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coco to fathomnet first draft #34

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68 changes: 68 additions & 0 deletions src/fathomnet/scripts/fathomnet_generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -276,6 +276,74 @@ def generate_coco_dataset(ims: List[AImageDTO], output_dir: str) -> bool:
return False


def generate_fathomnet_dataset_from_coco_object_detection(
coco_raw: str,
) -> Iterable[List[str]]:
"""
Generate a Fathomnet-compatible csv file from a COCO object detection json

COCO format reference:
https://cocodataset.org/#format-data

Fathomnet format reference:
https://www.fathomnet.org/post/how-to-submit-localized-image-annotations-to-the-fathomnet-database

Parameters
----------
coco_raw: str
The text contents of a coco json object detection annotation file
e.g. using `json.load(open('instances_val2017.json'))`

Returns
-------
Iterable[List[str]]
An iterable of rows in csv-reader style, including a header
Can be written to file with, e.g. `csv.writer(open('some_path','w')).writerows`

Examples
--------
>>> coco_raw = '''{
... "licenses": [{ "id": 1, "name": "license", "url": "example.com/license" }],
... "info": { "year": 2025, "version": "1.0", "description": "example", "contributor": "Arthur",
... "url": "https://example.com", "date_created": "2025/01/01"},
... "images": [
... { "id": 1, "file_name": "img1.jpg", "coco_url": "example.com/img1.jpg", "flickr_url": "",
... "width": 640, "height": 480, "date_captured": "2025-01-01 00:00:00", "license": 1},
... { "id": 2, "file_name": "img2.jpg", "coco_url": "example.com/img2.jpg", "flickr_url": "",
... "width": 800, "height": 600, "date_captured": "2025-01-01 00:00:00", "license": 1}
... ],
... "categories": [
... { "id": 1, "name": "cat", "supercategory": "animal" },
... { "id": 2, "name": "dog", "supercategory": "animal" }
... ],
... "annotations": [
... { "id": 1, "image_id": 1, "category_id": 1, "bbox": [5, 4, 2, 3], "area": 6, "iscrowd": 0, "segmentation": [[]]},
... { "id": 2, "image_id": 2, "category_id": 2, "bbox": [1, 2, 3, 3], "area": 9, "iscrowd": 0, "segmentation": [[]]},
... { "id": 3, "image_id": 2, "category_id": 1, "bbox": [0, 0, 5, 1], "area": 5, "iscrowd": 0, "segmentation": [[]]}
... ]
... }'''
>>> for row in generate_fathomnet_dataset_from_coco_object_detection(coco_raw):
... print(', '.join(map(str, row)))
...
concept, image, x, y, width, height
cat, example.com/img1.jpg, 5, 4, 2, 3
dog, example.com/img2.jpg, 1, 2, 3, 3
cat, example.com/img2.jpg, 0, 0, 5, 1
"""

HEADER = ["concept", "image", "x", "y", "width", "height"]
yield HEADER

coco = ObjectDetectionDataset.from_json(coco_raw)
imgIndex = {im.id: im for im in coco.images}
catIndex = {cat.id: cat for cat in coco.categories}
for annotation in coco.annotations:
concept = catIndex[annotation.category_id].name
image = imgIndex[annotation.image_id].coco_url
x, y, width, height = annotation.bbox
yield (concept, image, x, y, width, height)


def generate_yolo_dataset(ims: List[AImageDTO], output_dir: str) -> bool:
"""Generate a YOLO dataset (folder of annotation TXTs)"""
error_flag = False
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