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| import gradio as gr | |
| import cv2 | |
| import numpy | |
| import os | |
| import random | |
| from basicsr.archs.rrdbnet_arch import RRDBNet | |
| from basicsr.utils.download_util import load_file_from_url | |
| from realesrgan import RealESRGANer | |
| from realesrgan.archs.srvgg_arch import SRVGGNetCompact | |
| last_file = None | |
| img_mode = "RGBA" | |
| def realesrgan1(img, model_name, denoise_strength, face_enhance, outscale): | |
| denoise_strength = float(denoise_strength) | |
| if face_enhance == "False": | |
| face_enhance = False | |
| else: | |
| face_enhance = True | |
| outscale = int(outscale) | |
| """Real-ESRGAN function to restore (and upscale) images. | |
| """ | |
| if not img: | |
| return | |
| # Define model parameters | |
| if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'] | |
| elif model_name == 'RealESRNet_x4plus': # x4 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth'] | |
| elif model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth'] | |
| elif model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) | |
| netscale = 2 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'] | |
| elif model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size) | |
| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') | |
| netscale = 4 | |
| file_url = [ | |
| 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth', | |
| 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth' | |
| ] | |
| # Determine model paths | |
| model_path = os.path.join('weights', model_name + '.pth') | |
| if not os.path.isfile(model_path): | |
| ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| for url in file_url: | |
| # model_path will be updated | |
| model_path = load_file_from_url( | |
| url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None) | |
| # Use dni to control the denoise strength | |
| dni_weight = None | |
| if model_name == 'realesr-general-x4v3' and denoise_strength != 1: | |
| wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3') | |
| model_path = [model_path, wdn_model_path] | |
| dni_weight = [denoise_strength, 1 - denoise_strength] | |
| # Restorer Class | |
| upsampler = RealESRGANer( | |
| scale=netscale, | |
| model_path=model_path, | |
| dni_weight=dni_weight, | |
| model=model, | |
| tile=0, | |
| tile_pad=10, | |
| pre_pad=10, | |
| half=False, | |
| gpu_id=None | |
| ) | |
| # Use GFPGAN for face enhancement | |
| if face_enhance: | |
| from gfpgan import GFPGANer | |
| face_enhancer = GFPGANer( | |
| model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth', | |
| upscale=outscale, | |
| arch='clean', | |
| channel_multiplier=2, | |
| bg_upsampler=upsampler) | |
| # Convert the input PIL image to cv2 image, so that it can be processed by realesrgan | |
| cv_img = numpy.array(img) | |
| img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA) | |
| # Apply restoration | |
| try: | |
| if face_enhance: | |
| _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) | |
| else: | |
| output, _ = upsampler.enhance(img, outscale=outscale) | |
| except RuntimeError as error: | |
| print('Error', error) | |
| print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') | |
| else: | |
| # Save restored image and return it to the output Image component | |
| if img_mode == 'RGBA': # RGBA images should be saved in png format | |
| extension = 'png' | |
| else: | |
| extension = 'jpg' | |
| out_filename = f"output_{rnd_string(8)}.{extension}" | |
| cv2.imwrite(out_filename, output) | |
| global last_file | |
| last_file = out_filename | |
| return out_filename | |
| def rnd_string(x): | |
| """Returns a string of 'x' random characters | |
| """ | |
| characters = "abcdefghijklmnopqrstuvwxyz_0123456789" | |
| result = "".join((random.choice(characters)) for i in range(x)) | |
| return result | |
| def reset(): | |
| """Resets the Image components of the Gradio interface and deletes | |
| the last processed image | |
| """ | |
| global last_file | |
| if last_file: | |
| print(f"Deleting {last_file} ...") | |
| os.remove(last_file) | |
| last_file = None | |
| return gr.update(value=None), gr.update(value=None) | |
| def has_transparency(img): | |
| """This function works by first checking to see if a "transparency" property is defined | |
| in the image's info -- if so, we return "True". Then, if the image is using indexed colors | |
| (such as in GIFs), it gets the index of the transparent color in the palette | |
| (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas | |
| (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in | |
| it, but it double-checks by getting the minimum and maximum values of every color channel | |
| (img.getextrema()), and checks if the alpha channel's smallest value falls below 255. | |
| https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent | |
| """ | |
| if img.info.get("transparency", None) is not None: | |
| return True | |
| if img.mode == "P": | |
| transparent = img.info.get("transparency", -1) | |
| for _, index in img.getcolors(): | |
| if index == transparent: | |
| return True | |
| elif img.mode == "RGBA": | |
| extrema = img.getextrema() | |
| if extrema[3][0] < 255: | |
| return True | |
| return False | |
| def image_properties(img): | |
| """Returns the dimensions (width and height) and color mode of the input image and | |
| also sets the global img_mode variable to be used by the realesrgan function | |
| """ | |
| global img_mode | |
| if img: | |
| if has_transparency(img): | |
| img_mode = "RGBA" | |
| else: | |
| img_mode = "RGB" | |
| properties = f"Width: {img.size[0]}, Height: {img.size[1]} | Color Mode: {img_mode}" | |
| return properties | |
| # Gradio Interface | |
| with gr.Blocks(title="Real-ESRGAN Gradio Demo", theme="dark") as demo: | |
| gr.Markdown( | |
| """# <div align="center"> Real-ESRGAN Demo for Image Restoration and Upscaling </div> | |
| <div align="center"><img width="200" height="74" src="https://github.com/xinntao/Real-ESRGAN/raw/master/assets/realesrgan_logo.png"></div> | |
| This Gradio Demo was built as my Final Project for **CS50's Introduction to Programming with Python**. | |
| Please visit the [Real-ESRGAN GitHub page](https://github.com/xinntao/Real-ESRGAN) for detailed information about the project. | |
| """ | |
| ) | |
| with gr.Accordion("Options/Parameters"): | |
| with gr.Row(): | |
| model_name = gr.Dropdown(label="Real-ESRGAN inference model to be used", | |
| choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B", | |
| "RealESRGAN_x2plus", "realesr-general-x4v3"], | |
| value="realesr-general-x4v3", show_label=True) | |
| denoise_strength = gr.Slider(label="Denoise Strength (Used only with the realesr-general-x4v3 model)", | |
| minimum=0, maximum=1, step=0.1, value=0.5) | |
| outscale = gr.Slider(label="Image Upscaling Factor", | |
| minimum=1, maximum=10, step=1, value=2, show_label=True) | |
| face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN (Doesn't work for anime images)", | |
| value=False, show_label=True) | |
| with gr.Row(): | |
| with gr.Group(): | |
| input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA") | |
| input_image_properties = gr.Textbox(label="Image Properties", max_lines=1) | |
| output_image = gr.Image(label="Restored Image", image_mode="RGBA") | |
| with gr.Row(): | |
| restore_btn = gr.Button("Restore Image") | |
| reset_btn = gr.Button("Reset") | |
| # Event listeners: | |
| #input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties) | |
| restore_btn.click(fn=realesrgan1, | |
| inputs=[input_image, model_name, denoise_strength, face_enhance, outscale], | |
| outputs=output_image) | |
| #reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image]) | |
| # reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n") | |
| # Undocumented method to clear a component's value using Javascript | |
| gr.Markdown( | |
| """*Please note that support for animated GIFs is not yet implemented. Should an animated GIF is chosen for restoration, | |
| the demo will output only the first frame saved in PNG format (to preserve probable transparency).* | |
| """ | |
| ) | |
| demo.launch() | |