Create app.py

app.py

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+import streamlit as st
+import face_recognition
+import os
+import cv2
+import insightface
+import pickle
+
+from insightface.app import FaceAnalysis
+
+
+PATH_TMP = 'tmp'
+PATH_MODEL = 'bin'
+DATA_IMAGE_PICKLE = 'data_images.pkl'
+ONNX_SWAPPER_MODEL = 'inswapper_128.onnx'
+
+
+def face_swapper(image_background, image_customer):
+    app = FaceAnalysis(name='buffalo_l')
+    app.prepare(ctx_id=0, det_size=(640, 640))
+    swapper = insightface.model_zoo.get_model(os.path.join(os.getcwd(), PATH_MODEL, ONNX_SWAPPER_MODEL), download=False)
+    face_customer = app.get(image_customer)[0]
+    faces = app.get(image_background)
+
+    for face in faces:
+        image_background = swapper.get(image_background, face, face_customer, paste_back=True)
+
+    return image_background
+
+def process(image):
+    with open(os.path.join(os.getcwd(), PATH_MODEL, DATA_IMAGE_PICKLE), 'rb') as file:
+        data_images = pickle.load(file)
+
+    images_background_encoding, images_background_contents = data_images['encodings'], data_images['content']
+    image_loaded = face_recognition.load_image_file(image)
+    face_encoding = face_recognition.face_encodings(image_loaded)[0]
+    face_distances = face_recognition.face_distance(images_background_encoding, face_encoding)
+
+    tmp_distance = face_distances[0]
+    tmp_content = images_background_contents[0]
+    for face_distance, images_background_content in zip(face_distances[1:], images_background_contents[1:]):
+        if tmp_distance > face_distance:
+            tmp_distance = face_distance
+            tmp_content = images_background_content
+
+    output_image = face_swapper(tmp_content, image_loaded)
+    return output_image
+
+image_output = None
+
+st.title('Change Faces')
+
+option = st.radio('How would you like to upload your image?', ('File', 'WebCam'), horizontal=True)
+
+if option=='File':
+    uploaded_file = st.file_uploader('Choose your image', type=['jpg', 'png', 'jpeg'])
+else:
+    uploaded_file = st.camera_input("Take a picture")
+
+
+if uploaded_file is not None:
+    bytes_data = uploaded_file.getvalue()
+    if option=='File':
+        st.image(uploaded_file)
+    if st.button('Process'):
+        image_output = process(uploaded_file)
+        st.image(image_output)
+
+if image_output is not None:
+    image_output_to_download = cv2.cvtColor(image_output, cv2.COLOR_BGR2RGB)
+    _, image_output_to_download = cv2.imencode('.jpg', image_output_to_download)
+    st.download_button('Download image', image_output_to_download.tobytes(), file_name=f'output_{uploaded_file.name}')