def create_tf_example(image,
labels,
points,
img_file,
id):
image_height = image['height']
image_width = image['width']
filename = image['file_name']
if USE_INDEX_IN_FILE:
file_index = int(filename[filename.find("_") + 1:])
else:
file_index = id
with tf.gfile.GFile(img_file, 'rb') as fid:
encoded_jpg = fid.read()
xs = []
ys = []
category_ids = []
encoded_mask_png = []
num_annotations_skipped = 0
print("ann size:", len(labels))
for label,point in zip(labels,points):
x,y = point
category_id = int(label)
if not category_id_filter(category_id):
num_annotations_skipped += 1
continue
xs.append(float(x) / image_width)
ys.append(float(y) / image_height)
category_ids.append(category_id)
feature_dict = {
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'image/filename':
dataset_util.bytes_feature(filename.encode('utf8')),
'image/file_index': dataset_util.int64_feature(file_index),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/point/x':
dataset_util.float_list_feature(xs),
'image/object/point/y':
dataset_util.float_list_feature(ys),
'image/object/class/label':
dataset_util.int64_list_feature(category_ids),
}
example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
if example is None:
print("None example")
return None, None
return example, num_annotations_skipped
def create_tf_example(image, annotations):
global src_file_index
image_height = image['img_height']
image_width = image['img_width']
img_path = image['img_path']
if RECORD_IMG_SIZE is None:
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
else:
img = wmli.imread(img_path)
img = wmli.resize_img(img, RECORD_IMG_SIZE, keep_aspect_ratio=True)
encoded_jpg = wmli.encode_img(img)
xmin = []
xmax = []
ymin = []
ymax = []
is_crowd = []
category_ids = []
for l, box in annotations:
xmin.append(box[1])
xmax.append(box[3])
ymin.append(box[0])
ymax.append(box[2])
is_crowd.append(False)
category_ids.append(l)
if len(xmin) == 0:
return None
feature_dict = {
'image/height': dataset_util.int64_feature(image_height),
'image/width': dataset_util.int64_feature(image_width),
'image/filename': dataset_util.bytes_feature(img_path.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
'image/object/class/label':
dataset_util.int64_list_feature(category_ids),
'image/object/is_crowd': dataset_util.int64_list_feature(is_crowd),
}
example = tf.train.Example(features=tf.train.Features(
feature=feature_dict))
return example
def create_tf_example(image,
annotations_list,
image_dir,
category_index,
include_masks=False):
"""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'segmentation', u'area', u'iscrowd', u'image_id',
u'bbox', u'category_id', u'id']
Notice that bounding box coordinates in the official 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 expected by the Tensorflow Object Detection API (which is
which is [ymin, xmin, ymax, xmax] with coordinates normalized relative
to image size).
image_dir: directory containing the image files.
category_index: a dict containing COCO category information keyed
by the 'id' field of each category. See the
label_map_util.create_category_index function.
include_masks: Whether to include instance segmentations masks
(PNG encoded) in the result. default: False.
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
"""
global src_file_index
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 = []
is_crowd = []
category_names = []
category_ids = []
area = []
encoded_mask_png = []
num_annotations_skipped = 0
repeat_nr = 1
for object_annotations in annotations_list:
(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
category_id = int(object_annotations['category_id'])
category_id = trans_id(category_id)
if not category_id_filter(category_id):
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)
is_crowd.append(object_annotations['iscrowd'])
category_ids.append(category_id)
category_names.append(category_index[reverse_trans_id(category_id)]
['name'].encode('utf8'))
area.append(object_annotations['area'])
if include_masks:
run_len_encoding = mask.frPyObjects(
object_annotations['segmentation'], image_height, image_width)
binary_mask = mask.decode(run_len_encoding)
if not object_annotations['iscrowd']:
binary_mask = np.amax(binary_mask, axis=2)
pil_image = PIL.Image.fromarray(binary_mask)
output_io = io.BytesIO()
pil_image.save(output_io, format='PNG')
encoded_mask_png.append(output_io.getvalue())
feature_dict = {
'image/height': dataset_util.int64_feature(image_height),
'image/width': dataset_util.int64_feature(image_width),
'image/filename': dataset_util.bytes_feature(filename.encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(str(image_id).encode('utf8')),
'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
'image/object/class/label':
dataset_util.int64_list_feature(category_ids),
'image/object/is_crowd': dataset_util.int64_list_feature(is_crowd),
'image/object/area': dataset_util.float_list_feature(area),
'image/file_index': dataset_util.int64_feature(src_file_index),
}
if include_masks:
feature_dict['image/object/mask'] = (
dataset_util.bytes_list_feature(encoded_mask_png))
example = tf.train.Example(features=tf.train.Features(
feature=feature_dict))
#category_id is the key of ID_TO_TEXT
if len(category_ids) == 0:
return None, None, None, None
src_file_index += 1
return key, example, num_annotations_skipped, repeat_nr
def create_tf_example(image, annotations_list, img_file, id):
image_height = image['height']
image_width = image['width']
filename = image['file_name']
if USE_INDEX_IN_FILE:
file_index = int(filename[filename.find("_") + 1:])
else:
file_index = id
with tf.gfile.GFile(img_file, 'rb') as fid:
encoded_jpg = fid.read()
xmin = []
xmax = []
ymin = []
ymax = []
category_ids = []
encoded_mask_png = []
num_annotations_skipped = 0
print("ann size:", len(annotations_list))
for object_annotations in annotations_list:
(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
category_id = int(object_annotations['category_id'])
if not category_id_filter(category_id):
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_ids.append(category_id)
binary_mask = object_annotations["segmentation"]
#cv2.imwrite(wmlu.home_dir("x.jpg"),binary_mask*255)
pil_image = PIL.Image.fromarray(binary_mask)
output_io = io.BytesIO()
pil_image.save(output_io, format='PNG')
encoded_mask_png.append(output_io.getvalue())
#for test
#if len(xmin) == 0:
#return None,None
feature_dict = {
'image/height': dataset_util.int64_feature(image_height),
'image/width': dataset_util.int64_feature(image_width),
'image/filename': dataset_util.bytes_feature(filename.encode('utf8')),
'image/file_index': dataset_util.int64_feature(file_index),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
'image/object/class/label':
dataset_util.int64_list_feature(category_ids),
}
feature_dict['image/object/mask'] = (
dataset_util.bytes_list_feature(encoded_mask_png))
example = tf.train.Example(features=tf.train.Features(
feature=feature_dict))
if example is None:
print("None example")
return None, None
return example, num_annotations_skipped
