update

app.py

@@ -1,7 +1,76 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Copyright (c) Meta Platforms, Inc. All Rights Reserved
+
+import multiprocessing as mp
+
+import numpy as np
+from PIL import Image
+
+from detectron2.config import get_cfg
+
+from detectron2.projects.deeplab import add_deeplab_config
+from detectron2.data.detection_utils import read_image
+from open_vocab_seg import add_ovseg_config
+from open_vocab_seg.utils import VisualizationDemo
+
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def setup_cfg(config_file):
+    # load config from file and command-line arguments
+    cfg = get_cfg()
+    add_deeplab_config(cfg)
+    add_ovseg_config(cfg)
+    cfg.merge_from_file(config_file)
+    cfg.freeze()
+    return cfg
+
+
+def inference(class_names, input_img):
+    mp.set_start_method("spawn", force=True)
+    config_file = './configs/ovseg_swinB_vitL_demo.yaml'
+    cfg = setup_cfg(config_file)
+
+    demo = VisualizationDemo(cfg)
+
+    class_names = class_names.split(',')
+    img = read_image(input_img, format="BGR")
+    _, visualized_output = demo.run_on_image(img, class_names)
+
+    return Image.fromarray(np.uint8(visualized_output.get_image())).convert('RGB')
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo.launch()
+
+
+examples = [['Oculus, Ukulele', './resources/demo_samples/sample_03.jpeg'],]
+output_labels = ['segmentation map']
+
+title = 'OVSeg'
+
+description = """
+Gradio Demo for Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP \n
+You may click on of the examples or upload your own image. \n
+OVSeg could perform open vocabulary segmentation, you may input more classes (seperate by comma).
+"""
+
+article = """
+<p style='text-align: center'>
+<a href='https://arxiv.org/abs/2210.04150' target='_blank'>
+Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
+</a>
+|
+<a href='https://github.com' target='_blank'>Github Repo</a></p>
+"""
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+gr.Interface(
+    inference,
+    inputs=[
+        gr.inputs.Textbox(
+            lines=1, placeholder=None, default='', label='class names'),
+        gr.inputs.Image(type='filepath')
+    ],
+    outputs=gr.outputs.Image(label='segmentation map'),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples).launch(enable_queue=True)