Upload app.py

app.py

@@ -1,50 +1,54 @@
-import gradio as gr
+# app.py
 import cv2
 import numpy as np
 from PIL import Image
+import gradio as gr
+
+def vton_tryon(human: Image.Image, garment: Image.Image) -> Image.Image:
+    # Convert PIL to OpenCV format
+    human = cv2.cvtColor(np.array(human), cv2.COLOR_RGB2BGR)
+    garment = cv2.cvtColor(np.array(garment), cv2.COLOR_RGB2BGR)
+
+    # Resize garment to fit human image width
+    scale_factor = human.shape[1] / garment.shape[1] * 0.5  # garment takes half the human width
+    new_size = (int(garment.shape[1] * scale_factor), int(garment.shape[0] * scale_factor))
+    garment_resized = cv2.resize(garment, new_size, interpolation=cv2.INTER_AREA)
+
+    # Position: top center of human image
+    x_offset = (human.shape[1] - garment_resized.shape[1]) // 2
+    y_offset = human.shape[0] // 4  # roughly chest area
 
-# ======= Dummy try-on function =======
-# Replace this with your IDM-VTON model call
-def vton_tryon(human_img, garment_img, auto_mask=True, auto_resize=True, description=""):
-    """
-    human_img: PIL.Image
-    garment_img: PIL.Image
-    Returns: PIL.Image of human with garment overlay (dummy)
-    """
-    # Convert to OpenCV format
-    human = cv2.cvtColor(np.array(human_img), cv2.COLOR_RGB2BGR)
-    garment = cv2.cvtColor(np.array(garment_img), cv2.COLOR_RGB2BGR)
-
-    # Resize garment to human size if auto_resize
-    if auto_resize:
-        garment = cv2.resize(garment, (human.shape[1], human.shape[0]))
-
-    # Dummy overlay: just blend human + garment
-    output = cv2.addWeighted(human, 0.7, garment, 0.3, 0)
+    # Create mask for garment
+    gray = cv2.cvtColor(garment_resized, cv2.COLOR_BGR2GRAY)
+    _, mask = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
+    mask_inv = cv2.bitwise_not(mask)
+
+    # Region of interest (ROI) on human
+    roi = human[y_offset:y_offset+garment_resized.shape[0], x_offset:x_offset+garment_resized.shape[1]]
+
+    # Mask human and garment
+    human_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
+    garment_fg = cv2.bitwise_and(garment_resized, garment_resized, mask=mask)
+
+    # Merge garment with human
+    dst = cv2.add(human_bg, garment_fg)
+    human[y_offset:y_offset+garment_resized.shape[0], x_offset:x_offset+garment_resized.shape[1]] = dst
 
     # Convert back to PIL
-    output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-    return Image.fromarray(output)
-
-# ======= Gradio Interface =======
-with gr.Blocks() as demo:
-    gr.Markdown("## IDM-VTON 👕👔👚 Virtual Try-On with Gradio")
-    with gr.Row():
-        with gr.Column():
-            human_img = gr.Image(label="Upload Human Image", type="pil")
-            auto_mask = gr.Checkbox(label="Use Auto-Mask", value=True)
-            auto_resize = gr.Checkbox(label="Auto Crop & Resize", value=True)
-        with gr.Column():
-            garment_img = gr.Image(label="Upload Garment Image", type="pil")
-            description = gr.Textbox(label="Garment Description", placeholder="Short Sleeve Tee, etc.")
-    
-    tryon_btn = gr.Button("Generate Try-On")
-    output_img = gr.Image(label="Try-On Result")
-    
-    tryon_btn.click(
-        fn=vton_tryon,
-        inputs=[human_img, garment_img, auto_mask, auto_resize, description],
-        outputs=output_img
-    )
-
-demo.launch()
+    result = cv2.cvtColor(human, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(result)
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=vton_tryon,
+    inputs=[
+        gr.Image(type="pil", label="Upload Human Image"),
+        gr.Image(type="pil", label="Upload Garment Image")
+    ],
+    outputs=gr.Image(type="pil", label="Try-On Result"),
+    title="IDM-VTON 👕 Virtual Try-On",
+    description="Upload a human image and a garment image to see the try-on result dynamically."
+)
+
+if __name__ == "__main__":
+    iface.launch()