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Commit 98d9f3b8 authored by Kaushik Shivakumar's avatar Kaushik Shivakumar
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lint more, clean up, push

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Showing with 126 additions and 123 deletions
research/object_detection/dataset_tools/create_ava_actions_tf_record.py
View file @ 98d9f3b8
......@@ -37,7 +37,7 @@ numbered TFRecord files.
Generating the data on disk can take considerable time and disk space.
(Image compression quality is the primary determiner of disk usage.
If using the Tensorflow Object Detection API, set the input_type field
If using the Tensorflow Object Detection API, set the input_type field
in the input_reader to TF_SEQUENCE_EXAMPLE. If using this script to generate
data for Context R-CNN scripts, the --examples_for_context flag should be
set to true, so that properly-formatted tf.example objects are written to disk.
......@@ -50,7 +50,7 @@ Note that the number of videos changes in the data set over time, so it will
likely be necessary to change the expected number of examples.
The argument video_path_format_string expects a value as such:
"/path/to/videos/{0}"
'/path/to/videos/{0}'
"""
......@@ -78,28 +78,28 @@ from object_detection.utils import dataset_util
from object_detection.utils import label_map_util
POSSIBLE_TIMESTAMPS = range(902, 1798)
ANNOTATION_URL = "https://research.google.com/ava/download/ava_v2.2.zip"
ANNOTATION_URL = 'https://research.google.com/ava/download/ava_v2.2.zip'
SECONDS_TO_MILLI = 1000
FILEPATTERN = "ava_actions_%s_1fps_rgb"
FILEPATTERN = 'ava_actions_%s_1fps_rgb'
SPLITS = {
"train": {
"shards": 1000,
"examples": 862663,
"csv": '',
"excluded-csv": ''
'train': {
'shards': 1000,
'examples': 862663,
'csv': '',
'excluded-csv': ''
},
"val": {
"shards": 100,
"examples": 243029,
"csv": '',
"excluded-csv": ''
'val': {
'shards': 100,
'examples': 243029,
'csv': '',
'excluded-csv': ''
},
#Test doesn't have ground truth, so TF Records can't be created
"test": {
"shards": 100,
"examples": 0,
"csv": '',
"excluded-csv": ''
'test': {
'shards': 100,
'examples': 0,
'csv': '',
'excluded-csv': ''
}
}
......@@ -115,12 +115,12 @@ class Ava(object):
def __init__(self, path_to_output_dir, path_to_data_download):
if not path_to_output_dir:
raise ValueError("You must supply the path to the data directory.")
raise ValueError('You must supply the path to the data directory.')
self.path_to_data_download = path_to_data_download
self.path_to_output_dir = path_to_output_dir
def generate_and_write_records(self,
splits_to_process="train,val,test",
splits_to_process='train,val,test',
video_path_format_string=None,
seconds_per_sequence=10,
hop_between_sequences=10,
......@@ -133,47 +133,48 @@ class Ava(object):
original data files can be deleted.
Args:
splits_to_process: csv string of which splits to process. Allows providing
a custom CSV with the CSV flag. The original data is still downloaded
to generate the label_map.
splits_to_process: csv string of which splits to process. Allows
providing a custom CSV with the CSV flag. The original data is still
downloaded to generate the label_map.
video_path_format_string: The format string for the path to local files.
seconds_per_sequence: The length of each sequence, in seconds.
hop_between_sequences: The gap between the centers of
successive sequences.
