Update app.py

app.py

@@ -1,50 +1,39 @@
 import insightface
-import numpy as np
 import cv2
 import gradio as gr
-from insightface.app import FaceAnalysis
+import numpy as np
 import os
 
-# Initialize InsightFace
-app = FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'])
+# Initialize InsightFace model
+app = insightface.app.FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'])
 app.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.3)
 
-# Load InSwapper model
 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'])
 
-# Denoising to remove noise from the images
-def denoise_image(img):
-    return cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
+# Preprocess image to enhance it
+def preprocess_image(img):
+    alpha = 1.3
+    beta = 15
+    adjusted = cv2.convertScaleAbs(img, alpha=alpha, beta=beta)
+    return adjusted
 
-# Color transfer to match the source image to the target's color tone
-def color_transfer(src, dst):
-    src_mean, src_std = cv2.meanStdDev(src)
-    dst_mean, dst_std = cv2.meanStdDev(dst)
-    for c in range(3):
-        src[:, :, c] = ((src[:, :, c] - src_mean[c]) * (dst_std[c] / (src_std[c] + 1e-5)) + dst_mean[c])
-    return np.clip(src, 0, 255).astype(np.uint8)
-
-# Sharpening to enhance the details on the swapped face
+# Sharpen the image to make it look better
 def sharpen_image(img):
     kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
-    return cv2.filter2D(img, -1, kernel)
-
-# Seamless Cloning for better blending
-def seamless_cloning(src, dst, mask, center):
-    return cv2.seamlessClone(src, dst, mask, center, cv2.NORMAL_CLONE)
+    sharpened = cv2.filter2D(img, -1, kernel)
+    return np.clip(sharpened, 0, 255).astype(np.uint8)
 
-# Swap faces and improve blending
+# Perform face swapping
 def swap_faces(src_img, dst_img, blur_strength=5, sharpen=False):
     try:
-        # Preprocessing (denoise and color match)
-        src = denoise_image(src_img)
-        dst = denoise_image(dst_img)
-        src = color_transfer(src, dst)
+        # Preprocess images to improve lighting and details
+        src = preprocess_image(src_img)
+        dst = preprocess_image(dst_img)
 
-        # Convert to RGB
+        # Convert images to RGB
         src_rgb = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
         dst_rgb = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
 
@@ -53,44 +42,35 @@ def swap_faces(src_img, dst_img, blur_strength=5, sharpen=False):
         dst_faces = app.get(dst_rgb)
 
         if not src_faces or not dst_faces:
-            raise ValueError("No faces detected in one of the images.")
+            raise ValueError("No faces detected in one or both of the images.")
 
-        # Select the first detected faces
+        # Get the first detected face from each image
         src_face = src_faces[0]
         dst_face = dst_faces[0]
 
-        # Get the swapped image
+        # Perform the face swap
         swapped_img = swapper.get(dst_rgb, dst_face, src_face, paste_back=True)
 
-        # Apply blur if necessary
+        # Apply blur if specified
         if blur_strength > 0:
             swapped_img = cv2.GaussianBlur(swapped_img, (blur_strength, blur_strength), 0)
 
-        # Apply sharpening if requested
+        # Apply sharpening if specified
         if sharpen:
             swapped_img = sharpen_image(swapped_img)
 
-        # Convert back to BGR for final output
-        swapped_bgr = cv2.cvtColor(swapped_img, cv2.COLOR_RGB2BGR)
-
-        # Create mask for seamless cloning
-        mask = np.zeros(swapped_bgr.shape, dtype=np.uint8)
-        mask[dst_face.ymin:dst_face.ymax, dst_face.xmin:dst_face.xmax] = 255
-        center = (dst_face.center[0], dst_face.center[1])
-
-        # Perform seamless cloning
-        final_result = seamless_cloning(swapped_bgr, dst_img, mask, center)
-
-        return final_result
+        # Convert back to BGR for output
+        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)
 
-# Gradio interface setup
-title = "🧠 Futuristic Face Swapper with Enhanced Blending"
+# Define Gradio interface
+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 "
-    "with enhancements such as color matching, sharpening, and seamless blending for smoother results."
+    "for clean, smooth results. Adjust blur strength or enable sharpening for enhanced output."
 )
 
 demo = gr.Interface(