Add dataset loading compatible with finetuning folder structure
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import os
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import random
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import numpy as np
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import torch
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import torch.utils.data as data
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import utils.utils_image as util
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from utils.DTutils import is_natural_patch
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import itertools
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class DatasetFFDNet(data.Dataset):
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"""
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# -----------------------------------------
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# Get L/H/M for denosing on AWGN with a range of sigma.
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# Only dataroot_H is needed.
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# -----------------------------------------
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# e.g., FFDNet, H = f(L, sigma), sigma is noise level
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# -----------------------------------------
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"""
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def __init__(self, opt):
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super(DatasetFFDNet, self).__init__()
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self.opt = opt
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self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
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self.n_channels_datasetload = opt['n_channels_datasetload'] if opt['n_channels_datasetload'] else 3
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self.patch_size = self.opt['H_size'] if opt['H_size'] else 64
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self.sigma = opt['sigma'] if opt['sigma'] else [0, 75]
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self.sigma_min, self.sigma_max = self.sigma[0], self.sigma[1]
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self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else 0
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self.use_all_patches = opt['use_all_patches'] if opt['use_all_patches'] else False
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self.num_patches_per_image = opt['num_patches_per_image'] if opt['num_patches_per_image'] else 100
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self.skip_natural_patches = opt['skip_natural_patches'] if opt['skip_natural_patches'] else False
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# -------------------------------------
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# Dataset path contains all H images and subfolders for every single one with one or more
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# -------------------------------------
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"""
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Dataset path includes all H images and L-subfolders.
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Every H image has one L-subfolder assosiated, which
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contains one or more L representations of the H image.
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"""
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self.paths_H = [f for f in os.listdir(opt['dataroot_H']) if os.path.isfile(f)]
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#------------------------------------------------------------------------------------------------------
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# For the above step one can use util.get_image_paths(), but it goes recursevely thought the tree dirs
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#------------------------------------------------------------------------------------------------------
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paths_H_aux = []
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self.paths_L = []
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# Iterate over all image paths
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for H_file in self.paths_H:
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# filename = os.path.basename(H_file)
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filename = H_file.split(".png") # TODO: the correct way to do it is with os.path.basename()
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L_folder = os.path.join(opt['dataroot_H'],filename)
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# For image at subfolder, append to L paths and repeat current H path
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for L_file in os.listdir(L_folder):
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L_filepath = os.path.join(L_folder,L_file)
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paths_H_aux.append(H_file)
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self.paths_L.append(L_filepath)
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# Update H paths
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self.paths_H = paths_H_aux
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# Repeat every image in path list to get more than one patch per image
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if self.opt['phase'] == 'train':
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listOfLists = [list(itertools.repeat(path, self.num_patches_per_image)) for path in self.paths_H]
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self.paths_H = list(itertools.chain.from_iterable(listOfLists))
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listOfLists = [list(itertools.repeat(path, self.num_patches_per_image)) for path in self.paths_L]
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self.paths_L = list(itertools.chain.from_iterable(listOfLists))
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def __getitem__(self, index):
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# -------------------------------------
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# get H and L image
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# -------------------------------------
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H_path = self.paths_H[index]
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L_path = self.paths_L[index]
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H_file, L_file = H_path.split('/')[-1], L_path.split('/')[-1]
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H_name, L_name = H_file.split('.')[0], L_file.split('.')[0]
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assert H_name==L_name, f'Both high and low quality images MUST have same name.\nGot {H_name} and {L_name} respectively.'
