detectron2
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# .gitattributes |
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# Source files |
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# ============ |
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*.pxd text diff=python |
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*.msgpack filter=lfs diff=lfs merge=lfs -text |
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### Linux ### |
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*~ |
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### Python ### |
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nosetests.xml |
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docs/_build/ |
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# PyBuilder |
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.pybuilder/ |
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target/ |
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# Jupyter Notebook |
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.ipynb_checkpoints |
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# IPython |
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profile_default/ |
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ipython_config.py |
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# pyenv |
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# For a library or package, you might want to ignore these files since the code is |
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# intended to run in multiple environments; otherwise, check them in: |
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# .python-version |
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# pipenv |
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. |
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# However, in case of collaboration, if having platform-specific dependencies or dependencies |
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# having no cross-platform support, pipenv may install dependencies that don't work, or not |
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# install all needed dependencies. |
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#Pipfile.lock |
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow |
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__pypackages__/ |
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# Celery stuff |
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celerybeat-schedule |
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celerybeat.pid |
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*.sage.py |
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# Environments |
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.env |
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.venv |
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env/ |
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venv/ |
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ENV/ |
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env.bak/ |
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venv.bak/ |
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# Spyder project settings |
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.spyderproject |
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# Rope project settings |
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.ropeproject |
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# mkdocs documentation |
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/site |
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# mypy |
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.mypy_cache/ |
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.dmypy.json |
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dmypy.json |
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# Pyre type checker |
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.pyre/ |
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# pytype static type analyzer |
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.pytype/ |
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# Cython debug symbols |
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cython_debug/ |
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### Windows ### |
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# Windows thumbnail cache files |
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Thumbs.db |
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Thumbs.db:encryptable |
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ehthumbs.db |
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ehthumbs_vista.db |
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# Dump file |
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*.stackdump |
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# Folder config file |
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[Dd]esktop.ini |
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# Recycle Bin used on file shares |
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$RECYCLE.BIN/ |
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# Windows Installer files |
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*.cab |
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*.msi |
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*.msix |
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*.msm |
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*.msp |
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# Windows shortcuts |
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*.lnk |
@ -1,3 +1,35 @@ |
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# detectron2 |
# detectron2 |
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2 |
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# Object Detection using Detectron2 |
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## Description |
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This operator uses Facebook's [Detectron2](https://github.com/facebookresearch/detectron2) library to compute bounding boxes, class labels, and class scores for detected objects in a given image. |
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## Code Example |
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```python |
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import towhee |
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towhee.glob('./towhee.jpg') \ |
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.image_decode.cv2() \ |
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.object_detection.detectron2(model_name='retinanet_resnet50') \ |
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.show() |
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``` |
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## Factory Constructor |
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Create the operator via the following factory method |
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***object_detection.detectron2(model_name='retinanet_resnet50', thresh=0.5, num_classes=1000, skip_preprocess=False)*** |
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**Parameters:** |
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***model_name:*** *str* |
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A string indicating which model to use. |
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***thresh:*** *float* |
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The threshold value for which an object is detected (default value: `0.5`). Set this value lower to detect more objects at the expense of accuracy, or higher to reduce the total number of detections but increase the quality of detected objects. |
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from typing import List, Tuple |
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from detectron2 import model_zoo |
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from detectron2.config import get_cfg |
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from detectron2.engine.defaults import DefaultPredictor |
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import numpy as np |
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import torch |
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from towhee._types import Image |
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from towhee.operator import NNOperator, OperatorFlag |
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CFG_YAMLS = { |
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'faster_rcnn_resnet50_c4': 'COCO-Detection/faster_rcnn_R_50_C4_3x.yaml', |
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'faster_rcnn_resnet50_dc5': 'COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml', |
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'faster_rcnn_resnet50_fpn': 'COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml', |
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'faster_rcnn_resnet101_c4': 'COCO-Detection/faster_rcnn_R_101_C4_3x.yaml', |
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'faster_rcnn_resnet101_dc5': 'COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml', |
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'faster_rcnn_resnet101_fpn': 'COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml', |
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'faster_rcnn_resnext101': 'COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml', |
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'retinanet_resnet50': 'COCO-Detection/retinanet_R_50_FPN_3x.yaml', |
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'retinanet_resnet101': 'COCO-Detection/retinanet_R_101_FPN_3x.yaml' |
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} |
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@register(outputschema=['boxes', 'classes', 'scores'], |
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flag=OperatorFlag.STATELESS | OperatorFlag.REUSABLE) |
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class Detectron2ObjectDetection(NNOperator): |
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""" |
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This Operator implements object detection using the Detectron2 library. |
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Args: |
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model_name (`str`): |
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Detectron2-based model to use. For a full list, see `CFG_YAMLS`. |
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""" |
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def __init__(self, model_name: str = 'retinanet_resnet50', thresh: int = 0.5): |
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super().__init__() |
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cfg = get_cfg() |
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cfg.merge_from_file(model_zoo.get_config_file(CFG_YAMLS[model_name])) |
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cfg.MODEL.DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu' |
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cfg.MODEL.RETINANET.SCORE_THRESH_TEST = thresh |
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cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 |
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cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5 |
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cfg.freeze() |
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self._predictor = DefaultPredictor(cfg) |
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def __call__(self, image: 'towhee._types.Image') -> Tuple[List]: |
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# Detectron2 uses BGR-formatted images |
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res = self._predictor(image.to_ndarray()[:,:,::-1]) |
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boxes = res.get('pred_boxes').tensor.numpy() |
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classes = res.get('pred_classes').numpy() |
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scores = res.get('scores').numpy() |
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return (boxes, classes, scores) |
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torch >= 1.8 |
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torchvision >= 0.9.0 |
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https://github.com/facebookresearch/detectron2/archive/refs/tags/v0.6.tar.gz |
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