Update app.py

app.py

@@ -1,11 +1,7 @@
 import gradio as gr
 from PIL import Image
 import os
-import cv2
-import numpy as np
-import mediapipe as mp
 
-# Load hairstyles from 'hairstyles' folder
 def load_hairstyles():
     folder = "hairstyles"
     if not os.path.exists(folder):
@@ -16,65 +12,47 @@ def load_hairstyles():
     ]
 
 hairstyles = load_hairstyles()
-mp_face_detection = mp.solutions.face_detection
 
-# Apply hairstyle using face detection to auto-align on head
-def apply_hairstyle(user_img, style_index):
+def apply_hairstyle(user_img, style_index, x_offset, y_offset, scale):
     if user_img is None or not hairstyles:
         return None
 
-    # Convert to CV2 image for detection
-    img_cv2 = np.array(user_img.convert("RGB"))
-    img_cv2 = cv2.cvtColor(img_cv2, cv2.COLOR_RGB2BGR)
-    h_img, w_img = img_cv2.shape[:2]
+    user_img = user_img.convert("RGBA")
+    base_w, base_h = user_img.size
 
-    with mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) as face_detection:
-        results = face_detection.process(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
+    hairstyle = hairstyles[style_index]
 
-        if not results.detections:
-            return user_img  # No face detected, return original
+    # Resize the hairstyle based on scale
+    new_size = (int(base_w * scale), int(hairstyle.height * (base_w * scale / hairstyle.width)))
+    hairstyle = hairstyle.resize(new_size)
 
-        # Use first face
-        detection = results.detections[0]
-        bbox = detection.location_data.relative_bounding_box
-        x = int(bbox.xmin * w_img)
-        y = int(bbox.ymin * h_img)
-        w = int(bbox.width * w_img)
-        h = int(bbox.height * h_img)
+    # Create a blank transparent image to position the hairstyle
+    composite = Image.new("RGBA", user_img.size)
+    paste_x = int((base_w - new_size[0]) / 2 + x_offset)
+    paste_y = int(y_offset)
+    composite.paste(hairstyle, (paste_x, paste_y), hairstyle)
 
-        # Estimate head top
-        top_y = max(y - int(h * 0.6), 0)
+    # Overlay it
+    result = Image.alpha_composite(user_img, composite)
+    return result.convert("RGB")
 
-        # Load and resize hairstyle
-        hairstyle = hairstyles[style_index]
-        new_width = int(w * 1.1)  # Slightly wider than face
-        new_height = int(hairstyle.height * (new_width / hairstyle.width))
-        resized_hair = hairstyle.resize((new_width, new_height))
-
-        # Create output image
-        user_img = user_img.convert("RGBA")
-        composite = Image.new("RGBA", user_img.size)
-        paste_x = x - int((new_width - w) / 2)
-        paste_y = top_y
-        composite.paste(resized_hair, (paste_x, paste_y), resized_hair)
-
-        final = Image.alpha_composite(user_img, composite)
-        return final.convert("RGB")
-
-# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## 💇 Salon Virtual Hairstyle Try-On (Face-Aligned, No Adjustment Needed)")
+    gr.Markdown("## 💇 Salon Virtual Hairstyle Try-On (Adjustable)")
+
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(type="pil", label="📷 Upload Your Image")
+            image_input = gr.Image(type="pil", label="📷 Upload an Image")
             style_slider = gr.Slider(0, max(len(hairstyles)-1, 0), step=1, label="🎨 Select Hairstyle")
+            x_offset = gr.Slider(-200, 200, value=0, step=1, label="⬅️➡️ Move Left / Right")
+            y_offset = gr.Slider(-200, 200, value=0, step=1, label="⬆️⬇️ Move Up / Down")
+            scale = gr.Slider(0.3, 2.0, value=1.0, step=0.05, label="📏 Scale Hairstyle")
             apply_btn = gr.Button("✨ Apply Hairstyle")
         with gr.Column():
             result_output = gr.Image(label="🔍 Result Preview")
 
     apply_btn.click(
         fn=apply_hairstyle,
-        inputs=[image_input, style_slider],
+        inputs=[image_input, style_slider, x_offset, y_offset, scale],
         outputs=result_output
     )