"""
example_function = self._generate_sequence_examples
example_function = self._generate_sequence_examples
if examples_for_context:
example_function = self._generate_examples
logging.info("Downloading data.")
logging.info('Downloading data.')
download_output = self._download_data()
for key in splits_to_process.split(","):
logging.info("Generating examples for split: %s", key)
for key in splits_to_process.split(','):
logging.info('Generating examples for split: %s', key)
all_metadata = list(example_function(
download_output[0][key][0], download_output[0][key][1],
download_output[1], seconds_per_sequence, hop_between_sequences,
video_path_format_string))
logging.info("An example of the metadata: ")
logging.info('An example of the metadata: ')
logging.info(all_metadata[0])
random.seed(47)
random.shuffle(all_metadata)
shards = SPLITS[key]["shards"]
shards = SPLITS[key]['shards']
shard_names = [os.path.join(
self.path_to_output_dir, FILEPATTERN % key + "-%05d-of-%05d" % (
self.path_to_output_dir, FILEPATTERN % key + '-%05d-of-%05d' % (
i, shards)) for i in range(shards)]
writers = [tf.io.TFRecordWriter(shard_name) for shard_name in shard_names]
writers = [tf.io.TFRecordWriter(shard) for shard in shard_names]
with _close_on_exit(writers) as writers:
for i, seq_ex in enumerate(all_metadata):
writers[i % len(writers)].write(seq_ex.SerializeToString())
logging.info("Data extraction complete.")
logging.info('Data extraction complete.')
def _generate_sequence_examples(self, annotation_file, excluded_file, label_map,
seconds_per_sequence, hop_between_sequences,
def _generate_sequence_examples(self, annotation_file, excluded_file,
label_map, seconds_per_sequence,
hop_between_sequences,
video_path_format_string):
"""For each row in the annotation CSV, generates the corresponding examples.
"""For each row in the annotation CSV, generates corresponding examples.
When iterating through frames for a single sequence example, skips over
excluded frames. When moving to the next sequence example, also skips over
When iterating through frames for a single sequence example, skips over
excluded frames. When moving to the next sequence example, also skips over
excluded frames as if they don't exist. Generates equal-length sequence
examples, each with length seconds_per_sequence (1 fps) and gaps of
hop_between_sequences frames (and seconds) between them, possible greater
......@@ -189,32 +190,32 @@ class Ava(object):
Yields:
Each prepared tf.SequenceExample of metadata also containing video frames
"""
fieldnames = ["id", "timestamp_seconds", "xmin", "ymin", "xmax", "ymax",
"action_label"]
fieldnames = ['id', 'timestamp_seconds', 'xmin', 'ymin', 'xmax', 'ymax',
'action_label']
frame_excluded = {}
# create a sparse, nested map of videos and frame indices.
with open(excluded_file, "r") as excluded:
with open(excluded_file, 'r') as excluded:
reader = csv.reader(excluded)
for row in reader:
frame_excluded[(row[0], int(float(row[1])))] = True
with open(annotation_file, "r") as annotations:
with open(annotation_file, 'r') as annotations:
reader = csv.DictReader(annotations, fieldnames)
frame_annotations = collections.defaultdict(list)
ids = set()
# aggreggate by video and timestamp:
for row in reader:
ids.add(row["id"])
key = (row["id"], int(float(row["timestamp_seconds"])))
ids.add(row['id'])
key = (row['id'], int(float(row['timestamp_seconds'])))
frame_annotations[key].append(row)
# for each video, find aggregates near each sampled frame.:
logging.info("Generating metadata...")
logging.info('Generating metadata...')
media_num = 1
for media_id in ids:
logging.info("%d/%d, ignore warnings.\n" % (media_num, len(ids)))
logging.info('%d/%d, ignore warnings.\n' % (media_num, len(ids)))
media_num += 1
filepath = glob.glob(
video_path_format_string.format(media_id) + "*")[0]
video_path_format_string.format(media_id) + '*')[0]
cur_vid = cv2.VideoCapture(filepath)
width = cur_vid.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cur_vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
......@@ -237,7 +238,7 @@ class Ava(object):
if (media_id, windowed_timestamp) in frame_excluded:
end_time += 1
windowed_timestamp += 1
logging.info("Ignoring and skipping excluded frame.")
logging.info('Ignoring and skipping excluded frame.')
