Yolov3之darknet下训练与预测

在做一些实时性要求比较高的目标检测时候，经常会选择Yolov3。本文介绍其训练和预测过程：
官网：https://pjreddie.com/darknet/ <https://pjreddie.com/darknet/>
github地址：https://github.com/pjreddie/darknet
<https://github.com/pjreddie/darknet>

一、制作数据集

数据生成直接参见另一篇博文 Yolov3之生成训练数据
<https://blog.csdn.net/oTengYue/article/details/81364034>

二、修改配置文件

本文以人手检测为例配置（只有一个label：hand）
1、添加或修改data/hand.names文件，此文件记录label，每行一个label(注意：对应于训练数据的labels)
2、添加或修改cfg/hand.data文件
classes= 1 # 自己数据集的类别数(不包含背景类) train = /home/xxx/darknet/train.txt # train文件的路径
valid = /home/xxx/darknet/test.txt # test文件的路径 names =
/home/xxx/darknet/data/hand.names backup = /home/xxx/darknet/backup #
生成权重存放文件夹，如果不存在，需提前创建
3、修改cfg/yolov3.cfg文件

(1)基本修改



(2)修改3处，直接根据“yolo”关键字查询定位，然后根据注释修改



三、训练

下载预训练模型放在models文件夹下

darknet53.conv.74下载链接：https://pjreddie.com/media/files/darknet53.conv.74
<https://pjreddie.com/media/files/darknet53.conv.74>

终端访问dartnet目录，输入一下命令：
# 从头开始训练 ./darknet detector train cfg/hand.data cfg/yolov3_hand.cfg
models/darknet53.conv.74 # 从某个权重快照继续训练 ./darknet detector train cfg/hand.data
cfg/yolov3_hand.cfg models/yolov3_hand_150000.weights #
测试单张照片，会在当前目录生成一个predictions.jpg的测试图片 ./darknet detector test cfg/hand.data
cfg/yolov3_hand.cfg models/yolov3_hand_150000.weights data/tmp_hand.jpg
四、训练日志可视化

主要根据日志文件生成loss和iou曲线，当然日志需要训练时候从定向来生成日志文件

详情见另一博文：https://blog.csdn.net/oTengYue/article/details/81365185
<https://blog.csdn.net/oTengYue/article/details/81365185>

五、预测过程


由于github上的master版本的python/darknet.py针对yolov3版本目前不太完善（截止2018-08-02），如果darknet采用master分支编译的话，建议把yolov3分支的python/darknet.py替换掉master版本的该文件。
同时需要更改该脚本中的libdarknet.so路径为本机编译后的路径，不修改可能引起报错。


