import os import zipfile import shutil import time from PIL import Image, ImageDraw import io from rembg import remove import gradio as gr from concurrent.futures import ThreadPoolExecutor from transformers import pipeline import numpy as np import json import os def remove_background_rembg(input_path): print(f"Removing background using rembg for image: {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): print(f"Removing background using bria for image: {input_path}") pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True, device=0) pillow_image = pipe(input_path) return pillow_image def get_bounding_box_with_threshold(image, threshold): # Convert image to numpy array img_array = np.array(image) # Get alpha channel alpha = img_array[:,:,3] # Find rows and columns where alpha > threshold rows = np.any(alpha > threshold, axis=1) cols = np.any(alpha > threshold, axis=0) # Find the bounding box top, bottom = np.where(rows)[0][[0, -1]] left, right = np.where(cols)[0][[0, -1]] if left < right and top < bottom: return (left, top, right, bottom) else: return None def position_logic(image_path, use_threshold=True): image = Image.open(image_path) image = image.convert("RGBA") # Get the bounding box of the non-blank area with threshold if use_threshold: bbox = get_bounding_box_with_threshold(image, threshold=10) else: bbox = image.getbbox() log = [] if bbox: # Check 1 pixel around the image for non-transparent pixels width, height = image.size cropped_sides = [] # Define tolerance for transparency tolerance = 30 # Adjust this value as needed # Check top edge if any(image.getpixel((x, 0))[3] > tolerance for x in range(width)): cropped_sides.append("top") # Check bottom edge if any(image.getpixel((x, height-1))[3] > tolerance for x in range(width)): cropped_sides.append("bottom") # Check left edge if any(image.getpixel((0, y))[3] > tolerance for y in range(height)): cropped_sides.append("left") # Check right edge if any(image.getpixel((width-1, y))[3] > tolerance for y in range(height)): cropped_sides.append("right") if cropped_sides: info_message = f"Info for {os.path.basename(image_path)}: The following sides of the image may contain cropped objects: {', '.join(cropped_sides)}" print(info_message) log.append({"info": info_message}) else: info_message = f"Info for {os.path.basename(image_path)}: The image is not cropped." print(info_message) log.append({"info": info_message}) # Crop the image to the bounding box image = image.crop(bbox) log.append({"action": "crop", "bbox": [str(bbox[0]), str(bbox[1]), str(bbox[2]), str(bbox[3])]}) # Calculate the new size to expand the image padding = 125 target_size = 1080 aspect_ratio = image.width / image.height if len(cropped_sides) == 4: # If the image is cropped on all sides, center crop it to fit the canvas if aspect_ratio > 1: # Landscape new_height = target_size new_width = int(new_height * aspect_ratio) left = (new_width - target_size) // 2 image = image.resize((new_width, new_height), Image.LANCZOS) image = image.crop((left, 0, left + target_size, target_size)) else: # Portrait or square new_width = target_size new_height = int(new_width / aspect_ratio) top = (new_height - target_size) // 2 image = image.resize((new_width, new_height), Image.LANCZOS) image = image.crop((0, top, target_size, top + target_size)) log.append({"action": "center_crop_resize", "new_size": f"{target_size}x{target_size}"}) x, y = 0, 0 elif not cropped_sides: # If the image is not cropped, expand it from center until it touches the padding new_height = 1080 - 2 * padding # Ensure it touches top and bottom padding new_width = int(new_height * aspect_ratio) if new_width > 1080 - 2 * padding: # If width exceeds available space, adjust based on width new_width = 1080 - 2 * padding new_height = int(new_width / aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) x = (1080 - new_width) // 2 y = 1080 - new_height - padding else: # New logic for handling cropped top and left, or top and right if set(cropped_sides) == {"top", "left"} or set(cropped_sides) == {"top", "right"}: new_height = target_size - padding # Ensure bottom padding new_width = int(new_height * aspect_ratio) # If new width exceeds canvas width, adjust based on width if new_width > target_size: new_width = target_size new_height = int(new_width / aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set position if "left" in cropped_sides: x = 0 else: # right in cropped_sides x = target_size - new_width y = 0 # If the resized image is taller than the canvas minus padding, crop from the bottom if new_height > target_size - padding: crop_bottom = new_height - (target_size - padding) image = image.crop((0, 0, new_width, new_height - crop_bottom)) new_height = target_size - padding