image/object/bbox/x3': float_feature(charbb[0,3,:].tolist()),
'image/object/bbox/y0': float_feature(charbb[1,0,:].tolist()),
'image/object/bbox/y1': float_feature(charbb[1,1,:].tolist()),
'image/object/bbox/y2': float_feature(charbb[1,2,:].tolist()),
'image/object/bbox/y3': float_feature(charbb[1,3,:].tolist()),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/label': int64_feature(label),
'image/format': bytes_feature('jpeg'),
'image/encoded': bytes_feature(image_data),
'image/name': bytes_feature(imname.tostring()),
}))
return example
def build_example(user, image, text, label, file):
return tf.train.Example(features=tf.train.Features(
feature={
user_key: dataset_utils.bytes_feature(user),
image_key: dataset_utils.float_feature(image),
text_key: dataset_utils.int64_feature(text),
label_key: dataset_utils.int64_feature(label),
file_key: dataset_utils.bytes_feature(file),
}))
def my_image_to_tfexample(image_data, image_format, filename, height, width):
return tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': bytes_feature(image_data),
'image/format': bytes_feature(image_format),
'image/filename': bytes_feature(filename),
'image/height': int64_feature(height),
'image/width': int64_feature(width),
}))
def _process_image(directory, split, name):
# Read the image file.
filename = os.path.join(directory, 'image_2', name + '.png')
image_data = tf.gfile.FastGFile(filename, 'r').read()
# Get shape
img = cv2.imread(filename)
shape = np.shape(img)
label_list = []
type_list = []
bbox_x1_list = []
bbox_y1_list = []
bbox_x2_list = []
bbox_y2_list = []
# If 'test' split, skip annotations
if re.findall(r'train', split):
# Read the txt annotation file.
filename = os.path.join(directory, 'label_2', name + '.txt')
with open(filename) as anno_file:
objects = anno_file.readlines()
for object in objects:
obj_anno = object.split(' ')
type_txt = obj_anno[0].encode('ascii')
if type_txt in CLASSES:
label_list.append(CLASSES[type_txt])
type_list.append(type_txt)
# Bounding Box
bbox_x1 = float(obj_anno[4])
bbox_y1 = float(obj_anno[5])
bbox_x2 = float(obj_anno[6])
bbox_y2 = float(obj_anno[7])
bbox_x1_list.append(bbox_x1)
bbox_y1_list.append(bbox_y1)
bbox_x2_list.append(bbox_x2)
bbox_y2_list.append(bbox_y2)
image_format = b'PNG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': bytes_feature(image_data),
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(bbox_x1_list),
'image/object/bbox/xmax': float_feature(bbox_x2_list),
'image/object/bbox/ymin': float_feature(bbox_y1_list),
'image/object/bbox/ymax': float_feature(bbox_y2_list),
'image/object/bbox/label': int64_feature(label_list),
'image/object/bbox/label_text': bytes_feature(type_list),
}))
return example
def _convert_to_example(image_data, labels, bboxes, mask_data):
"""Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
# pylint: enable=expression-not-assigned
# image_format = b'JPEG'
image_format = b'PNG'
if mask_data is None:
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(512),
'image/width': int64_feature(512),
'image/channels': int64_feature(3),
'image/shape': int64_feature([512, 512, 3]),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)}))
else:
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(512),
'image/width': int64_feature(512),
'image/channels': int64_feature(3),
'image/shape': int64_feature([512, 512, 3]),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data),
'maskimage/encoded': bytes_feature(mask_data)}))
return example
def image_to_tfexample(image_data, image_format, height, width, class_id,
fname):
return tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': dataset_utils.bytes_feature(image_data),
'image/format': dataset_utils.bytes_feature(image_format),
'image/class/label': dataset_utils.int64_feature(class_id),
'image/height': dataset_utils.int64_feature(height),
'image/width': dataset_utils.int64_feature(width),
'image/name': dataset_utils.bytes_feature(fname),
}))
def dict_to_tf_example(image_name,
dataset_directory,
