face_dataset.py

import os
import json
import numpy as np
from PIL import Image
import cv2
import paddle
import paddle.vision.datasets as datasets
from paddle.vision import transforms
from paddle.vision.transforms import functional as F
from paddle.io import Dataset
from .common_dataset import create_operators
from ppcls.data.preprocess import transform as transform_func

# code is based on AdaFace: https://github.com/mk-minchul/AdaFace


def _get_image_size(img):
    if F._is_pil_image(img):
        return img.size
    elif F._is_numpy_image(img):
        return img.shape[:2][::-1]
    elif F._is_tensor_image(img):
        return img.shape[1:][::-1]  # chw
    else:
        raise TypeError("Unexpected type {}".format(type(img)))


class AdaFaceDataset(Dataset):
    def __init__(
            self,
            root_dir,
            label_path,
            transform=None,
            low_res_augmentation_prob=0.0,
            crop_augmentation_prob=0.0,
            photometric_augmentation_prob=0.0, ):
        self.root_dir = root_dir
        self.low_res_augmentation_prob = low_res_augmentation_prob
        self.crop_augmentation_prob = crop_augmentation_prob
        self.photometric_augmentation_prob = photometric_augmentation_prob
        self.random_resized_crop = transforms.RandomResizedCrop(
            size=(112, 112),
            scale=(0.2, 1.0),
            ratio=(0.75, 1.3333333333333333))
        self.photometric = transforms.ColorJitter(
            brightness=0.5, contrast=0.5, saturation=0.5, hue=0)
        self.transform = create_operators(transform)

        self.tot_rot_try = 0
        self.rot_success = 0
        with open(label_path) as fd:
            lines = fd.readlines()
        self.samples = []
        for l in lines:
            l = l.strip().split()
            self.samples.append([os.path.join(root_dir, l[0]), int(l[1])])

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

    def __getitem__(self, index):
        """
        Args:
            index (int): Index

        Returns:
            tuple: (sample, target) where target is class_index of the target class.
        """
        [path, target] = self.samples[index]
        with open(path, 'rb') as f:
            img = Image.open(f)
            sample = img.convert('RGB')

        # if 'WebFace' in self.root:
        #     # swap rgb to bgr since image is in rgb for webface
        #     sample = Image.fromarray(np.asarray(sample)[:, :, ::-1])

        sample, _ = self.augment(sample)
        if self.transform is not None:
            sample = transform_func(sample, self.transform)

        return sample, target

    def augment(self, sample):

        # crop with zero padding augmentation
        if np.random.random() < self.crop_augmentation_prob:
            # RandomResizedCrop augmentation
            new = np.zeros_like(np.array(sample))
            #  orig_W, orig_H = F._get_image_size(sample)
            orig_W, orig_H = _get_image_size(sample)
            i, j, h, w = self.random_resized_crop._get_param(sample)
            cropped = F.crop(sample, i, j, h, w)
            new[i:i + h, j:j + w, :] = np.array(cropped)
            sample = Image.fromarray(new.astype(np.uint8))
            crop_ratio = min(h, w) / max(orig_H, orig_W)
        else:
            crop_ratio = 1.0

        # low resolution augmentation
        if np.random.random() < self.low_res_augmentation_prob:
            # low res augmentation
            img_np, resize_ratio = low_res_augmentation(np.array(sample))
            sample = Image.fromarray(img_np.astype(np.uint8))
        else:
            resize_ratio = 1

        # photometric augmentation
        if np.random.random() < self.photometric_augmentation_prob:
            sample = self.photometric(sample)
        information_score = resize_ratio * crop_ratio
        return sample, information_score


def low_res_augmentation(img):
    # resize the image to a small size and enlarge it back
    img_shape = img.shape
    side_ratio = np.random.uniform(0.2, 1.0)
    small_side = int(side_ratio * img_shape[0])
    interpolation = np.random.choice([
        cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_AREA, cv2.INTER_CUBIC,
        cv2.INTER_LANCZOS4
    ])
    small_img = cv2.resize(
        img, (small_side, small_side), interpolation=interpolation)
    interpolation = np.random.choice([
        cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_AREA, cv2.INTER_CUBIC,
        cv2.INTER_LANCZOS4
    ])
    aug_img = cv2.resize(
        small_img, (img_shape[1], img_shape[0]), interpolation=interpolation)

