utils/dataloader.py

from matplotlib import artist
import cv2
import numpy as np
import torch
import torch.utils.data as data
import pandas as pd
import os
from PIL import Image

from .utils import cvtColor, preprocess_input
from .utils_aug import CenterCrop, ImageNetPolicy, RandomResizedCrop, Resize


class DataGenerator(data.Dataset):
    def __init__(self, annotation_lines, input_shape, random=True, autoaugment_flag=True):
        self.artwork_data       = annotation_lines
        self.input_shape        = input_shape
        self.random             = random
        #------------------------------#
        #   是否使用数据增强
        #------------------------------#
        self.autoaugment_flag   = autoaugment_flag
        if self.autoaugment_flag:
            self.resize_crop = RandomResizedCrop(input_shape)
            self.policy      = ImageNetPolicy()
            
            self.resize      = Resize(input_shape[0] if input_shape[0] == input_shape[1] else input_shape)
            self.center_crop = CenterCrop(input_shape)
        self.all_features    = self.get_all_features(self.artwork_data)

    def __len__(self):
        return self.artwork_data.shape[0]

    def __getitem__(self, index):
        # 从数据集中获取图像地址
        annotation_path = self.artwork_data['Duration (s)'][index]
        annotation_path = os.path.join('datasets/archive/Dataset', annotation_path)
        image = Image.open(annotation_path)
        #------------------------------#
        #   读取图像并转换成RGB图像
        #------------------------------#
        image   = cvtColor(image)
        if self.autoaugment_flag:
            image = self.AutoAugment(image, random=self.random)
        else:
            image = self.get_random_data(image, self.input_shape, random=self.random)
        # 去除价格特征
        other_features = self.all_features.drop(labels='Prices', axis=1)
        # 取其它特征作为输入
        other_features = other_features.iloc[index].values
        other_features = np.resize(np.array(other_features, dtype=np.float32), self.input_shape)
        other_features = np.expand_dims(other_features, 0)
        image          = np.transpose(preprocess_input(np.array(image, dtype=np.float32)), [2, 0, 1])
        all_features   = np.concatenate((image, other_features), axis=0)
        # 从数据集中获取价格标签
        y = np.expand_dims(np.array(self.all_features['Prices'][index]), axis=-1)
        return all_features, y

    def rand(self, a=0, b=1):
        return np.random.rand()*(b-a) + a

    '''
    @description: 
    @param {*} self
    @param {*} all_features
    @return {*} 数据集预处理
    '''        
    def get_all_features(self, all_features):
        all_features = all_features.iloc[:, [2,4,5,7,8,9,10,12,16]]
        all_features = all_features.copy()
        numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index
        all_features[numeric_features] = all_features[numeric_features].apply(
            lambda x: (x - x.mean()) / (x.std()))
        # 标准化后，每个特征的均值变为0，所以可以直接用0来替换缺失值
        all_features[numeric_features] = all_features[numeric_features].fillna(0)
        # dummy_na=True将缺失值也当作合法的特征值并为其创建指示特征
        all_features = pd.get_dummies(all_features, dummy_na=True)
        return all_features

    def get_random_data(self, image, input_shape, jitter=.3, hue=.1, sat=1.5, val=1.5, random=True):
        #------------------------------#
        #   获得图像的高宽与目标高宽
        #------------------------------#
        iw, ih  = image.size
        h, w    = input_shape

        if not random:
            scale = min(w/iw, h/ih)
            nw = int(iw*scale)
            nh = int(ih*scale)
            dx = (w-nw)//2
            dy = (h-nh)//2

            #---------------------------------#
            #   将图像多余的部分加上灰条
            #---------------------------------#
            image       = image.resize((nw,nh), Image.BICUBIC)
            new_image   = Image.new('RGB', (w,h), (128,128,128))
            new_image.paste(image, (dx, dy))
            image_data  = np.array(new_image, np.float32)

            return image_data

        #------------------------------------------#
        #   对图像进行缩放并且进行长和宽的扭曲
        #------------------------------------------#
        new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter)
        scale = self.rand(.75, 1.5)
        if new_ar < 1:
            nh = int(scale*h)
            nw = int(nh*new_ar)
        else:
            nw = int(scale*w)
            nh = int(nw/new_ar)
        image = image.resize((nw,nh), Image.BICUBIC)

        #------------------------------------------#
        #   将图像多余的部分加上灰条
        #------------------------------------------#
        dx = int(self.rand(0, w-nw))
        dy = int(self.rand(0, h-nh))
        new_image = Image.new('RGB', (w,h), (128,128,128))
        new_image.paste(image, (dx, dy))
        image = new_image

        #------------------------------------------#
        #   翻转图像
        #------------------------------------------#
        flip = self.rand()<.5
        if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
        
        rotate = self.rand()<.5
        if rotate: 
            angle = np.random.randint(-15,15)
            a,b = w/2,h/2
            M = cv2.getRotationMatrix2D((a,b),angle,1)
            image = cv2.warpAffine(np.array(image), M, (w,h), borderValue=[128, 128, 128]) 

        image_data      = np.array(image, np.uint8)
        #---------------------------------#
        #   对图像进行色域变换
        #   计算色域变换的参数
        #---------------------------------#
        r               = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1
        #---------------------------------#
        #   将图像转到HSV上
        #---------------------------------#
        hue, sat, val   = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV))
        dtype           = image_data.dtype
        #---------------------------------#
        #   应用变换
        #---------------------------------#
        x       = np.arange(0, 256, dtype=r.dtype)
        lut_hue = ((x * r[0]) % 180).astype(dtype)
        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)

        image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
        image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB)
        return image_data
    
    def AutoAugment(self, image, random=True):
        if not random:
            image = self.resize(image)
            image = self.center_crop(image)
            return image

        #------------------------------------------#
        #   resize并且随即裁剪
        #------------------------------------------#
        image = self.resize_crop(image)
        
        #------------------------------------------#
        #   翻转图像
        #------------------------------------------#
        flip = self.rand()<.5
        if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
        
        #------------------------------------------#
        #   随机增强
        #------------------------------------------#
        image = self.policy(image)
        return image
            
def detection_collate(batch):
    images = []
    targets = []
    for image, y in batch:
        images.append(image)
        targets.append(y)
    images  = torch.from_numpy(np.array(images)).type(torch.FloatTensor)
    targets = torch.from_numpy(np.array(targets)).type(torch.FloatTensor)
    return images, targets