label_map=None,
image_subdirectory='train'):
"""
Args:
image: a single image name
dataset_directory: Path to root directory holding PCam dataset
label_map: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
PCam dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by image is not a valid PNG
"""
# full_path = os.path.join(dataset_directory, image_subdirectory, image_name)
full_path = os.path.join(dataset_directory, image_name)
with tf.io.gfile.GFile(full_path, 'rb') as fid:
encoded = fid.read()
encoded_io = io.BytesIO(encoded)
image = Image.open(encoded_io)
width, height = image.size
format = image.format
image_stat = ImageStat.Stat(image)
mean = image_stat.mean
std = image_stat.stddev
key = hashlib.sha256(encoded).hexdigest()
# if image_subdirectory.lower() == 'test':
# label = -1
# else:
# label = int(label_map[image_name])
label = int(label_map[full_path])
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_utils.int64_feature(height),
'image/width': dataset_utils.int64_feature(width),
'image/filename': dataset_utils.bytes_feature(image_name.encode('utf8')),
'image/fullpath': dataset_utils.bytes_feature(full_path.encode('utf8')),
'image/source_id': dataset_utils.bytes_feature(image_name.encode('utf8')),
'image/key/sha256': dataset_utils.bytes_feature(key.encode('utf8')),
'image/encoded': dataset_utils.bytes_feature(encoded),
'image/format': dataset_utils.bytes_feature(format.encode('utf8')),
'image/class/label': dataset_utils.int64_feature(label),
# 'image/text': dataset_util.bytes_feature('label_text'.encode('utf8'))
'image/mean': dataset_utils.float_list_feature(mean),
'image/std': dataset_utils.float_list_feature(std)
}))
return example
def _convert_to_example(filename, image_data, height, width, current_file_info, common_info):
colorspace = 'RGB'
channels = 3
image_format = 'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_utils.int64_feature(height),
'image/width': dataset_utils.int64_feature(width),
'image/colorspace': dataset_utils.bytes_feature(colorspace),
'image/channels': dataset_utils.int64_feature(channels),
'image/format': dataset_utils.bytes_feature(image_format),
'image/filename': dataset_utils.bytes_feature(os.path.basename(filename)),
'image/encoded': dataset_utils.bytes_feature(image_data)}))
return example
def _convert_to_example(img_data, label):
example = tf.train.Example(features=tf.train.Features(
feature={
'image/data': bytes_feature(img_data),
'image/label': int64_feature(label)
}))
return example
def __convert_to_example(self, image_data, label, name,image_format):
example = tf.train.Example(features=tf.train.Features(feature={
'image/label': int64_feature(label),
'image/filename': bytes_feature(name.encode('utf-8')),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)}))
return example
def _convert_to_example(data_example):
"""Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
xmin = []
ymin = []
xmax = []
ymax = []
shape = []
difficult = []
truncated = []
label_text =[]
for i in range(len(data_example['bboxes'])):
difficult.append(0)
truncated.append(0)
label_text.append(b'face')
for s in data_example['shape']:
shape.append(s)
# print(shape)
image_data = data_example['image']
filename = data_example['name']
for bbox in data_example['bboxes']:
assert len(bbox) == 4
# pylint: disable=expression-not-assigned
xmin.append(bbox['xmin'] / shape[1])
ymin.append(bbox['ymin'] / shape[0])
xmax.append(bbox['xmax'] / shape[1])
ymax.append(bbox['ymax'] / shape[0])
# pylint: enable=expression-not-assigned
# print(xmin)
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(data_example['labels']),
'image/object/bbox/label_text': bytes_feature(label_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/filename': bytes_feature(filename.encode('utf-8')),
'image/encoded': bytes_feature(image_data)}))
return example
def _convert_to_example(image_data, labels, labels_text, bboxes, shape,
difficult, truncated):
"""Build an Example proto for an image example.
:return: Example proto.
