├── LR └── .gitkeep ├── results └── .gitkeep ├── demo.png ├── .vscode └── settings.json ├── .gitignore ├── models └── README.md ├── app ├── net_interp.py ├── transer_RRDB_models.py └── RRDBNet_arch.py ├── README.md ├── main.py └── LICENSE /LR/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /results/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/josh-truong/AI-Upscaler/HEAD/demo.png -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "python.analysis.extraPaths": [ 3 | "./app" 4 | ] 5 | } -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # folder 2 | env 3 | LR 4 | results 5 | 6 | !LR/.gitkeep 7 | !results/.gitkeep 8 | 9 | # file type 10 | *.svg 11 | *.pyc 12 | *.pth 13 | *.t7 14 | *.caffemodel 15 | *.mat 16 | *.npy -------------------------------------------------------------------------------- /models/README.md: -------------------------------------------------------------------------------- 1 | ## Place pretrained models here. 2 | 3 | We provide two pretrained models: 4 | 5 | 1. `RRDB_ESRGAN_x4.pth`: the final ESRGAN model we used in our [paper](https://arxiv.org/abs/1809.00219). 6 | 2. `RRDB_PSNR_x4.pth`: the PSNR-oriented model with **high PSNR performance**. 7 | 8 | *Note that* the pretrained models are trained under the `MATLAB bicubic` kernel. 9 | If the downsampled kernel is different from that, the results may have artifacts. 10 | -------------------------------------------------------------------------------- /app/net_interp.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import torch 3 | from collections import OrderedDict 4 | 5 | alpha = float(sys.argv[1]) 6 | 7 | net_PSNR_path = './models/RRDB_PSNR_x4.pth' 8 | net_ESRGAN_path = './models/RRDB_ESRGAN_x4.pth' 9 | net_interp_path = './models/interp_{:02d}.pth'.format(int(alpha*10)) 10 | 11 | net_PSNR = torch.load(net_PSNR_path) 12 | net_ESRGAN = torch.load(net_ESRGAN_path) 13 | net_interp = OrderedDict() 14 | 15 | print('Interpolating with alpha = ', alpha) 16 | 17 | for k, v_PSNR in net_PSNR.items(): 18 | v_ESRGAN = net_ESRGAN[k] 19 | net_interp[k] = (1 - alpha) * v_PSNR + alpha * v_ESRGAN 20 | 21 | torch.save(net_interp, net_interp_path) 22 | -------------------------------------------------------------------------------- /app/transer_RRDB_models.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import RRDBNet_arch as arch 4 | 5 | pretrained_net = torch.load('./models/RRDB_ESRGAN_x4.pth') 6 | save_path = './models/RRDB_ESRGAN_x4.pth' 7 | 8 | crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) 9 | crt_net = crt_model.state_dict() 10 | 11 | load_net_clean = {} 12 | for k, v in pretrained_net.items(): 13 | if k.startswith('module.'): 14 | load_net_clean[k[7:]] = v 15 | else: 16 | load_net_clean[k] = v 17 | pretrained_net = load_net_clean 18 | 19 | print('###################################\n') 20 | tbd = [] 21 | for k, v in crt_net.items(): 22 | tbd.append(k) 23 | 24 | # directly copy 25 | for k, v in crt_net.items(): 26 | if k in pretrained_net and pretrained_net[k].size() == v.size(): 27 | crt_net[k] = pretrained_net[k] 28 | tbd.remove(k) 29 | 30 | crt_net['conv_first.weight'] = pretrained_net['model.0.weight'] 31 | crt_net['conv_first.bias'] = pretrained_net['model.0.bias'] 32 | 33 | for k in tbd.copy(): 34 | if 'RDB' in k: 35 | ori_k = k.replace('RRDB_trunk.', 'model.1.sub.') 36 | if '.weight' in k: 37 | ori_k = ori_k.replace('.weight', '.0.weight') 38 | elif '.bias' in k: 39 | ori_k = ori_k.replace('.bias', '.0.bias') 40 | crt_net[k] = pretrained_net[ori_k] 41 | tbd.remove(k) 42 | 43 | crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight'] 44 | crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias'] 45 | crt_net['upconv1.weight'] = pretrained_net['model.3.weight'] 46 | crt_net['upconv1.bias'] = pretrained_net['model.3.bias'] 47 | crt_net['upconv2.weight'] = pretrained_net['model.6.weight'] 48 | crt_net['upconv2.bias'] = pretrained_net['model.6.bias'] 49 | crt_net['HRconv.weight'] = pretrained_net['model.8.weight'] 50 | crt_net['HRconv.bias'] = pretrained_net['model.8.bias'] 51 | crt_net['conv_last.weight'] = pretrained_net['model.10.weight'] 52 | crt_net['conv_last.bias'] = pretrained_net['model.10.bias'] 53 | 54 | torch.save(crt_net, save_path) 55 | print('Saving to ', save_path) 56 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## ESRGAN (Enhanced SRGAN) [:rocket: [BasicSR](https://github.com/xinntao/BasicSR)] [[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)] 2 | 3 | Cloned repository of [ESRGAN](https://github.com/xinntao/ESRGAN) 4 | 5 | Here are some examples for Real-ESRGAN: 6 | 7 |

