Create app.py

app.py

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+import os 
+os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
+import gradio as gr
+
+# Install detectron2
+import torch
+
+# clone and install Detic
+os.system("git clone https://github.com/facebookresearch/Detic.git --recurse-submodules")
+os.chdir("Detic")
+os.system("pip install -r requirements.txt")
+
+# 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
+from google.colab.patches import cv2_imshow
+
+# 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
+
+# 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"))
+    cv2_imshow(out.get_image()[:, :, ::-1])
+
+    return Image.fromarray(np.uint8(out.get_image())).convert('RGB')
+    
+title = "Detectron 2"
+description = "Gradio demo for Detectron 2: A PyTorch-based modular object detection library. 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://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/' target='_blank'>Detectron2: A PyTorch-based modular object detection library</a> | <a href='https://github.com/facebookresearch/detectron2' 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()