"""
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)
}))
return example
def _convert2example(image_data, shape, bboxes, labels,
labels_text, difficult, truncated):
y_min = []
x_min = []
y_max = []
x_max = []
for b in bboxes:
assert len(b) == 4
[xy.append(point) for xy, point in zip([y_min, x_min, y_max, x_max], b)]
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(x_min),
'image/object/bbox/xmax': float_feature(x_max),
'image/object/bbox/ymin': float_feature(y_min),
'image/object/bbox/ymax': float_feature(y_max),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)
}))
return example
def _conver_to_example(image_data, label):
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': dataset_utils.bytes_feature(image_data),
'image/format': dataset_utils.bytes_feature(image_format),
'image/classes/label': dataset_utils.int64_feature(label)
}))
return example
def _convert_to_example(image_data, shape, bbox, label):
nbbox = np.array(bbox)
ymin = list(nbbox[:, 0])
xmin = list(nbbox[:, 1])
ymax = list(nbbox[:, 2])
xmax = list(nbbox[:, 3])
#print 'shape: {}, height:{}, width:{}'.format(shape,shape[0],shape[1])
example = tf.train.Example(features=tf.train.Features(feature={
'image/shape': int64_feature(list(shape)),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/label': int64_feature(label),
'image/format': bytes_feature('jpeg'),
'image/encoded': bytes_feature(image_data),
}))
return example
def _convert_to_example(filename, image_buffer, height, width,
current_file_info, common_info):
"""Build an Example proto for an example.
Args:
filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
height: integer, image height in pixels
width: integer, image width in pixels
current_file_info: equivalent to label: integer, identifier for the ground truth for the network
common_info: a list of tags with format: ('type', 'ambiguous', 'count', 'name', 'id')
Returns:
Example proto
"""
colorspace = 'RGB'
channels = 3
image_format = 'JPEG'
human_readable_tags = DanbooruDataConverter._tag_to_human_readable(
current_file_info, common_info)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_utils.int64_feature(height),
'image/width':
dataset_utils.int64_feature(width),
'image/colorspace':
dataset_utils.bytes_feature(colorspace),
'image/channels':
dataset_utils.int64_feature(channels),
'image/class/label':
dataset_utils.int64_feature(current_file_info),
'image/class/text':
dataset_utils.bytes_feature(human_readable_tags),
'image/format':
dataset_utils.bytes_feature(image_format),
'image/filename':
dataset_utils.bytes_feature(os.path.basename(filename)),
'image/encoded':
dataset_utils.bytes_feature(image_buffer)
}))
return example
def oneshot_to_tfexample(feature_map, class_id):
num = len(
feature_map
) # samples num in one train item, including support set and target
feature = {}
for i in range(num):
feature['feature%d' % i] = dataset_utils.bytes_feature(
feature_map[i].tostring())
feature['label%d' % i] = dataset_utils.int64_feature(class_id[i])
return tf.train.Example(features=tf.train.Features(feature=feature))
def _convert_to_example(image_data, labels, labels_text, bboxes, shape):
"""Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([xmin, ymin, xmax, ymax], b)]
# pylint: enable=expression-not-assigned
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)}))
return example
def _convert_to_example(filename, image_data, height, width,
current_file_info, shared_info):
colorspace = 'RGB'
channels = 3
image_format = 'JPEG'
(x_expanded, y_expanded, w_expanded, h_expanded, image_w, image_h,
tags_id, original_image, face_xywh) = current_file_info
feature = {
'image/x':
dataset_utils.int64_feature(x_expanded),
'image/y':
dataset_utils.int64_feature(y_expanded),
'image/height':
dataset_utils.int64_feature(h_expanded),
'image/width':
dataset_utils.int64_feature(w_expanded),
'image/face_xywh':
dataset_utils.float_feature(face_xywh),
# 'image/left_eye_xywh': dataset_utils.float_feature(left_eye_xywh),
# 'image/right_eye_xywh': dataset_utils.float_feature(right_eye_xywh),
# 'image/mouth_xywh': dataset_utils.float_feature(mouth_xywh),
'image/colorspace':
dataset_utils.bytes_feature(colorspace),
'image/channels':
dataset_utils.int64_feature(channels),
'image/format':
dataset_utils.bytes_feature(image_format),
'image/filename':
dataset_utils.bytes_feature(os.path.basename(filename)),
'image/encoded':
