Application init

app.py

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+import cv2
+import gradio as gr
+
+from detectron2.config import get_cfg
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog
+
+
+def predict(input_image):
+    # Initialise model
+    cfg = get_cfg()
+    cfg.merge_from_file(model_zoo.get_config_file("COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml"))
+    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml")
+    cfg.MODEL.DEVICE = "cpu"
+    predictor = DefaultPredictor(cfg)
+
+    assert input_image.shape[2] == 3
+    height, width, _ = input_image.shape
+
+    # Apply Panoptic segmentation
+    panoptic_seg, segments_info = predictor(input_image)["panoptic_seg"]
+    v = Visualizer(input_image[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
+    out = v.draw_panoptic_seg_predictions(panoptic_seg.to("cpu"), segments_info)
+    segmented_image = out.get_image()[:, :, ::-1]
+
+    # Resize image if required
+    if not segmented_image.shape[:2] == (height, width):
+        segmented_image = cv2.resize(segmented_image, (height, width))
+
+    # Combine the segmented and original image
+    combined_image = cv2.hconcat([segmented_image, input_image])
+
+    return combined_image
+
+
+# Create Gradio interface
+image_input = gr.Image(type="pil", label="Input Image")
+
+iface = gr.Interface(fn=predict,
+                     inputs=[image_input],
+                     outputs=gr.Image(type="pil"),
+                     examples=[["examples/trump-fake.jpeg"]])
+iface.launch()

requirements.txt

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+detectron2==0.6
+gradio==4.29.0
+numpy==1.23.5
+opencv_python==4.8.0.76