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"""PixelUP DEMO.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1RufGXUNy-7zx5biALX7zEgXb0rM9cWnb |
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""" |
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import gradio as gr |
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import cv2 |
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import numpy |
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import os |
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import random |
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from basicsr.archs.rrdbnet_arch import RRDBNet |
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from basicsr.utils.download_util import load_file_from_url |
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from realesrgan import RealESRGANer |
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact |
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last_file = None |
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img_mode = "RGBA" |
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def realesrgan(img, model_name, denoise_strength, face_enhance, outscale): |
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"""Real-ESRGAN function to restore (and upscale) images. |
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""" |
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if not img: |
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return |
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if model_name == 'RealESRGAN_x4plus': |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) |
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netscale = 4 |
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'] |
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elif model_name == 'RealESRNet_x4plus': |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) |
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netscale = 4 |
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth'] |
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elif model_name == 'RealESRGAN_x4plus_anime_6B': |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) |
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netscale = 4 |
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth'] |
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elif model_name == 'RealESRGAN_x2plus': |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) |
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netscale = 2 |
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'] |
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elif model_name == 'realesr-general-x4v3': |
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') |
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netscale = 4 |
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file_url = [ |
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'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth', |
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'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth' |
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] |
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model_path = os.path.join('weights', model_name + '.pth') |
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if not os.path.isfile(model_path): |
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ROOT_DIR = os.path.dirname(os.path.abspath(".")) |
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for url in file_url: |
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model_path = load_file_from_url( |
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url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None) |
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dni_weight = None |
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if model_name == 'realesr-general-x4v3' and denoise_strength != 1: |
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wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3') |
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model_path = [model_path, wdn_model_path] |
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dni_weight = [denoise_strength, 1 - denoise_strength] |
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upsampler = RealESRGANer( |
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scale=netscale, |
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model_path=model_path, |
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dni_weight=dni_weight, |
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model=model, |
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tile=0, |
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tile_pad=10, |
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pre_pad=10, |
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half=False, |
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gpu_id=None |
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) |
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if face_enhance: |
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from gfpgan import GFPGANer |
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face_enhancer = GFPGANer( |
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model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth', |
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upscale=outscale, |
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arch='clean', |
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channel_multiplier=2, |
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bg_upsampler=upsampler) |
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cv_img = numpy.array(img) |
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img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA) |
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try: |
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if face_enhance: |
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) |
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else: |
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output, _ = upsampler.enhance(img, outscale=outscale) |
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except RuntimeError as error: |
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print('Error', error) |
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print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') |
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else: |
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if img_mode == 'RGBA': |
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extension = 'png' |
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else: |
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extension = 'jpg' |
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out_filename = f"output_{rnd_string(8)}.{extension}" |
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cv2.imwrite(out_filename, output) |
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global last_file |
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last_file = out_filename |
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return out_filename |
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def rnd_string(x): |
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"""Returns a string of 'x' random characters |
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""" |
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characters = "abcdefghijklmnopqrstuvwxyz_0123456789" |
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result = "".join((random.choice(characters)) for i in range(x)) |
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return result |
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def reset(): |
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"""Resets the Image components of the Gradio interface and deletes |
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the last processed image |
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""" |
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global last_file |
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if last_file: |
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print(f"Deleting {last_file} ...") |
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os.remove(last_file) |
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last_file = None |
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return gr.update(value=None), gr.update(value=None) |
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def has_transparency(img): |
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"""This function works by first checking to see if a "transparency" property is defined |
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in the image's info -- if so, we return "True". Then, if the image is using indexed colors |
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(such as in GIFs), it gets the index of the transparent color in the palette |
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(img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas |
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(img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in |
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it, but it double-checks by getting the minimum and maximum values of every color channel |
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(img.getextrema()), and checks if the alpha channel's smallest value falls below 255. |
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https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent |
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""" |
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if img.info.get("transparency", None) is not None: |
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return True |
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if img.mode == "P": |
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transparent = img.info.get("transparency", -1) |
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for _, index in img.getcolors(): |
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if index == transparent: |
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return True |
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elif img.mode == "RGBA": |
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extrema = img.getextrema() |
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if extrema[3][0] < 255: |
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return True |
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return False |
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def image_properties(img): |
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"""Returns the dimensions (width and height) and color mode of the input image and |
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also sets the global img_mode variable to be used by the realesrgan function |
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""" |
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global img_mode |
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if img: |
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try: |
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img_mode = img.mode |
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properties = f"Width: {img.size[0]}, Height: {img.size[1]} | Color Mode: {img_mode}" |
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return properties |
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except Exception as e: |
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print(f"Error processing image: {e}") |
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return "Invalid image" |
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def main(): |
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with gr.Blocks(title="PixelUP Demo", theme="dark") as demo: |
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gr.Markdown( |
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"""# <div align="center"> </div> |
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<div align="center"><img width="200" height="74" src="https://i.imgur.com/UzFFDdL.png"></div> |
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""" |
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) |
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with gr.Accordion("Options/Parameters"): |
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with gr.Row(): |
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model_name = gr.Dropdown(label="Choose Model", |
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choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B", |
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"RealESRGAN_x2plus", "realesr-general-x4v3"], |
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value="realesr-general-x4v3", show_label=True) |
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denoise_strength = gr.Slider(label="Denoise Strength", |
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minimum=0, maximum=1, step=0.1, value=0.5) |
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outscale = gr.Slider(label="Image Upscaling Factor", |
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minimum=1, maximum=10, step=1, value=2, show_label=True) |
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face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN ", |
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value=False, show_label=True) |
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with gr.Row(): |
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with gr.Group(): |
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input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA") |
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input_image_properties = gr.Textbox(label="Image Properties", max_lines=1) |
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output_image = gr.Image(label="Restored Image", image_mode="RGBA") |
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with gr.Row(): |
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restore_btn = gr.Button("Restore Image") |
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reset_btn = gr.Button("Reset") |
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input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties) |
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restore_btn.click(fn=realesrgan, |
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inputs=[input_image, model_name, denoise_strength, face_enhance, outscale], |
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outputs=output_image) |
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reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image]) |
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gr.Markdown( |
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"""*Please note that support for animated GIFs is not yet implemented. Should an animated GIF is chosen for restoration, |
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the demo will output only the first frame saved in PNG format (to preserve probable transparency).* |
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""" |
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) |
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demo.launch(share=True) |
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if __name__ == "__main__": |
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main() |
