fast fix

app.py

@@ -1,137 +1,125 @@
-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 -q -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 -q -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 -q -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 -q -P .")
-if not os.path.exists('RestoreFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -q -P .")
-if not os.path.exists('CodeFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -q -P .")
-
-torch.hub.download_url_to_file(
-    'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
-    'lincoln.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
-    'AI-generate.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
-    'Blake_Lively.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):
-    weight /= 100
-    print(img, version, scale, weight)
-    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)
-        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
-    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 😊
-"""
-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)
-"""
-
-demo = gr.Interface(
-        inference, [
-            gr.Image(type="filepath", label="Input"),
-            gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
-            gr.Number(label="Rescaling factor", value=2),
-            gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
-        ], [
-            gr.Image(type="numpy", label="Output (The whole image)", format="png"),
-            gr.File(label="Download the output image")
-        ],
-        title=title,
-        description=description,
-        article=article,
-        examples=[['AI-generate.jpg', 'v1.4', 2, 50], ],
-        flagging_mode="never",
-        cache_mode="lazy",
-        delete_cache=(1800, 3600),)
-demo.queue()
-demo.launch(show_error=True)
+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 -q -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 -q -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 -q -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 -q -P .")
+if not os.path.exists('RestoreFormer.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -q -P .")
+if not os.path.exists('CodeFormer.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -q -P .")
+
+
+# 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):
+    weight /= 100
+    print(img, version, scale, weight)
+    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)
+        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
+    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 😊
+"""
+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)
+"""
+
+demo = gr.Interface(
+        inference, [
+            gr.Image(type="filepath", label="Input"),
+            gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
+            gr.Number(label="Rescaling factor", value=2),
+            gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
+        ], [
+            gr.Image(type="numpy", label="Output (The whole image)", format="png"),
+            gr.File(label="Download the output image")
+        ],
+        title=title,
+        description=description,
+        article=article,
+        examples=[['AI-generate.jpg', 'v1.4', 2, 50], ],
+        flagging_mode="never",
+        cache_mode="lazy",
+        delete_cache=(1800, 3600),)
+demo.queue()
+demo.launch(show_error=True)