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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| def hex_to_rgb(hex_code): | |
| # Remove the '#' symbol if present | |
| hex_code = hex_code.lstrip('#') | |
| # Convert the hex code to RGB | |
| return tuple(int(hex_code[i:i+2], 16) for i in (0, 2, 4)) | |
| def crop_image(image, crop_top_percentage, crop_bottom_percentage): | |
| height, width = image.shape[:2] | |
| # Calculate the number of pixels to crop from the top and bottom as a percentage of the image height | |
| crop_top = int(height * crop_top_percentage / 100) | |
| crop_bottom = int(height * crop_bottom_percentage / 100) | |
| # Crop the input image | |
| cropped_image = image[crop_top:height - crop_bottom, :] | |
| return cropped_image | |
| def resize_and_overlay_image(input_image, reduction_percentage, shift_percentage_lr, shift_percentage_ud, background_color, crop_top_percentage, crop_bottom_percentage): | |
| # Check if the input image is empty | |
| if input_image.size == 0: | |
| return None | |
| img = np.array(input_image) | |
| # Check if the image has shape information | |
| if img.ndim < 2: | |
| return None | |
| # Crop the input image | |
| cropped_img = crop_image(img, crop_top_percentage, crop_bottom_percentage) | |
| # Get the dimensions of the cropped image | |
| height, width = cropped_img.shape[:2] | |
| # Calculate the new dimensions based on the reduction percentage | |
| new_height = int(height * reduction_percentage / 100) | |
| new_width = int(width * reduction_percentage / 100) | |
| # Calculate the shift values as a percentage of the image dimensions | |
| shift_x = int(width * shift_percentage_lr / 100) | |
| shift_y = int(height * shift_percentage_ud / 100) | |
| # Create a new background image with the same dimensions as the input image and specified color | |
| background_rgb = hex_to_rgb(background_color) | |
| background_img = np.ones_like(img) * background_rgb | |
| # Resize the cropped image while maintaining aspect ratio | |
| aspect_ratio = width / height | |
| if new_width / new_height > aspect_ratio: | |
| resized_height = new_height | |
| resized_width = int(new_height * aspect_ratio) | |
| else: | |
| resized_width = new_width | |
| resized_height = int(new_width / aspect_ratio) | |
| resized_img = cv2.resize(cropped_img, (resized_width, resized_height)) | |
| # Calculate the position to overlay the resized image on the background image | |
| x = int((width - resized_width) / 2) + shift_x | |
| y = int((height - resized_height) / 2) + shift_y | |
| # Overlay the resized image on the background image | |
| background_img[y:y + resized_height, x:x + resized_width] = resized_img | |
| # Return the resulting image as a NumPy array | |
| return background_img | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=resize_and_overlay_image, | |
| inputs=[ | |
| gr.inputs.Image(type="pil", label="Input Image"), | |
| gr.inputs.Slider(minimum=30, maximum=100, step=5, default=80, label="Percentage of Original"), | |
| gr.inputs.Slider(minimum=-30, maximum=30, step=1, default=0, label="Shift Left / Right (%)"), | |
| gr.inputs.Slider(minimum=-30, maximum=30, step=1, default=0, label="Shift Up / Down (%)"), | |
| gr.inputs.Textbox(default="#ffffff", label="Background Color (Hex Code)"), | |
| gr.inputs.Slider(minimum=0, maximum=25, step=1, default=0, label="Crop Top (%)"), | |
| gr.inputs.Slider(minimum=0, maximum=25, step=1, default=0, label="Crop Bottom (%)") | |
| ], | |
| outputs=gr.outputs.Image(type="numpy", label="Result"), | |
| title="Image Resizer", | |
| description="Crop the input image, overlay it on a new background of the specified color, start centered, and shift it as a percentage without stretching." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |