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app.py

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+import os
+
+import sys
+from torchvision.transforms import functional
+sys.modules["torchvision.transforms.functional_tensor"] = functional
+
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+from gfpgan.utils import GFPGANer
+from realesrgan.utils import RealESRGANer
+
+import torch
+import cv2
+import gradio as gr
+
+
+#Download Required Models
+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 .")
+
+
+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)
+
+
+# Save Image to the Directory
+# os.makedirs('output', exist_ok=True)
+
+def upscaler(img, version, scale):
+
+    try:
+        
+        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:
+            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)
+
+        
+        face_enhancer = GFPGANer(
+            model_path=f'{version}.pth', 
+            upscale=2, 
+            arch='RestoreFormer' if version=='RestoreFormer' else 'clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler
+        )
+
+
+        try:
+            _, _, 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)
+
+        # Save Image to the Directory
+        # ext = os.path.splitext(os.path.basename(str(img)))[1]
+        # if img_mode == 'RGBA':
+        #     ext = 'png'
+        # else:
+        #     ext = 'jpg'
+        #
+        # save_path = f'output/out.{ext}'
+        # cv2.imwrite(save_path, output)
+        # return output, save_path
+
+        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        return output
+    except Exception as error:
+        print('global exception', error)
+        return None, None
+
+if __name__ == "__main__":
+
+    title = "Image Upscaler & Restoring [GFPGAN Algorithm]"
+
+    demo = gr.Interface(
+            upscaler, [
+                gr.Image(type="filepath", label="Input"),
+                gr.Radio(['GFPGANv1.2', 'GFPGANv1.3', 'GFPGANv1.4', 'RestoreFormer'], type="value", label='version'),
+                gr.Number(label="Rescaling factor"),
+            ], [
+                gr.Image(type="numpy", label="Output (The whole image)"),
+            ],
+            title=title,
+            allow_flagging="never"
+        )
+
+    demo.queue()
+    demo.launch()

requirements.txt

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+absl-py==2.1.0
+addict==2.4.0
+aiofiles==23.2.1
+altair==5.3.0
+annotated-types==0.6.0
+anyio==4.3.0
+attrs==23.2.0
+basicsr==1.4.2
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+contourpy==1.2.1
+cycler==0.12.1
+facexlib==0.3.0
+fastapi==0.110.2
+ffmpy==0.3.2
+filelock==3.13.4
+filterpy==1.4.5
+fonttools==4.51.0
+fsspec==2024.3.1
+future==1.0.0
+gfpgan==1.3.8
+gradio==4.28.3
+gradio_client==0.16.0
+grpcio==1.62.2
+h11==0.14.0
+httpcore==1.0.5
+httpx==0.27.0
+huggingface-hub==0.22.2
+idna==3.7
+imageio==2.34.1
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+Jinja2==3.1.3
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+lazy_loader==0.4
+llvmlite==0.42.0
+lmdb==1.4.1
+Markdown==3.6
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.4
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.3
+numba==0.59.1
+numpy==1.26.4
+opencv-python==4.9.0.80
+orjson==3.10.1
+packaging==24.0
+pandas==2.2.2
+pillow==10.3.0
+platformdirs==4.2.1
+protobuf==4.25.3
+pydantic==2.7.1
+pydantic_core==2.18.2
+pydub==0.25.1
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+realesrgan==0.3.0
+referencing==0.35.0
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.0
+ruff==0.4.2
+scikit-image==0.23.2
+scipy==1.13.0
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+starlette==0.37.2
+sympy==1.12
+tb-nightly==2.17.0a20240428
+tensorboard-data-server==0.7.2
+tifffile==2024.4.24
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.3.0
+torchvision==0.18.0
+tqdm==4.66.2
+typer==0.12.3
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==2.2.1
+uvicorn==0.29.0
+websockets==11.0.3
+Werkzeug==3.0.2
+yapf==0.40.2
+zipp==3.18.1