continue
cur_vid.set(cv2.CAP_PROP_POS_MSEC,
......@@ -247,7 +248,7 @@ class Ava(object):
bufstring = buffer.tostring()
total_images.append(bufstring)
source_id = str(windowed_timestamp) + "_" + media_id
source_id = str(windowed_timestamp) + '_' + media_id
total_source_ids.append(source_id)
total_is_annotated.append(1)
......@@ -256,14 +257,14 @@ class Ava(object):
label_strings = []
confidences = []
for row in frame_annotations[(media_id, windowed_timestamp)]:
if len(row) > 2 and int(row["action_label"]) in label_map:
boxes.append([float(row["ymin"]), float(row["xmin"]),
float(row["ymax"]), float(row["xmax"])])
labels.append(int(row["action_label"]))
label_strings.append(label_map[int(row["action_label"])])
if len(row) > 2 and int(row['action_label']) in label_map:
boxes.append([float(row['ymin']), float(row['xmin']),
float(row['ymax']), float(row['xmax'])])
labels.append(int(row['action_label']))
label_strings.append(label_map[int(row['action_label'])])
confidences.append(1)
else:
logging.warning("Unknown label: %s", row["action_label"])
logging.warning('Unknown label: %s', row['action_label'])
total_boxes.append(boxes)
total_labels.append(labels)
......@@ -272,9 +273,10 @@ class Ava(object):
windowed_timestamp += 1
if len(total_boxes) > 0:
yield seq_example_util.make_sequence_example("AVA", media_id, total_images,
int(height), int(width), 'jpeg', total_source_ids, None, total_is_annotated,
total_boxes, total_label_strings, use_strs_for_source_id=True)
yield seq_example_util.make_sequence_example(
'AVA', media_id, total_images, int(height), int(width), 'jpeg',
total_source_ids, None, total_is_annotated, total_boxes,
total_label_strings, use_strs_for_source_id=True)
#Move middle_time_frame, skipping excluded frames
frames_mv = 0
......@@ -307,33 +309,33 @@ class Ava(object):
Yields:
Each prepared tf.Example of metadata also containing video frames
"""
fieldnames = ["id", "timestamp_seconds", "xmin", "ymin", "xmax", "ymax",
"action_label"]
fieldnames = ['id', 'timestamp_seconds', 'xmin', 'ymin', 'xmax', 'ymax',
'action_label']
frame_excluded = {}
# create a sparse, nested map of videos and frame indices.
with open(excluded_file, "r") as excluded:
with open(excluded_file, 'r') as excluded:
reader = csv.reader(excluded)
for row in reader:
frame_excluded[(row[0], int(float(row[1])))] = True
with open(annotation_file, "r") as annotations:
with open(annotation_file, 'r') as annotations:
reader = csv.DictReader(annotations, fieldnames)
frame_annotations = collections.defaultdict(list)
ids = set()
# aggreggate by video and timestamp:
for row in reader:
ids.add(row["id"])
key = (row["id"], int(float(row["timestamp_seconds"])))
ids.add(row['id'])
key = (row['id'], int(float(row['timestamp_seconds'])))
frame_annotations[key].append(row)
# for each video, find aggreggates near each sampled frame.:
logging.info("Generating metadata...")
logging.info('Generating metadata...')
media_num = 1
for media_id in ids:
logging.info("%d/%d, ignore warnings.\n" % (media_num, len(ids)))
logging.info('%d/%d, ignore warnings.\n' % (media_num, len(ids)))
media_num += 1
filepath = glob.glob(
video_path_format_string.format(media_id) + "*")[0]
filename = filepath.split("/")[-1]
video_path_format_string.format(media_id) + '*')[0]
filename = filepath.split('/')[-1]
cur_vid = cv2.VideoCapture(filepath)
width = cur_vid.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cur_vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
......@@ -348,7 +350,7 @@ class Ava(object):
cur_frame_num = 0
while middle_frame_time < POSSIBLE_TIMESTAMPS[-1]:
cur_vid.set(cv2.CAP_PROP_POS_MSEC,
(middle_frame_time) * SECONDS_TO_MILLI)
middle_frame_time * SECONDS_TO_MILLI)
success, image = cur_vid.read()
success, buffer = cv2.imencode('.jpg', image)
......@@ -356,11 +358,11 @@ class Ava(object):
if (media_id, middle_frame_time) in frame_excluded:
middle_frame_time += 1
logging.info("Ignoring and skipping excluded frame.")