dataset_utils.bytes_feature(image_data),
# Encoding original takes up too much space. Not recommended.
# 'image/original': dataset_utils.bytes_feature(original_image),
}
example = tf.train.Example(features=tf.train.Features(feature=feature))
return example
def _convert_to_example(image_data, gt_lanes, y_samples, shape):
"""Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
if len(gt_lanes) < 5:
padding_list = [-2] * 56
padding = 5 - len(gt_lanes)
for i in range(padding):
gt_lanes.append(padding_list)
image_format = b'JPG'
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/line1': float_feature(gt_lanes[0]),
'image/line2': float_feature(gt_lanes[1]),
'image/line3': float_feature(gt_lanes[2]),
'image/line4': float_feature(gt_lanes[3]),
'image/line5': float_feature(gt_lanes[4]),
'image/ysamples': float_feature(y_samples),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)
}))
return example
def _convert_to_example(image_data, shape, labels, labels_text,
bboxes):
"""Build an Example proto for an image example.
Args:
image_data: string, PNG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
# Transpose bboxes, dimensions and locations.
bboxes = list(map(list, zip(*bboxes)))
# Iterators.
it_bboxes = iter(bboxes)
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data),
'object/label': int64_feature(labels),
'object/label_text': bytes_feature(labels_text),
'object/bbox/xmin': float_feature(next(it_bboxes, [])),
'object/bbox/ymin': float_feature(next(it_bboxes, [])),
'object/bbox/xmax': float_feature(next(it_bboxes, [])),
'object/bbox/ymax': float_feature(next(it_bboxes, [])),
}))
return example
def _convert_to_example(image_data_np, tumor_fully_mask_np, mask_np, liver_mask_np, bbox_np):
"""Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
bbox_np = np.asarray(np.asarray(bbox_np, np.float32) / 512., np.float32)
xmin = []
ymin = []
xmax = []
ymax = []
labels = []
for b in bbox_np:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
labels.append(1)
image_format = b'PNG'
example = tf.train.Example(features=tf.train.Features(feature={
# 'image/height': int64_feature(512),
# 'image/width': int64_feature(512),
# 'image/channels': int64_feature(3),
# 'image/shape': int64_feature([512, 512, 3]),
'image/format': bytes_feature(image_format),
# 'image/encoded': bytes_feature(np.asarray(image_data_np, np.float32).tostring()),
# 'livermask/encoded': bytes_feature(np.asarray(liver_mask_np, np.uint8).tostring()),
# 'fullAnnTumorMask/encoded': bytes_feature(np.asarray(tumor_fully_mask_np, np.uint8).tostring()),
# 'maskimage/encoded': bytes_feature(np.asarray(mask_np, np.uint8).tostring())}))
'image/encoded': EncodedFloatFeature(np.asarray(image_data_np, np.float32)),
'livermask/encoded': EncodedInt64Feature(np.asarray(liver_mask_np, np.int64)),
'fullAnnTumorMask/encoded': EncodedInt64Feature(np.asarray(tumor_fully_mask_np, np.int64)),
'maskimage/encoded': EncodedInt64Feature(np.asarray(mask_np, np.int64)),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels)}))
return example
def _convert_to_example_multiphase_multislice_mask(
nc_image_data, art_image_data, pv_image_data, mask_image_data, labels,
labels_text, bboxes, shape, difficult, truncated):
"""Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
# pylint: enable=expression-not-assigned
image_format = suffix_type
print('image_format is ', image_format)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/mask/encoded': bytes_feature(mask_image_data),
'image/nc/encoded': bytes_feature(nc_image_data),
'image/art/encoded': bytes_feature(art_image_data),
'image/pv/encoded': bytes_feature(pv_image_data)
}))
return example
def create_sequence_examples(question, answer, distractors, word_model):
""" Creates multiple sequence examples for distractor encoder model
Args:
question: string containing question
answer: string containing answer
distractors: list of strings containing distractors
Yields:
tf.train.SequenceExample
"""
try:
sampled_negatives = word_model.most_similar_cosmul(
positive=[answer], topn=200)
except KeyError:
return
distractors_lower = set([d.lower() for d in distractors])
sampled_negatives = [
word_model[w] for w, _ in sampled_negatives
if w.lower() not in distractors_lower
][:100]
for d in distractors:
try:
dist_vector = word_model[d]
except KeyError:
continue
question_feature = bytes_feature(question.encode("utf-8"))
answer_feature = bytes_feature(answer.encode("utf-8"))
distractor_feature = float_list_feature(list(dist_vector))
negative_features = [
float_list_feature(list(n)) for n in sampled_negatives
]
context = tf.train.Features(
feature={
"question": question_feature, "answer": answer_feature,
"distractor": distractor_feature,
"negatives_length": int64_feature(len(negative_features)),
}
)
feature_list = {
"negatives": tf.train.FeatureList(feature=negative_features)
}
yield tf.train.SequenceExample(
feature_lists=tf.train.FeatureLists(feature_list=feature_list),
context=context)
def _convert_to_example(image_data, shape, labels, labels_text,
truncated, occluded, alpha, bboxes,
dimensions, locations, rotation_y):
"""Build an Example proto for an image example.
