def testDecodeEmptyPngInstanceMasks(self):
image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
encoded_masks = []
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/mask':
dataset_util.bytes_list_feature(encoded_masks),
'image/height':
dataset_util.int64_feature(10),
'image/width':
dataset_util.int64_feature(10),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape,
[0, 10, 10])
def create_tf_example(example, writer):
height = example['height']
width = example['width']
filename = example['filename']
encoded_image_data = example['encoded_image_data']
image_format = example['image_format']
bboxes = example['bbox']
xmins = [bbox[0]/float(width) for bbox in bboxes] # List of normalized left x coordinates in bounding box (1 per box)
xmaxs = [bbox[2]/float(width) for bbox in bboxes] # List of normalized right x coordinates in bounding box
ymins = [bbox[1]/float(height) for bbox in bboxes] # List of normalized top y coordinates in bounding box (1 per box)
ymaxs = [bbox[3]/float(height) for bbox in bboxes] # List of normalized bottom y coordinates in bounding box
classes_text = example['class_text']
classes = example['class_idx']
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(bytes(filename, "utf-8")),
'image/source_id': dataset_util.bytes_feature(bytes(filename, "utf-8")),
'image/encoded': dataset_util.bytes_feature(encoded_image_data),
'image/format': dataset_util.bytes_feature(bytes(image_format, "utf-8")),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature([bytes(t, "utf-8") for t in classes_text]),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
writer.write(tf_example.SerializeToString())
def create_tf_example(row):
full_path = os.path.join(os.getcwd(), 'images', '{}'.format(row['filename']))
with tf.gfile.GFile(full_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = row['filename'].encode('utf8')
image_format = b'jpg'
xmins = [row['xmin'] / width]
xmaxs = [row['xmax'] / width]
ymins = [row['ymin'] / height]
ymaxs = [row['ymax'] / height]
classes_text = [row['class'].encode('utf8')]
classes = [class_text_to_int(row['class'])]
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
def testDecodeInstanceSegmentation(self):
num_instances = 4
image_height = 5
image_width = 3
# Randomly generate image.
image_tensor = np.random.randint(
256, size=(image_height, image_width, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
# Randomly generate instance segmentation masks.
instance_masks = (
np.random.randint(2, size=(num_instances, image_height,
image_width)).astype(np.float32))
instance_masks_flattened = np.reshape(instance_masks, [-1])
# Randomly generate class labels for each instance.
object_classes = np.random.randint(
100, size=(num_instances)).astype(np.int64)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'image/object/mask':
dataset_util.float_list_feature(instance_masks_flattened),
'image/object/class/label':
dataset_util.int64_list_feature(object_classes)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
.get_shape().as_list()), [4, 5, 3])
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
.get_shape().as_list()), [4])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
instance_masks.astype(np.float32),
tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
self.assertAllEqual(object_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def dict_to_tf_example(data,
label_map_dict,
data_dir):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
"""
img_path = os.path.join(data_dir, data.replace("mask", "images"))
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = 512
height = 512
classes = []
classes_text = []
encoded_mask_png_list = []
mask_png = cv2.imread(os.path.join(data_dir, data), 0)/255
output = io.BytesIO()
encoded_mask_png_list.append(mask_png.save(output, mask_png))
class_name = 'water'
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
feature_dict = {
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].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/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}
feature_dict['image/object/mask'] = (
dataset_util.bytes_list_feature(encoded_mask_png_list))
example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
return example
def createTfExample(singleFileData, path):
# use TensorFlow's GFile function to open the .jpg image matching the current box data
with tf.gfile.GFile(os.path.join(path, '{}'.format(singleFileData.filename)), 'rb') as tensorFlowImageFile:
tensorFlowImage = tensorFlowImageFile.read()
# end with
# get the image width and height via converting from a TensorFlow image to an io library BytesIO image,
# then to a PIL Image, then breaking out the width and height
bytesIoImage = io.BytesIO(tensorFlowImage)
pilImage = Image.open(bytesIoImage)
width, height = pilImage.size
# get the file name from the file data passed in, and set the image format to .jpg
fileName = singleFileData.filename.encode('utf8')
imageFormat = b'jpg'
# declare empty lists for the box x, y, mins and maxes, and the class as text and as an integer
xMins = []
xMaxs = []
yMins = []
yMaxs = []
classesAsText = []
classesAsInts = []
# for each row in the current .xml file's data . . . (each row in the .xml file corresponds to one box)
for index, row in singleFileData.object.iterrows():
xMins.append(row['xmin'] / width)
xMaxs.append(row['xmax'] / width)
yMins.append(row['ymin'] / height)
yMaxs.append(row['ymax'] / height)
classesAsText.append(row['class'].encode('utf8'))
classesAsInts.append(classAsTextToClassAsInt(row['class']))
# end for
# finally we can calculate and return the TensorFlow Example
tfExample = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(fileName),
'image/source_id': dataset_util.bytes_feature(fileName),
'image/encoded': dataset_util.bytes_feature(tensorFlowImage),
'image/format': dataset_util.bytes_feature(imageFormat),
'image/object/bbox/xmin': dataset_util.float_list_feature(xMins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xMaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(yMins),
'image/object/bbox/ymax': dataset_util.float_list_feature(yMaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classesAsText),
'image/object/class/label': dataset_util.int64_list_feature(classesAsInts)}))
return tfExample
def create_tf_example(filename, writer):
lines = open(filename).readlines()
image_filename = lines[0].strip()[1:]
classes_text = []
classes = []
xmins = []
xmaxs = []
ymins = []
ymaxs = []
im = Image.open(image_filename)
arr = io.BytesIO()
im.save(arr, format='PNG')
height = im.height
width = im.width
encoded_image_data = arr.getvalue()
image_format = 'png'
for line in lines[1:]:
line = line.strip()
if line == '':
continue
data = line.split(",")
bbox = list(map(int, map(float, data[:4])))
class_text = data[4].strip()
class_idx = labels.index(class_text)
classes_text.append(class_text)
classes.append(class_idx)
xmins.append(bbox[0]/float(width))
xmaxs.append(bbox[2]/float(width)) # List of normalized right x coordinates in bounding box
ymins.append(bbox[1]/float(height)) # List of normalized top y coordinates in bounding box (1 per box)
ymaxs.append(bbox[3]/float(height)) # List of normalized bottom y coordinates in bounding box
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(bytes(filename, "utf-8")),
'image/source_id': dataset_util.bytes_feature(bytes(filename, "utf-8")),
'image/encoded': dataset_util.bytes_feature(encoded_image_data),
'image/format': dataset_util.bytes_feature(bytes(image_format, "utf-8")),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature([bytes(t, "utf-8") for t in classes_text]),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
writer.write(tf_example.SerializeToString())
def testInstancesNotAvailableByDefault(self):
num_instances = 4
image_height = 5
image_width = 3
# Randomly generate image.
image_tensor = np.random.randint(
256, size=(image_height, image_width, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
# Randomly generate instance segmentation masks.
instance_masks = (
np.random.randint(2, size=(num_instances, image_height,
image_width)).astype(np.float32))
instance_masks_flattened = np.reshape(instance_masks, [-1])
# Randomly generate class labels for each instance.
object_classes = np.random.randint(
100, size=(num_instances)).astype(np.int64)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'image/object/mask':
dataset_util.float_list_feature(instance_masks_flattened),
'image/object/class/label':
dataset_util.int64_list_feature(object_classes)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertTrue(
fields.InputDataFields.groundtruth_instance_masks not in tensor_dict)
def create_tf_example(group, path):
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
xmins.append(row['xmin'] / width)
xmaxs.append(row['xmax'] / width)
ymins.append(row['ymin'] / height)
ymaxs.append(row['ymax'] / height)
classes_text.append(row['class'].encode('utf8'))
classes.append(class_text_to_int(row['class']))
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
def create_tf_record(self):
path = os.path.join(self.get_temp_dir(), 'tfrecord')
writer = tf.python_io.TFRecordWriter(path)
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
flat_mask = (4 * 5) * [1.0]
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/height': dataset_util.int64_feature(4),
'image/width': dataset_util.int64_feature(5),
'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
'image/object/class/label': dataset_util.int64_list_feature([2]),
'image/object/mask': dataset_util.float_list_feature(flat_mask),
}))
writer.write(example.SerializeToString())
writer.close()
return path
def prepare_example(image_path, annotations, label_map_dict):
"""Converts a dictionary with annotations for an image to tf.Example proto.
Args:
image_path: The complete path to image.
annotations: A dictionary representing the annotation of a single object
that appears in the image.
label_map_dict: A map from string label names to integer ids.
Returns:
example: The converted tf.Example.