8 | 9 |

10 | :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data 11 | 12 | > [[Paper](https://arxiv.org/abs/2107.10833)]
13 | > [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)
14 | > Applied Research Center (ARC), Tencent PCG
15 | > Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 16 | 17 | ----- 18 | 19 | ## Quick Test 20 | #### Dependencies 21 | - Python 3 22 | - [PyTorch >= 1.0](https://pytorch.org/) (CUDA version >= 7.5 if installing with CUDA. [More details](https://pytorch.org/get-started/previous-versions/)) 23 | - ```pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu``` 24 | - Python packages: `pip install numpy opencv-python` 25 | 26 | ### Test models 27 | 1. Clone this github repo. 28 | ``` 29 | git clone https://github.com/xinntao/ESRGAN 30 | cd ESRGAN 31 | ``` 32 | 2. Place your own **low-resolution images** in `./LR` folder. (There are two sample images - baboon and comic). 33 | 3. Download pretrained models from [Google Drive](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) or [Baidu Drive](https://pan.baidu.com/s/1-Lh6ma-wXzfH8NqeBtPaFQ). Place the models in `./models`. We provide two models with high perceptual quality and high PSNR performance (see [model list](https://github.com/xinntao/ESRGAN/tree/master/models)). 34 | 4. Run test. We provide ESRGAN model and RRDB_PSNR model and you can config in the `test.py`. 35 | ``` 36 | python test.py 37 | ``` 38 | 5. The results are in `./results` folder. 39 | ### Network interpolation demo 40 | You can interpolate the RRDB_ESRGAN and RRDB_PSNR models with alpha in [0, 1]. 41 | 42 | 1. Run `python net_interp.py 0.8`, where *0.8* is the interpolation parameter and you can change it to any value in [0,1]. 43 | 2. Run `python test.py models/interp_08.pth`, where *models/interp_08.pth* is the model path. 44 | 45 |

46 | 47 |

-------------------------------------------------------------------------------- /app/RRDBNet_arch.py: -------------------------------------------------------------------------------- 1 | import functools 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | 6 | 7 | def make_layer(block, n_layers): 8 | layers = [] 9 | for _ in range(n_layers): 10 | layers.append(block()) 11 | return nn.Sequential(*layers) 12 | 13 | 14 | class ResidualDenseBlock_5C(nn.Module): 15 | def __init__(self, nf=64, gc=32, bias=True): 16 | super(ResidualDenseBlock_5C, self).__init__() 17 | # gc: growth channel, i.e. intermediate channels 18 | self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias) 19 | self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias) 20 | self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias) 21 | self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias) 22 | self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias) 23 | self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) 24 | 25 | # initialization 26 | # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) 27 | 28 | def forward(self, x): 29 | x1 = self.lrelu(self.conv1(x)) 30 | x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) 31 | x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) 32 | x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) 33 | x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) 34 | return x5 * 0.2 + x 35 | 36 | 37 | class RRDB(nn.Module): 38 | '''Residual in Residual Dense Block''' 39 | 40 | def __init__(self, nf, gc=32): 41 | super(RRDB, self).__init__() 42 | self.RDB1 = ResidualDenseBlock_5C(nf, gc) 43 | self.RDB2 = ResidualDenseBlock_5C(nf, gc) 44 | self.RDB3 = ResidualDenseBlock_5C(nf, gc) 45 | 46 | def forward(self, x): 47 | out = self.RDB1(x) 48 | out = self.RDB2(out) 49 | out = self.RDB3(out) 50 | return out * 0.2 + x 51 | 52 | 53 | class RRDBNet(nn.Module): 54 | def __init__(self, in_nc, out_nc, nf, nb, gc=32): 55 | super(RRDBNet, self).