Delete generate_noise.py

generate_noise.py

@@ -1,308 +0,0 @@
-import argparse
-import glob
-import os
-from PIL import Image
-
-import cv2
-import math
-import numpy as np
-import os
-import os.path as osp
-import random
-import time
-import torch
-from tqdm import tqdm
-
-from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
-from basicsr.data.transforms import augment
-from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-from torchvision.transforms.functional import center_crop
-import torchvision.transforms as T
-from torchvision.utils import save_image
-
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
-from basicsr.data.transforms import paired_random_crop
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from collections import OrderedDict
-from torch.nn import functional as F
-
-cfg = {
-    # dataset info.
-    "name": "DF2K+OST",
-    "type": "RealESRGANDataset",
-    "dataroot_gt": "/home/CORP/hsiang.chen/Project/Datasets/IR/Denoise",
-    "meta_train": [
-                "BSD68/metas/BSD68.list",
-                "BSD400/metas/BSD400.list",
-                "CBSD68/metas/CBSD68.list",
-                "Kodak/metas/Kodak.list",
-                "McMaster/metas/McMaster.list",
-                "Set12/metas/Set12.list",
-                "Urban100/metas/Urban100.list",
-                "WaterlooED/metas/WaterlooED.list",
-    ],               
-    # "meta_test": ["DIV2K/metas/DIV2K_valid_HR.list"],
-
-    # the first degradation process
-    "resize_prob": [0.2, 0.7, 0.1],  # up, down, keep
-    "resize_range": [0.15, 1.5],
-    "gaussian_noise_prob": 0.5,
-    "noise_range": [1, 30],
-    "poisson_scale_range": [0.05, 3],
-    "gray_noise_prob": 0.4,
-    "jpeg_range": [30, 95],
-
-    "blur_kernel_size": 21,
-    "kernel_list": ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'],
-    "kernel_prob": [0.45, 0.25, 0.12, 0.03, 0.12, 0.03],
-    "sinc_prob": 0.1,
-    "blur_sigma": [0.2, 3],
-    "betag_range": [0.5, 4],
-    "betap_range": [1, 2],
-
-    # the second degradation process
-    "second_blur_prob": 0.8,
-    "resize_prob2": [0.3, 0.4, 0.3],  # up, down, keep
-    "resize_range2": [0.3, 1.2],
-    "gaussian_noise_prob2": 0.5,
-    "noise_range2": [1, 25],
-    "poisson_scale_range2": [0.05, 2.5],
-    "gray_noise_prob2": 0.4,
-    "jpeg_range2": [30, 95],
-
-    "blur_kernel_size2": 21,
-    "kernel_list2": ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'],
-    "kernel_prob2": [0.45, 0.25, 0.12, 0.03, 0.12, 0.03],
-    "sinc_prob2": 0.1,
-    "blur_sigma2": [0.2, 1.5],
-    "betag_range2": [0.5, 4],
-    "betap_range2": [1, 2],
-
-    "final_sinc_prob": 0.8,
-
-    "gt_size": 512,
-    "keep_ratio": True,
-    "use_hflip": True,
-    "use_rot": False,
-
-    # data loader
-    "use_shuffle": True,
-    "num_worker_per_gpu": 5,
-    "batch_size_per_gpu": 12,
-    "dataset_enlarge_ratio": 1,
-    "prefetch_mode": None,
-}
-
-def set_seed(seed=42):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-@DATASET_REGISTRY.register()
-class NoiseDataset(data.Dataset):
-    """Dataset used for Denoise model:
-    synthetic Gaussian and Poisson noise dataset.