"""
with tf.gfile.GFile(image_path, 'rb') as fid:
encoded_png = fid.read()
encoded_png_io = io.BytesIO(encoded_png)
image = pil.open(encoded_png_io)
image = np.asarray(image)
key = hashlib.sha256(encoded_png).hexdigest()
width = int(image.shape[1])
height = int(image.shape[0])
xmin_norm = annotations['2d_bbox_left'] / float(width)
ymin_norm = annotations['2d_bbox_top'] / float(height)
xmax_norm = annotations['2d_bbox_right'] / float(width)
ymax_norm = annotations['2d_bbox_bottom'] / float(height)
difficult_obj = [0] * len(xmin_norm)
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(image_path.encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(image_path.encode('utf8')),
'image/key/sha256':
dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded':
dataset_util.bytes_feature(encoded_png),
'image/format':
dataset_util.bytes_feature('png'.encode('utf8')),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmin_norm),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmax_norm),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymin_norm),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymax_norm),
'image/object/class/text':
dataset_util.bytes_list_feature(
[x.encode('utf8') for x in annotations['type']]),
'image/object/class/label':
dataset_util.int64_list_feature(
[label_map_dict[x] for x in annotations['type']]),
'image/object/difficult':
dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated':
dataset_util.float_list_feature(annotations['truncated']),
'image/object/alpha':
dataset_util.float_list_feature(annotations['alpha']),
'image/object/3d_bbox/height':
dataset_util.float_list_feature(annotations['3d_bbox_height']),
'image/object/3d_bbox/width':
dataset_util.float_list_feature(annotations['3d_bbox_width']),
'image/object/3d_bbox/length':
dataset_util.float_list_feature(annotations['3d_bbox_length']),
'image/object/3d_bbox/x':
dataset_util.float_list_feature(annotations['3d_bbox_x']),
'image/object/3d_bbox/y':
dataset_util.float_list_feature(annotations['3d_bbox_y']),
'image/object/3d_bbox/z':
dataset_util.float_list_feature(annotations['3d_bbox_z']),
'image/object/3d_bbox/rot_y':
dataset_util.float_list_feature(annotations['3d_bbox_rot_y']),
}))
return example
def dict_to_tf_example(data,
label_map_dict,
image_subdirectory,
ignore_difficult_instances=False):
img_path = os.path.join(image_subdirectory, data['filename'])
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
for obj in data['object']:
difficult_obj.append(int(0))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
class_name = obj['name']
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
truncated.append(int(0))
poses.append('Unspecified'.encode('utf8'))
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(data['filename'].encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(data['filename'].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/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
'image/object/difficult':
dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated':
dataset_util.int64_list_feature(truncated),
'image/object/view':
dataset_util.bytes_list_feature(poses),
}))
return example
def prepare_example(image_path, annotations, label_map_dict):
"""
Converts a dictionary with annotations for an image to tf.Example proto.
:param image_path: full path to the image
:param annotations: a list object obtained by reading the annotation csv file
:param label_map_dict: a map from string label names to integer ids.
:return: example: The converted tf.Example.
"""
print("encoding %s" % image_path)
with tf.gfile.GFile(image_path, 'rb') as fid:
encoded_png = fid.read()
encoded_png_io = io.BytesIO(encoded_png)
image = pil.open(encoded_png_io)
if image.format != 'PNG':
raise ValueError('Image format error')
key = hashlib.sha256(encoded_png).hexdigest()
# obtain attributes
width, height = image.size
img_filename = image_path.split('/')[-1]
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
occlud = []
xmin.append(int(annotations[2]) / width)
ymin.append(int(annotations[3]) / height)
xmax.append(int(annotations[4]) / width)
ymax.append(int(annotations[5]) / height)
class_name = annotations[1]
classes_text.append(class_name)
classes.append(label_map_dict[class_name])
classes_text = [class_text.encode('utf-8') for class_text in classes_text]
trun, occ = annotations[6].split(',')
truncated.append(int(trun))
occlud.append(int(occ))
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(img_filename.encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(img_filename.encode('utf8')),
'image/key/sha256':
dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded':
dataset_util.bytes_feature(encoded_png),
'image/format':
dataset_util.bytes_feature('png'.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/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
'image/object/truncated':
dataset_util.int64_list_feature(truncated),
'image/object/view':
dataset_util.int64_list_feature(occlud),
}))
return example
def dict_to_tf_example(filename,
mask_path,
label_map_dict,
image_subdirectory,
ignore_difficult_instances=False,
mask_type='png'):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
mask_path: String path to PNG encoded mask.
label_map_dict: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
Pascal dataset directory holding the actual image data.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
smaller file sizes.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(image_subdirectory, filename + '.jpg')
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
mask_cv = cv2.imread(mask_path)
height, width, channels = mask_cv.shape
'''
with tf.gfile.GFile(mask_path, 'rb') as fid:
encoded_mask_png = fid.read()
encoded_png_io = io.BytesIO(encoded_mask_png)
mask = PIL.Image.open(encoded_png_io)
# print("mask pixel", mask[400][200])
if mask.format != 'PNG':
raise ValueError('Mask format not PNG')
print("img_path:", img_path)
print("mask_path:", mask_path)
mask_np = np.asarray(mask)
width = int(data['size']['width'])
height = int(data['size']['height'])
'''
xmins = []
ymins = []
xmaxs = []
ymaxs = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
# masks = []
print('-filename', filename)
bndboxes, masks, buttons_list = analize_mask(mask_cv)
print(
'--bndboxes length is {}, masks length is {} and buttons_list length is {}'
.format(len(bndboxes), len(masks), len(buttons_list)))
for index, bndbox in enumerate(bndboxes):
xmin, xmax, ymin, ymax = bndbox
difficult = bool(0)
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmins.append(xmin / width)
ymins.append(ymin / height)
xmaxs.append(xmax / width)
ymaxs.append(ymax / height)
# classes_text.append(obj['name'].encode('utf8'))
# classes.append(label_map_dict[obj['name']])
# print("label_map_dict[obj['name']]", label_map_dict[obj['name']])
class_name = buttons_list[index]
print('---box ' + str(index + 1) + ' is class_name ' + class_name +
' with label_map_dict[class_name] # ' +
str(label_map_dict[class_name]) + ' xmin: ' + str(xmin) +
', ymin: ' + str(ymin) + ', xmax: ' + str(xmax) + ', ymax: ' +
str(ymax))
count_element(class_name)
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
truncated.append(0)
poses.append('Unspecified'.encode('utf8'))
# mask_remapped = (mask_np != 2).astype(np.uint8)
# masks.append(mask_remapped)
feature_dict = {
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(filename.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(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult':
dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}
if mask_type == 'png':
encoded_mask_png_list = []
for mask in masks:
img = PIL.Image.fromarray(mask)
output = io.BytesIO()
img.save(output, format='PNG')
encoded_mask_png_list.append(output.getvalue())
feature_dict['image/object/mask'] = (
dataset_util.bytes_list_feature(encoded_mask_png_list))
example = tf.train.Example(features=tf.train.Features(
feature=feature_dict))
return example
def _create_tfexample(label_map_dict, image_id, encoded_image,
encoded_next_image, depth, next_depth, flow,
segmentation, extrinsics_dict, next_extrinsics_dict,
tracking_rows, next_tracking_rows,
segmentation_color_map, first_extrinsics_dict):
