Yolo 目標檢測總結帖（yolov3,yolov2）
阿新 • • 發佈：2019-01-10
由於專案的需求，需要完成一個目標檢測的任務，經過個人一段時間的實踐，現將自己實現的功能以及體驗過的事情在這裡做個總結，以便後續檢視，也讓其它人少走一些彎路，在這個過程中參考了一些部落格，便於入門與提升。
個人將大多數的時間花費在yolov3上，其精度效果會比yolov2的效果要好，但模擬和測試時間會花費一倍左右的時間，並且將yolov3的過程弄明白之後，yolov2如何跑動只是更改部分引數和預訓練模型罷了。注意，如果想訓練自己的資料集，最好是有一臺帶GPU的伺服器，一般執行到3w次左右其損失值會下降到0.0x的量級，CPU跑一個batchsize很慢，不建議使用CPU訓練，但可以使用CPU進行測試，使用CPU進行測試時有個小技巧，能夠加快一倍的測試時間。
文章分為以下幾個部分：
1.準備工具
軟硬體環境：本地MacBook Pro，阿里雲伺服器（P100顯示卡）
1.1 yolo網路下載
1.2 labelImg（有缺陷）
2.安裝
2.1 yolo包的安裝
2.1.1 CPU版本
git clone https://github.com/pjreddie/darknet.git
cd darkness
make這裡如果自己的電腦支援Openmp的話,也可以更改Makefile檔案將其中的OPENMP的值更改為1，會加快訓練和測試速度
GPU=0 CUDNN=0 OPENCV=0 OPENMP=0 # 若電腦支援Openmp時，可以將其設定為1 DEBUG=0
2.1.2 GPU版本
git clone https://github.com/pjreddie/darknet.git
cd darknet
vim Makefile
make對於GPU的Makefile更改的地方較多：
GPU=1 # 設定為1 CUDNN=1 # 設定為1 OPENCV=0 # 若後續想用opencv進行處理，可以將其設定為1 OPENMP=0 DEBUG=0 ARCH= -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_52,code=[sm_52,compute_52] \ -gencode arch=compute_60,code=[sm_60,compute_60] # 這個地方是根據自己的GPU架構進行設定，不同架構的GPU的運算能力不一樣，本文使用的是帕斯卡結構，查閱英偉達官網檢視對應的計算能力為6.0 # -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated? # This is what I use, uncomment if you know your arch and want to specify # ARCH= -gencode arch=compute_52,code=compute_52 VPATH=./src/:./examples SLIB=libdarknet.so ALIB=libdarknet.a EXEC=darknet OBJDIR=./obj/ CC=gcc NVCC=nvcc # 這個地方若沒有定義為環境變數，最好是使用絕對路徑，大概位於`/usr/local/cuda/bin/nvcc`
對於GPU版本的安裝，需要根據對應的地方更改Makefile檔案。
2.2 labelImg的安裝
兩種安裝方式：
2.2.1 檔案包安裝的方式：
2.2.2 pip安裝：
pip install labelImg
or
brew install labelImg注意，經過實踐，發現labelImg對.png格式影象不友好，不支援對.png影象的標註，即使標註出來其標籤檔案也不對。
3.資料集的準備與製作
資料集的準備安裝網上教程即可：
3.1 資料集標註
有幾個關鍵的地方需要強調一下：
OpenDir 是要標註影象的檔案地址
Change Save Dir 是修改儲存標記檔案的地址
Next Image 標註完點選這個進行下一張的標註
Prev Image 想檢視之前標註的情況
PascalVOC/YOLO 這個可選，前一種是得到的格式為xml的標籤檔案，後一種是直接得到格式為txt的標籤檔案，後一種適用於YOLO網路，前一種適合RCNN系列文章，根據自身選擇，本文由於之前嘗試過使用RCNN系列模型，就先標記為xml檔案，這裡不用擔心，darknet提供了轉換程式./scripts/voc_label.py。
3.2 資料集xml轉成yolo
對於使用tensorfloe-objection detection api的人來說，標籤格式是xml，好在darknet中提供了將xml格式的標籤轉換為txt標籤的函式，darknet提供了轉換程式./scripts/voc_label.py。注意這裡需要修改的地方：
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
# sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
# 前一個表示年份，後一個表示訓練或測試集檔案
sets=[('2007', 'train'),('2007', 'test')]
# classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
classes = ["1", "2", "3"]
# classes表示自己的類別名稱
def convert(size, box):
dw = 1./(size[0])
dh = 1./(size[1])
x = (box[0] + box[1])/2.0 - 1
y = (box[2] + box[3])/2.0 - 1
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(year, image_id):
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult)==1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:
if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w')
for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
convert_annotation(year, image_id)
list_file.close()
#這裡將最後兩行注射掉，執行後得到的訓練集和測試集 組合在一起是整個資料集，而不是將訓練集和測試集和一塊作為訓練集
#os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt")
#os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt")
執行之後，在./scripts資料夾就得到訓練集和測試集txt
對應的label資料夾中有了轉換好的txt格式的label：
4. 網路模型的訓練與測試
4.1 網路模型的訓練
4.1.1 需要更改的地方
修改cfg/voc.data
# 注意路徑，相對路徑和絕對路徑都可以
classes= n #類別數為n 你分幾類就將n設定為幾
train = ./scripts/2007_train.txt #對應剛才生成的訓練集txt
valid = ./scripts/2007_test.txt
names = data/voc.names
backup = ./results/ #網路模型訓練好的引數儲存路徑修改data/voc.names
#在這個地方輸入你標籤的名稱，每類一行，比如我分三類，分別為“ni”,"hao","ma",則下面是
ni
has
ma修改cfg/yolov3-voc.cfg 網路模型引數
[net]
# 將頭部程式碼更改為train，batch數量根據你自身的電腦能力設定，預設設定是64
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 50200 #最大迭代batches數
policy=steps
steps=20000,35000 # 每迭代多少次改變一次學習率，這裡是*0.1
scales=.1,.1
[convolutional]
size=1
stride=1
pad=1
filters=24 #這裡filters數量更改，與類別有關，一般公式是(classes_nums + 5) *3