Args:
image_data: string, PNG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
"""
# Transpose bboxes, dimensions and locations.
bboxes = list(map(list, zip(*bboxes)))
dimensions = list(map(list, zip(*dimensions)))
locations = list(map(list, zip(*locations)))
# Iterators.
it_bboxes = iter(bboxes)
it_dims = iter(dimensions)
its_locs = iter(locations)
image_format = b'PNG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data),
'object/label': int64_feature(labels),
'object/label_text': bytes_feature(labels_text),
'object/truncated': float_feature(truncated),
'object/occluded': int64_feature(occluded),
'object/alpha': float_feature(alpha),
'object/bbox/xmin': float_feature(next(it_bboxes, [])),
'object/bbox/ymin': float_feature(next(it_bboxes, [])),
'object/bbox/xmax': float_feature(next(it_bboxes, [])),
'object/bbox/ymax': float_feature(next(it_bboxes, [])),
'object/dimensions/height': float_feature(next(it_dims, [])),
'object/dimensions/width': float_feature(next(it_dims, [])),
'object/dimensions/length': float_feature(next(it_dims, [])),
'object/location/x': float_feature(next(its_locs, [])),
'object/location/y': float_feature(next(its_locs, [])),
'object/location/z': float_feature(next(its_locs, [])),
'object/rotation_y': float_feature(rotation_y),
}))
return example
def _convert_to_example(image_data, shape, bboxes, labels, labels_text,
difficult, truncated):
"""
Build an Example proto for an image example.
:param image_data:
:param shape:
:param bboxes:
:param labels:
:param labels_text:
:param difficult:
:param truncated:
:return: Example proto
"""
xmin = []
ymin = []
xmax = []
ymax = []
# This three lines convert tuple (which contains a serial of bounding boxes into
# list format.
# e.g. [(1, 2, 3, 4), (5, 6, 7, 8)] ==> a list, each element is a bounding box.
# will be convert to ymin=[1, 5], xmin=[2, 6], ymax=[3, 7] and xmax=[4, 8]
for b in bboxes:
assert len(b) == 4
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)
}))
return example
def _convert_to_example(image_data, labels, labels_text, bboxes, shape,
difficult, truncated):
"""Build an Example proto for an image example. 对于一张图片样例创建一个样例原型
Args:
image_data: string, JPEG encoding of RGB image; JPEG编码格式的RGB图片
labels: list of integers, identifier for the ground truth; 类别码标签集合
labels_text: list of strings, human-readable labels; 类别标签集合
bboxes: list of bounding boxes; each box is a list of integers; bbox集合
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels. 以像素为单位的图片形状
Returns:
Example proto 样本原型
"""
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes: #将bbox的值以元组的方式进行打包进以上四个列表中
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
# pylint: enable=expression-not-assigned
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)
}))
return example
def _convert_to_example(image_data, shape, bboxes, labels,
labels_text, difficult, truncated):
"""
Build an Example proto for an image example.
