create app run with Real Esrgan

.gitignore

@@ -9,4 +9,5 @@
 /gfpgan
 /output
 */__pycache__/*
-/utils/__pycache__/*
+/utils/__pycache__/*
+__pycache__/*

app.py

@@ -0,0 +1,102 @@
+from fastapi import FastAPI
+import os
+import shutil
+import uuid
+import cv2
+import gradio as gr
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from gfpgan.utils import GFPGANer
+from realesrgan.utils import RealESRGANer
+import uvicorn
+
+# download weights for RealESRGAN
+if not os.path.exists('model_zoo/real/RealESRGAN_x4plus.pth'):
+    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P model_zoo/real")
+if not os.path.exists('model_zoo/gan/GFPGANv1.4.pth'):
+    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P model_zoo/gan")
+if not os.path.exists('model_zoo/swinir/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth'):
+    os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth -P model_zoo/swinir')
+
+model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+model_path = 'model_zoo/real/RealESRGAN_x4plus.pth'
+netscale = 4
+tile = 400 if torch.cuda.is_available() else 0
+dni_weight = None
+# restorer
+upsampler = RealESRGANer(
+    scale=netscale,
+    model_path=model_path,
+    dni_weight=dni_weight,
+    model=model,
+    tile=tile,
+    tile_pad=10,
+    pre_pad=0,
+    half=False, #Use fp32 precision during inference. Default: fp16 (half precision).
+    gpu_id=None) #gpu device to use (default=None) can be 0,1,2 for multi-gpu
+
+def inference(img, scale):
+    # background enhancer with RealESRGAN
+    os.makedirs('output', exist_ok=True)
+    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)
+        try:
+            face_enhancer = GFPGANer(
+                model_path='model_zoo/gan/GFPGANv1.4.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+            _, _, 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'
+
+        filename = str(uuid.uuid4())    
+        save_path = f'output/out_{filename}.{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 = "Real Esrgan Restore Ai Face Restoration by appsgenz.com"
+description = ""
+article = "AppsGenz"
+grApp = gr.Interface(
+    inference, [
+        gr.Image(type="filepath", label="Input"),
+        gr.Number(label="Rescaling factor. Note max rescaling factor is 4", value=2),
+    ], [
+        gr.Image(type="numpy", label="Output (The whole image)"),
+        gr.File(label="Download the output image")
+    ],
+    title=title,
+    description=description,
+    article=article)
+grApp.queue(concurrency_count=2)
+grApp.launch()

download-weights.sh

@@ -0,0 +1,26 @@
+
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DIV2K_s48w8_SwinIR-M_x2.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DIV2K_s48w8_SwinIR-M_x3.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DIV2K_s48w8_SwinIR-M_x4.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DIV2K_s48w8_SwinIR-M_x8.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DF2K_s64w8_SwinIR-M_x2.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DF2K_s64w8_SwinIR-M_x3.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DF2K_s64w8_SwinIR-M_x4.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DF2K_s64w8_SwinIR-M_x8.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/002_lightweightSR_DIV2K_s64w8_SwinIR-S_x2.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/002_lightweightSR_DIV2K_s64w8_SwinIR-S_x3.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/002_lightweightSR_DIV2K_s64w8_SwinIR-S_x4.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/002_lightweightSR_DIV2K_s64w8_SwinIR-S_x4.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x2_GAN.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth -P model_zoo/swinir
+wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth -P model_zoo/swinir

requirement.txt

@@ -0,0 +1,16 @@
+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
+fastapi
+gradio
+uuid