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import streamlit as st |
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import os.path |
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os.system("mkdir _input") |
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os.system("mkdir _output") |
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os.system("mkdir _outputf") |
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os.system("ls") |
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if not os.path.isfile("./_input/imagem-0001.png"): |
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os.system("ffmpeg -i vivi.mp4 -compression_level 10 -pred mixed -pix_fmt rgb24 -sws_flags +accurate_rnd+full_chroma_int -s 1080x1920 -r 0.12 ./_input/imagem-%4d.png") |
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os.system("ls ./_input") |
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if 'myVar' not in globals(): |
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myVar="" |
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os.system("pip install git+https://github.com/TencentARC/GFPGAN.git") |
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import cv2 |
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import glob |
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import numpy as np |
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from basicsr.utils import imwrite |
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from gfpgan import GFPGANer |
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import random |
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from PIL import Image |
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import torch |
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bg_upsampler = None |
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print(f"Is CUDA available: {torch.cuda.is_available()}") |
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if 'restorer' not in globals(): |
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restorer = GFPGANer( |
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model_path='GFPGANv1.3.pth', |
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upscale=2, |
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arch='clean', |
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channel_multiplier=2, |
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bg_upsampler=bg_upsampler) |
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img_list = sorted(glob.glob(os.path.join("./_input", '*'))) |
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for img_path in img_list: |
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img_name = os.path.basename(img_path) |
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print(f'Processing {img_name} ...') |
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basename, ext = os.path.splitext(img_name) |
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input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) |
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cropped_faces, restored_faces, restored_img = restorer.enhance( |
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input_img, |
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has_aligned='store_true', |
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only_center_face='store_true', |
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paste_back=True, |
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weight=0.5) |
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for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): |
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save_crop_path = os.path.join("_output", 'cropped_faces', f'{basename}_{idx:02d}.png') |
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imwrite(cropped_face, save_crop_path) |
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if None is not None: |
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save_face_name = f'{basename}_{idx:04d}_{args.suffix}.png' |
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else: |
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save_face_name = f'{basename}_{idx:04d}.png' |
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save_restore_path = os.path.join("_output", 'restored_faces', save_face_name) |
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imwrite(restored_face, save_restore_path) |
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cmp_img = np.concatenate((cropped_face, restored_face), axis=1) |
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imwrite(cmp_img, os.path.join("_output", 'cmp', f'{basename}_{idx:04d}.png')) |
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if restored_img is not None: |
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print('encontrou**************') |
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if args.ext == 'auto': |
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extension = ext[1:] |
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else: |
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extension = args.ext |
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if None is not None: |
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save_restore_path = os.path.join("_output", 'restored_imgs', f'{basename}_{args.suffix}.{extension}') |
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else: |
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save_restore_path = os.path.join("_output", 'restored_imgs', f'{basename}.{extension}') |
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imwrite(restored_img, save_restore_path) |
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os.system("ls ./_output") |
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os.system("echo ----") |
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os.system("ls ./_output/cmp") |
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os.system("echo ----") |
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os.system("ls ./_output/restored_imgs") |
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os.system("echo ----") |
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def inference(): |
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random.randint(0, 9) |
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input_img = cv2.imread("./_output/cmp/imagem-000"+str(random.randint(1, 4))+"_0000.png" , cv2.IMREAD_COLOR) |
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input_img= cv2.cvtColor(input_img,cv2.COLOR_BGR2RGB) |
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st.image(input_img) |
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title = "Melhoria de imagens" |
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os.system("ls") |
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description = "Sistema para automação。" |
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article = "<p style='text-align: center'><a href='https://huggingface.co/spaces/akhaliq/GFPGAN/' target='_blank'>clone from akhaliq@huggingface with little change</a> | <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>GFPGAN Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>" |
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st.button('Comparacao',on_click=inference) |
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