app.py

import os
import zipfile
import shutil
import time
from PIL import Image
import io
from rembg import remove
import gradio as gr
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
from transformers import pipeline

def resize_and_crop_image(image_path, target_size=(1080, 1080), crop_mode='center'):
    with Image.open(image_path) as img:
        width, height = img.size

        # Calculate the scaling factor
        scaling_factor = max(target_size[0] / width, target_size[1] / height)

        # Resize the image with high-quality resampling
        new_size = (int(width * scaling_factor), int(height * scaling_factor))
        resized_img = img.resize(new_size, Image.LANCZOS)

        if crop_mode == 'center':
            left = (resized_img.width - target_size[0]) / 2
            top = (resized_img.height - target_size[1]) / 2
        elif crop_mode == 'top':
            left = (resized_img.width - target_size[0]) / 2
            top = 0
        elif crop_mode == 'bottom':
            left = (resized_img.width - target_size[0]) / 2
            top = resized_img.height - target_size[1]
        elif crop_mode == 'left':
            left = 0
            top = (resized_img.height - target_size[1]) / 2
        elif crop_mode == 'right':
            left = resized_img.width - target_size[0]
            top = (resized_img.height - target_size[1]) / 2

        right = left + target_size[0]
        bottom = top + target_size[1]

        # Crop the image
        cropped_img = resized_img.crop((left, top, right, bottom))

        return cropped_img

def remove_background_rembg(input_path):
    with open(input_path, 'rb') as i:
        input_image = i.read()
    output_image = remove(input_image)
    img = Image.open(io.BytesIO(output_image)).convert("RGBA")
    return img

def remove_background_bria(input_path):
    pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
    pillow_image = pipe(input_path)  # applies mask on input and returns a pillow image
    return pillow_image

def process_single_image(image_path, output_folder, crop_mode, remove_bg, bg_method, output_format, bg_choice, watermark_path=None):
    filename = os.path.basename(image_path)
    try:
        if remove_bg == 'yes':
            if bg_method == 'rembg':
                # Remove background using rembg
                image_with_no_bg = remove_background_rembg(image_path)
            elif bg_method == 'bria':
                # Remove background using bria
                image_with_no_bg = remove_background_bria(image_path)
                
            temp_image_path = os.path.join(output_folder, f"temp_{filename}")
            image_with_no_bg.save(temp_image_path, format='PNG')
        else:
            temp_image_path = image_path

        # Resize and crop the image with or without background removal
        new_image = resize_and_crop_image(temp_image_path, crop_mode=crop_mode)

        # Save both versions of the image (with and without watermark)
        images_paths = []

        # Save without watermark
        output_ext = 'jpg' if output_format == 'JPG' else 'png'
        output_path_without_watermark = os.path.join(output_folder, f"without_watermark_{os.path.splitext(filename)[0]}.{output_ext}")
        if output_format == 'JPG':
            new_image.convert('RGB').save(output_path_without_watermark, format='JPEG')
        else:
            new_image.save(output_path_without_watermark, format='PNG')
        images_paths.append(output_path_without_watermark)

        # Apply watermark if provided and save the version with watermark
        if watermark_path:
            watermark = Image.open(watermark_path).convert("RGBA")
            new_image_with_watermark = new_image.copy()
            new_image_with_watermark.paste(watermark, (0, 0), watermark)
            output_path_with_watermark = os.path.join(output_folder, f"with_watermark_{os.path.splitext(filename)[0]}.{output_ext}")
            if output_format == 'JPG':
                new_image_with_watermark.convert('RGB').save(output_path_with_watermark, format='JPEG')
            else:
                new_image_with_watermark.save(output_path_with_watermark, format='PNG')
            images_paths.append(output_path_with_watermark)

        if remove_bg == 'yes':
            # Remove the temporary file
            os.remove(temp_image_path)

        return images_paths

    except Exception as e:
        print(f"Error processing {filename}: {e}")
        return None

def process_images(zip_file, crop_mode='center', remove_bg='yes', bg_method='rembg', watermark_path=None, output_format='PNG', bg_choice='transparent', num_workers=4, progress=gr.Progress()):
    start_time = time.time()
    
    # Create a temporary directory
    input_folder = "temp_input"
    output_folder = "temp_output"
    if os.path.exists(input_folder):
        shutil.rmtree(input_folder)
    if os.path.exists(output_folder):
        shutil.rmtree(output_folder)
    os.makedirs(input_folder)
    os.makedirs(output_folder)
    