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=3 # 修改成你自己的類別數
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=04.1.2 預訓練模型訓練
目前都是使用遷移學習，將成熟網路的部分引數直接用過來，這裡也一樣：
下裝與訓練模型：
wget https://pjreddie.com/media/files/darknet53.conv.74訓練：
./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.744.1.3 中斷後繼續訓練
當訓練進行到一半的時候，可能中途中斷或者是停了想繼續進行時，只需將上面的語句最後的預訓練權重更換為之前在voc.data中設定的模型訓練儲存路徑中存在的權重即可，這裡以yolov3.weights表示：
./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg results/yolov3.weights4.2 網路模型的測試
4.2.1 單張測試
單張測試就是指定一張影象名稱進行測試，可類似於darknet網站中給定的例子那樣，只不過需要修改相關路徑及被測圖片名稱：
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg./darknet detect cfg/yolov3.cfg result/yolov3.weights /path/to/your picture4.2.2 批量測試
但存在一個問題是無法將檢測後的影象儲存時，其名稱與原始名稱一樣，有時候出錯為null,在其基礎上對其GetFilename函式進行修改。
#include "darknet.h"
static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
//獲取檔案的名字
char* GetFilename(char *fullname)
{
int from,to,i;
char *newstr,*temp;
if(fullname!=NULL)
{
if((temp=strchr(fullname,'.'))==NULL)//if not find dot
newstr = fullname;
else
{
from = strlen(fullname) - 1;
to = (temp-fullname); //the first dot's index;
for(i=from;i--;i<=to)
if(fullname[i]=='.')break;//find the last dot
newstr = (char*)malloc(i+1);
strncpy(newstr,fullname,i);
*(newstr+i)=0;
}
}
char name[50] = {""};
char *q = strrchr(newstr,'/') + 1;
strncpy(name,q,40);
return name;
}
void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
list *options = read_data_cfg(datacfg);
char *train_images = option_find_str(options, "train", "data/train.list");
char *backup_directory = option_find_str(options, "backup", "/backup/");
srand(time(0));
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network **nets = calloc(ngpus, sizeof(network));
srand(time(0));
int seed = rand();
int i;
for(i = 0; i < ngpus; ++i){
srand(seed);
#ifdef GPU
cuda_set_device(gpus[i]);
#endif
nets[i] = load_network(cfgfile, weightfile, clear);
nets[i]->learning_rate *= ngpus;
}
srand(time(0));
network *net = nets[0];
int imgs = net->batch * net->subdivisions * ngpus;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
data train, buffer;
layer l = net->layers[net->n - 1];
int classes = l.classes;
float jitter = l.jitter;
list *plist = get_paths(train_images);
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = get_base_args(net);
args.coords = l.coords;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.classes = classes;
args.jitter = jitter;
args.num_boxes = l.max_boxes;
args.d = &buffer;
args.type = DETECTION_DATA;
//args.type = INSTANCE_DATA;
args.threads = 64;
pthread_t load_thread = load_data(args);
double time;
int count = 0;
//while(i*imgs < N*120){
while(get_current_batch(net) < net->max_batches){
if(l.random && count++%10 == 0){
printf("Resizing\n");
int dim = (rand() % 10 + 10) * 32;
if (get_current_batch(net)+200 > net->max_batches) dim = 608;
//int dim = (rand() % 4 + 16) * 32;
printf("%d\n", dim);
args.w = dim;
args.h = dim;
pthread_join(load_thread, 0);
train = buffer;
free_data(train);
load_thread = load_data(args);
#pragma omp parallel for
for(i = 0; i < ngpus; ++i){
resize_network(nets[i], dim, dim);
}
net = nets[0];
}
time=what_time_is_it_now();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data(args);
/*
int k;
for(k = 0; k < l.max_boxes; ++k){
box b = float_to_box(train.y.vals[10] + 1 + k*5);
if(!b.x) break;
printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
}
*/
/*
int zz;
for(zz = 0; zz < train.X.cols; ++zz){
image im = float_to_image(net->w, net->h, 3, train.X.vals[zz]);
int k;
for(k = 0; k < l.max_boxes; ++k){
box b = float_to_box(train.y.vals[zz] + k*5, 1);
printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
draw_bbox(im, b, 1, 1,0,0);
}
show_image(im, "truth11");
cvWaitKey(0);
save_image(im, "truth11");
}
*/
printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