:param image_data: string, JPEG encoding of RGB iamge;
:param shape: list of 3 integers, image shape in pixels
:param bboxes: list of tuples, each tuple is bounding box os an object
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to
belong to the same label as the image label.
:param labels: list of integers, identifier for the ground truth
:param labels_text: list of strings, human-readable labels.
:return:
An Example proto.
"""
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)
}))
return example
def image_example(image_data_iris, image_format_iris, height_iris, width_iris,
class_id_iris, image_data_face, image_format_face,
height_face, width_face, class_id_face):
return tf.train.Example(features=tf.train.Features(
feature={
'image_iris/encoded': dataset_utils.bytes_feature(image_data_iris),
'image_iris/format': dataset_utils.bytes_feature(
image_format_iris),
'image_iris/class/label': dataset_utils.int64_feature(
class_id_iris),
'image_iris/height': dataset_utils.int64_feature(height_iris),
'image_iris/width': dataset_utils.int64_feature(width_iris),
'image_face/encoded': dataset_utils.bytes_feature(image_data_face),
'image_face/format': dataset_utils.bytes_feature(
image_format_face),
'image_face/class/label': dataset_utils.int64_feature(
class_id_face),
'image_face/height': dataset_utils.int64_feature(height_face),
'image_face/width': dataset_utils.int64_feature(width_face),
}))
def _create_tf_example(image, annotations_list, image_dir):
"""Converts image and annotations to a tf.Example proto.
Args:
image: dict with keys: [u'license', u'file_name', u'coco_url', u'height',
u'width', u'date_captured', u'flickr_url', u'id']
annotations_list:
list of dicts with keys: [u'image_id', u'bbox', u'label',
object[{"category_id", "area", "bbox" : [x,y,width,height],}]]. Notice
that bounding box coordinates in the COCO dataset are given as [x, y,
width, height] tuples using absolute coordinates where x, y represent
the top-left (0-indexed) corner. This function converts to the format
that can be used by the Tensorflow Object Detection API (which is [ymin,
xmin, ymax, xmax] with coordinates normalized relative to image size).
image_dir: directory containing the image files.
Returns:
example: The converted tf.Example
num_annotations_skipped: Number of (invalid) annotations that were ignored.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
image_height = image['height']
image_width = image['width']
filename = image['file_name']
image_id = image['id']
full_path = os.path.join(image_dir, filename)
with tf.gfile.GFile(full_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
key = hashlib.sha256(encoded_jpg).hexdigest()
xmin = []
xmax = []
ymin = []
ymax = []
category_ids = []
area = []
num_annotations_skipped = 0
label = annotations_list['label']
for object_annotations in annotations_list['object']:
(x, y, width, height) = tuple(object_annotations['bbox'])
if width <= 0 or height <= 0:
num_annotations_skipped += 1
continue
if x + width > image_width or y + height > image_height:
num_annotations_skipped += 1
continue
xmin.append(float(x) / image_width)
xmax.append(float(x + width) / image_width)
ymin.append(float(y) / image_height)
ymax.append(float(y + height) / image_height)
category_id = int(object_annotations['category_id'])
category_ids.append(category_id)
area.append(object_annotations['area'])
feature_dict = {
'image/height':
dataset_utils.int64_feature(image_height),
'image/width':
dataset_utils.int64_feature(image_width),
'image/filename':
dataset_utils.bytes_feature(filename.encode('utf8')),
'image/source_id':
dataset_utils.bytes_feature(str(image_id).encode('utf8')),
'image/key/sha256':
dataset_utils.bytes_feature(key.encode('utf8')),
'image/encoded':
dataset_utils.bytes_feature(encoded_jpg),
'image/format':
dataset_utils.bytes_feature('jpeg'.encode('utf8')),
'image/class/label':
dataset_utils.int64_feature(label),
