Create app.py

app.py

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+import cv2
+import numpy as np
+import mediapipe as mp
+import gradio as gr
+
+mp_selfie_segmentation = mp.solutions.selfie_segmentation
+BG_COLOR = (255, 255, 255) 
+
+def selfie_segmentation(image):
+    # Wenden Sie das Modell auf das Eingabebild an
+    with mp_selfie_segmentation.SelfieSegmentation(
+    model_selection=0) as selfie_segmentation:
+      image = cv2.imread(image)
+      image_height, image_width, _ = image.shape
+
+      # Convert the BGR image to RGB before processing.
+      results = selfie_segmentation.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+      condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
+      bg_image = np.zeros(image.shape, dtype=np.uint8)
+      bg_image[:] = BG_COLOR
+      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+      return np.where(condition, image, bg_image)
+      #im = Image.fromarray(output_image)
+      #img_var /= im.save("example.png")
+      #bla = "/content/example.png"
+      #bla = Image.open("/content/example.png")
+      #return img_var
+
+      #pil_image = Image.fromarray(output_image)
+      #byte_io = BytesIO()
+      #pil_image.save(byte_io, format='PNG')
+      #byte_io.seek(0)
+      #return byte_io.read()
+
+
+# Start a interface in gradio
+iface = gr.Interface(fn=selfie_segmentation, 
+                     inputs="image", 
+                     outputs="image")
+
+# starten Sie die Schnittstelle
+iface.launch()