log.append({"action": "crop_vertical", "bottom_pixels_removed": str(crop_bottom)}) log.append({"action": "position", "x": str(x), "y": str(y)}) elif set(cropped_sides) == {"bottom", "left"} or set(cropped_sides) == {"bottom", "right"}: # Handle bottom & left or bottom & right cropped images new_height = target_size - padding # Ensure top padding new_width = int(new_height * aspect_ratio) # If new width exceeds canvas width, adjust based on width if new_width > target_size - padding: new_width = target_size - padding new_height = int(new_width / aspect_ratio) # Resize the image without cropping or stretching image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set position if "left" in cropped_sides: x = 0 else: # right in cropped_sides x = target_size - new_width y = target_size - new_height log.append({"action": "position", "x": str(x), "y": str(y)}) elif set(cropped_sides) == {"bottom", "left", "right"}: # Expand the image from the center new_width = target_size new_height = int(new_width / aspect_ratio) if new_height < target_size: new_height = target_size new_width = int(new_height * aspect_ratio) image = image.resize((new_width, new_height), Image.LANCZOS) # Crop to fit the canvas left = (new_width - target_size) // 2 top = 0 image = image.crop((left, top, left + target_size, top + target_size)) log.append({"action": "expand_and_crop", "new_size": f"{target_size}x{target_size}"}) x, y = 0, 0 elif cropped_sides == ["top"]: # New logic for handling only top-cropped images if image.width > image.height: new_width = target_size new_height = int(target_size / aspect_ratio) else: new_height = target_size - padding # Ensure bottom padding new_width = int(new_height * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) x = (1080 - new_width) // 2 y = 0 # Align to top # Apply padding only to non-cropped sides x = max(padding, min(x, 1080 - new_width - padding)) elif cropped_sides in [["right"], ["left"]]: # New logic for handling only right-cropped or left-cropped images if image.width > image.height: new_width = target_size - padding # Ensure padding on non-cropped side new_height = int(new_width / aspect_ratio) else: new_height = target_size - (2 * padding) # Ensure top and bottom padding new_width = int(new_height * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) if cropped_sides == ["right"]: x = 1080 - new_width # Align to right else: # cropped_sides == ["left"] x = 0 # Align to left y = 1080 - new_height - padding # Respect bottom padding # Ensure top padding is respected if y < padding: y = padding log.append({"action": "position", "x": str(x), "y": str(y)}) elif set(cropped_sides) == {"left", "right"}: # Logic for handling images cropped on both left and right sides new_width = 1080 # Expand to full width of canvas # Calculate the aspect ratio of the original image aspect_ratio = image.width / image.height # Calculate the new height while maintaining aspect ratio new_height = int(new_width / aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set horizontal position (always 0 as it spans full width) x = 0 # Calculate vertical position to respect bottom padding y = 1080 - new_height - padding # If the resized image is taller than the canvas, crop from the top only if new_height > 1080 - padding: crop_top = new_height - (1080 - padding) image = image.crop((0, crop_top, new_width, new_height)) new_height = 1080 - padding y = 0 log.append({"action": "crop_vertical", "top_pixels_removed": str(crop_top)}) else: # Align the image to the bottom with padding y = 1080 - new_height - padding log.append({"action": "position", "x": str(x), "y": str(y)}) elif cropped_sides == ["bottom"]: # Logic for handling images cropped on the bottom side # Calculate the aspect ratio of the original image aspect_ratio = image.width / image.height if aspect_ratio < 1: # Portrait orientation new_height = 1080 - padding # Full height with top padding new_width = int(new_height * aspect_ratio) # If the new width exceeds the canvas width, adjust it if new_width > 1080: new_width = 1080 new_height = int(new_width / aspect_ratio) else: # Landscape orientation new_width = 1080 - (2 * padding) new_height = int(new_width / aspect_ratio) # If the new height exceeds the canvas height, adjust it if new_height > 1080: new_height = 1080 new_width = int(new_height * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set horizontal position (centered) x = (1080 - new_width) // 2 # Set vertical position (touching bottom edge for all cases) y = 1080 - new_height log.append({"action": "position", "x": str(x), "y": str(y)}) else: # Use the original resizing logic for other partially cropped images if image.width > image.height: new_width = target_size new_height = int(target_size / aspect_ratio) else: new_height = target_size