'image/object/bbox/xmin':
dataset_utils.float_list_feature(xmin),
'image/object/bbox/xmax':
dataset_utils.float_list_feature(xmax),
'image/object/bbox/ymin':
dataset_utils.float_list_feature(ymin),
'image/object/bbox/ymax':
dataset_utils.float_list_feature(ymax),
'image/object/class/label':
dataset_utils.int64_feature(label),
'image/object/area':
dataset_utils.float_list_feature(area),
}
example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
return key, example, num_annotations_skipped
def write_images_from_directory(set_directory_name, set_directory_path,
annotations_json, tfrecord_writer):
sequences = sorted(os.listdir(set_directory_path))
for sequence in sequences:
annotations_frames = annotations_json[set_directory_name][sequence][
'frames']
image_path = os.path.join(set_directory_path, sequence + '/')
images = sorted(os.listdir(image_path))
input_height = 480
input_width = 640
input_depth = 3
bboxes = []
labels = []
labels_text = []
difficult = []
truncated = []
for frame in range(len(images)):
sys.stdout.write('\r>> Annotating image %d/%d' %
(frame + 1, len(images)))
bboxes_f = []
labels_f = []
labels_text_f = []
difficult_f = []
truncated_f = []
object_dicts_list = []
if str(frame) in annotations_frames:
object_dicts_list = annotations_frames[str(frame)]
for object_dict in object_dicts_list:
if object_dict['lbl'] == 'person':
#Classify further into person_full and person_occluded
label_f = 'person'
labels_f.append(int(LABELS[label_f][0]))
labels_text_f.append(label_f.encode('ascii'))
pos = object_dict['pos']
ymin = float(pos[1]) / input_height
if ymin < 0.0:
ymin = 0.0
if float(pos[1]) + float(pos[3]) > input_height:
print("FRAME height:", frame, pos[1], pos[3])
ymax = 1.0
else:
ymax = (float(pos[1]) + float(pos[3])) / input_height
xmin = float(pos[0]) / input_width
if xmin < 0.0:
xmin = 0.0
if float(pos[0]) + float(pos[2]) > input_width:
print("FRAME width:", frame, pos[0], pos[2])
xmax = 1.0
else:
xmax = (float(pos[0]) + float(pos[2])) / input_width
bboxes_f.append((ymin, xmin, ymax, xmax))
if object_dict['occl'] == 1:
truncated_f.append(1)
else:
truncated_f.append(0)
difficult_f.append(0)
#elif object_dict['lbl'] == 'person?':
# truncated_f.append(0)
# difficult_f.append(1)
#else:
# truncated_f.append(0)
# difficult_f.append(0)
# Can check whether the object is occluded or not by
# accessing object_dict['ocl'] == 1, if its 1, then it
# is occluded. The associated bbox for the predicted
# object (predicting stuff thats not occluded) is then
# object_dict['pos']. If you just want the bbox for
# what's visible, do object_dict['posv']
bboxes.append(bboxes_f)
labels.append(labels_f)
labels_text.append(labels_text_f)
difficult.append(difficult_f)
truncated.append(truncated_f)
for i, imagename in enumerate(images):
sys.stdout.write('\r>> Converting image %d/%d' %
(i + 1, len(images)))
sys.stdout.flush()
image_file = image_path + imagename
image_data = tf.gfile.FastGFile(image_file, 'rb').read()
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes[i]:
[
l.append(point)
for l, point in zip([xmin, ymin, xmax, ymax], b)
]
# if len(bboxes[i]) != 0:
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
int64_feature(input_height),
'image/width':
int64_feature(input_width),
'image/channels':
int64_feature(input_depth),
'image/shape':
int64_feature([input_height, input_width, input_depth]),
'image/object/bbox/xmin':
float_feature(xmin),
'image/object/bbox/xmax':
float_feature(xmax),
'image/object/bbox/ymin':
float_feature(ymin),
'image/object/bbox/ymax':
float_feature(ymax),
'image/object/bbox/label':
int64_feature(labels[i]),
'image/object/bbox/label_text':
bytes_feature(labels_text[i]),
'image/object/bbox/difficult':
int64_feature(difficult[i]),
'image/object/bbox/truncated':
int64_feature(truncated[i]),
'image/format':
bytes_feature(image_format),
'image/encoded':
bytes_feature(image_data)
}))
tfrecord_writer.write(example.SerializeToString())