frame_id = int(image_id.split('_')[1])
assert frame_id == extrinsics_dict[
'frame'] == next_extrinsics_dict['frame'] - 1
next_tracking_row_map = {row['tid']: row for row in next_tracking_rows}
height, width = depth.shape[:2]
extrinsics = np.reshape(
np.array(list(extrinsics_dict.values())[1:], dtype=np.float32), [4, 4])
next_extrinsics = np.reshape(
np.array(list(next_extrinsics_dict.values())[1:], dtype=np.float32),
[4, 4])
first_extrinsics = np.reshape(
np.array(list(first_extrinsics_dict.values())[1:], dtype=np.float32),
[4, 4])
camera_moving = not np.allclose(extrinsics, next_extrinsics)
q_cam1 = q_from_rotation_matrix(extrinsics[:3, :3])
q_cam2 = q_from_rotation_matrix(next_extrinsics[:3, :3])
trans_cam1 = extrinsics[:3, 3]
trans_cam2 = next_extrinsics[:3, 3]
q_cam1_to_cam2 = q_difference(q_cam1, q_cam2)
q_cam2_to_cam1 = q_conjugate(q_cam1_to_cam2)
trans_cam1_to_cam2 = trans_cam2 - q_rotate(q_cam1_to_cam2, trans_cam1)
trans_cam2_to_cam1 = trans_cam1 - q_rotate(q_cam2_to_cam1, trans_cam2)
print(q_cam1_to_cam2)
camera_motion = np.concatenate([
q_cam1_to_cam2, trans_cam1_to_cam2,
np.array([camera_moving], dtype=np.float32)
])
boxes = []
masks = []
classes = []
motions = []
diff = 0
for row in tracking_rows:
next_row = next_tracking_row_map.get(row['tid'])
label = row['orig_label']
tid = row['tid']
# ensure object still tracked in next frame and visible in original frame
if next_row is not None and row['occupr'] > 0.1:
assert frame_id == row['frame'] == next_row['frame'] - 1
box = np.array([row['t'], row['l'], row['b'], row['r']],
dtype=np.float64)
boxes.append(box)
class_id = label_map_dict[label.lower()]
classes.append(class_id)
# find out which color this object corresponds to in the segmentation image
seg_r, seg_g, seg_b = segmentation_color_map['{}:{}'.format(
label, tid)]
mask = ((segmentation[:, :, 0] == seg_r).astype(np.uint8) +
(segmentation[:, :, 1] == seg_g).astype(np.uint8) +
(segmentation[:, :, 2] == seg_b).astype(np.uint8))
mask = (mask == 3).astype(np.uint8)
masks.append(mask)
moving = int(row['moving'])
p1 = _get_pivot(row)
p2 = _get_pivot(next_row)
q1 = _get_q(row)
q2 = _get_q(next_row)
q = q_multiply(q2, q_multiply(q_cam1_to_cam2, q_conjugate(q1)))
p2_cam1 = q_rotate(q_cam2_to_cam1, p2) + trans_cam2_to_cam1
trans = p2_cam1 - q_rotate(q, p1)
if moving == 0:
q = np.array([1, 0, 0, 0], dtype=np.float32)
trans = np.array([0, 0, 0], dtype=np.float32)
mv = np.array([moving], dtype=np.float32)
motion = np.concatenate([q, trans, p1, mv])
diff += np.sum(
np.abs(
q_rotate(q_cam1_to_cam2,
q_rotate(q, p1) + trans) + trans_cam1_to_cam2 -
p2))
motions.append(motion)
print(diff)
if len(boxes) > 0:
boxes = np.stack(boxes, axis=0)
masks = np.stack(masks, axis=0)
motions = np.stack(motions, axis=0)
else:
boxes = np.zeros((0, 5), dtype=np.float32)
masks = np.zeros((0, height, width), dtype=np.float32)
motions = np.zeros((0, 15), dtype=np.float32)
num_instances = boxes.shape[0]
ymins = (boxes[:, 0] / height).tolist()
xmins = (boxes[:, 1] / width).tolist()
ymaxs = (boxes[:, 2] / height).tolist()
xmaxs = (boxes[:, 3] / width).tolist()
index_0, index_1, index_2 = np.nonzero(masks)
key = hashlib.sha256(encoded_image).hexdigest()
camera_intrinsics = np.array([725.0, 620.5, 187.0], dtype=np.float32)
if FLAGS.gt_rigid_flow_from_motion:
example_flow = dense_flow_from_motion(np.expand_dims(depth, 2),
motions, masks, camera_motion,
camera_intrinsics)
else:
example_flow = flow
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(image_id.encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(image_id.encode('utf8')),
'image/encoded':
dataset_util.bytes_feature(encoded_image),
'next_image/encoded':
dataset_util.bytes_feature(encoded_next_image),
'image/format':
dataset_util.bytes_feature('png'.encode('utf8')),
'image/key/sha256':
dataset_util.bytes_feature(key.encode('utf8')),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
'image/object/motion':
dataset_util.float_list_feature(motions.ravel().tolist()),
'image/segmentation/object/count':
dataset_util.int64_feature(num_instances),
'image/segmentation/object/index_0':
dataset_util.int64_list_feature(index_0.tolist()),
'image/segmentation/object/index_1':
dataset_util.int64_list_feature(index_1.tolist()),
'image/segmentation/object/index_2':
dataset_util.int64_list_feature(index_2.tolist()),
'image/segmentation/object/class':
dataset_util.int64_list_feature(classes),
'image/depth':
dataset_util.float_list_feature(depth.ravel().tolist()),
'next_image/depth':
dataset_util.float_list_feature(next_depth.ravel().tolist()),
'image/flow':
dataset_util.float_list_feature(example_flow.ravel().tolist()),
'image/camera/motion':
dataset_util.float_list_feature(camera_motion.tolist()),
'image/camera/intrinsics':
dataset_util.float_list_feature(camera_intrinsics.tolist())
}))
return example, num_instances
def dict_to_tf_example(labels_corners, labels_center, labels_data, params,
label_map_dict, image_dir, image_dir_beliefs,
image_prefix, image_prev_prefix):
width = round(params['pointcloud_grid_map_interface']['grids']['cartesian']
['range']['y'] / params['pointcloud_grid_map_interface']
['grids']['cartesian']['resolution']['y'])
height = round(
params['pointcloud_grid_map_interface']['grids']['cartesian']['range']
['x'] / params['pointcloud_grid_map_interface']['grids']['cartesian']
['resolution']['x'])
xmin = []
ymin = []
xmax = []
ymax = []
x_c = []
y_c = []
w = []
h = []
angle = []
sin_angle = []
cos_angle = []
classes = []
classes_text = []
for idx, label_corner in enumerate(labels_corners):
xmin.append(min(label_corner[0]) / width)
ymin.append(min(label_corner[1]) / height)
xmax.append(max(label_corner[0]) / width)
ymax.append(max(label_corner[1]) / height)
x_min = min(label_corner[0]) / width
y_min = min(label_corner[1]) / height
x_max = max(label_corner[0]) / width
y_max = max(label_corner[1]) / height
if (x_min >= 1) or (y_min >= 1) or (x_max >= 1) or (y_max >= 1):
print(x_min, y_min, x_max, y_max)
raise ValueError('Box Parameters greather than 1.0')
if (x_min <= 0) or (y_min <= 0) or (x_max <= 0) or (y_max <= 0):
raise ValueError('Box Parameters less than 0.0')
x_c.append(labels_center[idx][0])
y_c.append(labels_center[idx][1])
angle_rad = _flipAngle(labels_data[idx].rz)
angle.append(angle_rad)
sin_angle.append(math.sin(2 * angle_rad))
cos_angle.append(math.cos(2 * angle_rad))
vec_s_x = math.cos(angle_rad)
vec_s_y = math.sin(angle_rad)
w_p = labels_data[idx].w / params['pointcloud_grid_map_interface'][
'grids']['cartesian']['resolution']['y']
w_p_s = w_p * math.sqrt(vec_s_x * vec_s_x /
(width * width) + vec_s_y * vec_s_y /
(height * height))
w.append(w_p_s)
l_p = labels_data[idx].l / params['pointcloud_grid_map_interface'][
'grids']['cartesian']['resolution']['x']
l_p_s = l_p * math.sqrt(vec_s_x * vec_s_x /
(height * height) + vec_s_y * vec_s_y /
(width * width))
h.append(l_p_s)
class_name = labels_data[idx].type
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
return tf.train.Example(features=tf.train.Features(
feature={
'id':
dataset_util.bytes_feature(image_prefix.encode('utf8')),
'image/format':
dataset_util.bytes_feature('png'.encode('utf8')),
'layers/height':
dataset_util.int64_feature(height),
'layers/width':
dataset_util.int64_feature(width),
'layers/detections/encoded':
dataset_util.bytes_feature(
_readImage(image_dir, image_prefix, 'detections_cartesian')),
'layers/observations/encoded':
dataset_util.bytes_feature(
_readImage(image_dir, image_prefix, 'observations_cartesian')),
'layers/decay_rate/encoded':
dataset_util.bytes_feature(
_readImage(image_dir, image_prefix, 'decay_rate_cartesian')),
'layers/intensity/encoded':
dataset_util.bytes_feature(
_readImage(image_dir, image_prefix, 'intensity_cartesian')),
'layers/zmin/encoded':
dataset_util.bytes_feature(
_readImage(image_dir, image_prefix,
'z_min_detections_cartesian')),
'layers/zmax/encoded':
dataset_util.bytes_feature(
_readImage(image_dir, image_prefix,
'z_max_detections_cartesian')),
'layers/occlusions/encoded':
dataset_util.bytes_feature(
_readImage(image_dir, image_prefix,
'z_max_occlusions_cartesian')),
'layers/bel_O_FUSED/encoded':
dataset_util.bytes_feature(
_readImage(image_dir_beliefs, image_prefix,
'bel_O_FUSED_cartesian')),
'layers/bel_F_FUSED/encoded':
