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import gradio as gr
import os
import zipfile
import tempfile
import shutil
from PIL import Image
import io
from typing import List, Tuple, Optional
import numpy as np
class ImageResizer:
def __init__(self):
self.supported_formats = ('.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp')
def get_background_color(self, image: Image.Image) -> tuple:
"""
Smart background color detection from image corners.
Samples 10x10 pixel areas from all four corners and calculates average.
"""
width, height = image.size
# Define corner regions (10x10 pixels)
corner_size = min(10, width // 4, height // 4)
corners = [
(0, 0, corner_size, corner_size), # Top-left
(width - corner_size, 0, width, corner_size), # Top-right
(0, height - corner_size, corner_size, height), # Bottom-left
(width - corner_size, height - corner_size, width, height) # Bottom-right
]
total_r, total_g, total_b = 0, 0, 0
total_pixels = 0
for corner in corners:
corner_region = image.crop(corner)
# Convert to RGB if not already
if corner_region.mode != 'RGB':
corner_region = corner_region.convert('RGB')
# Get average color of this corner
pixels = list(corner_region.getdata())
for r, g, b in pixels:
total_r += r
total_g += g
total_b += b
total_pixels += 1
if total_pixels > 0:
avg_r = total_r // total_pixels
avg_g = total_g // total_pixels
avg_b = total_b // total_pixels
return (avg_r, avg_g, avg_b)
else:
return (255, 255, 255) # Default to white
def resize_image(self, image: Image.Image, width: int, height: int, maintain_aspect: bool = True, png_bg_option: str = "auto", custom_color: tuple = None, is_png: bool = False) -> Image.Image:
"""
Resize image using smart canvas padding instead of traditional resizing.
"""
original_width, original_height = image.size
# Calculate target dimensions
if maintain_aspect:
# Calculate aspect ratios
original_aspect = original_width / original_height
target_aspect = width / height
if original_aspect > target_aspect:
# Image is wider, fit to width
new_width = width
new_height = int(width / original_aspect)
else:
# Image is taller, fit to height
new_height = height
new_width = int(height * original_aspect)
else:
new_width = width
new_height = height
# Only resize if the image is larger than target dimensions
if new_width < original_width or new_height < original_height:
# Use LANCZOS for high-quality downsampling
resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
else:
# Keep original size if it's smaller than target
resized_image = image.copy()
new_width, new_height = resized_image.size
# Create canvas with padding
canvas = Image.new('RGB', (width, height), (255, 255, 255))
# Handle PNG background color
if is_png or (hasattr(image, 'format') and image.format == 'PNG'):
if png_bg_option == "auto":
bg_color = self.get_background_color(image)
elif png_bg_option == "black":
bg_color = (0, 0, 0)
elif png_bg_option == "white":
bg_color = (255, 255, 255)
elif png_bg_option == "custom" and custom_color:
bg_color = custom_color
else:
bg_color = (255, 255, 255)
canvas = Image.new('RGB', (width, height), bg_color)
# Calculate position to center the image
x = (width - new_width) // 2
y = (height - new_height) // 2
# Paste the resized image onto the canvas
if resized_image.mode == 'RGBA':
canvas.paste(resized_image, (x, y), resized_image)
else:
canvas.paste(resized_image, (x, y))
return canvas
def hex_to_rgb(self, hex_color: str) -> tuple:
"""Convert hex color to RGB tuple."""
hex_color = hex_color.lstrip('#')
return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
def process_single_image(image, width, height, maintain_aspect, png_bg_option, custom_color_hex):
"""Process a single image with Gradio interface."""
if image is None:
return None, "Please upload an image first."
try:
# Convert Gradio image to PIL Image
if isinstance(image, np.ndarray):
pil_image = Image.fromarray(image)
else:
pil_image = image
# Validate dimensions
if width <= 0 or height <= 0:
return None, "Width and height must be positive numbers."