-    """
-    def __init__(self, opt, train=True, level=None):
-        super(NoiseDataset, self).__init__()
-        self.opt = opt
-
-        # kernel define
-        self.data_rt = opt['dataroot_gt']
-
-        # dataload
-        self.train = train
-        if self.train:
-            self.metas = opt['meta_train']
-        else:
-            self.metas = opt['meta_test']
-
-        self.paths = []
-        for meta in self.metas:
-            with open(os.path.join(self.data_rt, meta)) as fin:
-                self.paths += [line.strip().split(' ')[1] for line in fin]
-
-        # hyperparameter
-
-        self.device = torch.cuda.current_device()
-        self.jpeger = DiffJPEG(differentiable=False).to(self.device)  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().to(self.device)  # do usm sharpening
-        self.resize = opt['gt_size']
-        self.keep_ratio = opt['keep_ratio']
-
-        # function
-        self.crop = T.RandomCrop((self.resize, self.resize))
-        self.flip = T.RandomHorizontalFlip()
-        self.transform = T.Compose(
-            [
-                # T.ToDtype(torch.float32, scale=True),  # only support for torch 2.++
-                T.ToTensor(),
-            ]
-        )
-
-        # noise
-        self.sigma = [0.0588, 0.0784, 0.098, 0.1451, 0.1961] # 5 levels: 15, 20, 25, 37, 50
-        if level:
-            self.level = [level]
-        else:
-            self.level = [1,2,3,4,5]
-
-    def __getitem__(self, index):
-        # -------------------------------- Load gt images -------------------------------- #
-        gt_path = self.paths[index]
-        img_gt = Image.open(gt_path).convert("RGB")
-
-        # -------------------------------- Image Process --------------------------------
-        # resize
-        h, w = img_gt.height, img_gt.width
-        if self.keep_ratio:
-            ratio = self.resize / min(h, w)
-            h_new, w_new = round(h * ratio * 1.2), round(w * ratio * 1.2)
-            img_gt = img_gt.resize((w_new, h_new), resample=Image.LANCZOS)
-        else:
-            img_gt = img_gt.resize((self.resize, self.resize), resample=Image.LANCZOS)
-        # crop and 
-        img_gt = self.crop(img_gt)
-        # flip (only for train)
-        if self.train:
-            img_gt = self.flip(img_gt)
-        # transform to tensor
-        img_gt = self.transform(img_gt).to(torch.float32)
-
-        # -------------------------------- Generate Noise --------------------------------
-        # Poisson Noise
-        peak = 255
-        lam = torch.clamp(img_gt, 0, 1) * peak
-        counts = torch.poisson(lam)
-        img_poisson = torch.clamp(counts / float(peak), 0.0, 1.0)
-        
-        # Gaussian Noise
-        level = random.choice(self.level)
-        noise = torch.randn(size=img_poisson.size())
-        img_poisson_gaussian = torch.clamp(img_poisson + self.sigma[level-1] * noise, 0., 1.)
-
-        return img_poisson_gaussian, img_gt, gt_path
-
-    def __len__(self):
-        return len(self.paths)
-
-def poisson_gaussian_sampler():
-    """
-    It is now used for DF2K dataset (DIV2K + Flickr 2K)
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--level', type=int, default=None, help='train: one to many')
-    args = parser.parse_args()
-
-    if args.level:
-        level = args.level 
-    else:
-        level = [1,3,5]
-
-    # generate training dataset
-    for number in level:
-        print("="*100)
-        print(f"Generate Noise Level {number}...")