dataset_util.bytes_feature(
_readImage(image_dir_beliefs, image_prefix,
'bel_F_FUSED_cartesian')),
'layers/bel_U_FUSED/encoded':
dataset_util.bytes_feature(
_readImage(image_dir_beliefs, image_prefix,
'bel_U_FUSED_cartesian')),
'layers/detections_drivingCorridor_FUSED/encoded':
dataset_util.bytes_feature(
_readImage(image_dir_beliefs, image_prefix,
'detections_drivingCorridor_FUSED_cartesian')),
'layers/z_max_detections_FUSED/encoded':
dataset_util.bytes_feature(
_readImage(image_dir_beliefs, image_prefix,
'z_max_detections_FUSED_cartesian')),
'layers/z_min_detections_FUSED/encoded':
dataset_util.bytes_feature(
_readImage(image_dir_beliefs, image_prefix,
'z_min_detections_FUSED_cartesian')),
'layers/observations_z_min_FUSED/encoded':
dataset_util.bytes_feature(
_readImage(image_dir_beliefs, image_prefix,
'observations_z_min_FUSED_cartesian')),
# 'layers_prev/detections/encoded': dataset_util.bytes_feature(
# _readImage(image_dir, image_prev_prefix, 'detections_cartesian')),
# 'layers_prev/observations/encoded': dataset_util.bytes_feature(
# _readImage(image_dir, image_prev_prefix, 'observations_cartesian')),
# 'layers_prev/decay_rate/encoded': dataset_util.bytes_feature(
# _readImage(image_dir, image_prev_prefix, 'decay_rate_cartesian')),
# 'layers_prev/intensity/encoded': dataset_util.bytes_feature(
# _readImage(image_dir, image_prev_prefix, 'intensity_cartesian')),
# 'layers_prev/zmin/encoded': dataset_util.bytes_feature(
# _readImage(image_dir, image_prev_prefix, 'z_min_detections_cartesian')),
# 'layers_prev/zmax/encoded': dataset_util.bytes_feature(
# _readImage(image_dir, image_prev_prefix, 'z_max_detections_cartesian')),
# 'layers_prev/occlusions/encoded': dataset_util.bytes_feature(
# _readImage(image_dir, image_prev_prefix, 'z_max_occlusions_cartesian')),
'boxes/aligned/x_min':
dataset_util.float_list_feature(xmin),
'boxes/aligned/x_max':
dataset_util.float_list_feature(xmax),
'boxes/aligned/y_min':
dataset_util.float_list_feature(ymin),
'boxes/aligned/y_max':
dataset_util.float_list_feature(ymax),
'boxes/inclined/x_c':
dataset_util.float_list_feature(x_c),
'boxes/inclined/y_c':
dataset_util.float_list_feature(y_c),
'boxes/inclined/w':
dataset_util.float_list_feature(w),
'boxes/inclined/h':
dataset_util.float_list_feature(h),
'boxes/inclined/angle':
dataset_util.float_list_feature(angle),
'boxes/inclined/sin_angle':
dataset_util.float_list_feature(sin_angle),
'boxes/inclined/cos_angle':
dataset_util.float_list_feature(cos_angle),
'boxes/class/text':
dataset_util.bytes_list_feature(classes_text),
'boxes/class/label':
dataset_util.int64_list_feature(classes),
}))
def create_tf_example(group, path):
with tf.gfile.GFile(
os.path.join(path, '{}'.format(group.filename.split("/")[-1])),
'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
xmins.append(row['Upper left corner X'] / width)
xmaxs.append(row['Lower right corner X'] / width)
ymins.append(row['Upper left corner Y'] / height)
ymaxs.append(row['Lower right corner Y'] / height)
lightColor = ""
if "go" in row['Annotation tag']:
lightColor = "Green"
if "stop" in row['Annotation tag']:
lightColor = "Red"
if "warning" in row['Annotation tag']:
lightColor = "Yellow"
classes_text.append(lightColor.encode())
classes.append(int(LABEL_DICT[lightColor]))
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
return tf_example
embed()
xmins.append(xmin / width)
xmaxs.append(xmax / width)
ymins.append(ymin / height)
ymaxs.append(ymax / height)
classes_text.append(tf.compat.as_bytes(text))
classes.append(index)
# cv2.rectangle(_img, (xmin, ymin), (xmax, ymax), (255,0,0), 2)
# cv2.putText(_img, text, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)
# _path = os.path.join(out_path, os.path.splitext(data[key]["filename"])[0]+"_ann"+os.path.splitext(data[key]["filename"])[1])
# cv2.imwrite(_path, _img)
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
def create_tf_record(self,
has_additional_channels=False,
num_shards=1,
num_examples_per_shard=1):
def dummy_jpeg_fn():
image_tensor = np.random.randint(255,
size=(4, 5, 3)).astype(np.uint8)
additional_channels_tensor = np.random.randint(
255, size=(4, 5, 1)).astype(np.uint8)
encoded_jpeg = tf.image.encode_jpeg(image_tensor)
encoded_additional_channels_jpeg = tf.image.encode_jpeg(
additional_channels_tensor)
return encoded_jpeg, encoded_additional_channels_jpeg
encoded_jpeg, encoded_additional_channels_jpeg = self.execute(
dummy_jpeg_fn, [])
tmp_dir = self.get_temp_dir()
flat_mask = (4 * 5) * [1.0]
for i in range(num_shards):
path = os.path.join(tmp_dir, '%05d.tfrecord' % i)
writer = tf.python_io.TFRecordWriter(path)
for j in range(num_examples_per_shard):
if num_shards > 1:
source_id = (str(i) + '_' + str(j)).encode()
else:
source_id = str(j).encode()
features = {
'image/source_id':
dataset_util.bytes_feature(source_id),
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/height':
dataset_util.int64_feature(4),
'image/width':
dataset_util.int64_feature(5),
'image/object/bbox/xmin':
dataset_util.float_list_feature([0.0]),
'image/object/bbox/xmax':
dataset_util.float_list_feature([1.0]),
'image/object/bbox/ymin':
dataset_util.float_list_feature([0.0]),
'image/object/bbox/ymax':
dataset_util.float_list_feature([1.0]),
'image/object/class/label':
dataset_util.int64_list_feature([2]),
'image/object/mask':
dataset_util.float_list_feature(flat_mask),
}
if has_additional_channels:
additional_channels_key = 'image/additional_channels/encoded'
features[
additional_channels_key] = dataset_util.bytes_list_feature(
[encoded_additional_channels_jpeg] * 2)
example = tf.train.Example(features=tf.train.Features(
feature=features))
writer.write(example.SerializeToString())
writer.close()
return os.path.join(self.get_temp_dir(), '?????.tfrecord')
def create_tf_example(example, mode):
# Some images referenced in the xml aren't in the dataset
try:
activ_image = Image.open(join(example['path_to_image'],
example['file_name']),
mode='r')
except:
print("Could not find {0}; skipping".format(
join(example['path_to_image'], example['file_name'])))
return None
if USE_GRAYSCALE:
activ_image = activ_image.convert('L').convert('RGB')
# Normalized x,y coordinates
width, height = activ_image.size
xmins = [x / float(width) for x in example['bbox_xmins']]
xmaxs = [x / float(width) for x in example['bbox_xmaxs']]
ymins = [y / float(height) for y in example['bbox_ymins']]
ymaxs = [y / float(height) for y in example['bbox_ymaxs']]
# Skip the image if it doesn't match INPUT_WIDTH x INPUT_HEIGHT
if mode != "test" and ONE_IMAGE_SIZE and (height != INPUT_HEIGHT
or width != INPUT_WIDTH):
#print("Input image does not match expected size {0}x{1}; skipping".format(INPUT_WIDTH,INPUT_HEIGHT))
return None
# If needed, resize now that the normalized box coordinates have been calculated
if width > 1000 or height > 1000:
basewidth = 1000
wpercent = (basewidth / float(width))
hsize = int((float(height) * float(wpercent)))
activ_image = activ_image.resize((basewidth, hsize), Image.ANTIALIAS)
width, height = activ_image.size
imgByteArr = io.BytesIO()
if example['extension'] in ['jpg', 'jpeg']:
activ_image.save(imgByteArr, format='JPEG')
else:
activ_image.save(imgByteArr, format='PNG')
encoded_image_data = imgByteArr.getvalue() # Encoded image bytes
filename = example['file_name'].encode(
'utf-8') # Filename of the image. Empty if image is not from file
image_format = example['image_format'] # b'jpeg' or b'png'
# List of string class name of bounding box (1 per box)
classes_text = [example['label'] for i in range(len(xmins))]
# List of integer class id of bounding box (1 per box)
classes = [example['label_num'] for i in range(len(xmins))]
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_image_data),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
return tf_example
def create_tf_record(data,
label_map_dict,
is_yaml=False,
ignore_difficult_instances=False):
"""
Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
:param data: dict holding (XML or YAML) fields for a single image (obtained by running dataset_util.recursive_parse_xml_to_dict)
:param label_map_dict: A map from string label names to integers ids.
:param ignore_difficult_instances: Whether to skip difficult instances in the dataset (default: False).