new_width = int(target_size * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Center horizontally for all images x = (1080 - new_width) // 2 y = 1080 - new_height - padding # Adjust positions for cropped sides if "top" in cropped_sides: y = 0 elif "bottom" in cropped_sides: y = 1080 - new_height if "left" in cropped_sides: x = 0 elif "right" in cropped_sides: x = 1080 - new_width # Apply padding only to non-cropped sides, but keep horizontal centering if "left" not in cropped_sides and "right" not in cropped_sides: x = (1080 - new_width) // 2 # Always center horizontally if "top" not in cropped_sides and "bottom" not in cropped_sides: y = max(padding, min(y, 1080 - new_height - padding)) return log, image, x, y def process_single_image(image_path, output_folder, bg_method, output_format, bg_choice, custom_color, watermark_path=None): add_padding_line = False padding = 125 filename = os.path.basename(image_path) try: print(f"Processing image: {filename}") if bg_method == 'rembg': image_with_no_bg = remove_background_rembg(image_path) elif bg_method == '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') log, new_image, x, y = position_logic(temp_image_path) # Create a new 1080x1080 canvas with the appropriate background if bg_choice == 'white': canvas = Image.new("RGBA", (1080, 1080), "WHITE") elif bg_choice == 'custom': canvas = Image.new("RGBA", (1080, 1080), custom_color) else: # transparent canvas = Image.new("RGBA", (1080, 1080), (0, 0, 0, 0)) # Paste the resized image onto the canvas canvas.paste(new_image, (x, y), new_image) log.append({"action": "paste", "position": [str(x), str(y)]}) # Add visible black line for padding when background is not transparent if add_padding_line: draw = ImageDraw.Draw(canvas) draw.rectangle([padding, padding, 1080 - padding, 1080 - padding], outline="black", width=5) log.append({"action": "add_padding_line"}) output_ext = 'jpg' if output_format == 'JPG' else 'png' output_filename = f"{os.path.splitext(filename)[0]}.{output_ext}" output_path = os.path.join(output_folder, output_filename) # Apply watermark only if the filename ends with "_01" and watermark_path is provided if os.path.splitext(filename)[0].endswith("_01") and watermark_path: watermark = Image.open(watermark_path).convert("RGBA") canvas.paste(watermark, (0, 0), watermark) log.append({"action": "add_watermark"}) if output_format == 'JPG': canvas.convert('RGB').save(output_path, format='JPEG') else: canvas.save(output_path, format='PNG') os.remove(temp_image_path) print(f"Processed image path: {output_path}") return [(output_path, image_path)], log except Exception as e: print(f"Error processing {filename}: {e}") return None, None def process_images(input_files, bg_method='rembg', watermark_path=None, output_format='PNG', bg_choice='transparent', custom_color="#ffffff", num_workers=4, progress=gr.Progress()): start_time = time.time() output_folder = "processed_images" if os.path.exists(output_folder): shutil.rmtree(output_folder) os.makedirs(output_folder) processed_images = [] original_images = [] all_logs = [] if isinstance(input_files, str) and input_files.lower().endswith(('.zip', '.rar')): # Handle zip file input_folder = "temp_input" if os.path.exists(input_folder): shutil.rmtree(input_folder) os.makedirs(input_folder) try: with zipfile.ZipFile(input_files, 'r') as zip_ref: zip_ref.extractall(input_folder) except zipfile.BadZipFile as e: print(f"Error extracting zip file: {e}") return [], None, 0 image_files = [os.path.join(input_folder, f) for f in os.listdir(input_folder) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif', '.webp'))] elif isinstance(input_files, list): # Handle multiple files image_files = input_files else: # Handle single file image_files = [input_files] total_images = len(image_files) print(f"Total images to process: {total_images}") avg_processing_time = 0 with ThreadPoolExecutor(max_workers=num_workers) as executor: future_to_image = {executor.submit(process_single_image, image_path, output_folder, bg_method, output_format, bg_choice, custom_color, watermark_path): image_path for image_path in image_files} for idx, future in enumerate(future_to_image): try: start_time_image = time.time() result, log = future.result() end_time_image = time.time() image_processing_time = end_time_image - start_time_image # Update average processing time avg_processing_time = (avg_processing_time * idx + image_processing_time) / (idx + 1) if result: processed_images.extend(result) original_images.append(future_to_image[future]) all_logs.append({os.path.basename(future_to_image[future]): log}) # Estimate remaining time remaining_images = total_images - (idx + 1) estimated_remaining_time = remaining_images * avg_processing_time progress((idx + 1) / total_images, f"{idx + 1}/{total_images} images processed. Estimated time remaining: {estimated_remaining_time:.2f} seconds") except Exception as e: print(f"Error processing image {future_to_image[future]}: {e}") output_zip_path = "processed_images.zip" with zipfile.ZipFile(output_zip_path, 'w') as zipf: for file, _ in processed_images: zipf.write(file, os.path.basename(file)) # Write the comprehensive log for all images with open(os.path.join(output_folder, 'process_log.json'), 'w') as log_file: json.dump(all_logs, log_file, indent=4) print("Comprehensive log saved to", os.path.join(output_folder, 'process_log.json')) end_time = time.time() processing_time = end_time - start_time print(f"Processing time: {processing_time} seconds") return original_images, processed_images, output_zip_path, processing_time def gradio_interface(input_files, bg_method, watermark, output_format, bg_choice, custom_color, num_workers): progress = gr.Progress() watermark_path = watermark.name if watermark else None # Check input_files, is it single image, list image, or zip/rar if isinstance(input_files, str) and input_files.lower().endswith(('.zip', '.rar')): return process_images(input_files, bg_method, watermark_path, output_format, bg_choice, custom_color, num_workers, progress) elif isinstance(input_files, list): return process_images(input_files, bg_method, watermark_path, output_format, bg_choice, custom_color, num_workers, progress) else: return process_images(input_files.name, bg_method, watermark_path, output_format, bg_choice, custom_color, num_workers, progress) def show_color_picker(bg_choice): if bg_choice == 'custom': return gr.update(visible=True) return gr.update(visible=False) def update_compare(evt: gr.SelectData): if isinstance(evt.value, dict) and 'caption' in evt.value: input_path = evt.value['caption'] output_path = evt.value['image']['path'] input_path = input_path.split("Input: ")[-1] # Open the original and processed images original_img = Image.open(input_path) processed_img = Image.open(output_path) # Calculate the aspect ratios original_ratio = f"{original_img.width}x{original_img.height}" processed_ratio = f"{processed_img.width}x{processed_img.height}" return gr.update(value=input_path), gr.update(value=output_path), gr.update(value=original_ratio), gr.update(value=processed_ratio) else: print("No caption found in selection") return gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None) def process(input_files, bg_method, watermark, output_format, bg_choice, custom_color, num_workers): _, processed_images, zip_path, time_taken = gradio_interface(input_files, bg_method, watermark, output_format, bg_choice, custom_color, num_workers) processed_images_with_captions = [(img, f"Input: {caption}") for img, caption in processed_images] return processed_images_with_captions, zip_path, f"{time_taken:.2f} seconds" with gr.Blocks() as iface: gr.Markdown("# Image Background Removal and Resizing with Optional Watermark") gr.Markdown("Choose to upload multiple images or a ZIP/RAR file, select the crop mode, optionally upload a watermark image, and choose the output format.") with gr.Row(): input_files = gr.File(label="Upload Image or ZIP/RAR file", file_types=[".zip", ".rar", "image"], interactive=True) watermark = gr.File(label="Upload Watermark Image (Optional)", file_types=[".png"]) with gr.Row(): output_format = gr.Radio(choices=["PNG", "JPG"], label="Output Format", value="JPG") num_workers = gr.Slider(minimum=1, maximum=16, step=1, label="Number of Workers", value=5) with gr.Row(): bg_method = gr.Radio(choices=["bria", "rembg"], label="Background Removal Method", value="bria") bg_choice = gr.Radio(choices=["transparent", "white", "custom"], label="Background Choice", value="white") custom_color = gr.ColorPicker(label="Custom Background Color", value="#ffffff", visible=False) process_button = gr.Button("Process Images") with gr.Row(): gallery_processed = gr.Gallery(label="Processed Images") with gr.Row(): image_original = gr.Image(label="Original Images", interactive=False) image_processed = gr.Image(label="Processed Images", interactive=False) with gr.Row(): original_ratio = gr.Textbox(label="Original Ratio") processed_ratio = gr.Textbox(label="Processed Ratio") with gr.Row(): output_zip = gr.File(label="Download Processed Images as ZIP") processing_time = gr.Textbox(label="Processing Time (seconds)") bg_choice.change(show_color_picker, inputs=bg_choice, outputs=custom_color) process_button.click(process, inputs=[input_files, bg_method, watermark, output_format, bg_choice, custom_color, num_workers], outputs=[gallery_processed, output_zip, processing_time]) gallery_processed.select(update_compare, outputs=[image_original, image_processed, original_ratio, processed_ratio]) iface.launch()