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| import os | |
| os.system("pip install gradio==2.4.6") | |
| import sys | |
| import gradio as gr | |
| os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html') | |
| # clone and install Detic | |
| os.system("git clone https://github.com/facebookresearch/Detic.git --recurse-submodules") | |
| os.chdir("Detic") | |
| # Install detectron2 | |
| import torch | |
| # Some basic setup: | |
| # Setup detectron2 logger | |
| import detectron2 | |
| from detectron2.utils.logger import setup_logger | |
| setup_logger() | |
| # import some common libraries | |
| import sys | |
| import numpy as np | |
| import os, json, cv2, random | |
| # import some common detectron2 utilities | |
| from detectron2 import model_zoo | |
| from detectron2.engine import DefaultPredictor | |
| from detectron2.config import get_cfg | |
| from detectron2.utils.visualizer import Visualizer | |
| from detectron2.data import MetadataCatalog, DatasetCatalog | |
| # Detic libraries | |
| sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/') | |
| from centernet.config import add_centernet_config | |
| from detic.config import add_detic_config | |
| from detic.modeling.utils import reset_cls_test | |
| from PIL import Image | |
| # Build the detector and download our pretrained weights | |
| cfg = get_cfg() | |
| add_centernet_config(cfg) | |
| add_detic_config(cfg) | |
| cfg.MODEL.DEVICE='cpu' | |
| cfg.merge_from_file("configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml") | |
| cfg.MODEL.WEIGHTS = 'https://dl.fbaipublicfiles.com/detic/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth' | |
| cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model | |
| cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH = 'rand' | |
| cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL = True # For better visualization purpose. Set to False for all classes. | |
| predictor = DefaultPredictor(cfg) | |
| # Setup the model's vocabulary using build-in datasets | |
| BUILDIN_CLASSIFIER = { | |
| 'lvis': 'datasets/metadata/lvis_v1_clip_a+cname.npy', | |
| 'objects365': 'datasets/metadata/o365_clip_a+cnamefix.npy', | |
| 'openimages': 'datasets/metadata/oid_clip_a+cname.npy', | |
| 'coco': 'datasets/metadata/coco_clip_a+cname.npy', | |
| } | |
| BUILDIN_METADATA_PATH = { | |
| 'lvis': 'lvis_v1_val', | |
| 'objects365': 'objects365_v2_val', | |
| 'openimages': 'oid_val_expanded', | |
| 'coco': 'coco_2017_val', | |
| } | |
| vocabulary = 'lvis' # change to 'lvis', 'objects365', 'openimages', or 'coco' | |
| metadata = MetadataCatalog.get(BUILDIN_METADATA_PATH[vocabulary]) | |
| classifier = BUILDIN_CLASSIFIER[vocabulary] | |
| num_classes = len(metadata.thing_classes) | |
| reset_cls_test(predictor.model, classifier, num_classes) | |
| os.system("wget https://web.eecs.umich.edu/~fouhey/fun/desk/desk.jpg") | |
| def inference(img): | |
| im = cv2.imread(img) | |
| outputs = predictor(im) | |
| v = Visualizer(im[:, :, ::-1], metadata) | |
| out = v.draw_instance_predictions(outputs["instances"].to("cpu")) | |
| return Image.fromarray(np.uint8(out.get_image())).convert('RGB') | |
| title = "Detic" | |
| description = "Gradio demo for Detic: Detecting Twenty-thousand Classes using Image-level Supervision. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.02605' target='_blank'>Detecting Twenty-thousand Classes using Image-level Supervision</a> | <a href='https://github.com/facebookresearch/Detic' target='_blank'>Github Repo</a></p>" | |
| examples = [['desk.jpg']] | |
| gr.Interface(inference, inputs=gr.inputs.Image(type="filepath"), outputs=gr.outputs.Image(type="pil"),enable_queue=True, title=title, | |
| description=description, | |
| article=article, | |
| examples=examples | |
| ).launch() |