Aggiungi 'Real-CUGAN'

Real-CUGAN

@@ -0,0 +1,119 @@
+#Real-CUGAN
+
+[-] Step 1: Preparation
+
+Before you begin, make sure that you have set the runtime type to GPU (Hardware acclerator: GPU).
+
+```
+ROOTPATH="/content/ailab/" # root dir (a constant)
+ModelPath=ROOTPATH+"Real-CUGAN/model/" # model dir
+PendingPath=ROOTPATH+"Real-CUGAN/pending/" # input dir
+FinishPath=ROOTPATH+"Real-CUGAN/finish/" # output dir
+ModelName="up2x-latest-no-denoise.pth" # default model
+Tile=4 #{0,1,2,3,4,auto}; the larger the number, the smaller the memory consumption
+# initialize environment
+!pip install torch opencv-python
+!git clone https://github.com/bilibili/ailab.git
+from google.colab import drive
+drive.mount('/content/gdrive')
+
+```
+
+[-] Step 2: Download 
+
+Download model files from here <https://drive.google.com/drive/folders/1UFgpV14uEAcgYvVw0fJuajzy1k7JIz6H> and save them - in a folder called updated_weights - under your Google Drive's root folder.
+
+[-] Step 3: upload
+
+Put/upload your image(s) under /content/aliab/Real-CUGAN/pending.
+
+[-] Step 4: Setup
+
+Run the following and choose the model you want.
+
+```
+import os
+if not os.path.exists(ModelPath):
+    os.mkdir(ModelPath)
+if not os.path.exists(PendingPath):
+    os.mkdir(PendingPath)
+if not os.path.exists(FinishPath):
+    os.mkdir(FinishPath)
+!cp -r /content/gdrive/MyDrive/updated_weights/* /content/ailab/Real-CUGAN/model/
+fileNames = os.listdir(PendingPath)
+print("Pending images:")
+for i in fileNames:
+  print("\t"+i)
+fileNames = os.listdir(ModelPath)
+print("Model files available:")
+for idx, i in enumerate(fileNames):
+  print(f"{idx+1}. \t {i}")
+choice = int(input("Select model (leave blank for default)："))
+if choice:
+  ModelName=fileNames[choice-1]
+Amplification=ModelName[2] # amplifying ratio
+if (not os.path.isfile(ModelPath+ModelName)):
+  print("Warning: selected model file does not exist")
+
+```
+
+[-] Execution
+
+Run the processing script.
+
+```
+import shutil
+import sys
+sys.path.append("/content/ailab/Real-CUGAN")
+import os
+source_path = '/content/gdrive/MyDrive/upscale'
+destination_path = '/content/ailab/Real-CUGAN'
+os.system(f'cp -v {source_path}/pending/*.png {destination_path}/pending')
+import matplotlib.pyplot as plt
+%matplotlib inline
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+import cv2
+import numpy as np
+from upcunet_v3 import RealWaifuUpScaler
+from time import time as ttime  
+fileNames = os.listdir(destination_path + '/pending')
+print(f"using model {ModelPath+ModelName}")
+upscaler = RealWaifuUpScaler(2, ModelPath+ModelName, half=True, device="cuda:0")
+t0 = ttime()
+for i in fileNames:
+    torch.cuda.empty_cache()
+    try:
+        img = cv2.imread(destination_path + '/pending/' + i)[:, :, [2, 1, 0]]
+        result = upscaler(img, tile_mode=5, cache_mode=2, alpha=1)
+        # Estrai il nome del file senza estensione
+        file_name, file_extension = os.path.splitext(i)
+        # Rinomina l'immagine con il nome del modello scelto
+        new_name = f"{file_name}_{ModelName}{file_extension}"
+        cv2.imwrite(os.path.join(destination_path, 'finish', 'Real_CUGAN', new_name), result[:, :, ::-1])
+        # Sposta l'immagine rinominata nella cartella di destinazione
+        source_file = os.path.join(destination_path, 'finish', 'Real_CUGAN', new_name)
+        destination_file = os.path.join(destination_path, 'finish', 'Real_CUGAN', new_name)
+        try:
+            os.rename(source_file, destination_file)
+            print(f'Moved {source_file} to {destination_file}')
+        except OSError as e:
+            print(f'Error moving {source_file} to {destination_file}: {e}')
+        # Sposta anche l'immagine dalla cartella "pending" alla cartella "done"
+        source_pending_file = os.path.join(source_path, 'pending', i)
+        destination_done_file = os.path.join(source_path, 'done', i)
+        try:
+            os.rename(source_pending_file, destination_done_file)
+            print(f'Moved {source_pending_file} to {destination_done_file}')
+        except OSError as e:
+            print(f'Error moving {source_pending_file} to {destination_done_file}: {e}')
+    except RuntimeError as e:
+        print (i+" FAILED")
+        print (e)
+    else:
+        print(i+" DONE")
+t1 = ttime()
+print("time_spent", t1 - t0)
+
+```