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app.py

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+import gradio as gr
+from PIL import Image
+import os
+# Import your core functions (estimate_pose, segment_clothing, inpaint_clothing, change_clothing) from your main script
+from main_code_script import change_clothing  # Replace your_main_script
+def predict(image_path, garment_image_path): # Changed input
+    """
+    The prediction function for Gradio.
+    """
+    try:
+        modified_image = change_clothing(image_path, garment_image_path) # Changed input
+        if modified_image:
+            return modified_image
+        else:
+            return "Failed to change clothing.  Please check the images."
+    except Exception as e:
+        return f"Error: {e}"
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="filepath", label="Input Image (Person)"), # Changed label
+        gr.Image(type="filepath", label="Garment Image"),  # Added input
+    ],
+    outputs=gr.Image(type="pil", label="Modified Image"),
+    title="AI Clothing Changer",
+    description="Try on different clothes with AI by uploading a garment image!", # Changed description
+)
+# Launch the Gradio interface
+if __name__ == "__main__":
+    iface.launch()

main_code_script.py

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+# Install necessary libraries (in your requirements.txt)
+# pillow opencv-python transformers mediapipe diffusers accelerate transformers
+# Example install command: pip install pillow opencv-python transformers mediapipe diffusers accelerate transformers
+from PIL import Image
+import cv2
+import mediapipe as mp
+import numpy as np
+from transformers import pipeline
+from diffusers import StableDiffusionInpaintPipeline
+import torch
+# --- 1. Pose Estimation (using Mediapipe) ---
+def estimate_pose(image_path):
+    """Detects the pose of a person in an image using Mediapipe.
+    Args:
+        image_path: Path to the input image.
+    Returns:
+        A list of landmarks (x, y, visibility) 
+        or None if no pose is detected.
+    """
+    mp_drawing = mp.solutions.drawing_utils
+    mp_pose = mp.solutions.pose
+    with mp_pose.Pose(
+        static_image_mode=True,
+        model_complexity=2,
+        enable_segmentation=True,
+        min_detection_confidence=0.5) as pose:
+        image = cv2.imread(image_path)
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        results = pose.process(image_rgb)
+        
+    if results.pose_landmarks:
+        # Example: Draw the pose landmarks on the image (for visualization)
+        annotated_image = image.copy()
+        mp_drawing.draw_landmarks(
+            annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+        #cv2.imwrite("pose_annotated.jpg", annotated_image)  # Save annotated image
+        #return results.pose_landmarks.landmark  
+        # Return the landmarks
+        return results, image # Return the entire result
+    else:
+        return None, None # or raise an exception
+# --- 2. Clothing Segmentation (Example - using a placeholder function) ---
+def segment_clothing(image, results): #Added result
+    """Segments the clothing region in the image.
+    This is a simplified example. In reality, you would use a pre-trained
+       segmentation model.
+    """
+    # 1. Create a mask where the person is present.
+    segmentation_mask = results.segmentation_mask
+    threshold = 0.5  # Adjust this threshold as needed.
+    # Threshold the segmentation mask to create a binary mask.
+    binary_mask = (segmentation_mask > threshold).astype(np.uint8) * 255
+    # Convert binary mask to a PIL Image
+    mask_img = Image.fromarray(binary_mask).convert("L")
+    return mask_img
+# --- 3. Image Inpainting (Replacing Clothing - using Stable Diffusion Inpainting) ---
+def inpaint_clothing(image, mask_img, garment_image_path, device="cuda" if torch.cuda.is_available() else "cpu"): # Changed input
+    """
+    Replaces the clothing region in the image with the uploaded garment image,
+    using Stable Diffusion Inpainting.
+    """
+    pipe = StableDiffusionInpaintPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-2-inpainting",
+        torch_dtype=torch.float16
+    )
+    pipe = pipe.to(device)
+    # Resize the image and mask to the same size.  Important for inpainting.
+    image = image.resize((512, 512))
+    mask_img = mask_img.resize((512, 512))
+    
+    # Load the garment image
+    garment_image = Image.open(garment_image_path).convert("RGB")
+    garment_image = garment_image.resize((512,512)) # Resize if necessary
+    
+    # Inpaint using the garment image as a guide (This part might need further refinement)
+    # A simple approach is to use the garment image in the prompt.  
+    # More advanced techniques might involve using the garment image as 
+    # a style reference or directly manipulating the latent space.
+    prompt = f"A photo of a person wearing the uploaded garment" 
+    image = pipe(prompt=prompt, image=image, mask_image=mask_img).images[0]
+    return image
+# --- 4. Main Function (Putting it all together) ---
+def change_clothing(image_path, garment_image_path): # Changed input
+    """
+    Main function to change the clothing in an image.
+    """
+    # 1. Load the image
+    image = Image.open(image_path).convert("RGB")
+    # 2. Estimate the pose
+    results, cv2_image = estimate_pose(image_path)
+    if results is None:
+        print("No pose detected.")
+        return None
+    # 3. Segment the clothing
+    mask_img = segment_clothing(image, results)
+    # 4. Inpaint the clothing
+    modified_image = inpaint_clothing(image, mask_img, garment_image_path) # Changed input
+    return modified_image
+# --- Example Usage ---
+if __name__ == "__main__":
+    input_image_path = "person.jpg"  # Replace with your image
+    garment_image_path = "garment.jpg" # Replace with your garment image
+    modified_image = change_clothing(input_image_path, garment_image_path)
+    if modified_image:
+        modified_image.save("modified_image.jpg")
+        print("Clothing changed and saved to modified_image.jpg")
+    else:
+        print("Failed to change clothing.")