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| # -------------------------------------------------------- | |
| # InstructDiffusion | |
| # Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix) | |
| # Modified by Chen Li (edward82@stu.xjtu.edu.cn) | |
| # -------------------------------------------------------- | |
| import os | |
| import numpy as np | |
| from torch.utils.data import Dataset | |
| import torch | |
| from PIL import Image | |
| import torchvision.transforms.functional as TF | |
| from pdb import set_trace as stx | |
| import random | |
| import cv2 | |
| from PIL import Image | |
| import torchvision | |
| def is_image_file(filename): | |
| return any(filename.endswith(extension) for extension in ['jpeg', 'JPEG', 'jpg', 'png', 'JPG', 'PNG', 'gif']) | |
| class SIDD(Dataset): | |
| def __init__(self, path, split="train", size=256, interpolation="pil_lanczos", | |
| flip_prob=0.5, sample_weight=1.0, instruct=False): | |
| super(SIDD, self).__init__() | |
| inp_files = sorted(os.listdir(os.path.join(path, split, 'input'))) | |
| tar_files = sorted(os.listdir(os.path.join(path, split, 'gt'))) | |
| self.inp_filenames = [os.path.join(path, split, 'input', x) for x in inp_files if is_image_file(x)] | |
| self.tar_filenames = [os.path.join(path, split, 'gt', x) for x in tar_files if is_image_file(x)] | |
| self.size = size | |
| self.flip_prob = flip_prob | |
| self.sample_weight = sample_weight | |
| self.instruct = instruct | |
| self.sizex = len(self.tar_filenames) # get the size of target | |
| self.interpolation = { | |
| "cv_nearest": cv2.INTER_NEAREST, | |
| "cv_bilinear": cv2.INTER_LINEAR, | |
| "cv_bicubic": cv2.INTER_CUBIC, | |
| "cv_area": cv2.INTER_AREA, | |
| "cv_lanczos": cv2.INTER_LANCZOS4, | |
| "pil_nearest": Image.NEAREST, | |
| "pil_bilinear": Image.BILINEAR, | |
| "pil_bicubic": Image.BICUBIC, | |
| "pil_box": Image.BOX, | |
| "pil_hamming": Image.HAMMING, | |
| "pil_lanczos": Image.LANCZOS, | |
| }[interpolation] | |
| prompt_path='dataset/prompt/prompt_denoise.txt' | |
| self.prompt_list=[] | |
| with open(prompt_path) as f: | |
| line=f.readline() | |
| while line: | |
| line=line.strip('\n') | |
| self.prompt_list.append(line) | |
| line=f.readline() | |
| print(f"SIDD has {len(self)} samples!!") | |
| def __len__(self): | |
| return int(self.sizex * self.sample_weight) | |
| def __getitem__(self, index): | |
| if self.sample_weight >= 1: | |
| index_ = index % self.sizex | |
| else: | |
| index_ = int(index / self.sample_weight) + random.randint(0, int(1 / self.sample_weight) - 1) | |
| inp_path = self.inp_filenames[index_] | |
| tar_path = self.tar_filenames[index_] | |
| inp_img = Image.open(inp_path) | |
| tar_img = Image.open(tar_path) | |
| width, height = inp_img.size | |
| tar_width, tar_height = tar_img.size | |
| assert tar_width == width and tar_height == height, "Input and target image mismatch" | |
| inp_img = np.array(inp_img).astype(np.float32).transpose(2, 0, 1) | |
| inp_img_tensor = torch.tensor((inp_img / 127.5 - 1.0).astype(np.float32)) | |
| tar_img = np.array(tar_img).astype(np.float32).transpose(2, 0, 1) | |
| tar_img_tensor = torch.tensor((tar_img / 127.5 - 1.0).astype(np.float32)) | |
| crop = torchvision.transforms.RandomCrop(self.size) | |
| flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob)) | |
| image_0, image_1 = flip(crop(torch.cat((inp_img_tensor, tar_img_tensor)))).chunk(2) | |
| prompt = random.choice(self.prompt_list) | |
| if self.instruct: | |
| prompt = "Image Denoising: " + prompt | |
| return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt)) |