time=what_time_is_it_now();
float loss = 0;
#ifdef GPU
if(ngpus == 1){
loss = train_network(net, train);
} else {
loss = train_networks(nets, ngpus, train, 4);
}
#else
loss = train_network(net, train);
#endif
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
i = get_current_batch(net);
printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, i*imgs);
if(i%100==0){
#ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(net, buff);
}
if(i%10000==0 || (i < 1000 && i%100 == 0)){
#ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
free_data(train);
}
#ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
}
static int get_coco_image_id(char *filename)
{
char *p = strrchr(filename, '/');
char *c = strrchr(filename, '_');
if(c) p = c;
return atoi(p+1);
}
static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
{
int i, j;
int image_id = get_coco_image_id(image_path);
for(i = 0; i < num_boxes; ++i){
float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
if (xmin < 0) xmin = 0;
if (ymin < 0) ymin = 0;
if (xmax > w) xmax = w;
if (ymax > h) ymax = h;
float bx = xmin;
float by = ymin;
float bw = xmax - xmin;
float bh = ymax - ymin;
for(j = 0; j < classes; ++j){
if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
}
}
}
void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
{
int i, j;
for(i = 0; i < total; ++i){
float xmin = dets[i].bbox.x - dets[i].bbox.w/2. + 1;
float xmax = dets[i].bbox.x + dets[i].bbox.w/2. + 1;
float ymin = dets[i].bbox.y - dets[i].bbox.h/2. + 1;
float ymax = dets[i].bbox.y + dets[i].bbox.h/2. + 1;
if (xmin < 1) xmin = 1;
if (ymin < 1) ymin = 1;
if (xmax > w) xmax = w;
if (ymax > h) ymax = h;
for(j = 0; j < classes; ++j){
if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
xmin, ymin, xmax, ymax);
}
}
}
void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
{
int i, j;
for(i = 0; i < total; ++i){
float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
if (xmin < 0) xmin = 0;
if (ymin < 0) ymin = 0;
if (xmax > w) xmax = w;
if (ymax > h) ymax = h;
for(j = 0; j < classes; ++j){
int class = j;
if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, dets[i].prob[class],
xmin, ymin, xmax, ymax);
}
}
}
void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{
int j;
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.list");
char *name_list = option_find_str(options, "names", "data/names.list");
char *prefix = option_find_str(options, "results", "results");
char **names = get_labels(name_list);
char *mapf = option_find_str(options, "map", 0);
int *map = 0;
if (mapf) map = read_map(mapf);
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 2);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
srand(time(0));
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
layer l = net->layers[net->n-1];
int classes = l.classes;
char buff[1024];
char *type = option_find_str(options, "eval", "voc");
FILE *fp = 0;
FILE **fps = 0;
int coco = 0;
int imagenet = 0;
if(0==strcmp(type, "coco")){
if(!outfile) outfile = "coco_results";
snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
fp = fopen(buff, "w");
fprintf(fp, "[\n");
coco = 1;
} else if(0==strcmp(type, "imagenet")){
if(!outfile) outfile = "imagenet-detection";
snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
fp = fopen(buff, "w");
imagenet = 1;
classes = 200;
} else {
if(!outfile) outfile = "comp4_det_test_";
fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
fps[j] = fopen(buff, "w");
}
}
int m = plist->size;
int i=0;
int t;
float thresh = .005;
float nms = .45;
int nthreads = 4;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
image input = make_image(net->w, net->h, net->c*2);
load_args args = {0};
args.w = net->w;
args.h = net->h;
//args.type = IMAGE_DATA;
args.type = LETTERBOX_DATA;
for(t = 0; t < nthreads; ++t){
