Create app.py

app.py

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+import numpy as np
+import cv2
+import streamlit as st
+import insightface
+from insightface.app import FaceAnalysis
+from PIL import Image
+
+# Initialize the FaceAnalysis model
+app = FaceAnalysis(name='buffalo_l')
+app.prepare(ctx_id=0, det_size=(640, 640))  # Ensure the model runs on the CPU if there's limited GPU
+
+# Load the face swapping model (downloading on-demand)
+swapper = insightface.model_zoo.get_model('insightface/inswapper_128.onnx', download=True)
+
+# Face swapping function
+def swap_faces(destination_image, source_image):
+    # Load the destination and source images from Streamlit inputs
+    img = cv2.cvtColor(np.array(destination_image), cv2.COLOR_RGB2BGR)
+    test = cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)
+    
+    # Detect faces in the destination and source images
+    faces = app.get(img)
+    test_faces = app.get(test)
+    
+    if not faces or not test_faces:
+        return "No faces detected in one or both images."
+
+    test_face = test_faces[0]
+
+    # Perform face swapping with error handling
+    res = img.copy()
+    try:
+        for face in faces:
+            res = swapper.get(res, face, test_face, paste_back=True)
+    except MemoryError:
+        return "Memory error: Face swapping operation failed due to memory overload."
+    
+    # Sharpen the output image by 20%
+    kernel = np.array([[0, -1, 0],
+                       [-1, 5, -1],
+                       [0, -1, 0]])
+    sharpened_res = cv2.filter2D(res, -1, kernel)
+
+    # Convert the result to RGB for display in Streamlit
+    sharpened_res_rgb = cv2.cvtColor(sharpened_res, cv2.COLOR_BGR2RGB)
+    
+    return sharpened_res_rgb
+
+# Streamlit app layout
+st.title("Face Swapping")
+st.write("Upload a source and a destination image to perform face swapping.")
+
+# Upload destination and source images
+destination_image = st.file_uploader("Upload Destination Image", type=["jpg", "png", "jpeg"])
+source_image = st.file_uploader("Upload Source Image", type=["jpg", "png", "jpeg"])
+
+if destination_image and source_image:
+    # Read images with PIL (Streamlit works with PIL images)
+    destination_img = Image.open(destination_image)
+    source_img = Image.open(source_image)
+    
+    st.image(destination_img, caption="Destination Image", use_column_width=True)
+    st.image(source_img, caption="Source Image", use_column_width=True)
+    
+    # Call the face swapping function
+    result = swap_faces(destination_img, source_img)
+    
+    if isinstance(result, str):
+        st.error(result)  # Display error message if no faces detected or memory error
+    else:
+        st.image(result, caption="Swapped Face", use_column_width=True)