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| import torch | |
| from PIL import Image | |
| from RealESRGAN import RealESRGAN | |
| import gradio as gr | |
| import numpy as np | |
| import tempfile | |
| import time | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| def load_model(scale): | |
| model = RealESRGAN(device, scale=scale) | |
| weights_path = f'weights/RealESRGAN_x{scale}.pth' | |
| try: | |
| model.load_weights(weights_path, download=True) | |
| print(f"Weights for scale {scale} loaded successfully.") | |
| except Exception as e: | |
| print(f"Error loading weights for scale {scale}: {e}") | |
| model.load_weights(weights_path, download=False) | |
| return model | |
| model2 = load_model(2) | |
| model4 = load_model(4) | |
| model8 = load_model(8) | |
| def enhance_image(image, scale): | |
| try: | |
| print(f"Enhancing image with scale {scale}...") | |
| start_time = time.time() | |
| image_np = np.array(image.convert('RGB')) | |
| print(f"Image converted to numpy array: shape {image_np.shape}, dtype {image_np.dtype}") | |
| if scale == '2x': | |
| result = model2.predict(image_np) | |
| elif scale == '4x': | |
| result = model4.predict(image_np) | |
| else: | |
| result = model8.predict(image_np) | |
| enhanced_image = Image.fromarray(np.uint8(result)) | |
| print(f"Image enhanced in {time.time() - start_time:.2f} seconds") | |
| return enhanced_image | |
| except Exception as e: | |
| print(f"Error enhancing image: {e}") | |
| return image | |
| def muda_dpi(input_image, dpi): | |
| dpi_tuple = (dpi, dpi) | |
| image = Image.fromarray(input_image.astype('uint8'), 'RGB') | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png') | |
| image.save(temp_file, format='PNG', dpi=dpi_tuple) | |
| temp_file.close() | |
| return Image.open(temp_file.name) | |
| def resize_image(input_image, width, height): | |
| image = Image.fromarray(input_image.astype('uint8'), 'RGB') | |
| resized_image = image.resize((width, height)) | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png') | |
| resized_image.save(temp_file, format='PNG') | |
| temp_file.close() | |
| return Image.open(temp_file.name) | |
| def process_image(input_image, enhance, scale, adjust_dpi, dpi, resize, width, height): | |
| original_image = Image.fromarray(input_image.astype('uint8'), 'RGB') | |
| if enhance: | |
| original_image = enhance_image(original_image, scale) | |
| if adjust_dpi: | |
| original_image = muda_dpi(np.array(original_image), dpi) | |
| if resize: | |
| original_image = resize_image(np.array(original_image), width, height) | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png') | |
| original_image.save(temp_file.name) | |
| return original_image, temp_file.name | |
| iface = gr.Interface( | |
| fn=process_image, | |
| inputs=[ | |
| gr.Image(label="Upload"), | |
| gr.Checkbox(label="Enhance Image (ESRGAN)"), | |
| gr.Radio(['2x', '4x', '8x'], type="value", value='2x', label='Resolution model'), | |
| gr.Checkbox(label="Adjust DPI"), | |
| gr.Number(label="DPI", value=300), | |
| gr.Checkbox(label="Resize"), | |
| gr.Number(label="Width", value=512), | |
| gr.Number(label="Height", value=512) | |
| ], | |
| outputs=[ | |
| gr.Image(label="Final Image"), | |
| gr.File(label="Download Final Image") | |
| ], | |
| title="Image Enhancer", | |
| description="Upload an image (.jpg, .png), enhance using AI, adjust DPI, resize and download the final result.", | |
| examples=[ | |
| ["gatuno.JPG"] | |
| ] | |
| ) | |
| iface.launch(debug=True) | |