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# Author: Ming Yang
# Date: 2023/01/20
# Description: Traverse the zip file but not decompress it.
# Suppose the zip file contains yolo format annotation files.
# /
# ├── classes.txt
# ├── images
# │ ├── 1.jpg
# │ ├── 2.jpg
# │ └── ...
# └── labels
# ├── 1.txt
# ├── 2.txt
# └── ...
from datetime import datetime
from typing import Optional
from zipfile import ZipFile
from PIL import Image
from pycocotools import coco
from pycocotools.coco import COCO
yolo_label = {
'class': str,
'class_id': int,
'x_center': float,
'y_center': float,
'width': float,
'height': float
}
yolo_image = {
'image_name': str,
'image': Image,
'labels': list[yolo_label]
}
coco_annotation = {
"id": int,
"image_id": int, # the id of the image that the annotation belongs to
"category_id": int, # the id of the category that the annotation belongs to
# "segmentation": RLE or [polygon],
"area": float,
"bbox": [float, float, float, float], # [x,y,width,height]
"iscrowd": bool, # 0 or 1,
}
coco_category = {
"id": int,
"name": str,
"supercategory": Optional[str],
}
coco_image = {
"id": int,
"width": int,
"height": int,
"file_name": str,
"date_captured": Optional[datetime],
}
coco_dataset = {
"images": list[coco_image], # list of all images in the dataset
"annotations": list[coco_annotation], # list of all annotations in the dataset
"categories": list[coco_category] # list of all categories
}
class YoloImage:
def __init__(self, image_name: str, image: Image, labels: list[yolo_label]):
self.image_name = image_name
self.image = image
self.labels = labels
def __repr__(self):
return f'YoloImage(image_name={self.image_name}, image={self.image}, labels={self.labels})'
def to_coco_image(self, id: int) -> coco_image:
return {
"id": id,
"width": self.image.width,
"height": self.image.height,
"file_name": self.image_name,
}
def to_coco_annotations(self, image_id: int, ann_id_start: int) -> list[coco_annotation]:
ann_id = ann_id_start
annotations: list[coco_annotation] = []
for label in self.labels:
ann_id = ann_id + 1
annotations.append({
"id": ann_id,
"image_id": image_id,
"category_id": label['class_id'],
"area": label['width'] * label['height'],
"bbox": [label['x_center'] - label['width'] / 2, label['y_center'] - label['height'] / 2,
label['width'], label['height']],
"iscrowd": False,
})
return annotations
class YoloDataset:
_zip_file: ZipFile
_classes: list[str]
_images: list[str]
_labels: list[str]
def __init__(self, zip_file: ZipFile, classes=None, images=None, labels=None):
if labels is None:
labels = []
if images is None:
images = []
if classes is None:
classes = []
self._zip_file = zip_file
self._classes = classes
self._images = images
self._labels = labels
@staticmethod
def from_zip_file(zip_file: ZipFile) -> 'YoloDataset':
namelist = zip_file.namelist()
root_name = namelist[0]
namelist = list(filter(lambda x: not zip_file.getinfo(x).is_dir(), namelist))
if 'classes.txt' in namelist:
classes = zip_file.read('classes.txt').decode('utf-8').split('\n')
else:
classes = []
images = list(filter(lambda x: x.startswith(root_name + 'images'), namelist))
labels = list(filter(lambda x: x.startswith(root_name + 'labels'), namelist))
assert len(images) == len(labels) and len(images) > 0
images.sort()
labels.sort()
for image, label in zip(images, labels):
image_name = image.split('/')[-1]
label_name = label.split('/')[-1]
assert image_name.split('.')[0] == label_name.split('.')[0]
return YoloDataset(zip_file, classes, images, labels)
@staticmethod
def from_path(path: str) -> 'YoloDataset':
zip_file = ZipFile(path, 'r')
return YoloDataset.from_zip_file(zip_file)
def __len__(self):
return len(self._images)
def __getitem__(self, index: int) -> YoloImage:
image_name = self._images[index]
labels = self._zip_file.read(self._labels[index]).decode('utf-8').split('\n')
labels = list(filter(lambda x: len(x) > 0, labels))
labels = list(map(lambda x: x.split(' '), labels))
labels = list(map(lambda x: {
'class': self._classes[int(x[0])] if len(self._classes) > int(x[0]) else 'unknown',
'class_id': int(x[0]),
'x_center': float(x[1]),
'y_center': float(x[2]),
'width': float(x[3]),
'height': float(x[4])
}, labels))
return YoloImage(image_name, Image.open(self._zip_file.open(self._images[index])), labels)
def __iter__(self):
for i in range(len(self)):
yield self[i]
def __deepcopy__(self, memodict=None):
return YoloDataset(self._zip_file, self._classes, self._images, self._labels)
def load_image(self, image_name: str) -> Image:
return Image.open(self._zip_file.open(image_name))
def to_coco(self) -> COCO:
images: list[coco_image] = []
annotations: list[coco_annotation] = []
categories: list[coco_category] = []
ann_id = 0
for i in range(len(self)):
image = self[i]
images.append(image.to_coco_image(i))
annotations.extend(image.to_coco_annotations(i, ann_id))
ann_id = ann_id + len(image.labels)
for i in range(len(self._classes)):
categories.append({
"id": i,
"name": self._classes[i],
"supercategory": None,
})
coco_ds = coco.COCO()
coco_ds.dataset = {
"images": images,
"annotations": annotations,
"categories": categories,
}
coco_ds.createIndex()
return coco_ds
if __name__ == '__main__':
dataset = YoloDataset.from_zip_file('tests/coco8.zip')
coco = dataset.to_coco()
print(coco)
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