Added tab for upload and resized image to ensure no runtime errors for Out of Memory.

app.py

@@ -1,10 +1,13 @@
 import gradio as gr
 import numpy as np
-from PIL import Image, ImageDraw, ImageFilter
-from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
+from PIL import Image, ImageDraw, ImageFilter, ImageOps
+from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation, pipeline
 import torchvision.transforms
 import torch
 
+
+# pipeline = pipeline(task="image-segmentation", model="shi-labs/oneformer_cityscapes_swin_large", label_ids_to_fuse=[11])
+
 person_processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_cityscapes_swin_large")
 person_model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_cityscapes_swin_large")
 transform = torchvision.transforms.ToPILImage()
@@ -18,6 +21,14 @@ def detect_person(image: Image):
     return mask  
 
 
+# def detect_person_pipeline():
+#     results = pipeline(image, label_ids_to_fuse=[11])
+#     for detection in results:
+#         if detection["label"] == "person":
+#             return ImageOps.invert(detection["mask"])
+#     return Image.new(mode="L", size=image.size, color=255)
+
+
 def detect_dummy(image: Image):
     return Image.new(mode="L", size=image.size, color=255)
 
@@ -27,21 +38,6 @@ detectors = {
     # "License Plate": detect_license_plate
 }
 
-
-def anonymize(path: str, detectors: list):
-    # Read image
-    image = Image.open(path)
-    # Run requested detectors 
-    masks = [implemented_detectors.get(det, detect_dummy)(image) for det in detectors]
-    # Combine masks
-    combined = np.minimum.reduce([np.array(m) for m in masks])
-    mask = Image.fromarray(combined)
-    # Apply blur through mask
-    blurred = image.filter(ImageFilter.GaussianBlur(15))
-    anonymized = Image.composite(image, blurred, mask)
-    return anonymized    
-
-
 def test_gradio(image):
     masks = [detect_person(image)]
     combined = np.minimum.reduce([np.array(m) for m in masks])
@@ -52,6 +48,27 @@ def test_gradio(image):
     return anonymized
 
 
-demo = gr.Interface(fn=test_gradio, inputs=gr.Image(source="webcam", type="pil"), outputs=gr.Image(type="pil"))
-demo.launch()
-# demo.launch(server_name="localhost", server_port=8080)
+demo_live = gr.Interface(
+    fn=test_gradio, 
+    inputs=gr.Image(source="webcam", type="pil", shape=(640, 480)), 
+    outputs=gr.Image(type="pil")
+)
+
+demo_upload = gr.Interface(
+    fn=test_gradio, 
+    inputs=gr.Image(type="pil", shape=(640, 480)), 
+    outputs=gr.Image(type="pil")
+)
+
+demo = gr.TabbedInterface(
+    interface_list=[demo_live, demo_upload], 
+    tab_names=["Webcam", "Bild hochladen"], 
+    title="Image Anonymizer"
+)
+
+# if __name__ == "__main__":
+    # demo.launch()
+    # 
+
+print(__name__)
+demo.launch(server_name="localhost", server_port=8080)