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')
+os.system('pip install torch==1.9.0 torchvision==0.10.0')
+
+import gradio as gr
+# check pytorch installation: 
+import torch, torchvision
+print(torch.__version__, torch.cuda.is_available())
+assert torch.__version__.startswith("1.9")   # please manually install torch 1.9 if Colab changes its default version
+# Some basic setup:
+# Setup detectron2 logger
+import detectron2
+from detectron2.utils.logger import setup_logger
+# import some common libraries
+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, ColorMode
+from detectron2.data import MetadataCatalog, DatasetCatalog
+from PIL import Image
+from pathlib import Path
+from detectron2.data.datasets import register_coco_instances
+from matplotlib import pyplot as plt
+
+
+cfg = get_cfg()
+cfg.MODEL.DEVICE='cpu'
+# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
+cfg.INPUT.MASK_FORMAT='bitmask'
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3 
+cfg.TEST.DETECTIONS_PER_IMAGE = 1000
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
+# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
+cfg.MODEL.WEIGHTS = "model_final.pth"
+
+predictor = DefaultPredictor(cfg)
+
+
+def inference(img):
+    # im = cv2.imread(img.name)
+    im = cv2.imread(img)
+    outputs = predictor(im)
+
+    take = outputs['instances'].scores >= 0.5 #Threshold
+    pred_masks = outputs['instances'].pred_masks[take].cpu().numpy()
+
+    mask = np.stack(pred_masks)
+    mask = np.any(mask == 1, axis=0)
+
+    p = plt.imshow(im,cmap='gray')
+    p1 = plt.imshow(mask, alpha=0.4)
+
+    return plt
+
+
+
+title = "Sartorius Cell Instance Segmentation"
+description = "Sartorius Cell Instance Segmentation Demo: Current Kaggle competition - kaggle.com/c/sartorius-cell-instance-segmentation"
+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 = [['0030fd0e6378.png']]  
+gr.Interface(inference, inputs=gr.inputs.Image(type="filepath"), outputs=gr.outputs.Image('plot')  ,enable_queue=True, title=title,
+    description=description,
+    article=article,
+    examples=examples).launch(debug=False)