darknet是如何对数据集做resize的?

人工智能机器学习

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2019-8-19

在准备数据集时,darknet并不要求我们预先对图片resize到固定的size. darknet自动帮我们做了图像的resize.

darknet训练前处理

本文所指的darknet版本:https://github.com/AlexeyAB/darknet

./darknet detector train data/trafficlights.data yolov3-tiny_trafficlights.cfg yolov3-tiny.conv.15
main函数位于darknet.c

训练时的入口函数为detector.c里

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs)
{
    load_args args = { 0 };
    args.type = DETECTION_DATA;
    args.letter_box = net.letter_box;
    
    load_thread = load_data(args);
    
    loss = train_network(net, train);
}

函数太长,只贴了几句关键的.注意args.type = DETECTION_DATA;

data.c中

void *load_thread(void *ptr)
{
    //srand(time(0));
    //printf("Loading data: %d\n", random_gen());
    load_args a = *(struct load_args*)ptr;
    if(a.exposure == 0) a.exposure = 1;
    if(a.saturation == 0) a.saturation = 1;
    if(a.aspect == 0) a.aspect = 1;

    if (a.type == OLD_CLASSIFICATION_DATA){
        *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
    } else if (a.type == CLASSIFICATION_DATA){
        *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
    } else if (a.type == SUPER_DATA){
        *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
    } else if (a.type == WRITING_DATA){
        *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
    } else if (a.type == REGION_DATA){
        *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
    } else if (a.type == DETECTION_DATA){
        *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.blur, a.mixup, a.jitter,
            a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.show_imgs);
    } else if (a.type == SWAG_DATA){
        *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter);
    } else if (a.type == COMPARE_DATA){
        *a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h);
    } else if (a.type == IMAGE_DATA){
        *(a.im) = load_image(a.path, 0, 0, a.c);
        *(a.resized) = resize_image(*(a.im), a.w, a.h);
    }else if (a.type == LETTERBOX_DATA) {
        *(a.im) = load_image(a.path, 0, 0, a.c);
        *(a.resized) = letterbox_image(*(a.im), a.w, a.h);
    } else if (a.type == TAG_DATA){
        *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.flip, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
    }
    free(ptr);
    return 0;
}

根据a.type不同,有不同的加载逻辑.在训练时,args.type = DETECTION_DATA,接着去看load_data_detection().

load_data_detection()有两套实现,用宏#ifdef OPENCV区别开来.我们看opencv版本

load_data_detection()
{
    src = load_image_mat_cv(filename, flag);
    image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp);

}

注意load_image_mat_cv()中imread读入的是bgr顺序的,用cv::cvtColor做了bgr–>rgb的转换.

if (mat.channels() == 3) cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR);

这里有个让人困惑的地方,为什么是cv::COLOR_RGB2BGR而不是cv::COLOR_BGR2RGB,实际上这两个enum值是一样的,都是4.
https://docs.opencv.org/3.1.0/d7/d1b/group__imgproc__misc.html

image_data_argmentation()的主要逻辑

            cv::Mat cropped(src_rect.size(), img.type());
            //cropped.setTo(cv::Scalar::all(0));
            cropped.setTo(cv::mean(img));

            img(new_src_rect).copyTo(cropped(dst_rect));

            // resize
            cv::resize(cropped, sized, cv::Size(w, h), 0, 0, cv::INTER_LINEAR);

其实主要就是cv::resize. 这里cropped的img是在原图上随机截取出来的一块区域(当然是有范围的).
在load_data_detection()中有这样一段逻辑,生成pleft,pright,ptop,pbot. 这些参数被传递给image_data_argmentation(),用以截取出cropped image.

       int oh = get_height_mat(src);
        int ow = get_width_mat(src);

        int dw = (ow*jitter);
        int dh = (oh*jitter);

        if(!augmentation_calculated || !track)
        {
            augmentation_calculated = 1;
            r1 = random_float();
            r2 = random_float();
            r3 = random_float();
            r4 = random_float();

            dhue = rand_uniform_strong(-hue, hue);
            dsat = rand_scale(saturation);
            dexp = rand_scale(exposure);

            flip = use_flip ? random_gen() % 2 : 0;
        }

        int pleft  = rand_precalc_random(-dw, dw, r1);
        int pright = rand_precalc_random(-dw, dw, r2);
        int ptop   = rand_precalc_random(-dh, dh, r3);
        int pbot   = rand_precalc_random(-dh, dh, r4);

        int swidth =  ow - pleft - pright;
        int sheight = oh - ptop - pbot;

        float sx = (float)swidth  / ow;
        float sy = (float)sheight / oh;

        float dx = ((float)pleft/ow)/sx;
        float dy = ((float)ptop /oh)/sy;

这么做的目的是,参考作者AlexeyAB大神的回复:
https://github.com/AlexeyAB/darknet/issues/3703

Your test images will not be the same as training images, so you should change training images as many times as possible. So maybe one of the modified training images of the object coincides with the test image.
这里,我此前一直有个错误的理解,在train和test时对image的preprocess应该是完全一致的.大神的回复意思是,并非如此,在train的时候应该尽可能多地使训练图片产生一些变化,因为测试图片不可能与训练图片是完全一致的,这样的话,才更有可能使测试图片与某个随机变化后的训练图片吻合.

但是之前,我在issue里有看到有人训练出来的模型效果并不好,改变了image的preprocess以后,效果就好了.这一点还有待研究.

原始的darknet里图像的preprocess用的是letterbox_image(),AlexeyAB的版本里用的是resize.据作者说这一改变使得对小目标的检测效果更好.
参考https://github.com/AlexeyAB/darknet/issues/1907 https://github.com/AlexeyAB/darknet/issues/232#issuecomment-336955485
resize()并不会保持宽高比,letterbox_image()会保持宽高比.作者认为如果你的dataset的train和test中图像分辨率一致的话,是没有必要保持宽高比的.

darknet 推导前处理

detector.c中

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
    float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile)
{
        image im = load_image(input, 0, 0, net.c);
        image sized = resize_image(im, net.w, net.h);
}

这里的resize_image是用C实现的,和cv::resize功能相同

作者:sdu20112013