__init__() 56 | RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) 57 | 58 | self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) 59 | self.RRDB_trunk = make_layer(RRDB_block_f, nb) 60 | self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) 61 | #### upsampling 62 | self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) 63 | self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) 64 | self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) 65 | self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True) 66 | 67 | self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) 68 | 69 | def forward(self, x): 70 | fea = self.conv_first(x) 71 | trunk = self.trunk_conv(self.RRDB_trunk(fea)) 72 | fea = fea + trunk 73 | 74 | fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest'))) 75 | fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) 76 | out = self.conv_last(self.lrelu(self.HRconv(fea))) 77 | 78 | return out 79 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import os 2 | import os.path as osp 3 | import glob 4 | import cv2 5 | import numpy as np 6 | import torch 7 | import sys 8 | sys.path.insert(0, '{0}/app'.format(os.path.dirname(__file__))) 9 | import RRDBNet_arch as arch 10 | 11 | # Settings 12 | model_path = 'models/RRDB_ESRGAN_x4.pth' # models/RRDB_ESRGAN_x4.pth OR models/RRDB_PSNR_x4.pth 13 | # device = torch.device('cuda') # if you want to run on CPU, change 'cuda' -> cpu 14 | device = torch.device('cpu') 15 | test_img_folder = 'LR/*' 16 | 17 | # ESRGAN Model 18 | model = arch.RRDBNet(3, 3, 64, 23, gc=32) 19 | model.load_state_dict(torch.load(model_path), strict=True) 20 | model.eval() 21 | model = model.to(device) 22 | 23 | # Supported Extensions 24 | img_ext = ['.bmp','.dib','.jpeg','.jpg','.jpe','.jp2','.png','.pbm','.pgm','.ppm','.sr','.ras','.tiff','.tif'] 25 | vid_ext = ['.mp4'] 26 | 27 | def ResizeImage(img, max=100): 28 | # Resize image to have a dimension less than 100 pixels 29 | height, width = img.shape[0], img.shape[1] 30 | scale_factor = max / (height if (height > width) else width) 31 | dim = (int(width*scale_factor), int(height*scale_factor)) 32 | resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 33 | return resized 34 | 35 | def ESRGAN(img, model): 36 | img = img * 1.0 / 255 37 | img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() 38 | img_LR = img.unsqueeze(0) 39 | img_LR = img_LR.to(device) 40 | 41 | with torch.no_grad(): 42 | output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy() 43 | output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) 44 | output = (output * 255.0).round() 45 | return output 46 | 47 | 48 | print('Model path {:s}.'.format(model_path)) 49 | MAXSIZE = 300 50 | for path in glob.glob(test_img_folder): 51 | base, ext = osp.splitext(osp.basename(path)) 52 | print('{0}{1}'.format(base, ext)) 53 | 54 | if (ext in img_ext): 55 | # read images 56 | img = cv2.imread(path, cv2.IMREAD_COLOR) 57 | img = img if (MAXSIZE in img.shape) else ResizeImage(img, MAXSIZE) 58 | cv2.imwrite('LR/{:s}.png'.format(base), img) 59 | output = ESRGAN(img, model) 60 | cv2.imwrite('results/{:s}.png'.format(base), output) 61 | elif (ext in vid_ext): 62 | # Read frames 63 | filename = "results/{:s}.avi".format(base) 64 | 65 | cap = cv2.VideoCapture(path) 66 | writer = None 67 | if not cap.isOpened(): 68 | exit() 69 | ret, frame = cap.read() 70 | 71 | while(cap.isOpened()): 72 | ret, frame = cap.read() 73 | if not ret: 74 | break 75 | frame = frame if (MAXSIZE in frame.shape) else ResizeImage(frame, MAXSIZE) 76 | frame = ESRGAN(frame, model) 77 | 78 | if (writer == None): 79 | codec = cv2.VideoWriter_fourcc(*'DIVX') 80 | framerate = cap.get(cv2.CAP_PROP_FPS) 81 | resolution = (frame.shape[1], frame.shape[0]) 82 | writer = cv2.VideoWriter(filename, codec, framerate, resolution) 83 | 84 | writer.write(frame.astype('uint8')) 85 | 86 | writer.release() 87 | cap.release() -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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