add gfp-gan

gfp-gan/Dockerfile

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+FROM python:3.8.4-slim
+
+WORKDIR /app
+
+COPY requirements.txt /app
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/ 
+
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y git
+
+RUN apt-get install -y tk
+
+RUN pip3 install --trusted-host pypi.python.org -r requirements.txt 
+
+COPY . /app
+
+CMD ["python", "gfp_gan.py"]

gfp-gan/gfp_gan.py

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+#超分辨率重建
+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
+
+
+# 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)
+
+# Use GFPGAN for face enhancement
+face_enhancer_v3 = GFPGANer(
+    model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+
+os.makedirs('output', exist_ok=True)
+
+
+def inference(img):
+    scale = 2
+    try:
+        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        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 = face_enhancer_v3
+        
+        try:
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        except RuntimeError as error:
+            print('Error', error)
+        else:
+            extension = 'png'
+
+        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
+    except Exception as error:
+        print('global exception', error)
+        return None
+
+
+demo = gr.Interface(
+            inference,
+            gr.inputs.Image(type="filepath"),
+            gr.Image(),
+            title = "图像超分辨率重建",
+	    allow_flagging="never",
+            examples = ['1.jpg', '2.jpg', '4.jpg'])
+
+
+if __name__ == '__main__':
+    demo.queue().launch(server_name = "0.0.0.0", server_port = 7004, max_threads=40, show_error=True)

gfp-gan/requirements.txt

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+gradio
+torch
+basicsr
+gfpgan
+realesrgan
+opencv-python-headless==4.5.3.56
+pillow
+numpy
+