Delete main_code_script.py

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, clothing_prompt, device="cpu" if torch.cuda.is_available() else "cpu"):
-    """
-    Replaces the clothing region in the image with new clothing based on a text prompt,
-    using Stable Diffusion Inpainting.
-    """
-    pipe = StableDiffusionInpaintPipeline.from_pretrained(
-         "stabilityai/stable-diffusion-2-inpainting",
-         "SimianLuo/LCM_Dreamshaper_v7"
-        torch_dtype=torch.float16
-    )
-    pipe = pipe.to(device)
-    # Resize the image and mask to a smaller size for faster inpainting
-    width, height = image.size
-    inpainted_size = (128, 128)  # Smaller size for faster inpainting
-    image = image.resize(inpainted_size)
-    mask_img = mask_img.resize(inpainted_size)
-    prompt = f"A photo of a person wearing {clothing_prompt}" #Add style or detail
-    image = pipe(prompt=prompt, image=image, mask_image=mask_img).images[0]
-    # Resize back to the original size (or a desired output size)
-    image = image.resize((width, height)) # Or resize to a target output size
-    return image
-# --- 4. Main Function (Putting it all together) ---
-def change_clothing(image_path, clothing_prompt):
-    """
-    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, clothing_prompt)
-    return modified_image
-# --- Example Usage ---
-if __name__ == "__main__":
-    input_image_path = "person.jpg"  # Replace with your image
-    clothing_description = "a red leather jacket"  # Replace with desired clothing
-    modified_image = change_clothing(input_image_path, clothing_description)
-    if modified_image:
-        modified_image.save("modified_image.jpg")
-        print("Clothing changed and saved to modified_image.jpg")
-    else:
-        print("Failed to change clothing.")