此处记录备份一下当前版本的darknet.py文件
from ctypes import * import math import random def sample(probs): s =
sum(probs) probs = [a/sfor a in probs] r = random.uniform(0, 1) for i in
range(len(probs)): r = r - probs[i]if r <= 0: return i return len(probs)-1 def
c_array(ctype, values): new_values = values.ctypes.data_as(POINTER(ctype))
return new_values def array_to_image(arr): import numpy as np # need to return
old values to avoid python freeing memory arr = arr.transpose(2,0,1) c =
arr.shape[0] h = arr.shape[1] w = arr.shape[2] arr =
np.ascontiguousarray(arr.flat, dtype=np.float32) /255.0 data =
arr.ctypes.data_as(POINTER(c_float)) im = IMAGE(w,h,c,data)return im, arr class
BOX(Structure): _fields_ = [("x", c_float), ("y", c_float), ("w", c_float), ("h"
, c_float)]class DETECTION(Structure): _fields_ = [("bbox", BOX), ("classes",
c_int), ("prob", POINTER(c_float)), ("mask", POINTER(c_float)), ("objectness",
c_float), ("sort_class", c_int)] class IMAGE(Structure): _fields_ = [("w",
c_int), ("h", c_int), ("c", c_int), ("data", POINTER(c_float))] class METADATA
(Structure): _fields_ = [("classes", c_int), ("names", POINTER(c_char_p))] #lib
= CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) lib =
CDLL("libdarknet.so", RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int predict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype =
POINTER(c_float) set_gpu = lib.cuda_set_device set_gpu.argtypes = [c_int]
make_image = lib.make_image make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE get_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float,
POINTER(c_int), c_int, POINTER(c_int)] get_network_boxes.restype =
POINTER(DETECTION) make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p] make_network_boxes.restype =
POINTER(DETECTION) free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int] free_ptrs =
lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] network_predict =
lib.network_predict network_predict.argtypes = [c_void_p, POINTER(c_float)]
reset_rnn = lib.reset_rnn reset_rnn.argtypes = [c_void_p] load_net =
lib.load_network load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p do_nms_obj = lib.do_nms_obj do_nms_obj.argtypes =
[POINTER(DETECTION), c_int, c_int, c_float] do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] free_image =
lib.free_image free_image.argtypes = [IMAGE] letterbox_image =
lib.letterbox_image letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p] lib.get_metadata.restype = METADATA
load_image = lib.load_image_color load_image.argtypes = [c_char_p, c_int,
c_int] load_image.restype = IMAGE rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE] predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE] predict_image.restype =
POINTER(c_float)def classify(net, meta, im): out = predict_image(net, im) res =
[]for i in range(meta.classes): res.append((meta.names[i], out[i])) res =
sorted(res, key=lambda x: -x[1]) return res def detect(net, meta, image, thresh=
.5, hier_thresh=.5, nms=.45): im = load_image(image, 0, 0) num = c_int(0) pnum
= pointer(num) predict_image(net, im) dets = get_network_boxes(net, im.w, im.h,
thresh, hier_thresh,None, 0, pnum) num = pnum[0] if (nms): do_nms_obj(dets,
num, meta.classes, nms); res = []for j in range(num): for i in
range(meta.classes):if dets[j].prob[i] > 0: b = dets[j].bbox
res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h))) res =
sorted(res, key=lambda x: -x[1]) free_image(im) free_detections(dets, num)
return res def detect_numpy(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45
): im, arr = array_to_image(image) num = c_int(0) pnum = pointer(num)
predict_image(net, im) dets = get_network_boxes(net, im.w, im.h, thresh,
hier_thresh,None, 0, pnum) num = pnum[0] if (nms): do_nms_obj(dets, num,
meta.classes, nms); res = []for j in range(num): for i in range(meta.classes):
if dets[j].prob[i] > 0: b = dets[j].bbox res.append((meta.names[i],
dets[j].prob[i], (b.x, b.y, b.w, b.h))) res = sorted(res, key=lambda x: -x[1])
free_detections(dets, num)return res if __name__ == "__main__": #net =
load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
#im = load_image("data/wolf.jpg", 0, 0) #meta = load_meta("cfg/imagenet1k.data")
#r = classify(net, meta, im) #print r[:10] net = load_net("cfg/tiny-yolo.cfg",
"tiny-yolo.weights", 0) meta = load_meta("cfg/coco.data") import scipy.misc
import time ''' t_start = time.time() for ii in range(100): r = detect(net,
meta, 'data/dog.jpg') print(time.time() - t_start) print(r) image =
scipy.misc.imread('data/dog.jpg') for ii in range(100):
scipy.misc.imsave('/tmp/image.jpg', image) r = detect(net, meta,
'/tmp/image.jpg') print(time.time() - t_start) print(r) ''' image =
scipy.misc.imread('data/dog.jpg') t_start = time.time() for ii in range(100): r
= detect_numpy(net, meta, image) print(time.time() - t_start) print(r)
人手检测预测代码：examples/yolov3_detector_hand.py
#coding=utf-8 import cv2 import sys, os sys.path.append(
'/export/songhongwei/code/darknet/python/') import scipy.misc import darknet as
dnfrom PIL import Image class Yolov3HandDetector: hand_cfg_path =
"/export/songhongwei/code/darknet/cfg/yolov3_hand.cfg" hand_weights_path =
"/export/songhongwei/code/darknet/backup/yolov3_hand_150000.weights"
hand_data_path ="/export/songhongwei/code/darknet/cfg/hand.data" def __init__
(self): # Darknet self.net = dn.load_net(self.hand_cfg_path,
self.hand_weights_path,0) self.meta = dn.load_meta(self.hand_data_path) def
img_cv2pil(self, cv_im): pil_im = Image.fromarray(cv2.cvtColor(cv_im,
cv2.COLOR_BGR2RGB))return pil_im def detect_hand(self,cv_im): im =
self.img_cv2pil(cv_im) im = scipy.misc.fromimage(im) res_infos =
dn.detect_numpy(self.net, self.meta, im) bbox_map = {}for label, probability,
bboxin res_infos: if label not in bbox_map: bbox_map[label] = []
bbox_map[label].append([int(bbox[0]-bbox[2]/2),int(bbox[1]-bbox[3]/2),int(bbox[0
]+bbox[2]/2),int(bbox[1]+bbox[3]/2)]) return bbox_map if __name__ == '__main__'
: handDetector = Yolov3HandDetector() pic_path =
'/export/songhongwei/code/darknet/data/tmp_hand.jpg' cv_im =
cv2.imread(pic_path) bbox_map = handDetector.detect_hand(cv_im) print(bbox_map)
输出：
{'hand': [[155, 180, 218, 265], [239, 241, 302, 291]]}
注：每个中括号内数字代表格式[top_left_x, top_left_y, bottom_right_x, bottom_right_y]

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