Create app.py

app.py

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+import streamlit as st
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+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
+
+# Set up Detectron2 model for panoptic segmentation
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5   # Set a threshold for the model's confidence
+predictor = DefaultPredictor(cfg)
+
+st.title("🎯 Smart Background Remover & Object Tagger with Panoptic Segmentation")
+
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+if uploaded_file:
+    # Read image
+    image = Image.open(uploaded_file).convert("RGB")
+    image_np = np.array(image)
+
+    st.image(image_np, caption="Original Image", use_container_width=True)
+
+    with st.spinner("Detecting and segmenting..."):
+        # Run panoptic segmentation
+        outputs = predictor(image_np)
+        panoptic_seg, segments_info = outputs["panoptic_seg"], outputs["instances"].to("cpu")
+
+        # Visualize the results
+        v = Visualizer(image_np[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
+        v = v.draw_panoptic_segmentation(panoptic_seg.to("cpu"), segments_info)
+        result_image = v.get_image()[:, :, ::-1]
+
+    st.markdown("---")
+    st.image(result_image, caption="Panoptic Segmentation Output", use_container_width=True)
+
+    # Download option for filtered image
+    result_img = Image.fromarray(result_image)
+    st.download_button("📥 Download Filtered Image", result_img.save, file_name="filtered_panoptic.png", mime="image/png")