args.path = paths[i+t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
double start = what_time_is_it_now();
for(i = nthreads; i < m+nthreads; i += nthreads){
fprintf(stderr, "%d\n", i);
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
pthread_join(thr[t], 0);
val[t] = buf[t];
val_resized[t] = buf_resized[t];
}
for(t = 0; t < nthreads && i+t < m; ++t){
args.path = paths[i+t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data, 1);
flip_image(val_resized[t]);
copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data + net->w*net->h*net->c, 1);
network_predict(net, input.data);
int w = val[t].w;
int h = val[t].h;
int num = 0;
detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &num);
if (nms) do_nms_sort(dets, num, classes, nms);
if (coco){
print_cocos(fp, path, dets, num, classes, w, h);
} else if (imagenet){
print_imagenet_detections(fp, i+t-nthreads+1, dets, num, classes, w, h);
} else {
print_detector_detections(fps, id, dets, num, classes, w, h);
}
free_detections(dets, num);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
}
}
for(j = 0; j < classes; ++j){
if(fps) fclose(fps[j]);
}
if(coco){
fseek(fp, -2, SEEK_CUR);
fprintf(fp, "\n]\n");
fclose(fp);
}
fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
}
void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{
int j;
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.list");
char *name_list = option_find_str(options, "names", "data/names.list");
char *prefix = option_find_str(options, "results", "results");
char **names = get_labels(name_list);
char *mapf = option_find_str(options, "map", 0);
int *map = 0;
if (mapf) map = read_map(mapf);
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
srand(time(0));
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
layer l = net->layers[net->n-1];
int classes = l.classes;
char buff[1024];
char *type = option_find_str(options, "eval", "voc");
FILE *fp = 0;
FILE **fps = 0;
int coco = 0;
int imagenet = 0;
if(0==strcmp(type, "coco")){
if(!outfile) outfile = "coco_results";
snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
fp = fopen(buff, "w");
fprintf(fp, "[\n");
coco = 1;
} else if(0==strcmp(type, "imagenet")){
if(!outfile) outfile = "imagenet-detection";
snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
fp = fopen(buff, "w");
imagenet = 1;
classes = 200;
} else {
if(!outfile) outfile = "comp4_det_test_";
fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
fps[j] = fopen(buff, "w");
}
}
int m = plist->size;
int i=0;
int t;
float thresh = .005;
float nms = .45;
int nthreads = 4;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
load_args args = {0};
args.w = net->w;
args.h = net->h;
//args.type = IMAGE_DATA;
args.type = LETTERBOX_DATA;
for(t = 0; t < nthreads; ++t){
args.path = paths[i+t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
double start = what_time_is_it_now();
for(i = nthreads; i < m+nthreads; i += nthreads){
fprintf(stderr, "%d\n", i);
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
pthread_join(thr[t], 0);
val[t] = buf[t];
val_resized[t] = buf_resized[t];
}
for(t = 0; t < nthreads && i+t < m; ++t){
args.path = paths[i+t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
float *X = val_resized[t].data;
network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
int nboxes = 0;
detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &nboxes);
if (nms) do_nms_sort(dets, nboxes, classes, nms);
if (coco){
print_cocos(fp, path, dets, nboxes, classes, w, h);
} else if (imagenet){
print_imagenet_detections(fp, i+t-nthreads+1, dets, nboxes, classes, w, h);
} else {
print_detector_detections(fps, id, dets, nboxes, classes, w, h);
}
free_detections(dets, nboxes);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
}
}
for(j = 0; j < classes; ++j){
