Update app.py

app.py

@@ -1,80 +1,59 @@
-import insightface
-import numpy as np
+import os
 import cv2
-import gradio as gr
+import numpy as np
+import insightface
 from insightface.app import FaceAnalysis
-import os
-
-# Suppress NumPy rcond warning (optional)
-import warnings
-warnings.filterwarnings("ignore", category=FutureWarning)
+import gradio as gr
 
-# Initialize InsightFace
+# Initialize InsightFace for face detection
 app = FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'])
 app.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.3)
 
-# Load inswapper model for face swapping
+# Load the InSwapper model (lightweight face swapper)
 inswapper_path = "checkpoints/inswapper_128.onnx"
 if not os.path.exists(inswapper_path):
     raise FileNotFoundError(f"Model not found at {inswapper_path}")
 swapper = insightface.model_zoo.get_model(inswapper_path, providers=['CPUExecutionProvider'])
 
+# Preprocessing function to adjust contrast and brightness
 def preprocess_image(img):
-    """Preprocess image to enhance it for better swap results."""
-    # Adjust contrast and brightness for better facial features extraction
-    alpha = 1.3  # contrast control
-    beta = 15  # brightness control
+    alpha = 1.3  # Contrast control
+    beta = 15    # Brightness control
     adjusted = cv2.convertScaleAbs(img, alpha=alpha, beta=beta)
-    
-    # Optional: histogram equalization to enhance details
-    adjusted = cv2.cvtColor(adjusted, cv2.COLOR_BGR2GRAY)
-    adjusted = cv2.equalizeHist(adjusted)
-    return cv2.cvtColor(adjusted, cv2.COLOR_GRAY2BGR)
+    return adjusted
 
+# Function to sharpen the image to enhance details
 def sharpen_image(img):
-    """Enhance the sharpness of the face swapped image."""
-    kernel = np.array([[0, -1, 0], [-1, 5,-1], [0, -1, 0]])
+    kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])  # Sharpening kernel
     sharpened = cv2.filter2D(img, -1, kernel)
     return np.clip(sharpened, 0, 255).astype(np.uint8)
 
-def color_correction(src_img, dst_img):
-    """Color correction to ensure the source face blends seamlessly with the target face."""
-    src_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB)
-    dst_img = cv2.cvtColor(dst_img, cv2.COLOR_BGR2RGB)
-    
-    src_mean, src_std = cv2.meanStdDev(src_img)
-    dst_mean, dst_std = cv2.meanStdDev(dst_img)
-    
-    for c in range(3):
-        src_img[:, :, c] = ((src_img[:, :, c] - src_mean[c]) * (dst_std[c] / (src_std[c] + 1e-5)) + dst_mean[c])
-    
-    return cv2.cvtColor(np.clip(src_img, 0, 255).astype(np.uint8), cv2.COLOR_RGB2BGR)
-
+# Function to perform face swapping
 def swap_faces(src_img, dst_img, blur_strength=5, sharpen=False):
     try:
-        # Preprocess images for better face swapping
+        # Preprocess both images
         src = preprocess_image(src_img)
         dst = preprocess_image(dst_img)
 
-        # Convert images to RGB for InsightFace processing
+        # Convert images to RGB
         src_rgb = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
         dst_rgb = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
 
-        # Detect faces in source and destination images
+        # Detect faces in both images
         src_faces = app.get(src_rgb)
         dst_faces = app.get(dst_rgb)
 
         if not src_faces or not dst_faces:
             raise ValueError("No faces detected in one of the images.")
 
-        # Extract the first face from each image
+        # Use the first detected faces
         src_face = src_faces[0]
         dst_face = dst_faces[0]
 
         # Perform face swapping using the inswapper model
         swapped_img = swapper.get(dst_rgb, dst_face, src_face, paste_back=True)
 
-        # Apply Gaussian blur if specified
+        # Apply Gaussian blur if required
         if blur_strength > 0:
             swapped_img = cv2.GaussianBlur(swapped_img, (blur_strength, blur_strength), 0)
 
@@ -82,17 +61,14 @@ def swap_faces(src_img, dst_img, blur_strength=5, sharpen=False):
         if sharpen:
             swapped_img = sharpen_image(swapped_img)
 
-        # Color correction to match the destination face
-        swapped_img = color_correction(swapped_img, dst)
-
-        # Return the final swapped face image
+        # Convert the final image back to BGR
         result = cv2.cvtColor(swapped_img, cv2.COLOR_RGB2BGR)
         return result
     except Exception as e:
         print(f"Error: {str(e)}")
-        return np.zeros((640, 640, 3), dtype=np.uint8)  # Fallback image
+        return np.zeros((640, 640, 3), dtype=np.uint8)  # Return a blank image on error
 
-# Gradio UI setup
+# Gradio interface setup
 title = "🧠 Futuristic Face Swapper with inswapper_128"
 description = (
     "Upload a source face and a target image. The AI swaps the face using inswapper_128.onnx "