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add coco subsampling, update docs, update tests (#46)
* add coco subsampling method * update docs * update deprecated np.bool * update test_utils to utils * update mmdet test config
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| Original file line number | Diff line number | Diff line change |
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| # COCO Utilities | ||
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| ## COCO dataset creation steps: | ||
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| - import required classes: | ||
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| ```python | ||
| from sahi.utils.coco import Coco, CocoCategory, CocoImage, CocoAnnotation | ||
| ``` | ||
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| - init Coco object: | ||
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| ```python | ||
| coco = Coco() | ||
| ``` | ||
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| - add categories starting from id 0: | ||
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| ```python | ||
| coco.add_category(CocoCategory(id=0, name='human')) | ||
| coco.add_category(CocoCategory(id=1, name='vehicle')) | ||
| ``` | ||
|
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| - create a coco image: | ||
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| ```python | ||
| coco_image = CocoImage(file_name="image1.jpg", height=1080, width=1920) | ||
| ``` | ||
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| - add annotations to coco image: | ||
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| ```python | ||
| coco_image.add_annotation( | ||
| CocoAnnotation( | ||
| bbox=[x_min, y_min, width, height], | ||
| category_id=0, | ||
| category_name='human' | ||
| ) | ||
| ) | ||
| coco_image.add_annotation( | ||
| CocoAnnotation( | ||
| bbox=[x_min, y_min, width, height], | ||
| category_id=1, | ||
| category_name='vehicle' | ||
| ) | ||
| ) | ||
| ``` | ||
|
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| - add coco image to Coco object: | ||
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| ```python | ||
| coco.add_image(coco_image) | ||
| ``` | ||
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| - after adding all images, convert coco object to coco json: | ||
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| ```python | ||
| coco_json = coco.json | ||
| ``` | ||
|
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| - you can export it as json file: | ||
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| ```python | ||
| from sahi.utils.file import save_json | ||
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| save_json(coco_json, "coco_dataset.json") | ||
| ``` | ||
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| ## Slice COCO dataset images and annotations into grids: | ||
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| ```python | ||
| from sahi.slicing import slice_coco | ||
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| coco_dict, coco_path = slice_coco( | ||
| coco_annotation_file_path="coco.json", | ||
| image_dir="source/coco/image/dir", | ||
| slice_height=256, | ||
| slice_width=256, | ||
| overlap_height_ratio=0.2, | ||
| overlap_width_ratio=0.2, | ||
| ) | ||
| ``` | ||
|
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| ## Split COCO dataset into train/val: | ||
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| ```python | ||
| from sahi.utils.coco import Coco | ||
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| # specify coco dataset path | ||
| coco_path = "coco.json" | ||
|
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| # init Coco object | ||
| coco = Coco.from_coco_dict_or_path(coco_path) | ||
|
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| # split and export COCO dataset into given target_dir with a 85% train/15% val split | ||
| coco.split_coco_as_train_val( | ||
| file_name="splitted_coco", | ||
| target_dir="output/folder/dir", | ||
| train_split_rate=0.85 | ||
| ) | ||
| ``` | ||
|
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| ## Combine COCO dataset files: | ||
|
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| ```python | ||
| from sahi.utils.coco import Coco | ||
| from sahi.utils.file import save_json | ||
|
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| # specify coco dataset json paths | ||
| coco_path_1 = "coco1.json" | ||
| coco_path_2 = "coco2.json" | ||
| coco_path_3 = "coco3.json" | ||
|
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| # init Coco object with a list of Coco path | ||
| combined_coco = Coco.from_coco_dict_or_path([coco_path_1, coco_path_2, coco_path_3]) | ||
|
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| # export combined COCO dataset | ||
| save_json(combined_coco.json, "combined_coco.json") | ||
| ``` | ||
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| ## Convert COCO dataset to ultralytics/yolov5 format: | ||
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| ```python | ||
| from sahi.utils.coco import Coco | ||
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| # specify coco dataset path | ||
| coco_path = "coco.json" | ||
|
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| # init Coco object | ||
| coco = Coco.from_coco_dict_or_path(coco_path) | ||
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| # export converted YoloV5 formatted dataset into given output_dir with a 85% train/15% val split | ||
| coco.export_as_yolov5( | ||
| image_dir="source/coco/image/dir", | ||
| output_dir="output/folder/dir", | ||
| train_split_rate=0.85 | ||
| ) | ||
| ``` | ||
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| ## Subsample COCO dataset file: | ||
|
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| ```python | ||
| from sahi.utils.coco import Coco | ||
|
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| # specify coco dataset path | ||
| coco_path = "coco.json" | ||
|
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| # init Coco object | ||
| coco = Coco.from_coco_dict_or_path(coco_path) | ||
|
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| # create a Coco object with 1/10 of total images | ||
| subsampled_coco = coco.get_subsampled_coco(subsample_ratio=10) | ||
|
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| # export subsampled COCO dataset | ||
| save_json(subsampled_coco.json, "subsampled_coco.json") | ||
| ``` |
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