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@ -1,3 +1,35 @@ |
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# 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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