Duplicate from vicalloy/GFPGAN

Co-authored-by: vicalloy <vicalloy@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: GFPGAN
+emoji: 😁
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 3.1.7
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: vicalloy/GFPGAN
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+
+import cv2
+import gradio as gr
+import torch
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+from gfpgan.utils import GFPGANer
+from realesrgan.utils import RealESRGANer
+
+os.system("pip freeze")
+# download weights
+if not os.path.exists('realesr-general-x4v3.pth'):
+    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
+if not os.path.exists('GFPGANv1.2.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
+if not os.path.exists('GFPGANv1.3.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
+if not os.path.exists('GFPGANv1.4.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
+if not os.path.exists('RestoreFormer.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
+if not os.path.exists('CodeFormer.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
+
+if not os.path.exists('HanamichiSakuragi.jpg'):
+    torch.hub.download_url_to_file(
+        'https://haoluobo.com/wp-content/uploads/2023/01/%E6%A8%B1%E6%9C%A8%E8%8A%B1%E9%81%93.jpg',
+        'HanamichiSakuragi.jpg')
+    torch.hub.download_url_to_file(
+        'https://haoluobo.com/wp-content/uploads/2023/01/%E6%9D%8E%E4%B8%96%E6%B0%91.jpg',
+        'LiShiming.jpg')
+    torch.hub.download_url_to_file(
+        'https://haoluobo.com/wp-content/uploads/2023/01/%E4%B9%BE%E9%9A%86.jpg',
+        'QianLong.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
+        '10045.png')
+
+# background enhancer with RealESRGAN
+model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+model_path = 'realesr-general-x4v3.pth'
+half = True if torch.cuda.is_available() else False
+upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
+
+os.makedirs('output', exist_ok=True)
+
+
+# def inference(img, version, scale, weight):
+def inference(img, version, scale, blur_face):
+    blur_face = int(blur_face)
+    if blur_face % 2 != 1:
+        blur_face += 1
+    if blur_face < 3:
+        blur_face = 0
+    # weight /= 100
+    print(img, version, scale)
+    if scale > 4:
+        scale = 4  # avoid too large scale value
+    try:
+        extension = os.path.splitext(os.path.basename(str(img)))[1]
+        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        elif len(img.shape) == 2:  # for gray inputs
+            img_mode = None
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        else:
+            img_mode = None
+
+        h, w = img.shape[0:2]
+        if h < 300:
+            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+
+        if version == 'v1.2':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.3':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.4':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'RestoreFormer':
+            face_enhancer = GFPGANer(
+            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
+        # elif version == 'CodeFormer':
+        #     face_enhancer = GFPGANer(
+        #     model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
+
+        try:
+            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
+            face_helper = face_enhancer.face_helper
+            align_warp_face = face_helper.align_warp_face
+
+            def new_align_warp_face(*args, **kwargs):
+                align_warp_face(*args, **kwargs)  # save_cropped_path
+                face_helper.org_cropped_faces = face_helper.cropped_faces
+                if blur_face >= 3:
+                    face_helper.cropped_faces = [cv2.GaussianBlur(e, (blur_face, blur_face), 0) for e in face_helper.cropped_faces]
+                print("find face count:", len(face_helper.cropped_faces))
+
+            face_helper.align_warp_face = new_align_warp_face
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        except RuntimeError as error:
+            print('Error', error)
+
+        try:
+            if scale != 2:
+                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
+                h, w = img.shape[0:2]
+                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
+        except Exception as error:
+            print('wrong scale input.', error)
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
+        else:
+            extension = 'jpg'
+        save_path = f'output/out.{extension}'
+        cv2.imwrite(save_path, output)
+
+        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        return (
+            output,
+            save_path,
+            [cv2.cvtColor(e, cv2.COLOR_BGR2RGB) for e in face_enhancer.face_helper.org_cropped_faces],
+            [cv2.cvtColor(e, cv2.COLOR_BGR2RGB) for e in face_enhancer.face_helper.restored_faces]
+        )
+    except Exception as error:
+        print('global exception', error)
+        return None, None
+
+
+title = "GFPGAN: Practical Face Restoration Algorithm"
+description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
+It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
+To use it, simply upload your image.<br>
+If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊<br>
+This demo was forked by [vicalloy](https://github.com/vicalloy), add `face blur` param to optimize painting face enhance.
+"""
+article = r"""
+
+[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
+
+If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
+"""
+with gr.Blocks() as demo:
+    gr.Markdown("<center><h1>%s</h1></center>" % title)
+    gr.Markdown(description)
+    with gr.Row(equal_height=False):
+        with gr.Column():
+            file_path = gr.components.Image(type="filepath", label="Input")
+            version = gr.components.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version')
+            rescaling_factor = gr.components.Number(label="Rescaling factor", value=2)
+            blur_face = gr.components.Number(label="Blur face", value=25)
+            submit = gr.Button("Submit")
+        with gr.Column():
+            output_img = gr.components.Image(type="numpy", label="Output (The whole image)")
+            download = gr.components.File(label="Download the output image")
+    with gr.Row():
+        with gr.Column():
+            input_faces = gr.Gallery(label="Input faces").style(height="auto")
+        with gr.Column():
+            output_faces = gr.Gallery(label="Output faces").style(height="auto")
+    gr.Examples([['HanamichiSakuragi.jpg', 'v1.4', 2, 31], ['LiShiming.jpg', 'v1.4', 2, 3], ['QianLong.jpg', 'v1.4', 2, 3],
+              ['10045.png', 'v1.4', 2, 0]], [file_path, version, rescaling_factor, blur_face])
+    gr.Markdown(article)
+    submit.click(
+        inference,
+        inputs=[file_path, version, rescaling_factor, blur_face],
+        outputs=[output_img, download, input_faces, output_faces]
+    )
+demo.queue(concurrency_count=4)
+demo.launch()

packages.txt

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+ffmpeg
+libsm6
+libxext6

requirements.txt

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+torch>=1.7
+basicsr>=1.4.2
+facexlib>=0.2.5
+gfpgan>=1.3.7
+realesrgan>=0.2.5
+numpy
+opencv-python
+torchvision
+scipy
+tqdm
+lmdb
+pyyaml
+yapf