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#!/usr/bin/env python3
# ============================================================================
# This script takes two paths as input. The first path is to the annotation file
# in json format. This annotation file is the validation data used in the 2017 COCO
# competition for object detection, downloaded from https://cocodataset.org/
# The second path indicates the folder where the user wants to store the converted
# annotation files in plain text format. Each file in the destination folder contains
# the true label and the bounding boxes for its corresponding validation image.
# To use the average precision calculation in the dla_benchmark, you must
# provide the text-formatted annotation files.
# Note that 91 classes are used in the mscoco paper https://arxiv.org/pdf/1405.0312.pdf,
# whereas 80 are used in validation 2014/2017 dataset.
# ============================================================================
import json
import sys
def cat80(cat: int) -> int:
'''
The validation dataset omits 11 classes and causes mismatches with
the predicted classes in the benckmark_app. This function maps the
class id from the json annotation file to those used in the dla_benchmark.
'''
diff = 1
if cat > 11:
diff += 1
if cat > 25:
diff += 1
if cat > 28:
diff += 2
if cat > 44:
diff += 1
if cat > 65:
diff += 1
if cat > 67:
diff += 2
if cat > 70:
diff += 1
if cat > 82:
diff += 1
if cat > 90:
diff += 1
return cat - diff
def parse_annotation_file(path_to_annotation: str, destination_folder: str) -> int:
fin = open(path_to_annotation)
json_data = json.load(fin)
per_image_data = dict()
# Gets all bounding boxes and labels w.r.t. each validation image.
for annotation in json_data["annotations"]:
image_id = annotation["image_id"]
bbox_data = [str(cat80(annotation["category_id"]))] + list(map(str, annotation["bbox"]))
if image_id in per_image_data:
per_image_data[image_id].append(bbox_data)
else:
per_image_data[image_id] = [bbox_data]
fin.close()
# Creates and writes to text files.
for image_meta in json_data["images"]:
file_path = rf'{destination_folder}/{image_meta["file_name"][:-4]}.txt'
if image_meta["id"] in per_image_data:
bboxes = per_image_data[image_meta["id"]]
else:
bboxes = []
with open(file_path, "w") as fout:
fout.write("\n".join([" ".join(bbox) for bbox in bboxes]))
if __name__ == "__main__":
if len(sys.argv) != 3:
sys.exit(
("Usage: {0} "
"<path to the validation file in json format> "
"<path to the folder to store the annotation text files> "
)
.format(sys.argv[0]))
json_instances = sys.argv[1]
destination = sys.argv[2]
try:
parse_annotation_file(json_instances, destination)
except Exception as err:
print(err)
else:
print("Finished.")
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