    # Extract the zip file
    with zipfile.ZipFile(zip_file, 'r') as zip_ref:
        zip_ref.extractall(input_folder)
    
    processed_images = []
    image_files = [os.path.join(input_folder, f) for f in os.listdir(input_folder) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif'))]
    total_images = len(image_files)

    # Process images using ThreadPoolExecutor for I/O-bound tasks and ProcessPoolExecutor for CPU-bound tasks
    with ThreadPoolExecutor(max_workers=num_workers) as thread_executor:
        with ProcessPoolExecutor(max_workers=num_workers) as process_executor:
            future_to_image = {thread_executor.submit(process_single_image, image_path, output_folder, crop_mode, remove_bg, bg_method, output_format, bg_choice, watermark_path): image_path for image_path in image_files}
            for idx, future in enumerate(future_to_image):
                result = future.result()
                if result:
                    processed_images.extend(result)
                # Update progress
                progress((idx + 1) / total_images, f"{idx + 1}/{total_images} images processed")

    # Create a zip file of the processed images
    output_zip_path = "processed_images.zip"
    with zipfile.ZipFile(output_zip_path, 'w') as zipf:
        for file in processed_images:
            if "with_watermark" in file:
                zipf.write(file, os.path.join("with_watermark", os.path.basename(file)))
            else:
                zipf.write(file, os.path.join("without_watermark", os.path.basename(file)))

    end_time = time.time()
    processing_time = end_time - start_time
    
    # Return the images, the zip file path, and processing time
    return processed_images, output_zip_path, processing_time

def gradio_interface(zip_file, crop_mode, remove_bg, bg_method, watermark, output_format, bg_choice, num_workers):
    progress = gr.Progress()  # Initialize progress
    watermark_path = watermark.name if watermark else None
    return process_images(zip_file.name, crop_mode, remove_bg, bg_method, watermark_path, output_format, bg_choice, num_workers, progress)

def show_bg_choice(remove_bg, output_format):
    if remove_bg == 'yes' and output_format == 'PNG':
        return gr.update(visible=True)
    return gr.update(visible=False)

def show_bg_method(remove_bg):
    if remove_bg == 'yes':
        return gr.update(visible=True)
    return gr.update(visible=False)

# Create the Gradio interface
with gr.Blocks() as iface:
    gr.Markdown("# Image Background Removal and Resizing with Optional Watermark")
    gr.Markdown("Upload a ZIP or RAR file containing images, choose the crop mode, optionally upload a watermark image, and select the output format.")

    with gr.Row():
        zip_file = gr.File(label="Upload ZIP/RAR file of images", file_types=[".zip", ".rar"])
        watermark = gr.File(label="Upload Watermark Image (Optional)", file_types=[".png"])
    
    with gr.Row():
        crop_mode = gr.Radio(choices=["center", "top", "bottom", "left", "right"], label="Crop Mode", value="center")
        remove_bg = gr.Radio(choices=["yes", "no"], label="Remove Background", value="yes")
        
        output_format = gr.Radio(choices=["PNG", "JPG"], label="Output Format", value="PNG")
        num_workers = gr.Slider(minimum=1, maximum=16, step=1, label="Number of Workers", value=2)

    with gr.Row():
        bg_method = gr.Radio(choices=["bria", "rembg"], label="Background Removal Method", value="bria", visible=True)
        bg_choice = gr.Radio(choices=["transparent", "white"], label="Background Choice", value="transparent", visible=True)

    remove_bg.change(show_bg_choice, inputs=[remove_bg, output_format], outputs=bg_choice)
    remove_bg.change(show_bg_method, inputs=remove_bg, outputs=bg_method)
    output_format.change(show_bg_choice, inputs=[remove_bg, output_format], outputs=bg_choice)

    gallery = gr.Gallery(label="Processed Images")
    output_zip = gr.File(label="Download Processed Images as ZIP")
    processing_time = gr.Textbox(label="Processing Time (seconds)")

    def process(zip_file, crop_mode, remove_bg, bg_method, watermark, output_format, bg_choice, num_workers):
        processed_images, zip_path, time_taken = gradio_interface(zip_file, crop_mode, remove_bg, bg_method, watermark, output_format, bg_choice, num_workers)
        return processed_images, zip_path, f"{time_taken:.2f} seconds"

    process_button = gr.Button("Process Images")
    process_button.click(process, inputs=[zip_file, crop_mode, remove_bg, bg_method, watermark, output_format, bg_choice, num_workers], outputs=[gallery, output_zip, processing_time])

# Launch the interface
iface.launch()