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img_H = util.imread_uint(H_path, self.n_channels_datasetload)
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img_L = util.imread_uint(L_path, self.n_channels_datasetload)[:,:,:2]
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# Temp solution for blanking images
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L_v, L_h = img_L.shape[:2]
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if L_v==1000 and L_h==1800:
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img_L = img_L[(1000-900)//2:-(1000-900)//2,(1800-1600)//2:-(1800-1600)//2,:]
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# Get module of complex image, stretch and to uint8
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# img_L = img_L.astype('float')
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# img_L = np.abs(img_L[:,:,0]+1j*img_L[:,:,1])
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# img_L = 255*(img_L - img_L.min())/(img_L.max() - img_L.min())
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# img_L = img_L.astype('uint8')
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if self.opt['phase'] == 'train':
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"""
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# --------------------------------
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# get L/H/M patch pairs
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# --------------------------------
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"""
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H, W = img_H.shape[:2]
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if self.use_all_patches or (img_H.shape[0] <= self.patch_size) or (img_H.shape[1] <= self.patch_size):
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# ---------------------------------
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# Start or continue image patching
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# ---------------------------------
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img_patch_index = index % self.num_patches_per_image # Resets to 0 every time index overflows num_patches
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# Upper-left corner of patch
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h_index = self.patch_size * ( (img_patch_index * self.patch_size) // W)
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w_index = self.patch_size * ( ( (img_patch_index * self.patch_size) % W ) // self.patch_size)
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# Dont exceed the image limit
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h_index = min(h_index, H - self.patch_size)
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w_index = min(w_index, W - self.patch_size)
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### Keep text patches only (non-natural images)
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if self.skip_natural_patches:
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# Check if selected patch is natural, based on RGB entropy
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is_natural = is_natural_patch(img_H[h_index:h_index + self.patch_size, w_index:w_index + self.patch_size, :])
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# If natural, select random patch and keep trying until non-natural or reaching max attempts
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attempt = 0
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max_attempts = 10
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while is_natural and (attempt < max_attempts):
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h_index = random.randint(0, max(0, H - self.patch_size))
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w_index = random.randint(0, max(0, W - self.patch_size))
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is_natural = is_natural_patch(img_H[h_index:h_index + self.patch_size, w_index:w_index + self.patch_size, :])
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attempt += 1
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else:
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# ---------------------------------
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# randomly crop the patch
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# ---------------------------------
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h_index = random.randint(0, max(0, H - self.patch_size))
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w_index = random.randint(0, max(0, W - self.patch_size))
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# Ground-truth as channels mean
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patch_H = np.mean(img_H[h_index:h_index + self.patch_size, w_index:w_index + self.patch_size, :],axis=2)
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# Get the patch from the simulation
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patch_L = img_L[h_index:h_index + self.patch_size, w_index:w_index + self.patch_size,:]
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# ---------------------------------
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# HWC to CHW, numpy(uint) to tensor
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# ---------------------------------
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img_H = util.uint2tensor3(patch_H)
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img_L = util.uint2tensor3(patch_L)
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# ---------------------------------
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# get noise level
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# ---------------------------------
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noise_level = torch.FloatTensor([int(np.random.uniform(self.sigma_min, self.sigma_max))])/255.0
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# noise_level = torch.FloatTensor([np.random.randint(self.sigma_min, self.sigma_max)])/255.0
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if (self.sigma_max != 0):
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# ---------------------------------
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# add noise
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# ---------------------------------
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noise = torch.randn(img_L.size()).mul_(noise_level).float()
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img_L.add_(noise)
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else:
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"""
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# --------------------------------
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# get L/H/sigma image pairs
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# --------------------------------
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"""
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# Ground-truth as mean value of RGB channels
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img_H = np.mean(img_H,axis=2)
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img_H = img_H[:,:,np.newaxis]
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# ---------------------------------
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# HWC to CHW, numpy(uint) to tensor
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# ---------------------------------
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img_H = util.uint2tensor3(img_H)
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img_L = util.uint2tensor3(img_L)
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# img_H = util.uint2single(img_H)
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# img_L = util.uint2single(img_L)
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# ---------------------------------
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# get noise level
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# ---------------------------------
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noise_level = torch.FloatTensor([int(self.sigma_test)])/255.0
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if self.sigma_test != 0:
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# noise_level = torch.FloatTensor([np.random.randint(self.sigma_min, self.sigma_max)])/255.0
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# ---------------------------------
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# add noise
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# ---------------------------------
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noise = torch.randn(img_L.size()).mul_(noise_level).float()
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img_L.add_(noise)
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noise_level = noise_level.unsqueeze(1).unsqueeze(1)
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return {'L': img_L, 'H': img_H, 'C': noise_level, 'L_path': L_path, 'H_path': H_path}
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def __len__(self):
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return len(self.paths_H)
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