if(fps) fclose(fps[j]);
}
if(coco){
fseek(fp, -2, SEEK_CUR);
fprintf(fp, "\n]\n");
fclose(fp);
}
fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
}
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
srand(time(0));
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.list");
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
layer l = net->layers[net->n-1];
int j, k;
int m = plist->size;
int i=0;
float thresh = .001;
float iou_thresh = .5;
float nms = .4;
int total = 0;
int correct = 0;
int proposals = 0;
float avg_iou = 0;
for(i = 0; i < m; ++i){
char *path = paths[i];
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net->w, net->h);
char *id = basecfg(path);
network_predict(net, sized.data);
int nboxes = 0;
detection *dets = get_network_boxes(net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes);
if (nms) do_nms_obj(dets, nboxes, 1, nms);
char labelpath[4096];
find_replace(path, "images", "labels", labelpath);
find_replace(labelpath, "JPEGImages", "labels", labelpath);
find_replace(labelpath, ".jpg", ".txt", labelpath);
find_replace(labelpath, ".JPEG", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for(k = 0; k < nboxes; ++k){
if(dets[k].objectness > thresh){
++proposals;
}
}
for (j = 0; j < num_labels; ++j) {
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
for(k = 0; k < l.w*l.h*l.n; ++k){
float iou = box_iou(dets[k].bbox, t);
if(dets[k].objectness > thresh && iou > best_iou){
best_iou = iou;
}
}
avg_iou += best_iou;
if(best_iou > iou_thresh){
++correct;
}
}
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
free(id);
free_image(orig);
free_image(sized);
}
}
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
image **alphabet = load_alphabet();
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
double time;
char buff[256];
char *input = buff;
float nms=.45;
int i=0;
while(1){
if(filename){
strncpy(input, filename, 256);
image im = load_image_color(input,0,0);
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
float *X = sized.data;
time=what_time_is_it_now();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
//printf("%d\n", nboxes);
//if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
if(outfile)
{
save_image(im, outfile);
}
else{
//save_image(im, "predictions");
char image[2048];
sprintf(image,"./data/predict/%s",GetFilename(filename));
save_image(im,image);
printf("predict %s successfully!\n",GetFilename(filename));
#ifdef OPENCV
cvNamedWindow("predictions", CV_WINDOW_NORMAL);
if(fullscreen){
cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
}
show_image(im, "predictions");
cvWaitKey(0);
cvDestroyAllWindows();
#endif
}
free_image(im);
free_image(sized);
if (filename) break;
}
else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
list *plist = get_paths(input);
char **paths = (char **)list_to_array(plist);
printf("Start Testing!\n");
int m = plist->size;
if(access("./data/out",0)==-1)//"/home/FENGsl/darknet/data"修改成自己的路徑
{
if (mkdir("./data/out",0777))//"/home/FENGsl/darknet/data"修改成自己的路徑
{
printf("creat file bag failed!!!");
}
}
for(i = 0; i < m; ++i){
char *path = paths[i];
image im = load_image_color(path,0,0);
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
float *X = sized.data;
time=what_time_is_it_now();
network_predict(net, X);
printf("Try Very Hard:");
printf("%s: Predicted in %f seconds.\n", path, what_time_is_it_now()-time);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
//printf("%d\n", nboxes);
//if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
if(outfile){
save_image(im, outfile);
}
else{
char b[2048];
sprintf(b,"./data/out/%s",GetFilename(path));//"/home/FENGsl/darknet/data"修改成自己的路徑
save_image(im, b);
printf("save %s successfully!\n",GetFilename(path));
#ifdef OPENCV
cvNamedWindow("predictions", CV_WINDOW_NORMAL);
