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34 lines
1.5 KiB
Python
34 lines
1.5 KiB
Python
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# Objects365 https://www.objects365.org labels JSON to YOLO script
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# 1. Download Object 365 from the Object 365 website And unpack all images in datasets/object365/images
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# 2. Place this file and zhiyuan_objv2_train.json file in datasets/objects365
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# 3. Execute this file from datasets/object365 path
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# /datasets
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# /objects365
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# /images
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# /labels
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from pycocotools.coco import COCO
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coco = COCO("zhiyuan_objv2_train.json")
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cats = coco.loadCats(coco.getCatIds())
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nms = [cat["name"] for cat in cats]
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print("COCO categories: \n{}\n".format(" ".join(nms)))
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for categoryId, cat in enumerate(nms):
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catIds = coco.getCatIds(catNms=[cat])
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imgIds = coco.getImgIds(catIds=catIds)
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print(cat)
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# Create a subfolder in this directory called "labels". This is where the annotations will be saved in YOLO format
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for im in coco.loadImgs(imgIds):
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width, height = im["width"], im["height"]
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path = im["file_name"].split("/")[-1] # image filename
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try:
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with open("labels/train/" + path.replace(".jpg", ".txt"), "a+") as file:
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annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
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for a in coco.loadAnns(annIds):
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x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
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x, y = x + w / 2, y + h / 2 # xy to center
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file.write(f"{categoryId} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
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except Exception as e:
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print(e)
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