logging.info('Ignoring and skipping excluded frame.')
continue
cur_frame_num += 1
source_id = str(middle_frame_time) + "_" + media_id
source_id = str(middle_frame_time) + '_' + media_id
xmins = []
xmaxs = []
......@@ -371,18 +373,18 @@ class Ava(object):
label_strings = []
confidences = []
for row in frame_annotations[(media_id, middle_frame_time)]:
if len(row) > 2 and int(row["action_label"]) in label_map:
xmins.append(float(row["xmin"]))
xmaxs.append(float(row["xmax"]))
ymins.append(float(row["ymin"]))
ymaxs.append(float(row["ymax"]))
areas.append(float((xmaxs[-1] - xmins[-1]) *
if len(row) > 2 and int(row['action_label']) in label_map:
xmins.append(float(row['xmin']))
xmaxs.append(float(row['xmax']))
ymins.append(float(row['ymin']))
ymaxs.append(float(row['ymax']))
areas.append(float((xmaxs[-1] - xmins[-1]) *
(ymaxs[-1] - ymins[-1])) / 2)
labels.append(int(row["action_label"]))
label_strings.append(label_map[int(row["action_label"])])
labels.append(int(row['action_label']))
label_strings.append(label_map[int(row['action_label'])])
confidences.append(1)
else:
logging.warning("Unknown label: %s", row["action_label"])
logging.warning('Unknown label: %s', row['action_label'])
middle_frame_time += 1/3
if abs(middle_frame_time - round(middle_frame_time) < 0.0001):
......@@ -390,8 +392,8 @@ class Ava(object):
key = hashlib.sha256(bufstring).hexdigest()
date_captured_feature = (
"2020-06-17 00:%02d:%02d" % ((middle_frame_time - 900)*3 // 60,
(middle_frame_time - 900)*3 % 60))
'2020-06-17 00:%02d:%02d' % ((middle_frame_time - 900)*3 // 60,
(middle_frame_time - 900)*3 % 60))
context_feature_dict = {
'image/height':
dataset_util.int64_feature(int(height)),
......@@ -400,9 +402,9 @@ class Ava(object):
'image/format':
dataset_util.bytes_feature('jpeg'.encode('utf8')),
'image/source_id':
dataset_util.bytes_feature(source_id.encode("utf8")),
dataset_util.bytes_feature(source_id.encode('utf8')),
'image/filename':
dataset_util.bytes_feature(source_id.encode("utf8")),
dataset_util.bytes_feature(source_id.encode('utf8')),
'image/encoded':
dataset_util.bytes_feature(bufstring),
'image/key/sha256':
......@@ -424,7 +426,8 @@ class Ava(object):
'image/location':
dataset_util.bytes_feature(media_id.encode('utf8')),
'image/date_captured':
dataset_util.bytes_feature(date_captured_feature.encode('utf8')),
dataset_util.bytes_feature(
date_captured_feature.encode('utf8')),
'image/seq_num_frames':
dataset_util.int64_feature(total_non_excluded),
'image/seq_frame_num':
......@@ -444,28 +447,28 @@ class Ava(object):
urlretrieve = urllib.request.urlretrieve
else:
urlretrieve = urllib.request.urlretrieve
logging.info("Creating data directory.")
logging.info('Creating data directory.')
tf.io.gfile.makedirs(self.path_to_data_download)
logging.info("Downloading annotations.")
logging.info('Downloading annotations.')