# Initialize resizer
resizer = ImageResizer()
# Handle custom color
custom_color = None
if png_bg_option == "custom":
try:
custom_color = resizer.hex_to_rgb(custom_color_hex)
except:
custom_color = (255, 255, 255) # Default to white if invalid
# Check if image is PNG
is_png = hasattr(pil_image, 'format') and pil_image.format == 'PNG'
# Resize image
resized_image = resizer.resize_image(
pil_image, width, height, maintain_aspect,
png_bg_option, custom_color, is_png
)
return resized_image, f"β
Image resized successfully to {width}x{height}!"
except Exception as e:
return None, f"β Error processing image: {str(e)}"
def process_folder_images(files, width, height, maintain_aspect, png_bg_option, custom_color_hex):
"""Process multiple images from folder upload."""
if not files:
return [], "Please upload image files first."
try:
# Validate dimensions
if width <= 0 or height <= 0:
return [], "Width and height must be positive numbers."
# Initialize resizer
resizer = ImageResizer()
# Handle custom color
custom_color = None
if png_bg_option == "custom":
try:
custom_color = resizer.hex_to_rgb(custom_color_hex)
except:
custom_color = (255, 255, 255) # Default to white if invalid
processed_images = []
processed_count = 0
for file in files:
try:
# Open image
image = Image.open(file.name)
# Check if image is PNG
is_png = file.name.lower().endswith('.png')
# Resize image
resized_image = resizer.resize_image(
image, width, height, maintain_aspect,
png_bg_option, custom_color, is_png
)
processed_images.append(resized_image)
processed_count += 1
except Exception as e:
print(f"Error processing {file.name}: {str(e)}")
continue
status_msg = f"β
Successfully processed {processed_count} out of {len(files)} images to {width}x{height}!"
return processed_images, status_msg
except Exception as e:
return [], f"β Error processing folder: {str(e)}"
def process_zip_file(zip_file, width, height, maintain_aspect, png_bg_option, custom_color_hex):
"""Process images from uploaded ZIP file."""
if zip_file is None:
return [], "Please upload a ZIP file first."
try:
# Validate dimensions
if width <= 0 or height <= 0:
return [], "Width and height must be positive numbers."
# Initialize resizer
resizer = ImageResizer()
# Handle custom color
custom_color = None
if png_bg_option == "custom":
try:
custom_color = resizer.hex_to_rgb(custom_color_hex)
except:
custom_color = (255, 255, 255) # Default to white if invalid
processed_images = []
processed_count = 0
# Create temporary directory
with tempfile.TemporaryDirectory() as temp_dir:
# Extract ZIP file
with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
zip_ref.extractall(temp_dir)
# Process all images in the extracted folder
for root, dirs, files in os.walk(temp_dir):
for file in files:
if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp')):
try:
file_path = os.path.join(root, file)
image = Image.open(file_path)
# Check if image is PNG
is_png = file.lower().endswith('.png')
# Resize image
resized_image = resizer.resize_image(
image, width, height, maintain_aspect,
png_bg_option, custom_color, is_png
)
processed_images.append(resized_image)
processed_count += 1
except Exception as e:
print(f"Error processing {file}: {str(e)}")
continue
status_msg = f"β
Successfully processed {processed_count} images from ZIP file to {width}x{height}!"
return processed_images, status_msg
except Exception as e:
return [], f"β Error processing ZIP file: {str(e)}"
# Create Gradio interface
def create_gradio_app():
with gr.Blocks(title="πΌοΈ Image Resizer Pro - Gradio Edition", theme=gr.themes.Soft()) as app:
gr.Markdown("# πΌοΈ Image Resizer Pro - Gradio Edition")
gr.Markdown("**Smart Canvas Padding & PNG Background Selector** - Resize images with intelligent padding instead of stretching")
with gr.Tabs():
# Single Image Tab
with gr.TabItem("π· Single Image"):
with gr.Row():
with gr.Column(scale=1):
single_image_input = gr.Image(type="pil", label="Upload Image")
with gr.Row():
single_width = gr.Number(value=800, label="Width", minimum=1, maximum=10000)
single_height = gr.Number(value=600, label="Height", minimum=1, maximum=10000)
