目标检测数据集预处理什么 目标检测数据增强方法

睿智的目标检测28——YoloV4当中的Mosaic数据增强方法

• 学习前言
• 代码下载
• 什么是Mosaic数据增强方法
• 实现思路
• 全部代码

• 1、数据增强
• 2、调用代码

什么是Mosaic数据增强方法

Yolov4的mosaic数据增强参考了CutMix数据增强方式，理论上具有一定的相似性！

CutMix数据增强方式利用两张图片进行拼接。

但是mosaic利用了四张图片，根据论文所说其拥有一个巨大的优点是丰富检测物体的背景！且在BN计算的时候一下子会计算四张图片的数据！

就像下图这样：

实现思路

1、每次读取四张图片。

2、分别对四张图片进行翻转、缩放、色域变化等，并且按照四个方向位置摆好。

3、进行图片的组合和框的组合

全部代码

这里的代码直接复制无法使用，请到Github下载。

1、数据增强

该部分为数据增强。

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 merge_bboxes(bboxes, cutx, cuty):
    merge_bbox = []
    for i in range(len(bboxes)):
        for box in bboxes[i]:
            tmp_box = []
            x1, y1, x2, y2 = box[0], box[1], box[2], box[3]

            if i == 0:
                if y1 > cuty or x1 > cutx:
                    continue
                if y2 >= cuty and y1 <= cuty:
                    y2 = cuty
                if x2 >= cutx and x1 <= cutx:
                    x2 = cutx

            if i == 1:
                if y2 < cuty or x1 > cutx:
                    continue
                if y2 >= cuty and y1 <= cuty:
                    y1 = cuty
                if x2 >= cutx and x1 <= cutx:
                    x2 = cutx

            if i == 2:
                if y2 < cuty or x2 < cutx:
                    continue
                if y2 >= cuty and y1 <= cuty:
                    y1 = cuty
                if x2 >= cutx and x1 <= cutx:
                    x1 = cutx

            if i == 3:
                if y1 > cuty or x2 < cutx:
                    continue
                if y2 >= cuty and y1 <= cuty:
                    y2 = cuty
                if x2 >= cutx and x1 <= cutx:
                    x1 = cutx
            tmp_box.append(x1)
            tmp_box.append(y1)
            tmp_box.append(x2)
            tmp_box.append(y2)
            tmp_box.append(box[-1])
            merge_bbox.append(tmp_box)
    return merge_bbox

def get_random_data_with_Mosaic(annotation_line, input_shape, jitter=0.3, hue=.1, sat=0.7, val=0.4):
    h, w = input_shape
    min_offset_x = rand(0.3, 0.7)
    min_offset_y = rand(0.3, 0.7)

    image_datas = [] 
    box_datas   = []
    index       = 0
    for line in annotation_line:
        #---------------------------------#
        #   每一行进行分割
        #---------------------------------#
        line_content = line.split()
        #---------------------------------#
        #   打开图片
        #---------------------------------#
        image = Image.open(line_content[0])
        image = image.convert('RGB')
        
        #---------------------------------#
        #   图片的大小
        #---------------------------------#
        iw, ih = image.size
        #---------------------------------#
        #   保存框的位置
        #---------------------------------#
        box = np.array([np.array(list(map(int,box.split(',')))) for box in line_content[1:]])
        
        #---------------------------------#
        #   是否翻转图片
        #---------------------------------#
        flip = rand()<.5
        if flip and len(box)>0:
            image = image.transpose(Image.FLIP_LEFT_RIGHT)
            box[:, [0,2]] = iw - box[:, [2,0]]

        #------------------------------------------#
        #   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(.4, 1)
        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)

        #-----------------------------------------------#
        #   将图片进行放置，分别对应四张分割图片的位置
        #-----------------------------------------------#
        if index == 0:
            dx = int(w*min_offset_x) - nw
            dy = int(h*min_offset_y) - nh
        elif index == 1:
            dx = int(w*min_offset_x) - nw
            dy = int(h*min_offset_y)
        elif index == 2:
            dx = int(w*min_offset_x)
            dy = int(h*min_offset_y)
        elif index == 3:
            dx = int(w*min_offset_x)
            dy = int(h*min_offset_y) - nh
        
        new_image = Image.new('RGB', (w,h), (128,128,128))
        new_image.paste(image, (dx, dy))
        image_data = np.array(new_image)

        index = index + 1
        box_data = []
        #---------------------------------#
        #   对box进行重新处理
        #---------------------------------#
        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)]
            box_data = np.zeros((len(box),5))
            box_data[:len(box)] = box
        
        image_datas.append(image_data)
        box_datas.append(box_data)

    #---------------------------------#
    #   将图片分割，放在一起
    #---------------------------------#
    cutx = int(w * min_offset_x)
    cuty = int(h * min_offset_y)

    new_image = np.zeros([h, w, 3])
    new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :]
    new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :]
    new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :]
    new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :]

    new_image       = np.array(new_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(new_image, cv2.COLOR_RGB2HSV))
    dtype           = new_image.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)

    new_image = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
    new_image = cv2.cvtColor(new_image, cv2.COLOR_HSV2RGB)

    #---------------------------------#
    #   对框进行进一步的处理
    #---------------------------------#
    new_boxes = merge_bboxes(box_datas, cutx, cuty)

    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_Mosaic
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4个图像与标签
    #------------------------------#
    sample_xmls     = sample(xml_names, 4)
    unique_labels   = get_classes(sample_xmls, Origin_Annotations_path)
    
    annotation_line = []
    for xml in sample_xmls:
        line = convert_annotation(os.path.join(Origin_JPEGImages_path, os.path.splitext(xml)[0] + '.jpg'), os.path.join(Origin_Annotations_path, xml), unique_labels)
        annotation_line.append(line)

    #------------------------------#
    #   合并mosaic
    #------------------------------#
    image_data, box_data = get_random_data_with_Mosaic(annotation_line, input_shape)
    
    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()