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import csv
import json
import argparse
from tqdm import tqdm
# Formats the raw openimages csv bounding box
# annotations and image files into filtered, parsed json.
# Lets extract not only each annotation, but a list of image id's.
# This id index will be used to filter out images that don't have valid annotations.
def format_annotations(annotations_path, trainable_classes_path):
annotations = []
ids = []
with open(trainable_classes_path, 'rb') as file:
trainable_classes ='\n')
with open(annotations_path, 'rb') as annofile:
for row in csv.reader(annofile):
if row[2] in trainable_classes:
annotation = {'id': row[0], 'label': row[2], 'confidence': row[3], 'x0': row[4],
'x1': row[5], 'y0': row[6], 'y1': row[7]}
ids = dedupe(ids)
return annotations, ids
def dedupe(seq):
seen = set()
seen_add = seen.add
return [x for x in seq if not (x in seen or seen_add(x))]
def format_images(images_path):
images = []
with open(images_path, 'rb') as f:
reader = csv.reader(f)
dataset = list(reader)
for row in tqdm(dataset, desc="reformatting image data"):
image = {'id': row[0], 'url': row[2]}
return images
# Lets check each image and only keep it if it's ID has a bounding box annotation associated with it.
def filter_images(dataset, ids):
output_list = []
unique_ids = set(ids)
for element in tqdm(dataset, desc="filtering out non-essential images"):
if element['id'] in unique_ids:
return output_list
def save_data(data, out_path):
with open(out_path, 'w+') as f:
json.dump(data, f)
# Gathers annotations for each image id, to be easier to work with.
def points_maker(annotations):
by_id = {}
for anno in tqdm(annotations, desc="grouping annotations"):
if anno['id'] in by_id:
by_id[anno['id']] = []
groups = []
while len(by_id) >= 1:
key, value = by_id.popitem()
groups.append({'id': key, 'annotations': value})
return groups
parser = argparse.ArgumentParser()
parser.add_argument('--annotations_input_path', dest='anno_path', required=True)
parser.add_argument('--image_index_input_path', dest='index_in_path', required=True)
parser.add_argument('--point_output_path', dest='point_path', required=True)
parser.add_argument('--image_index_output_path', dest='index_out_path', required=True)
parser.add_argument('--trainable_classes_path', dest='trainable_path', required=True)
if __name__ == "__main__":
args = parser.parse_args()
anno_input_path = args.anno_path
image_index_input_path = args.index_in_path
point_output_path = args.point_path
image_index_output_path = args.index_out_path
trainable_classes_path = args.trainable_path
annotations, valid_image_ids = format_annotations(anno_input_path, trainable_classes_path)
images = format_images(image_index_input_path)
points = points_maker(annotations)
filtered_images = filter_images(images, valid_image_ids)
save_data(filtered_images, image_index_output_path)
save_data(points, point_output_path)
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