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02_🎭_Face_Feature_Swap.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+from utils.face_utils import swap_facial_features
+
+# Page configuration
+st.set_page_config(
+    page_title="🎭 Face Feature Swapper",
+    page_icon=":smiley:",
+    layout="wide"
+)
+
+# Title and description
+st.title("🎭 Face Feature Swapper")
+st.subheader("Combine facial features from different photos")
+st.markdown("""
+Upload two photos to swap facial features:
+1. **Base Photo**: The main face that will be modified
+2. **Donor Photo**: Provides features to transfer
+""")
+
+# Initialize session state
+if 'result_image' not in st.session_state:
+    st.session_state.result_image = None
+if 'base_img' not in st.session_state:
+    st.session_state.base_img = None
+if 'donor_img' not in st.session_state:
+    st.session_state.donor_img = None
+
+# Upload widgets
+col1, col2 = st.columns(2)
+with col1:
+    base_file = st.file_uploader("Base Photo", type=["jpg", "jpeg", "png"])
+    if base_file:
+        st.session_state.base_img = np.array(Image.open(base_file))
+        st.image(st.session_state.base_img, caption="Base Photo", use_column_width=True)
+
+with col2:
+    donor_file = st.file_uploader("Donor Photo", type=["jpg", "jpeg", "png"])
+    if donor_file:
+        st.session_state.donor_img = np.array(Image.open(donor_file))
+        st.image(st.session_state.donor_img, caption="Donor Photo", use_column_width=True)
+
+# Feature selection and options
+if st.session_state.base_img is not None and st.session_state.donor_img is not None:
+    st.subheader("Feature Selection")
+    features = st.multiselect(
+        "Select features to swap:",
+        ["Eyes", "Lips", "Eyebrows", "Nose"],
+        default=["Eyes", "Lips"]
+    )
+    
+    blend_intensity = st.slider("Blending Intensity", 0.0, 1.0, 0.9)
+    show_landmarks = st.checkbox("Show Facial Landmarks", True)
+    
+    if st.button("Swap Features", use_container_width=True):
+        with st.spinner("Creating your custom face..."):
+            # Swap features
+            result = swap_facial_features(
+                st.session_state.base_img.copy(),
+                st.session_state.donor_img.copy(),
+                features,
+                blend_intensity
+            )
+            
+            # Convert to PIL for display
+            st.session_state.result_image = Image.fromarray(result)
+            
+        st.success("Feature swap complete!")
+
+# Display results
+if st.session_state.result_image:
+    st.subheader("Result")
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        st.image(st.session_state.result_image, caption="Modified Face", use_column_width=True)
+        
+        # Download button
+        buf = BytesIO()
+        st.session_state.result_image.save(buf, format="JPEG")
+        st.download_button(
+            label="Download Result",
+            data=buf.getvalue(),
+            file_name="custom_face.jpg",
+            mime="image/jpeg",
+            use_container_width=True
+        )
+    
+    with col2:
+        if show_landmarks:
+            st.subheader("Feature Visualization")
+            from utils.face_utils import detect_landmarks, get_feature_mask
+            
+            # Create visualization
+            vis_img = st.session_state.base_img.copy()
+            landmarks = detect_landmarks(vis_img)
+            
+            if landmarks:
+                for feature in features:
+                    mask = get_feature_mask(landmarks, feature.lower(), vis_img.shape)
+                    color = {
+                        'eyes': (0, 255, 0),
+                        'lips': (0, 0, 255),
+                        'eyebrows': (255, 0, 0),
+                        'nose': (255, 255, 0)
+                    }[feature.lower()]
+                    
+                    # Draw contours
+                    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                    cv2.drawContours(vis_img, contours, -1, color, 2)
+                
+                st.image(vis_img, caption="Feature Regions", use_column_width=True)
+            else:
+                st.warning("Could not detect facial features in base image")
+
+# Preset management
+st.sidebar.subheader("Presets")
+preset_name = st.sidebar.text_input("Save current settings as preset:")
+if st.sidebar.button("Save Preset"):
+    if preset_name:
+        st.session_state[preset_name] = {
+            'features': features,
+            'blend_intensity': blend_intensity
+        }
+        st.sidebar.success(f"Preset '{preset_name}' saved!")
+    else:
+        st.sidebar.warning("Please enter a preset name")
+
+# Load presets
+preset_names = [k for k in st.session_state.keys() if k not in ['base_img', 'donor_img', 'result_image']]
+selected_preset = st.sidebar.selectbox("Load preset", preset_names)
+if selected_preset and st.sidebar.button("Apply Preset"):
+    preset = st.session_state[selected_preset]
+    features = preset['features']
+    blend_intensity = preset['blend_intensity']
+    st.experimental_rerun()

packages.txt

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+libgl1-mesa-glx
+libglib2.0-0

requirements.txt

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+mediapipe==0.10.9
+opencv-python==4.9.0.80
+numpy==1.26.4
+streamlit==1.32.0
+Pillow==10.3.0

tempCodeRunnerFile.py

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+BytesIO