数据处理——图像处理

数据处理——图像处理

引入库

import os
import cv2
import random
import torch
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
from imgaug import augmenters as iaa
from utils.process_labels import encode_labels, decode_labels, decode_color_labels

imgaug是一个图像数据扩增工具

 剪裁图像

def crop_resize_data(image, label=None, image_size=(1024, 384), offset=690):
    roi_image = image[offset:, :]
    if label is not None:
        roi_label = label[offset:, :]
        train_image = cv2.resize(roi_image, image_size, interpolation=cv2.INTER_LINEAR) #image做的是线性插值
        train_label = cv2.resize(roi_label, image_size, interpolation=cv2.INTER_NEAREST) #label做的是最邻近插值
        return train_image, train_label
    else:
        train_image = cv2.resize(roi_image, image_size, interpolation=cv2.INTER_LINEAR)
        return train_image

图像可能会有部分是没用的，比如说，我们想实现车道线分割任务，但是，图像中天空占比很大，这时候需要将天空剪裁掉来节省我们的显存资源。 

 图像扩增

Guassian Noise, Sharpen Augmentation

class ImageAug(object):
    image, mask = sample
    
    if np.random.uniform(0,1) > 0.5:
        seq = iaa.Sequential([iaa.OneOf([
            iaa.AdditiveGaussianNoise(scale=(0, 0.2 * 255)), # 加高斯白噪声
            iaa.Sharpen(alpha=(alpha=(0.1,0.3), lightness=(0.7,1.3)), # 锐化
            iaa.GaussianBlur(sigma=(o,1.0))])]) # 高斯模糊

        image = seq.augment_image(image)
    return image, mask

Deformation Augmentation

class DeformAug(object):
    def __call__(self, sample):
        image, mask = sample
        seq = iaa.Sequential([iaa.CropAndPad(percent=(-0.05,0.1))])
        seg_to = seq.to_deterministic()
        image = seg_to.augment_image(image)
        mask = seg_to.augment_image(mask)
        return image, mask

Scale Augmentation

class ScaleAug(object):
    def __cal__(self, sample):
        image, mask = sample
        scale = random.uniform(0.7,1.5) # 按照均匀分布在0.7~1.5之间取值
        h,w,_ = image.shape

        aug_image = image.copy() # 复制一下，不要修改原数据
        aug_mask = mask.copy()

        aug_image = cv2.resize(aug_image, (int(scale * w), int(scale * h)))
        aug_mask = cv2.resize(aug_mask, (int(scale * w, int(scale * h)))
        if (scale < 1.0):
            new_h, new_w, _ = aug_image.shape
            pre_h_pad = int((h - new_h) / 2)
            pre_w_pad = int((w - new_w) / 2)
            pad_list = [[pre_h_pad, h - new_h - pre_h_pad], [pre_w_pad, w - new_w - pre_w_pad], [0, 0]]
            aug_image = np.pad(aug_image, pad_list, mode="constant")
            aug_mask = np.pad(aug_mask, pad_list[:2], mode="constant")
        if (scale > 1.0):
            new_h, new_w, _ = aug_image.shape
            pre_h_crop = int ((new_h - h) / 2)
            pre_w_crop = int ((new_w - w) / 2)
            post_h_crop = h + pre_h_crop
            post_w_crop = w + pre_w_crop
            aug_image = aug_image[pre_h_crop:post_h_crop, pre_w_crop:post_w_crop]
            aug_mask = aug_mask[pre_h_crop:post_h_crop, pre_w_crop:post_w_crop]
        return aug_image, aug_mask            

向量化

class ToTensor(object):
    def __call__(self, sample):
        image, mask = sample
        image = np.transpose(image, (2,0,1))
        image = image.astype(np.float32)
        mask = mask.astype(np.long)
        return {'image':torch.from_numpy(image.copy()),
                'mask':torch.from_numpy(mask.copy())}