睿智的目标检测64——目标检测中的MixUp数据增强方法

学习前言

哈哈哈！我再来一次数据增强！

代码下载

https://github.com/bubbliiiing/object-detection-augmentation

什么是MixUp数据增强方法

MixUp数据增强方法在最新的几个Yolo算法中得到了广泛的应用，特别在YoloX中，s、m、l、x四个型号的网络都使用了MixUp数据增强。nano和tiny由于模型的拟合能力一般没有使用MixUp，但也说明了MixUp具有强大的数据增强能力。

MixUp的思路较为简单，主要是将两张图像按比例进行混合，如图所示：

图片混合完成后，原来两幅图片的真实框此时也位于一幅图像上。

实现思路

1、每次读取两张的图片。


2、分别对两张图片进行翻转、缩放、色域变化等数据增强。

3、将二者的真实框堆叠到一起。

全部代码

1、数据增强与MixUp

该部分为普通数据增强与MixUp的代码

import cv2
import numpy as np
from PIL import Image, ImageDraw


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

def get_random_data(annotation_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True):
    line    = annotation_line.split()
    #------------------------------#
    #   读取图像并转换成RGB图像
    #------------------------------#
    image   = Image.open(line[0])
    image   = image.convert('RGB')

    #------------------------------#
    #   获得图像的高宽与目标高宽
    #------------------------------#
    iw, ih  = image.size
    h, w    = input_shape
    #------------------------------#
    #   获得预测框
    #------------------------------#
    box     = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])

    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)

        #---------------------------------#
        #   对真实框进行调整
        #---------------------------------#
        if len(box)>0:
            np.random.shuffle(box)
            box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
            box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
            box[:, 0:2][box[:, 0:2]<0] = 0
            box[:, 2][box[:, 2]>w] = w
            box[:, 3][box[:, 3]>h] = h
            box_w = box[:, 2] - box[:, 0]
            box_h = box[:, 3] - box[:, 1]
            box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box

        return image_data, box
            
    #------------------------------------------#
    #   对图像进行缩放并且进行长和宽的扭曲
    #------------------------------------------#
    new_ar = iw/ih * rand(1-jitter,1+jitter) / rand(1-jitter,1+jitter)
    scale = rand(.25, 2)
    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(rand(0, w-nw))
    dy = int(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 = rand()<.5
    if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)

    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)

    #---------------------------------#
    #   对真实框进行调整
    #---------------------------------#
    if len(box)>0:
        np.random.shuffle(box)
        box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
        box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
        if flip: box[:, [0,2]] = w - box[:, [2,0]]
        box[:, 0:2][box[:, 0:2]<0] = 0
        box[:, 2][box[:, 2]>w] = w
        box[:, 3][box[:, 3]>h] = h
        box_w = box[:, 2] - box[:, 0]
        box_h = box[:, 3] - box[:, 1]
        box = box[np.logical_and(box_w>1, box_h>1)] 
    
    return image_data, box

def get_random_data_with_MixUp(image_1, box_1, image_2, box_2):
    new_image = np.array(image_1, np.float32) * 0.5 + np.array(image_2, np.float32) * 0.5
    new_boxes = np.concatenate([box_1, box_2], axis=0)
    return new_image, new_boxes

2、调用代码

该部分为调用代码

import os
from random import sample

import numpy as np
from PIL import Image, ImageDraw

from utils.random_data import get_random_data, get_random_data_with_MixUp
from utils.utils import convert_annotation, get_classes

#-----------------------------------------------------------------------------------#
#   Origin_VOCdevkit_path   原始数据集所在的路径
#-----------------------------------------------------------------------------------#
Origin_VOCdevkit_path   = "VOCdevkit_Origin"
#-----------------------------------------------------------------------------------#
#   input_shape             生成的图片大小。
#-----------------------------------------------------------------------------------#
input_shape             = [640, 640]

if __name__ == "__main__":
    Origin_JPEGImages_path  = os.path.join(Origin_VOCdevkit_path, "VOC2007/JPEGImages")
    Origin_Annotations_path = os.path.join(Origin_VOCdevkit_path, "VOC2007/Annotations")
    
    #---------------------------#
    #   遍历标签并赋值
    #---------------------------#
    xml_names = os.listdir(Origin_Annotations_path)

    #------------------------------#
    #   获取两个图像与标签
    #------------------------------#
    sample_xmls     = sample(xml_names, 2)
    unique_labels   = get_classes(sample_xmls, Origin_Annotations_path)
    jpg_name_1  = os.path.join(Origin_JPEGImages_path, os.path.splitext(sample_xmls[0])[0] + '.jpg')
    jpg_name_2  = os.path.join(Origin_JPEGImages_path, os.path.splitext(sample_xmls[1])[0] + '.jpg')
    xml_name_1  = os.path.join(Origin_Annotations_path, sample_xmls[0])
    xml_name_2  = os.path.join(Origin_Annotations_path, sample_xmls[1])
    
    line_1 = convert_annotation(jpg_name_1, xml_name_1, unique_labels)
    line_2 = convert_annotation(jpg_name_2, xml_name_2, unique_labels)

    #------------------------------#
    #   各自数据增强
    #------------------------------#
    image_1, box_1  = get_random_data(line_1, input_shape) 
    image_2, box_2  = get_random_data(line_2, input_shape) 
    
    #------------------------------#
    #   合并mixup
    #------------------------------#
    image_data, box_data = get_random_data_with_MixUp(image_1, box_1, image_2, box_2)
    
    img = Image.fromarray(image_data.astype(np.uint8))
    for j in range(len(box_data)):
        thickness = 3
        left, top, right, bottom  = box_data[j][0:4]
        draw = ImageDraw.Draw(img)
        for i in range(thickness):
            draw.rectangle([left + i, top + i, right - i, bottom - i],outline=(255, 255, 255))
    img.show()