datapipe/ffhq_degradation_dataset.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-

'''
This code is borrowed from:
https://github.com/TencentARC/GFPGAN/blob/master/gfpgan/data/ffhq_degradation_dataset.py
'''

import cv2
import math
import numpy as np
import os.path as osp
import torch
import torch.utils.data as data
from basicsr.data import degradations as degradations
from basicsr.data.data_util import paths_from_folder
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torchvision.transforms.functional import (
        adjust_brightness,
        adjust_contrast,
        adjust_hue,
        adjust_saturation,
        normalize
        )

from utils import util_common

@DATASET_REGISTRY.register()
class FFHQDegradationDataset(data.Dataset):
    """FFHQ dataset for GFPGAN.
    It reads high resolution images, and then generate low-quality (LQ) images on-the-fly.
    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            io_backend (dict): IO backend type and other kwarg.
            mean (list | tuple): Image mean.
            std (list | tuple): Image std.
            use_hflip (bool): Whether to horizontally flip.
            Please see more options in the codes.
    """

    def __init__(self, opt):
        super(FFHQDegradationDataset, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.need_gt_path = opt['need_gt_path']

        self.mean = opt['mean']
        self.std = opt['std']
        self.out_size = opt['out_size']

        self.crop_components = opt.get('crop_components', False)  # facial components
        self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1)  # whether enlarge eye regions

        if self.crop_components:
            # load component list from a pre-process pth files
            self.components_list = torch.load(opt.get('component_path'))

        # file client
        self.paths = util_common.readline_txt(opt['files_txt'])

        # degradation configurations
        self.blur_kernel_size = opt['blur_kernel_size']
        self.kernel_list = opt['kernel_list']
        self.kernel_prob = opt['kernel_prob']
        self.blur_sigma = opt['blur_sigma']
        self.downsample_range = opt['downsample_range']
        self.noise_range = opt['noise_range']
        self.jpeg_range = opt['jpeg_range']

        # color jitter
        self.color_jitter_prob = opt.get('color_jitter_prob')
        self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob')
        self.color_jitter_shift = opt.get('color_jitter_shift', 20)
        # to gray
        self.gray_prob = opt.get('gray_prob')

        self.color_jitter_shift /= 255.

    @staticmethod
    def color_jitter(img, shift):
        """jitter color: randomly jitter the RGB values, in numpy formats"""
        jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32)
        img = img + jitter_val
        img = np.clip(img, 0, 1)
        return img

    @staticmethod
    def color_jitter_pt(img, brightness, contrast, saturation, hue):
        """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats"""
        fn_idx = torch.randperm(4)
        for fn_id in fn_idx:
            if fn_id == 0 and brightness is not None:
                brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()
                img = adjust_brightness(img, brightness_factor)

            if fn_id == 1 and contrast is not None:
                contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()
                img = adjust_contrast(img, contrast_factor)

            if fn_id == 2 and saturation is not None:
                saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()
                img = adjust_saturation(img, saturation_factor)

            if fn_id == 3 and hue is not None:
                hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()
                img = adjust_hue(img, hue_factor)
        return img

    def get_component_coordinates(self, index, status):
        """Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file"""
        components_bbox = self.components_list[f'{index:08d}']
        if status[0]:  # hflip
            # exchange right and left eye
            tmp = components_bbox['left_eye']
            components_bbox['left_eye'] = components_bbox['right_eye']
            components_bbox['right_eye'] = tmp
            # modify the width coordinate
            components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0]
            components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0]
            components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0]

        # get coordinates
        locations = []
        for part in ['left_eye', 'right_eye', 'mouth']:
            mean = components_bbox[part][0:2]
            half_len = components_bbox[part][2]
            if 'eye' in part:
                half_len *= self.eye_enlarge_ratio
            loc = np.hstack((mean - half_len + 1, mean + half_len))
            loc = torch.from_numpy(loc).float()
            locations.append(loc)
        return locations

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # load gt image
        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
        gt_path = self.paths[index]
        img_bytes = self.file_client.get(gt_path)
        img_gt = imfrombytes(img_bytes, float32=True)   # [0, 1]

        # random horizontal flip
        img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True)
        h, w, _ = img_gt.shape

        # get facial component coordinates
        if self.crop_components:
            locations = self.get_component_coordinates(index, status)
            loc_left_eye, loc_right_eye, loc_mouth = locations

        # ------------------------ generate lq image ------------------------ #
        # blur
        kernel = degradations.random_mixed_kernels(
            self.kernel_list,
            self.kernel_prob,
            self.blur_kernel_size,
            self.blur_sigma,
            self.blur_sigma, [-math.pi, math.pi],
            noise_range=None)
        img_lq = cv2.filter2D(img_gt, -1, kernel)
        # downsample
        scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1])
        img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR)
        # noise
        if self.noise_range is not None:
            img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range)
        # jpeg compression
        if self.jpeg_range is not None:
            img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range)

        # resize to original size
        img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR)

        # random color jitter (only for lq)
        if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob):
            img_lq = self.color_jitter(img_lq, self.color_jitter_shift)
        # random to gray (only for lq)
        if self.gray_prob and np.random.uniform() < self.gray_prob:
            img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY)
            img_lq = np.tile(img_lq[:, :, None], [1, 1, 3])
            if self.opt.get('gt_gray'):  # whether convert GT to gray images
                img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY)
                img_gt = np.tile(img_gt[:, :, None], [1, 1, 3])  # repeat the color channels

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)

        # random color jitter (pytorch version) (only for lq)
        if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob):
            brightness = self.opt.get('brightness', (0.5, 1.5))
            contrast = self.opt.get('contrast', (0.5, 1.5))
            saturation = self.opt.get('saturation', (0, 1.5))
            hue = self.opt.get('hue', (-0.1, 0.1))
            img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue)

        # round and clip
        img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255.

        # normalize
        normalize(img_gt, self.mean, self.std, inplace=True)
        normalize(img_lq, self.mean, self.std, inplace=True)

        out_dict = {'lq': img_lq, 'gt': img_gt }
        if self.need_gt_path:
            out_dict['gt_path'] = gt_path
        if self.crop_components:
            out_dict['loc_left_eye'] = loc_left_eye
            out_dict['loc_right_eye'] = loc_right_eye
            out_dict['loc_mouth'] = loc_mouth

        return out_dict

    def __len__(self):
        return len(self.paths)