    return aug_img, side_ratio


class FiveValidationDataset(Dataset):
    def __init__(self, val_data_path, concat_mem_file_name):
        '''
        concatenates all validation datasets from emore
        val_data_dict = {
        'agedb_30': (agedb_30, agedb_30_issame),
        "cfp_fp": (cfp_fp, cfp_fp_issame),
        "lfw": (lfw, lfw_issame),
        "cplfw": (cplfw, cplfw_issame),
        "calfw": (calfw, calfw_issame),
        }
        agedb_30: 0
        cfp_fp: 1
        lfw: 2
        cplfw: 3
        calfw: 4
        '''
        val_data = get_val_data(val_data_path)
        age_30, cfp_fp, lfw, age_30_issame, cfp_fp_issame, lfw_issame, cplfw, cplfw_issame, calfw, calfw_issame = val_data
        val_data_dict = {
            'agedb_30': (age_30, age_30_issame),
            "cfp_fp": (cfp_fp, cfp_fp_issame),
            "lfw": (lfw, lfw_issame),
            "cplfw": (cplfw, cplfw_issame),
            "calfw": (calfw, calfw_issame),
        }
        self.dataname_to_idx = {
            "agedb_30": 0,
            "cfp_fp": 1,
            "lfw": 2,
            "cplfw": 3,
            "calfw": 4
        }

        self.val_data_dict = val_data_dict
        # concat all dataset
        all_imgs = []
        all_issame = []
        all_dataname = []
        key_orders = []
        for key, (imgs, issame) in val_data_dict.items():
            all_imgs.append(imgs)
            dup_issame = [
            ]  # hacky way to make the issame length same as imgs. [1, 1, 0, 0, ...]
            for same in issame:
                dup_issame.append(same)
                dup_issame.append(same)
            all_issame.append(dup_issame)
            all_dataname.append([self.dataname_to_idx[key]] * len(imgs))
            key_orders.append(key)
        assert key_orders == ['agedb_30', 'cfp_fp', 'lfw', 'cplfw', 'calfw']

        if isinstance(all_imgs[0], np.memmap):
            self.all_imgs = read_memmap(concat_mem_file_name)
        else:
            self.all_imgs = np.concatenate(all_imgs)

        self.all_issame = np.concatenate(all_issame)
        self.all_dataname = np.concatenate(all_dataname)

    def __getitem__(self, index):
        x_np = self.all_imgs[index].copy()
        x = paddle.to_tensor(x_np)
        y = self.all_issame[index]
        dataname = self.all_dataname[index]
        return x, y, dataname, index

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


def read_memmap(mem_file_name):
    # r+ mode: Open existing file for reading and writing
    with open(mem_file_name + '.conf', 'r') as file:
        memmap_configs = json.load(file)
        return np.memmap(mem_file_name, mode='r+', \
                        shape=tuple(memmap_configs['shape']), \
                        dtype=memmap_configs['dtype'])


def get_val_pair(path, name, use_memfile=True):
    # installing bcolz should set proxy to access internet
    import bcolz
    if use_memfile:
        mem_file_dir = os.path.join(path, name, 'memfile')
        mem_file_name = os.path.join(mem_file_dir, 'mem_file.dat')
        if os.path.isdir(mem_file_dir):
            print('laoding validation data memfile')
            np_array = read_memmap(mem_file_name)
        else:
            os.makedirs(mem_file_dir)
            carray = bcolz.carray(rootdir=os.path.join(path, name), mode='r')
            np_array = np.array(carray)
            #  mem_array = make_memmap(mem_file_name, np_array)
            #  del np_array, mem_array
            del np_array
            np_array = read_memmap(mem_file_name)
    else:
        np_array = bcolz.carray(rootdir=os.path.join(path, name), mode='r')

    issame = np.load(os.path.join(path, '{}_list.npy'.format(name)))
    return np_array, issame


def get_val_data(data_path):
    agedb_30, agedb_30_issame = get_val_pair(data_path, 'agedb_30')
    cfp_fp, cfp_fp_issame = get_val_pair(data_path, 'cfp_fp')
    lfw, lfw_issame = get_val_pair(data_path, 'lfw')
    cplfw, cplfw_issame = get_val_pair(data_path, 'cplfw')
    calfw, calfw_issame = get_val_pair(data_path, 'calfw')
    return agedb_30, cfp_fp, lfw, agedb_30_issame, cfp_fp_issame, lfw_issame, cplfw, cplfw_issame, calfw, calfw_issame