basicsr

import os import cv2 import torch import numpy as np from basicsr.archs.rrdbnet_arch import RRDBNet from basicsr.utils.realesrgan_utils import RealESRGANer from basicsr.utils.download_util import load_file_from_url # 1. Create a dummy low-resolution image (e.g., 64x64, 3 channels) # In a real application, this would be loaded from a file (e.g., cv2.imread) dummy_lr_img = np.random.randint(0, 256, (64, 64, 3), dtype=np.uint8) # 2. Define model parameters and download URL for a pre-trained Real-ESRGAN model model_scale = 4 model_path_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth" output_filename = "dummy_sr_image.png" # 3. Download the model weights try: # Model weights will be saved to a 'weights' directory by default model_path = load_file_from_url(url=model_path_url, model_dir='weights', progress=True) except Exception as e: print(f"Could not download model from {model_path_url}: {e}") model_path = None # Set to None if download fails if model_path: # 4. Initialize the RealESRGANer for inference device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') upsampler = RealESRGANer( scale=model_scale, model_path=model_path, dni_weight=None, # Not used for standard RealESRGAN_x4plus model_arch=RRDBNet, tile=0, # Process image without tiling for small inputs tile_pad=10, pre_pad=0, half=False, # Use float32, set to True for float16 inference if supported device=device ) # 5. Perform inference (enhance expects a NumPy array (HWC, BGR, uint8)) output_image, _ = upsampler.enhance(dummy_lr_img, outscale=model_scale) # 6. Save the output image cv2.imwrite(output_filename, output_image) print(f"Super-resolved image saved to {output_filename}") else: print("Skipping quickstart inference due to model download failure or missing model path.") # Clean up downloaded weights directory if desired (optional) # import shutil # if os.path.exists('weights'): # shutil.rmtree('weights')