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import json
from collections import defaultdict
from pathlib import Path
from tqdm import tqdm
import numpy as np
import sys
import pathlib
CURRENT_DIR = pathlib.Path(__file__).parent
sys.path.append(str(CURRENT_DIR))
def make_dirs(dir="./datasets/coco"):
# Create folders
dir = Path(dir)
for p in [dir / "labels"]:
p.mkdir(parents=True, exist_ok=True) # make dir
return dir
def coco91_to_coco80_class(): # converts 80-index (val2014) to 91-index (paper)
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
None, 73, 74, 75, 76, 77, 78, 79, None]
return x
def convert_coco_json(
json_dir="../coco/annotations/", use_segments=False, cls91to80=False
):
save_dir = make_dirs() # output directory
coco80 = coco91_to_coco80_class()
# Import json
for json_file in sorted(Path(json_dir).resolve().glob("*.json")):
if not str(json_file).endswith("instances_val2017.json"):
continue
fn = (
Path(save_dir) / "labels" / json_file.stem.replace("instances_", "")
) # folder name
fn.mkdir()
with open(json_file) as f:
data = json.load(f)
# Create image dict
images = {"%g" % x["id"]: x for x in data["images"]}
# Create image-annotations dict
imgToAnns = defaultdict(list)
for ann in data["annotations"]:
imgToAnns[ann["image_id"]].append(ann)
txt_file = open(Path(save_dir / "val2017").with_suffix(".txt"), "a")
# Write labels file
for img_id, anns in tqdm(imgToAnns.items(), desc=f"Annotations {json_file}"):
img = images["%g" % img_id]
h, w, f = img["height"], img["width"], img["file_name"]
bboxes = []
segments = []
txt_file.write(
"./images/" + "/".join(img["coco_url"].split("/")[-2:]) + "\n"
)
for ann in anns:
if ann["iscrowd"]:
continue
# The COCO box format is [top left x, top left y, width, height]
box = np.array(ann["bbox"], dtype=np.float64)
box[:2] += box[2:] / 2 # xy top-left corner to center
box[[0, 2]] /= w # normalize x
box[[1, 3]] /= h # normalize y
if box[2] <= 0 or box[3] <= 0: # if w <= 0 and h <= 0
continue
cls = (
coco80[ann["category_id"] - 1]
if cls91to80
else ann["category_id"] - 1
) # class
box = [cls] + box.tolist()
if box not in bboxes:
bboxes.append(box)
# Segments
if use_segments:
if len(ann["segmentation"]) > 1:
s = merge_multi_segment(ann["segmentation"])
s = (
(np.concatenate(s, axis=0) / np.array([w, h]))
.reshape(-1)
.tolist()
)
else:
s = [
j for i in ann["segmentation"] for j in i
] # all segments concatenated
s = (
(np.array(s).reshape(-1, 2) / np.array([w, h]))
.reshape(-1)
.tolist()
)
s = [cls] + s
if s not in segments:
segments.append(s)
# Write
with open((fn / f).with_suffix(".txt"), "a") as file:
for i in range(len(bboxes)):
line = (
*(segments[i] if use_segments else bboxes[i]),
) # cls, box or segments
file.write(("%g " * len(line)).rstrip() % line + "\n")
txt_file.close()
def min_index(arr1, arr2):
"""Find a pair of indexes with the shortest distance.
Args:
arr1: (N, 2).
arr2: (M, 2).
Return:
a pair of indexes(tuple).
"""
dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
return np.unravel_index(np.argmin(dis, axis=None), dis.shape)
def merge_multi_segment(segments):
"""Merge multi segments to one list.
Find the coordinates with min distance between each segment,
then connect these coordinates with one thin line to merge all
segments into one.
Args:
segments(List(List)): original segmentations in coco's json file.
like [segmentation1, segmentation2,...],
each segmentation is a list of coordinates.
"""
s = []
segments = [np.array(i).reshape(-1, 2) for i in segments]
idx_list = [[] for _ in range(len(segments))]
# record the indexes with min distance between each segment
for i in range(1, len(segments)):
idx1, idx2 = min_index(segments[i - 1], segments[i])
idx_list[i - 1].append(idx1)
idx_list[i].append(idx2)
# use two round to connect all the segments
for k in range(2):
# forward connection
if k == 0:
for i, idx in enumerate(idx_list):
# middle segments have two indexes
# reverse the index of middle segments
if len(idx) == 2 and idx[0] > idx[1]:
idx = idx[::-1]
segments[i] = segments[i][::-1, :]
segments[i] = np.roll(segments[i], -idx[0], axis=0)
segments[i] = np.concatenate([segments[i], segments[i][:1]])
# deal with the first segment and the last one
if i in [0, len(idx_list) - 1]:
s.append(segments[i])
else:
idx = [0, idx[1] - idx[0]]
s.append(segments[i][idx[0] : idx[1] + 1])
else:
for i in range(len(idx_list) - 1, -1, -1):
if i not in [0, len(idx_list) - 1]:
idx = idx_list[i]
nidx = abs(idx[1] - idx[0])
s.append(segments[i][nidx:])
return s
if __name__ == "__main__":
convert_coco_json(
"./datasets/coco/annotations", # directory with *.json
use_segments=True,
cls91to80=True,
) |