paths = {}
zip_path = os.path.join(self.path_to_data_download,
ANNOTATION_URL.split("/")[-1])
ANNOTATION_URL.split('/')[-1])
urlretrieve(ANNOTATION_URL, zip_path)
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(self.path_to_data_download)
for split in ["train", "test", "val"]:
for split in ['train', 'test', 'val']:
csv_path = os.path.join(self.path_to_data_download,
"ava_%s_v2.2.csv" % split)
excl_name = "ava_%s_excluded_timestamps_v2.2.csv" % split
'ava_%s_v2.2.csv' % split)
excl_name = 'ava_%s_excluded_timestamps_v2.2.csv' % split
excluded_csv_path = os.path.join(self.path_to_data_download, excl_name)
SPLITS[split]["csv"] = csv_path
SPLITS[split]["excluded-csv"] = excluded_csv_path
SPLITS[split]['csv'] = csv_path
SPLITS[split]['excluded-csv'] = excluded_csv_path
paths[split] = (csv_path, excluded_csv_path)
label_map = self.get_label_map(os.path.join(
self.path_to_data_download,
"ava_action_list_v2.2_for_activitynet_2019.pbtxt"))
'ava_action_list_v2.2_for_activitynet_2019.pbtxt'))
return paths, label_map
def get_label_map(self, path):
......@@ -487,7 +490,7 @@ def _close_on_exit(writers):
def main(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
raise app.UsageError('Too many command-line arguments.')
Ava(flags.FLAGS.path_to_output_dir,
flags.FLAGS.path_to_download_data).generate_and_write_records(
flags.FLAGS.splits_to_process,
......@@ -496,34 +499,34 @@ def main(argv):
flags.FLAGS.hop_between_sequences,
flags.FLAGS.examples_for_context)
if __name__ == "__main__":
flags.DEFINE_string("path_to_download_data",
"",
"Path to directory to download data to.")
flags.DEFINE_string("path_to_output_dir",
"",
"Path to directory to write data to.")
flags.DEFINE_string("splits_to_process",
"train,val",
"Process these splits. Useful for custom data splits.")
flags.DEFINE_string("video_path_format_string",
if __name__ == '__main__':
flags.DEFINE_string('path_to_download_data',
'',
'Path to directory to download data to.')
flags.DEFINE_string('path_to_output_dir',
'',
'Path to directory to write data to.')
flags.DEFINE_string('splits_to_process',
'train,val',
'Process these splits. Useful for custom data splits.')
flags.DEFINE_string('video_path_format_string',
None,
"The format string for the path to local video files. "
"Uses the Python string.format() syntax with possible "
"arguments of {video}, {start}, {end}, {label_name}, and "
"{split}, corresponding to columns of the data csvs.")
flags.DEFINE_integer("seconds_per_sequence",
'The format string for the path to local video files. '
'Uses the Python string.format() syntax with possible '
'arguments of {video}, {start}, {end}, {label_name}, and '
'{split}, corresponding to columns of the data csvs.')
flags.DEFINE_integer('seconds_per_sequence',
10,
"The number of seconds per example in each example. Always"
"1 when examples_for_context is True.")
flags.DEFINE_integer("hop_between_sequences",
'The number of seconds per example in each example.'
'Always 1 when examples_for_context is True.')
flags.DEFINE_integer('hop_between_sequences',
10,
"The hop between sequences. If less than "
"seconds_per_sequence, will overlap. Always 1 when "
"examples_for_context is True.")
flags.DEFINE_boolean("examples_for_context",
False,
"Whether to generate examples instead of sequence examples. "
"If true, will generate tf.Example objects "
"for use in Context R-CNN.")
'The hop between sequences. If less than '
'seconds_per_sequence, will overlap. Always 1 when '
'examples_for_context is True.')
flags.DEFINE_boolean('examples_for_context',
False,
'Whether to generate examples instead of sequence '
'examples. If true, will generate tf.Example objects '
'for use in Context R-CNN.')
app.run(main)
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