if(fullscreen){
cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
}
show_image(im, "predictions");
cvWaitKey(0);
cvDestroyAllWindows();
#endif
}
free_image(im);
free_image(sized);
if (filename) break;
}
}
}
}
void run_detector(int argc, char **argv)
{
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .5);
float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
int avg = find_int_arg(argc, argv, "-avg", 3);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
}
char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
char *outfile = find_char_arg(argc, argv, "-out", 0);
int *gpus = 0;
int gpu = 0;
int ngpus = 0;
if(gpu_list){
printf("%s\n", gpu_list);
int len = strlen(gpu_list);
ngpus = 1;
int i;
for(i = 0; i < len; ++i){
if (gpu_list[i] == ',') ++ngpus;
}
gpus = calloc(ngpus, sizeof(int));
for(i = 0; i < ngpus; ++i){
gpus[i] = atoi(gpu_list);
gpu_list = strchr(gpu_list, ',')+1;
}
} else {
gpu = gpu_index;
gpus = &gpu;
ngpus = 1;
}
int clear = find_arg(argc, argv, "-clear");
int fullscreen = find_arg(argc, argv, "-fullscreen");
int width = find_int_arg(argc, argv, "-w", 0);
int height = find_int_arg(argc, argv, "-h", 0);
int fps = find_int_arg(argc, argv, "-fps", 0);
//int class = find_int_arg(argc, argv, "-class", 0);
char *datacfg = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
char *filename = (argc > 6) ? argv[6]: 0;
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile);
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg,cfg, weights);
else if(0==strcmp(argv[2], "demo")) {
list *options = read_data_cfg(datacfg);
int classes = option_find_int(options, "classes", 20);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen);
}
//else if(0==strcmp(argv[2], "extract")) extract_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
//else if(0==strcmp(argv[2], "censor")) censor_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
}
./darknet detector test cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_final.weights
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BFLOPs
1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BFLOPs
.......
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
105 conv 255 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 255 0.353 BFLOPs
106 detection
Loading weights from yolov3.weights...Done!
Enter Image Path:
這裡讓輸入影象路徑，一個txt儲存的路徑即可，我在這裡輸入的是之前生成的2007_test.txt
5. Python介面
darknet提供了python介面，直接使用python即可呼叫程式得到檢測結果，python介面在`./darknet/python`資料夾中,呼叫的是編譯時生成的libdarknet.so檔案，不同的機器平臺編譯生成的檔案不一樣，如果換機器或使用cpu或gpu執行時，請重新編譯一下。
該檔案有兩個資料夾：darknet.py 與provertbot.py，目前的版本支援python2.7，適當修改程式碼使其支援python3.x，個人做好的api已上傳到github上，方便使用。
python darknet.py輸出結果形式為： res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
依次為檢測出物體的名稱，概率，檢測框大小範圍（在原圖中所處的位置），其中x，y表示方框中心，w和h分別表示中心到兩邊的寬度和高度，如下圖所示：
本人的api對其進行了更改，輸出的是方框的橫縱座標的範圍（x1,x2,y1,y2），且個人只分三類，區分服裝上衣，下衣及全身裝。讀者有需要的話只需修改你訓練的model位置及配置檔案cfg即可。
7. 計算MAP和recall
1.生成檢測結果檔案
./darknet detector valid cfg/car.data cfg/car.cfg backup/car_final.weights -out car.txt -gpu 0 -thresh .52.把car.txt 用faster rcnn 中voc_eval計算mAP
/home/sam/src/caffeup2date_pyfasterrcnn/lib/datasets/compute_mAP.py
from voc_eval import voc_eval
print(voc_eval('/home/sam/src/darknet/results/{}.txt',/home/sam/datasets/car2/VOC2007/Annotations/{}.xml','/home/sam/datasets/car2/VOC2007/ImageSets/Main/test.txt', 'car', '.')第三個結果就是
如果只想計算大於0.3的輸出結果的mAP,把 voc_eval.py檔案中如下程式碼更改
sorted_ind = np.argsort(-confidence)
sorted_ind1 = np.where(confidence[sorted_ind] >= .3)[0]#np.argsort(-confidence<=-.3)
sorted_ind = sorted_ind[sorted_ind1]3.計算recall
./darknet detector recall cfg/car.data cfg/car.cfg backup/car_final.weights -out car.txt -gpu 0 -thresh .57.參考
YOLO V3
參考：
注：資料夾內容儲存圖片命名問題，*GetFilename(char *p)函式中限制了檔名長度，修改即可
* YOLO 網路終端輸出引數意義
YOLO V2
參考：
後續待完善...