95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
#超分辨率重建
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import os
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import cv2
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import gradio as gr
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import torch
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from basicsr.archs.srvgg_arch import SRVGGNetCompact
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from gfpgan.utils import GFPGANer
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from realesrgan.utils import RealESRGANer
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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# background enhancer with RealESRGAN
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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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model_path = 'realesr-general-x4v3.pth'
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half = True if torch.cuda.is_available() else False
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upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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# Use GFPGAN for face enhancement
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face_enhancer_v3 = GFPGANer(
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model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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os.makedirs('output', exist_ok=True)
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def inference(img):
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scale = 2
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try:
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img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
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if len(img.shape) == 3 and img.shape[2] == 4:
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img_mode = 'RGBA'
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else:
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img_mode = None
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h, w = img.shape[0:2]
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if h < 300:
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
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face_enhancer = face_enhancer_v3
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try:
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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except RuntimeError as error:
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print('Error', error)
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else:
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extension = 'png'
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try:
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if scale != 2:
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interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
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h, w = img.shape[0:2]
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output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
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except Exception as error:
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print('wrong scale input.', error)
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if img_mode == 'RGBA': # RGBA images should be saved in png format
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extension = 'png'
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else:
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extension = 'jpg'
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save_path = f'output/out.{extension}'
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cv2.imwrite(save_path, output)
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output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
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return output
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except Exception as error:
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print('global exception', error)
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return None
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>图像超分辨率重建</font></div>
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""")
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with gr.Row():
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with gr.Column():
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image = gr.Image(label="图片", type="filepath")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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box2 = gr.Image(label="图片")
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button.click(fn=inference, inputs=[image], outputs=box2)
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examples = gr.Examples(examples=[['1.jpg'], ['2.jpg'], ['4.jpg']], inputs=[image], label="例子")
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if __name__ == '__main__':
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demo.queue().launch(server_name = "0.0.0.0")
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