Returns:
:return tf_example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
with tf.gfile.GFile(data['path'], 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPG')
key = hashlib.sha256(encoded_jpg).hexdigest()
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
if is_yaml:
width = int(data['width'])
height = int(data['height'])
filename = data['path'].encode('utf8')
for box in data['boxes']:
difficult_obj.append(0)
xmin.append(float(box['x_min']) / width)
ymin.append(float(box['y_min']) / height)
xmax.append(float(box['x_max']) / width)
ymax.append(float(box['y_max']) / height)
classes_text.append(box['label'].encode('utf8'))
classes.append(label_map_dict[box['label']])
truncated.append(0)
poses.append(r'Unspecified'.encode('utf8'))
else:
width = int(data['size']['width'])
height = int(data['size']['height'])
filename = data['filename'].encode('utf8')
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
classes_text.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/key/sha256':
dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature(r'jpg'.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/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
'image/object/difficult':
dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated':
dataset_util.int64_list_feature(truncated),
'image/object/view':
dataset_util.bytes_list_feature(poses),
}))
return tf_example
def main(_):
xml_file = FLAGS.xml
logging.info('Reading from DLIB %s dataset.', xml_file)
# find all labels in xml
xml_data = xml.etree.ElementTree.parse( xml_file ).getroot()
labels = find_all_labels_recursive(xml_data)
labels = sorted(labels)
print(labels)
# create pbtxt
label_map_filename = os.path.splitext(FLAGS.output_path)[0] + '.pbtxt'
with open(label_map_filename,'w') as f:
for idx,lbl in enumerate(labels):
f.write("item {{\n id: {}\n name: '{}'\n}}\n\n".format(idx+1,lbl))
# create TF record file
writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
xml_file_dir = os.path.dirname(xml_file)
print('XML file directory is {}.'.format(xml_file_dir))
for img_node in xml_data.find('images'):
# get image path
img_filename = img_node.attrib['file']
if os.path.isabs( img_filename ):
img_path = img_filename
else:
img_path = os.path.join(xml_file_dir,img_filename)
# read image
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width,height = image.size
print("Image {}: {}x{}".format(os.path.basename(img_filename),width,height))
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
difficult_obj = []
# read annotations
for annot_node in img_node:
label_node = annot_node.find('label')
if label_node is not None:
label = label_node.text
else:
print("Skipping annotation because label is empty")
bbox = dict(ymin=int(annot_node.attrib['top']),
xmin=int(annot_node.attrib['left']),
xmax=int(annot_node.attrib['left'])+int(annot_node.attrib['width']),
ymax=int(annot_node.attrib['top'])+int(annot_node.attrib['height']))
is_truncated = bbox['xmin'] < 0 or bbox['xmax'] >= width or bbox['ymin'] < 0 or bbox['ymax'] >= height
is_difficult = False
xmin.append(float(bbox['xmin']) / width)
ymin.append(float(bbox['ymin']) / height)
xmax.append(float(bbox['xmax']) / width)
ymax.append(float(bbox['ymax']) / height)
classes_text.append(label.encode('utf8'))
classes.append(labels.index(label)+1)
truncated.append(int(is_truncated))
difficult_obj.append(int(is_difficult))
if len(classes) > 0:
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(img_filename.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(img_filename.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/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
#'image/object/view': dataset_util.bytes_list_feature(poses),
}))
writer.write(example.SerializeToString())
# print(data)
#examples_list = dataset_util.read_examples_list(examples_path)
# for idx, example in enumerate(examples_list):
# if idx % 100 == 0:
# logging.info('On image %d of %d', idx, len(examples_list))
# path = os.path.join(annotations_dir, example + '.xml')
# with tf.gfile.GFile(path, 'r') as fid:
# xml_str = fid.read()
# xml = etree.fromstring(xml_str)
# data = dataset_util.recursive_parse_xml_to_dict(xml)
# tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict,
# FLAGS.ignore_difficult_instances)
# writer.write(tf_example.SerializeToString())
writer.close()
def dict_to_tf_example(data, label_map_dict):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
label_map_dict: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
PASCAL dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
encoded_jpg_io = io.BytesIO()
image = data['image']
image.save(encoded_jpg_io, "JPEG", quality=80)
encoded_jpg = encoded_jpg_io.getvalue()
key = hashlib.sha256(encoded_jpg).hexdigest()
width, height = image.size
xmin = []
ymin = []
xmax = []
ymax = []
rotation = []
classes = []
classes_text = []
truncated = []
poses = []
masks = []
difficult_obj = []
for obj in data['object']:
difficult = bool(int(obj['difficult']))
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
rotation.append(float(obj['rotation']))
masks.append(obj['mask'])
classes_text.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
mask = np.stack(masks)
encoded_mask = pn_encode(mask.flatten()).tolist()
mask_length = len(encoded_mask)
print('mask encode:', mask.shape, '->', len(encoded_mask)) ###
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].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/rotation': dataset_util.float_list_feature(rotation),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
'image/segmentation/object': dataset_util.int64_list_feature(encoded_mask),
'image/segmentation/length': dataset_util.int64_feature(mask_length),
'image/segmentation/object/class': dataset_util.int64_list_feature(classes),
}))
return example
def create_tf_example(image,
annotations_list,
image_dir,
category_index,
include_masks=True):
"""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
"""
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
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
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_id = int(object_annotations['category_id'])
category_ids.append(category_id)
category_names.append(
category_index[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/text':
dataset_util.bytes_list_feature(category_names),
'image/object/is_crowd': dataset_util.int64_list_feature(is_crowd),
'image/object/area': dataset_util.float_list_feature(area),
}
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))
return key, example, num_annotations_skipped
def dict_to_tf_example(data,
dataset_directory,
set_name,
label_map_dict,
ignore_difficult_instances=False):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
set_name: name of the set training, validation or test
label_map_dict: A map from string label names to integers ids.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(set_name, data['filename'])
full_path = os.path.join(dataset_directory, img_path)
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)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
if 'object' not in data:
data['object'] = []
for obj in data['object']:
if obj['name'] in label_map_dict:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
classes_text.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].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/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}))
return example
def dict_to_tf_example(data,
dataset_directory,
label_map_dict,
ignore_difficult_instances=False,
image_subdirectory='JPEGImages'):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding COWFACE XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
label_map_dict: A map from string label names to integers ids.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
image_subdirectory: String specifying subdirectory within the
PASCAL dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']
tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict, FLAGS.ignore_difficult_instances)
data:
{'source':
{'database': 'Unknown'},
'object': [
{
'bndbox': {'xmin': '115', 'ymin': '5', 'ymax': '124', 'xmax': '261'}, 'difficult': '0', 'pose': 'Unspecified', 'name': 'dog', 'truncated': '0'
}
],
'filename': '1.jpg', 'segmented': '0', 'path': '/home/liuhy/Downloads/dog/1.jpg', 'folder': 'dog',
'size': {'width': '323', 'depth': '3', 'height': '156'}
}
"""
img_path = os.path.join(data['folder'], image_subdirectory, data['filename']) # dog/JPEGImages/1.jpg
full_path = os.path.join(dataset_directory, img_path) # FLAGS.data_dir/dog/JPEGImages/1.jpg
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)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
if 'object' in data:
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
classes_text.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].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/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}))
return example
def create_tf_example(example: list):
"""
Creates a tf.train.Example object from an image and its labels which can be used in
the training pipeline for the object detector.
Args:
example: list containing information about the image and its labels.
Returns: information of example parsed into a tf.train.Example object
"""
width = int(example[0])
height = int(example[1])
filename = str.encode(example[2])
with tf.gfile.GFile(example[3], 'rb') as f:
encoded_image_data = bytes(f.read())
image_format = b'jpg'
boxes = example[5]
xmins = []
ymins = []
xmaxs = []
ymaxs = []
for b in boxes:
xmins.append(b[0])
ymins.append(b[1])
xmaxs.append(b[2])
ymaxs.append(b[3])
xmins = [x / width for x in xmins]
xmaxs = [x / width for x in xmaxs]
ymins = [y / height for y in ymins]
ymaxs = [y / height for y in ymaxs]
class_reader = ClassReader(known_class_ids_annotation_predictor)
classes_text = example[4][:]
classes = []
none_vals = []
for i, cls in enumerate(classes_text):
if cls is None:
none_vals.append(i)
for index in sorted(none_vals, reverse=True):
classes_text.pop(index)
xmins.pop(index)
ymins.pop(index)
xmaxs.pop(index)
ymaxs.pop(index)
for i, cls in enumerate(classes_text):
classes.append(class_reader.get_index_of_class_from_label(cls))
class_encoded = str.encode(cls)
classes_text[i] = class_encoded
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_image_data),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
def dict_to_tf_example(data,
mask_path,
label_map_dict,
image_subdirectory,
ignore_difficult_instances=False,
faces_only=True,
mask_type='png'):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
mask_path: String path to PNG encoded mask.
label_map_dict: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
Pascal dataset directory holding the actual image data.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
faces_only: If True, generates bounding boxes for pet faces. Otherwise
generates bounding boxes (as well as segmentations for full pet bodies).
mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
smaller file sizes.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(image_subdirectory, data['filename'])
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
with tf.gfile.GFile(mask_path, 'rb') as fid:
encoded_mask_png = fid.read()
encoded_png_io = io.BytesIO(encoded_mask_png)
mask = PIL.Image.open(encoded_png_io)
if mask.format != 'PNG':
raise ValueError('Mask format not PNG')
mask_np = np.asarray(mask)
nonbackground_indices_x = np.any(mask_np != 2, axis=0)
nonbackground_indices_y = np.any(mask_np != 2, axis=1)
nonzero_x_indices = np.where(nonbackground_indices_x)
nonzero_y_indices = np.where(nonbackground_indices_y)
width = int(data['size']['width'])
height = int(data['size']['height'])
xmins = []
ymins = []
xmaxs = []
ymaxs = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
masks = []
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
if faces_only:
xmin = float(obj['bndbox']['xmin'])
xmax = float(obj['bndbox']['xmax'])
ymin = float(obj['bndbox']['ymin'])
ymax = float(obj['bndbox']['ymax'])
else:
xmin = float(np.min(nonzero_x_indices))
xmax = float(np.max(nonzero_x_indices))
ymin = float(np.min(nonzero_y_indices))
ymax = float(np.max(nonzero_y_indices))
xmins.append(xmin / width)
ymins.append(ymin / height)
xmaxs.append(xmax / width)
ymaxs.append(ymax / height)
class_name = get_class_name_from_filename(data['filename'])
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
if not faces_only:
mask_remapped = (mask_np != 2).astype(np.uint8)
masks.append(mask_remapped)
feature_dict = {
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].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(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}
if not faces_only:
if mask_type == 'numerical':
mask_stack = np.stack(masks).astype(np.float32)
masks_flattened = np.reshape(mask_stack, [-1])
feature_dict['image/object/mask'] = (
dataset_util.float_list_feature(masks_flattened.tolist()))
elif mask_type == 'png':
encoded_mask_png_list = []
for mask in masks:
img = PIL.Image.fromarray(mask)
output = io.BytesIO()
img.save(output, format='PNG')
encoded_mask_png_list.append(output.getvalue())
feature_dict['image/object/mask'] = (
dataset_util.bytes_list_feature(encoded_mask_png_list))
example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
return example
def dict_to_tf_example(data,
label_map_dict,
image_subdirectory,
ignore_difficult_instances=False):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
label_map_dict: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
Pascal dataset directory holding the actual image data.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(image_subdirectory, data['filename'])
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
for obj in data['object']:
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
#class_name = get_class_name_from_filename(data['filename'])
class_name = 'gazelle_head'
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(data['filename'].encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(data['filename'].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/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
return example
def dict_to_tf_example(data,
dataset_directory,
label_map_dict,
ignore_difficult_instances=False,
image_subdirectory='JPEGImages'):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
label_map_dict: A map from string label names to integers ids.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
image_subdirectory: String specifying subdirectory within the
PASCAL dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(data['folder'], image_subdirectory, data['filename'])
full_path = os.path.join(dataset_directory, img_path)
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)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
classes_text.append(obj['name'].encode('utf8'))
classes.append(label_map_dict[obj['name']])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
data['filename'].encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
data['filename'].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/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}))
return example
def dict_to_tf_example(data,
label_map_dict,
image_subdirectory,
example,
ignore_difficult_instances=False):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
label_map_dict: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
Pascal dataset directory holding the actual image data.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
# for embedded folders like images/bw1/image1.jpg
# path = data['path'].split(os.sep)[-2]
# print(path, data['filename'])
# if len(path) < 5 and 'bw' not in path:
# path = 'bw' + path
# img_path = os.path.join(image_subdirectory, path, data['filename'])
# print(img_path)
print(example)
img_name = example.split(".")[0]
img_path = os.path.join(image_subdirectory, img_name + '.jpg')
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = int(data['size']['width'])
height = int(data['size']['height'])
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin.append(float(obj['bndbox']['xmin']) / width)
ymin.append(float(obj['bndbox']['ymin']) / height)
xmax.append(float(obj['bndbox']['xmax']) / width)
ymax.append(float(obj['bndbox']['ymax']) / height)
# class_name = get_class_name_from_filename(data['filename'])
class_name = obj['name'].split(' ')[0]
# class_name = 'pack'
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(data['filename'].encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(data['filename'].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/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
'image/object/difficult':
dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated':
dataset_util.int64_list_feature(truncated),
'image/object/view':
dataset_util.bytes_list_feature(poses),
}))
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
"""
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
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
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_id = int(object_annotations['category_id'])
category_ids.append(category_id)
category_names.append(category_index[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),
}
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))
return key, example, num_annotations_skipped
def create_tf_example(group, path, is_pevid: bool = False):
"""Creates a TF example given a dataframe containing annotation information and input image path.
This is only used for TFExample creation from the PEViD-UHD dataset"""
if is_pevid:
# Get and add the volume and 'frames' to the path
volume_name = group.filename.split('_frame')[0]
path = os.path.join(path, volume_name, 'frames')
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)),
'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
xmins.append(row['xmin'] / width)
xmaxs.append(row['xmax'] / width)
ymins.append(row['ymin'] / height)
ymaxs.append(row['ymax'] / height)
classes_text.append(row['class'].encode('utf8'))
classes.append(class_text_to_int(row['class']))
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
return tf_example
def create_tf_examples(writer, anns, path, file_name, width, height,
encoded_jpg):
xmins, ymins = [], []
xmaxs, ymaxs = [], []
classes_text = []
classes = []
num_examples = 0
for ann in anns:
xmin = ann['bbox'][0]
ymin = ann['bbox'][1]
w = ann['bbox'][2]
h = ann['bbox'][3]
xmax = xmin + w
ymax = ymin + h
# normalize
xmin /= width
xmax /= width
ymin /= height
ymax /= height
if xmin < 1 and xmax < 1 and ymin < 1 and ymax < 1:
xmins.append(xmin)
xmaxs.append(xmax)
ymins.append(ymin)
ymaxs.append(ymax)
classes_text.append('Text'.encode('utf8'))
classes.append(1)
filename = os.path.join(path, file_name)
filename = filename.encode('utf8')
image_format = b'jpg'
if len(xmins) != 0:
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
writer.write(tf_example.SerializeToString())
num_examples += 1
return num_examples
def create_tf_example(_filename, _encoded_image_data, _width, _height,
_metadata):
height = _height # Image height
width = _width # Image width
# if height != 300 or width != 300:
# print "Invalid Image dimensions! " + str(width) + "x" + str(height)
# exit()
filename = _filename # Filename of the image. Empty if image is not from file
encoded_image_data = _encoded_image_data # Encoded image bytes
image_format = b'jpeg' # or b'png'
metadata = _metadata
xmins = [
] # List of normalized left x coordinates in bounding box (1 per box)
xmaxs = [] # List of normalized right x coordinates in bounding box
# (1 per box)
ymins = [
] # List of normalized top y coordinates in bounding box (1 per box)
ymaxs = [] # List of normalized bottom y coordinates in bounding box
# (1 per box)
classes_text = [] # List of string class name of bounding box (1 per box)
classes = [] # List of integer class id of bounding box (1 per box)
number_entries = int(next(metadata))
for i in range(0, number_entries):
next_line = next(metadata).strip('\n')
data = [float(entry) for entry in next_line.split(' ')[:-2]]
centre_x = data[3]
centre_y = data[4]
r_major = data[0] * math.cos(data[2] * math.pi / 180)
r_minor = data[1] * math.cos(data[2] * math.pi / 180)
x = float(centre_x - r_minor)
y = float(centre_y - r_major)
w = float(r_minor * 2)
h = float(r_major * 2)
if math.isnan(x) or math.isnan(y) or math.isnan(w) or math.isnan(
h) or height is 0 or width is 0:
print "NAN!"
exit()
xmins.append(x / float(width))
xmaxs.append((x + w) / float(width))
ymins.append(y / float(height))
ymaxs.append((y + h) / float(height))
classes_text.append('face')
classes.append(1)
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_image_data),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
return tf_example
def dict_to_tf_example(data,
mask_path,
label_map_dict,
image_subdirectory,
ignore_difficult_instances=False,
mask_type='png'):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
mask_path: String path to PNG encoded mask.
