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December 15, 2021 16:36
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kwcoco_vectorized_demo.py
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| def demo_vectorized_interface(): | |
| """ | |
| This demonstrates how to use the kwcoco vectorized interface for images / | |
| categories / annotations. | |
| """ | |
| import kwcoco | |
| import ubelt as ub | |
| # Dummy data for the demo | |
| coco_dset = kwcoco.CocoDataset.demo('vidshapes3', num_frames=5) | |
| # Reroot to make file-paths more readable in this demo as relative paths | |
| coco_dset.reroot(absolute=False) | |
| ### | |
| # Images | |
| ### | |
| # The :func:`images` method, returns information about multiple images in | |
| # a dataset. By default, all images are returned. But parameters can | |
| # be specified to query for particular images. | |
| images = coco_dset.images() | |
| print('images = {!r}'.format(images)) | |
| """ | |
| <Images(num=16) at 0x7f1f720407c0> | |
| """ | |
| # Vectorized objects are just lightweight pointers to the underlying | |
| # dataset. The only data they contain are object-ids and pointers | |
| # to the coco dataset. Iterating / indexing into them treats them as | |
| # integer object ids. | |
| print('list(images) = {!r}'.format(list(images))) | |
| """ | |
| list(images) = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] | |
| """ | |
| # Data in the underlying image dictionaries can be accessed via lookup | |
| # or via using the special ".objs" property | |
| # Query the frame index, video_id, and image-id for each image | |
| video_ids = images.lookup('id') | |
| image_ids = images.lookup('video_id') | |
| frame_idxs = images.lookup('frame_index') | |
| print('video_ids = {!r}'.format(video_ids)) | |
| print('image_ids = {!r}'.format(image_ids)) | |
| print('frame_idxs = {!r}'.format(frame_idxs)) | |
| """ | |
| video_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] | |
| image_ids = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3] | |
| frame_idxs = [0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4] | |
| """ | |
| images.objs | |
| print('images.objs = {}'.format(ub.repr2(images.objs, nl=1))) | |
| """ | |
| images.objs = [ | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00001.png', 'frame_index': 0, 'height': 600, 'id': 1, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00002.png', 'frame_index': 1, 'height': 600, 'id': 2, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00003.png', 'frame_index': 2, 'height': 600, 'id': 3, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00004.png', 'frame_index': 3, 'height': 600, 'id': 4, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00005.png', 'frame_index': 4, 'height': 600, 'id': 5, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00006.png', 'frame_index': 0, 'height': 600, 'id': 6, 'video_id': 2, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00007.png', 'frame_index': 1, 'height': 600, 'id': 7, 'video_id': 2, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00008.png', 'frame_index': 2, 'height': 600, 'id': 8, 'video_id': 2, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00009.png', 'frame_index': 3, 'height': 600, 'id': 9, 'video_id': 2, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00010.png', 'frame_index': 4, 'height': 600, 'id': 10, 'video_id': 2, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00011.png', 'frame_index': 0, 'height': 600, 'id': 11, 'video_id': 3, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00012.png', 'frame_index': 1, 'height': 600, 'id': 12, 'video_id': 3, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00013.png', 'frame_index': 2, 'height': 600, 'id': 13, 'video_id': 3, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00014.png', 'frame_index': 3, 'height': 600, 'id': 14, 'video_id': 3, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00015.png', 'frame_index': 4, 'height': 600, 'id': 15, 'video_id': 3, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| ] | |
| """ | |
| ### | |
| # Annotations | |
| ### | |
| # Annotations work similarly, find all annotations for a specific image | |
| gid = image_ids[-1] | |
| annots = coco_dset.annots(gid=gid) | |
| print('annots = {!r}'.format(annots)) | |
| """ | |
| <Annots(num=2) at 0x7f202f3c3e20> | |
| """ | |
| # The ".objs" property returns a list of the raw coco dictionaries | |
| # (this works on all vectorized objects) | |
| anns = annots.objs | |
| # Lets remove the segmentations for readability | |
| for ann in anns: | |
| ann.pop('segmentation', None) | |
| ann.pop('keypoints', None) | |
| print('annots.objs = {}'.format(ub.repr2(annots.objs, nl=1))) | |
| """ | |
| annots.objs = [ | |
| {'bbox': [-15.0, 192.4, 138.3, 50.9], 'category_id': 1, 'id': 3, 'image_id': 3, 'track_id': 0}, | |
| {'bbox': [440.7, -62.2, 65.1, 260.3], 'category_id': 1, 'id': 8, 'image_id': 3, 'track_id': 1}, | |
| ] | |
| """ | |
| # Annotations have a special method that returns kwimage detection objects | |
| dets = annots.detections | |
| print('dets.data = {}'.format(ub.repr2(dets.data, nl=1))) | |
| """ | |
| dets.data = { | |
| 'boxes': <Boxes(xywh, | |
| array([[-15. , 192.4, 138.3, 50.9], | |
| [440.7, -62.2, 65.1, 260.3]], dtype=float32))>, | |
| 'class_idxs': np.array([0, 0], dtype=np.int64), | |
| 'keypoints': <PointsList(n=2) at 0x7f1f72040d90>, | |
| 'segmentations': <PolygonList(n=2) at 0x7f1f72927310>, | |
| } | |
| """ | |
| ### | |
| # Images (Advanced) | |
| ### | |
| # First query for images (e.g. within a video) | |
| images = coco_dset.images(vidid=1) | |
| print('images = {!r}'.format(images)) | |
| print('images.objs = {}'.format(ub.repr2(images.objs, nl=1))) | |
| """ | |
| images = <Images(num=5) at 0x7f1f729bc490> | |
| images.objs = [ | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00001.png', 'frame_index': 0, 'height': 600, 'id': 1, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00002.png', 'frame_index': 1, 'height': 600, 'id': 2, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00003.png', 'frame_index': 2, 'height': 600, 'id': 3, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00004.png', 'frame_index': 3, 'height': 600, 'id': 4, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| {'channels': 'r|g|b', 'file_name': '_assets/images/img_00005.png', 'frame_index': 4, 'height': 600, 'id': 5, 'video_id': 1, 'warp_img_to_vid': {'type': 'affine'}, 'width': 600}, | |
| ] | |
| """ | |
| # with an Images you can also access Annotations objects directly | |
| # Queries against a group are naturally grouped | |
| annot_groups = images.annots | |
| aids_per_image = annot_groups.lookup('id') | |
| print('annot_groups = {!r}'.format(annot_groups)) | |
| print('aids_per_image = {!r}'.format(aids_per_image)) | |
| """ | |
| annot_groups = <AnnotGroups(n=5, m=2.0, s=0.0) at 0x7f1f7206ce80> | |
| aids_per_image = [[1, 6], [2, 7], [8, 3], [9, 4], [10, 5]] | |
| """ |
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