single_maintain_aspect = gr.Checkbox(value=True, label="Maintain aspect ratio")
with gr.Group():
gr.Markdown("**PNG Background Color**")
single_png_bg = gr.Radio(
choices=["auto", "black", "white", "custom"],
value="auto",
label="Background Option",
info="Choose background color for PNG images"
)
single_custom_color = gr.Textbox(
value="#FFFFFF",
label="Custom Color (Hex)",
placeholder="#FFFFFF",
visible=False
)
single_process_btn = gr.Button("π Resize Image", variant="primary")
with gr.Column(scale=1):
single_output = gr.Image(label="Resized Image")
single_status = gr.Textbox(label="Status", interactive=False)
# Show/hide custom color input
single_png_bg.change(
lambda x: gr.update(visible=(x == "custom")),
inputs=[single_png_bg],
outputs=[single_custom_color]
)
# Process single image
single_process_btn.click(
process_single_image,
inputs=[single_image_input, single_width, single_height, single_maintain_aspect, single_png_bg, single_custom_color],
outputs=[single_output, single_status]
)
# Folder Processing Tab
with gr.TabItem("π Folder Processing"):
with gr.Row():
with gr.Column(scale=1):
folder_files_input = gr.File(
file_count="multiple",
file_types=["image"],
label="Upload Multiple Images"
)
with gr.Row():
folder_width = gr.Number(value=800, label="Width", minimum=1, maximum=10000)
folder_height = gr.Number(value=600, label="Height", minimum=1, maximum=10000)
folder_maintain_aspect = gr.Checkbox(value=True, label="Maintain aspect ratio")
with gr.Group():
gr.Markdown("**PNG Background Color**")
folder_png_bg = gr.Radio(
choices=["auto", "black", "white", "custom"],
value="auto",
label="Background Option",
info="Choose background color for PNG images"
)
folder_custom_color = gr.Textbox(
value="#FFFFFF",
label="Custom Color (Hex)",
placeholder="#FFFFFF",
visible=False
)
folder_process_btn = gr.Button("π Process Folder", variant="primary")
with gr.Column(scale=1):
folder_output = gr.Gallery(label="Processed Images", columns=3, rows=2)
folder_status = gr.Textbox(label="Status", interactive=False)
# Show/hide custom color input
folder_png_bg.change(
lambda x: gr.update(visible=(x == "custom")),
inputs=[folder_png_bg],
outputs=[folder_custom_color]
)
# Process folder
folder_process_btn.click(
process_folder_images,
inputs=[folder_files_input, folder_width, folder_height, folder_maintain_aspect, folder_png_bg, folder_custom_color],
outputs=[folder_output, folder_status]
)
# ZIP Processing Tab
with gr.TabItem("π¦ ZIP Processing"):
with gr.Row():
with gr.Column(scale=1):
zip_file_input = gr.File(
file_types=[".zip"],
label="Upload ZIP File"
)
with gr.Row():
zip_width = gr.Number(value=800, label="Width", minimum=1, maximum=10000)
zip_height = gr.Number(value=600, label="Height", minimum=1, maximum=10000)
zip_maintain_aspect = gr.Checkbox(value=True, label="Maintain aspect ratio")
with gr.Group():
gr.Markdown("**PNG Background Color**")
zip_png_bg = gr.Radio(
choices=["auto", "black", "white", "custom"],
value="auto",
label="Background Option",
info="Choose background color for PNG images"
)
zip_custom_color = gr.Textbox(
value="#FFFFFF",
label="Custom Color (Hex)",
placeholder="#FFFFFF",
visible=False
)
zip_process_btn = gr.Button("π Process ZIP", variant="primary")
with gr.Column(scale=1):
zip_output = gr.Gallery(label="Processed Images", columns=3, rows=2)
zip_status = gr.Textbox(label="Status", interactive=False)
# Show/hide custom color input
zip_png_bg.change(
lambda x: gr.update(visible=(x == "custom")),
inputs=[zip_png_bg],
outputs=[zip_custom_color]
)
# Process ZIP
zip_process_btn.click(
process_zip_file,
inputs=[zip_file_input, zip_width, zip_height, zip_maintain_aspect, zip_png_bg, zip_custom_color],
outputs=[zip_output, zip_status]
)
# Footer
gr.Markdown("---")
gr.Markdown("**πΌοΈ Image Resizer Pro v3.2** - Smart Canvas Padding & PNG Background Selector Edition")
gr.Markdown("Features: Smart background detection, Canvas padding, Multi-format support (JPG, PNG, BMP, TIFF, WEBP)")
return app
if __name__ == "__main__":
app = create_gradio_app()
app.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
debug=True
) |