label_map_dict: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
Pascal dataset directory holding the actual image data.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
mask_type: 'numerical' or 'png'. 'png' is recommended because it leads to
smaller file sizes.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = os.path.join(image_subdirectory, data['filename'])
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
with tf.gfile.GFile(mask_path, 'rb') as fid:
encoded_mask_png = fid.read()
encoded_png_io = io.BytesIO(encoded_mask_png)
mask = PIL.Image.open(encoded_png_io)
'''
print("mask.size", mask.size)
px = mask.load()
print ("pixel", px[400, 200])
print ("pixel", px[0, 0])
'''
# print("mask pixel", mask[400][200])
if mask.format != 'PNG':
raise ValueError('Mask format not PNG')
print("img_path:", img_path)
print("mask_path:", mask_path)
mask_np = np.asarray(mask)
width = int(data['size']['width'])
height = int(data['size']['height'])
xmins = []
ymins = []
xmaxs = []
ymaxs = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
masks = []
if 'object' in data:
for obj in data['object']:
difficult = bool(int(obj['difficult']))
if ignore_difficult_instances and difficult:
continue
difficult_obj.append(int(difficult))
xmin = float(obj['bndbox']['xmin'])
xmax = float(obj['bndbox']['xmax'])
ymin = float(obj['bndbox']['ymin'])
ymax = float(obj['bndbox']['ymax'])
xmins.append(xmin / width)
ymins.append(ymin / height)
xmaxs.append(xmax / width)
ymaxs.append(ymax / height)
# classes_text.append(obj['name'].encode('utf8'))
# classes.append(label_map_dict[obj['name']])
# print("label_map_dict[obj['name']]", label_map_dict[obj['name']])
class_name = get_class_name_from_filename(data['filename'])
print('class_name', class_name, 'label_map_dict[class_name]',
label_map_dict[class_name])
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
truncated.append(int(obj['truncated']))
poses.append(obj['pose'].encode('utf8'))
mask_remapped = (mask_np != 2).astype(np.uint8)
masks.append(mask_remapped)
feature_dict = {
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(data['filename'].encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(data['filename'].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(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
'image/object/difficult':
dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated':
dataset_util.int64_list_feature(truncated),
'image/object/view':
dataset_util.bytes_list_feature(poses),
}
if mask_type == 'numerical':
mask_stack = np.stack(masks).astype(np.float32)
masks_flattened = np.reshape(mask_stack, [-1])
feature_dict['image/object/mask'] = (dataset_util.float_list_feature(
masks_flattened.tolist()))
elif mask_type == 'png':
encoded_mask_png_list = []
for mask in masks:
img = PIL.Image.fromarray(mask)
output = io.BytesIO()
img.save(output, format='PNG')
encoded_mask_png_list.append(output.getvalue())
feature_dict['image/object/mask'] = (
dataset_util.bytes_list_feature(encoded_mask_png_list))
example = tf.train.Example(features=tf.train.Features(
feature=feature_dict))
print('-------------------')
return example
def make_shape(max_num, width, height, bg_color=True, filename=None, label_map_dict=None):
if bg_color:
r_s = np.random.randint(20, 160)
r = np.random.randint(r_s, r_s + random.randint(10, 90), width * height, np.uint8).reshape((width, height))
g_s = np.random.randint(20, 160)
g = np.random.randint(g_s, g_s + +random.randint(10, 90), width * height, np.uint8).reshape((width, height))
b_s = np.random.randint(20, 160)
b = np.random.randint(b_s, b_s + +random.randint(10, 90), width * height, np.uint8).reshape((width, height))
else:
r = np.zeros((width, height), np.uint8)
g = np.zeros((width, height), np.uint8)
b = np.zeros((width, height), np.uint8)
bg = cv2.merge([r, g, b])
xmins = []
ymins = []
xmaxs = []
ymaxs = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
for i in range(max_num):
label = random.randint(1, 2)
x1, y1, x2, y2 = 0, 0, 0, 0
if label == 1:
shp = getCircleShape(bg)
x1, y1, x2, y2 = shp[0] - shp[2], shp[1] - shp[2], shp[0] + shp[2], shp[1] + shp[2]
cv2.circle(bg, (shp[0], shp[1]), shp[2], getColor(), -1)
label_text = 'circle'
elif label == 2:
shp = getRectShape(bg)
x1, y1, x2, y2 = shp[0], shp[1], shp[2], shp[3]
cv2.rectangle(bg, (shp[0], shp[1]), (shp[2], shp[3]), getColor(), -1)
label_text = 'rect'
xmins.append(float(x1 / width))
ymins.append(float(y1 / height))
xmaxs.append(float(x2 / width))
ymaxs.append(float(y2 / height))
classes.append(label_map_dict[label_text])
classes_text.append(label_text.encode('utf8'))
truncated.append(0)
poses.append("0".encode('utf8'))
difficult_obj.append(0)
filename=filename + '_temp.jpg'
img_path = os.path.join('./data', filename + '_temp.jpg')
cv2.imwrite(img_path, bg)
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
feature_dict = {
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(filename.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(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses)
}
example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
os.remove(img_path)
return example
def dict_to_tf_example(data,label_map_dict,image_dir):
# TODO(user): Populate the following variables from your example.
height = int(data['size']['height']) # Image height
width = int(data['size']['width']) # Image width
s = data['filename']
if s[(len(s)-3):len(s)] == 'png':
s = s[0 :(len(s)-3)]+'jpg'
filename = os.path.join(image_dir,s) # Filename of the image. Empty if image is not from file
# encoded_image_data = None # Encoded image bytes
with tf.gfile.GFile(filename, 'rb') as fid:
encoded_image_data = fid.read()
encoded_image_data_io = io.BytesIO(encoded_image_data)
image = PIL.Image.open(encoded_image_data_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
# key = hashlib.sha256(encoded_image_data).hexdigest()
image_format = b'jpeg' # b'jpeg' or b'png'
xmins = [] # List of normalized left x coordinates in bounding box (1 per box)
xmaxs = [] # List of normalized right x coordinates in bounding box
# (1 per box)
ymins = [] # List of normalized top y coordinates in bounding box (1 per box)
ymaxs = [] # List of normalized bottom y coordinates in bounding box
# (1 per box)
classes_text = [] # List of string class name of bounding box (1 per box)
classes = [] # List of integer class id of bounding box (1 per box)
if 'object' in data :
for obj in data['object']:
print(obj)
if obj:
xmin = float(obj['bndbox']['xmin'])
xmax = float(obj['bndbox']['xmax'])
ymin = float(obj['bndbox']['ymin'])
ymax = float(obj['bndbox']['ymax'])
xmins.append(xmin / width)
ymins.append(ymin / height)
xmaxs.append(xmax / width)
ymaxs.append(ymax / height)
class_name = obj['name']
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(filename.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_image_data),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
def json_to_tf_example(json_data,
dataset_directory,
label_map_dict ):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: dict holding PASCAL XML fields for a single image (obtained by
running dataset_util.recursive_parse_xml_to_dict)
dataset_directory: Path to root directory holding PASCAL dataset
label_map_dict: A map from string label names to integers ids.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
image_subdirectory: String specifying subdirectory within the
PASCAL dataset directory holding the actual image data.
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
width = int(json_data.get("image_width"))
height = int(json_data.get("image_height"))
filename=orig_filename = json_data.get("filename")
full_path=orig_full_path = os.path.join(FLAGS.data_dir,"photos", orig_filename)
with tf.gfile.GFile(orig_full_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
width,height=image.size
# filename = json_data.get("filename")+".scaled.jpg"
# #img_path = os.path.join(FLAGS.data_dir,"photos", filename)
# full_path = os.path.join(FLAGS.data_dir,"photos", filename)
# if not os.path.exists(full_path):
# #raise ValueError('Please scale image :convert abc.jpg -resize 756x1008 sss.jpg')
# orig_filename = json_data.get("filename")
# orig_full_path = os.path.join(FLAGS.data_dir,"photos", orig_filename)
# #image = PIL.Image.open(orig_full_path)
# ##image.resize((756,1008), resample=PIL.Image.BILINEAR).save(full_path)
# #image.resize((756,1008), resample=PIL.Image.NEAREST).save(full_path)
# os.system("convert "+orig_full_path+" -resize 756x1008 "+full_path)
#full_path = os.path.join(dataset_directory, img_path)
#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)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width,height=image.size;
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
poses = []
difficult_obj = []
for obj in json_data.get("bndboxes"):
difficult_obj.append(0)
xmin.append(float(obj.get("x")) / width)
ymin.append(float(obj.get("y")) / height)
xmax.append(numpy.clip(float(obj.get("x")+obj.get("w")) / width,0,1))
ymax.append(numpy.clip(float(obj.get("y")+obj.get("h")) / height,0,1))
classes_text.append(obj.get("id").encode('utf8'))
classes.append(label_map_dict[obj.get("id")])
truncated.append(int(0))
poses.append("Unspecified")
width,height=image.size
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(
filename.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(
filename.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/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses),
}))
return example
def dict_to_tf_example(example):
filename = example['filename']
filename = filename.encode()
with tf.gfile.GFile(example['filename'], 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
width, height = image.size
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
xmins = [] # left x-coordinate
ymins = [] # right x-coordinate
xmaxs = [] # top y-coordinate
ymaxs = [] # buttom y-coordinate
classes = [] # class id
classes_text = [] # class name
for box in example['annotations']:
xmins.append(float(box['xmin'] / width))
xmaxs.append(float((box['xmin'] + box['x_width']) / width))
ymins.append(float(box['ymin'] / height))
ymaxs.append(float((box['ymin'] + box['y_height']) / height))
classes_text.append(box['class'].encode())
classes.append(int(LABELS_MAP[box['class']]))
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'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(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes)
}))
return tf_example
def prepare_example(image_path, annotations, label_map_dict):
"""Converts a dictionary with annotations for an image to tf.Example proto.
Args:
image_path: The complete path to image.
annotations: A dictionary representing the annotation of a single object
that appears in the image.
label_map_dict: A map from string label names to integer ids.
Returns:
example: The converted tf.Example.