-
-        dataset = NoiseDataset(cfg, train=True, level=number)
-        data_dl = data.DataLoader( 
-            dataset,
-            batch_size = 1
-        )
-        print("Train Data:", dataset.data_rt, len(data_dl))
-        meta_info = {}
-        for sample in tqdm(data_dl):
-            lq, hq, path = sample
-            # /home/CORP/hsiang.chen/Project/Datasets/IR/SuperResolution/DIV2K/DIV2K_train_HR/0098.png
-            file_name = os.path.basename(path[0])
-            gt_folder = os.path.dirname(path[0]) # /home/CORP/hsiang.chen/Project/Datasets/IR/SuperResolution/DIV2K/DIV2K_train_HR/
-            if "DIV2K_train_HR" in gt_folder or "DIV2K_valid_HR" in gt_folder:
-                hq_folder = gt_folder.replace("HR", f"pair/Noise_L{number}/HQ")
-                lq_folder = gt_folder.replace("HR", f"pair/Noise_L{number}/LQ")
-            elif "Denoise" in gt_folder:
-                hq_folder = os.path.join(gt_folder.replace("image", f"image_pair/Noise_L{number}"), "HQ/")
-                lq_folder = os.path.join(gt_folder.replace("image", f"image_pair/Noise_L{number}"), "LQ/")
-            else:
-                hq_folder = os.path.join(gt_folder.replace("images", f"images_pair/Noise_L{number}"), "HQ/")
-                lq_folder = os.path.join(gt_folder.replace("images", f"images_pair/Noise_L{number}"), "LQ/")
-
-            os.makedirs(hq_folder, exist_ok=True)
-            os.makedirs(lq_folder, exist_ok=True)
-
-            hq_path = os.path.join(hq_folder, file_name)
-            lq_path = os.path.join(lq_folder, file_name)
-
-            save_image(hq[0], hq_path)
-            save_image(lq[0], lq_path)
-
-            dset = os.path.relpath(gt_folder, dataset.data_rt).split("/")[0]
-            if dset not in meta_info:
-                meta_info[dset] = [(lq_path, hq_path)]
-            else:
-                meta_info[dset].append((lq_path, hq_path))
-
-        for dset, dlist in meta_info.items():
-            with open(os.path.join(dataset.data_rt,'{}/metas/{}_train_Noise_L{}.list'.format(dset, dset, number)), 'w') as fp:
-                for item in dlist:
-                    fp.write('{} {} {}\n'.format(item[0], item[1], None))
-            print(os.path.join(dataset.data_rt,'{}/metas/{}_train_Noise_L{}.list'.format(dset, dset, number)), len(dlist))
-
-    # generate testing dataset
-    # dataset = NoiseDataset(cfg, train=False)
-    # data_dl = data.DataLoader( 
-    #     dataset,
-    #     batch_size = 1
-    # )
-    # print("Test Data:", dataset.data_rt, len(data_dl))
-    # print("="*100)
-    # print(f"Generate Testing Noise...")
-
-    # meta_info = {}
-    # for sample in tqdm(data_dl):
-    #     lq, hq, path = sample
-    #     # /home/CORP/hsiang.chen/Project/Datasets/IR/SuperResolution/DIV2K/DIV2K_train_HR/0098.png
-    #     file_name = os.path.basename(path[0])
-    #     gt_folder = os.path.dirname(path[0]) # /home/CORP/hsiang.chen/Project/Datasets/IR/SuperResolution/DIV2K/DIV2K_train_HR/
-    #     if "DIV2K_train_HR" in gt_folder or "DIV2K_valid_HR" in gt_folder:
-    #         hq_folder = gt_folder.replace("HR", f"pair/Noise/HQ")
-    #         lq_folder = gt_folder.replace("HR", f"pair/Noise/LQ")
-    #     else:
-    #         hq_folder = os.path.join(gt_folder.replace("images", f"images_pair/Noise"), "HQ/")
-    #         lq_folder = os.path.join(gt_folder.replace("images", f"images_pair/Noise"), "LQ/")
-
-    #     os.makedirs(hq_folder, exist_ok=True)
-    #     os.makedirs(lq_folder, exist_ok=True)
-
-    #     hq_path = os.path.join(hq_folder, file_name)
-    #     lq_path = os.path.join(lq_folder, file_name)
-
-    #     save_image(hq[0], hq_path)
-    #     save_image(lq[0], lq_path)
-
-    #     dset = os.path.relpath(gt_folder, dataset.data_rt).split("/")[0]
-    #     if dset not in meta_info:
-    #         meta_info[dset] = [(lq_path, hq_path)]
-    #     else:
-    #         meta_info[dset].append((lq_path, hq_path))
-
-    # for dset, dlist in meta_info.items():
-    #     with open(os.path.join(dataset.data_rt,'{}/metas/{}_valid_Noise.list'.format(dset, dset)), 'w') as fp:
-    #         for item in dlist:
-    #             fp.write('{} {} {}\n'.format(item[0], item[1], None))
-    #     print(os.path.join(dataset.data_rt,'{}/metas/{}_valid_Noise.list'.format(dset, dset)), len(dlist))
-
-if __name__ == '__main__':
-    set_seed(1229)
-    # poisson_gaussian for data generation
-    poisson_gaussian_sampler()
-
-# python 3_generate_noise.py