"""
with tf.gfile.GFile(image_path, 'rb') as fid:
encoded_png = fid.read()
encoded_png_io = io.BytesIO(encoded_png)
image = pil.open(encoded_png_io)
image = np.asarray(image)
key = hashlib.sha256(encoded_png).hexdigest()
width = int(image.shape[1])
height = int(image.shape[0])
xmin_norm = annotations['2d_bbox_left'] / float(width)
ymin_norm = annotations['2d_bbox_top'] / float(height)
xmax_norm = annotations['2d_bbox_right'] / float(width)
ymax_norm = annotations['2d_bbox_bottom'] / float(height)
difficult_obj = [0]*len(xmin_norm)
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(image_path.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(image_path.encode('utf8')),
'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
'image/encoded': dataset_util.bytes_feature(encoded_png),
'image/format': dataset_util.bytes_feature('png'.encode('utf8')),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmin_norm),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmax_norm),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymin_norm),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymax_norm),
'image/object/class/text': dataset_util.bytes_list_feature(
[x.encode('utf8') for x in annotations['type']]),
'image/object/class/label': dataset_util.int64_list_feature(
[label_map_dict[x] for x in annotations['type']]),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.float_list_feature(
annotations['truncated']),
'image/object/alpha': dataset_util.float_list_feature(
annotations['alpha']),
'image/object/3d_bbox/height': dataset_util.float_list_feature(
annotations['3d_bbox_height']),
'image/object/3d_bbox/width': dataset_util.float_list_feature(
annotations['3d_bbox_width']),
'image/object/3d_bbox/length': dataset_util.float_list_feature(
annotations['3d_bbox_length']),
'image/object/3d_bbox/x': dataset_util.float_list_feature(
annotations['3d_bbox_x']),
'image/object/3d_bbox/y': dataset_util.float_list_feature(
annotations['3d_bbox_y']),
'image/object/3d_bbox/z': dataset_util.float_list_feature(
annotations['3d_bbox_z']),
'image/object/3d_bbox/rot_y': dataset_util.float_list_feature(
annotations['3d_bbox_rot_y']),
}))
return example
def generate_tfrecord(group, path, label_dict):
# Open image file
with tf.io.gfile.GFile(os.path.join(path, '{}'.format(group.filename)),
'rb') as gf:
encoded_jpg = gf.read()
jpg_io = io.BytesIO(encoded_jpg)
img = Image.open(jpg_io)
image_width, image_height = img.size
filename = group.filename.encode('utf8')
img_format = b'jpg'
# For the data in regards to the image
# we need to store the minimum and maximum x and y positions of the bounding boxes
bx_min = []
bx_max = []
by_min = []
by_max = []
classes = []
classes_texts = []
# Itterate over every row of data in our given data in the dataset
for i, row in group.object.iterrows():
bx_min.append(row['xmin'] / image_width)
bx_max.append(row['xmax'] / image_width)
by_min.append(row['ymin'] / image_height)
by_max.append(row['ymax'] / image_height)
# Add Class text and the converted labels to
classes_texts.append(row['class'].encode('utf8'))
#print(row['class'], " Id from label_to_int: ", label_to_int(row['class'], label_dict))
classes.append(label_to_int(row['class'], label_dict))
# TF record example
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'img/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature(img_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bx_min),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bx_max),
'image/object/bbox/ymin':
dataset_util.float_list_feature(by_min),
'image/object/bbox/ymax':
dataset_util.float_list_feature(by_max),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_texts),
'image/object/class/label':
dataset_util.int64_list_feature(classes)
}))
return tf_example
def create_tf_record(output_filename, num_shards, examples):
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
tf_record_close_stack,
output_filename,
num_shards)
for idx, example in enumerate(examples):
img_path = os.path.join(read_bucket, example)
if not os.path.isfile(img_path):
continue
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width, height = image.size
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = [] # 'coke', 'pepsi', 'coke'...
classes = [] # 1, 2, 1...
difficult_obj = []
truncated = []
poses = []
for annotation in annotations[example]:
xmins.append(annotation['x'])
xmaxs.append(annotation['x2'])
ymins.append(annotation['y'])
ymaxs.append(annotation['y2'])
classes_text.append(annotation['label'].encode('utf8'))
classes.append(1) # temporary, I need to assign labels to actual ids
difficult_obj.append(0)
truncated.append(0)
poses.append(''.encode('utf8'))
try:
feature_dict = {
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(example.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(example.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(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
'image/object/truncated': dataset_util.int64_list_feature(truncated),
'image/object/view': dataset_util.bytes_list_feature(poses)
}
tf_example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
if tf_example:
shard_idx = idx % num_shards
output_tfrecords[shard_idx].write(tf_example.SerializeToString())
except ValueError:
print('Invalid example, ignoring.')
def create_tf_example(example):
# Bosch
#height = 720 # Image height
#width = 1280 # Image width
# Udacity data set
height = 600 # Image height
width = 800 # Image width
filename = example[
'filename'] # Filename of the image. Empty if image is not from file
filename = filename.encode()
with tf.gfile.GFile(example['filename'], 'rb') as fid:
encoded_image = fid.read()
image_format = 'jpg'.encode()
xmins = [
] # List of normalized left x coordinates in bounding box (1 per box)
xmaxs = [] # List of normalized right x coordinates in bounding box
# (1 per box)
ymins = [
] # List of normalized top y coordinates in bounding box (1 per box)
ymaxs = [] # List of normalized bottom y coordinates in bounding box
# (1 per box)
classes_text = [] # List of string class name of bounding box (1 per box)
classes = [] # List of integer class id of bounding box (1 per box)
for box in example['annotations']:
#if box['occluded'] is False:
#print("adding box")
xmins.append(float(box['xmin'] / width))
xmaxs.append(float((box['xmin'] + box['x_width']) / width))
ymins.append(float(box['ymin'] / height))
ymaxs.append(float((box['ymin'] + box['y_height']) / height))
classes_text.append(box['class'].encode())
classes.append(int(LABEL_DICT[box['class']]))
tf_example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(filename),
'image/source_id':
dataset_util.bytes_feature(filename),
'image/encoded':
dataset_util.bytes_feature(encoded_image),
'image/format':
dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin':
dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax':
dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin':
dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(ymaxs),
'image/object/class/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
}))
return tf_example
def dict_to_tf_example(data,
label_map_dict,
image_subdirectory,
ignore_difficult_instances=False):
"""Convert XML derived dict to tf.Example proto.
Notice that this function normalizes the bounding box coordinates provided
by the raw data.
Args:
data: data corresponding to each image file.
label_map_dict: A map from string label names to integers ids.
image_subdirectory: String specifying subdirectory within the
dataset directory holding the actual image data.
ignore_difficult_instances: Whether to skip difficult instances in the
dataset (default: False).
Returns:
example: The converted tf.Example.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG
"""
img_path = image_subdirectory
with tf.gfile.GFile(img_path) as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'PNG':
raise ValueError('Image format error')
# bg = PIL.Image.new("RGB", image.size, (255,255,255))
# x, y = image.size
# bg.paste(image)
# img_path = img_path[:-3] + 'jpg'
# bg.save(img_path)
# with tf.gfile.GFile(img_path) as fid:
# encoded_jpg = fid.read()
# encoded_jpg_io = io.BytesIO(encoded_jpg)
# image = PIL.Image.open(encoded_jpg_io)
# if image.format != 'JPEG':
# raise ValueError('sase')
key = hashlib.sha256(encoded_jpg).hexdigest()
width, height = image.size
img_filename = img_path.split('/')[-1]
xmin = []
ymin = []
xmax = []
ymax = []
classes = []
classes_text = []
truncated = []
occlud = []
xmin.append(int(data[2]) / width)
ymin.append(int(data[3]) / height)
xmax.append(int(data[4]) / width)
ymax.append(int(data[5]) / height)
class_name = data[1]
classes_text.append(class_name)
classes.append(label_map_dict[class_name])
trun, occ = data[6].split(',')
truncated.append(int(trun))
occlud.append(int(occ))
example = tf.train.Example(features=tf.train.Features(
feature={
'image/height':
dataset_util.int64_feature(height),
'image/width':
dataset_util.int64_feature(width),
'image/filename':
dataset_util.bytes_feature(img_filename),
'image/source_id':
dataset_util.bytes_feature(img_filename),
'image/key/sha256':
dataset_util.bytes_feature(key),
'image/encoded':
dataset_util.bytes_feature(encoded_jpg),
'image/format':
dataset_util.bytes_feature('png'),
'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/text':
dataset_util.bytes_list_feature(classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(classes),
'image/object/truncated':
dataset_util.int64_list_feature(truncated),
'image/object/view':
dataset_util.int64_list_feature(occlud),
}))
return example
def dict_to_tf_example(data, label_map_dict, img_path):
with tf.gfile.GFile(img_path, 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = PIL.Image.open(encoded_jpg_io)
if image.format != 'JPEG':
raise ValueError('Image format not JPEG')
key = hashlib.sha256(encoded_jpg).hexdigest()
width = image.width
height = image.height
xmins = []
ymins = []
xmaxs = []
ymaxs = []
classes = []
classes_text = []
for shape in data['Layers']['Layer']['Shapes']['Shape']:
text = shape['BlockText']['Text'].text
if not (text.startswith('Panel') or text.startswith('panel')):
continue
attrib = shape['Data']['Extent'].attrib
x = float(attrib['X'])
y = float(attrib['Y'])
w = float(attrib['Width'])
h = float(attrib['Height'])
xmin = x
xmax = x + w
ymin = y
ymax = y + h
xmin /= width
ymin /= height
xmax /= width
ymax /= height
if xmin < 0 or ymin < 0 or xmax > 1.01 or ymax > 1.01:
print(img_path)
xmins.append(xmin)
ymins.append(ymin)
xmaxs.append(xmax)
ymaxs.append(ymax)
class_name = 'Panel'
classes_text.append(class_name.encode('utf8'))
classes.append(label_map_dict[class_name])
feature_dict = {
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(img_path.encode('utf8')),
'image/source_id': dataset_util.bytes_feature(img_path.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(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}
example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
return example