YOLO目标检测快速上手

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介绍

YOLO 是基于深度学习端到端的实时目标检测系统,YOLO 将目标区域预测和目标类别预测整合于单个神经网络模型中,实现在准确率较高的情况下快速目标检测与识别,更加适合现场应用环境。本案例,我们快速实现一个视频目标检测功能,实现的具体原理我们将在单独的文章中详细介绍。
下载编译
我们首先下载 Darknet 开发框架,Darknet 开发框架是 YOLO 大神级作者自己用 C 语言编写的开发框架,支持 GPU 加速,有两种下载方式:

下载 Darknet 压缩包

git clone https://github.com/pjreddie/darknet

下载后,完整的文件内容,如下图所示:

编译:
cd darknet
# 编译
make

编译后的文件内容,如下图所示:

下载权重文件
我们这里下载的是“yolov3”版本,大小是 200 多 M,“yolov3-tiny”比较小,30 多 M。
wget https://pjreddie.com/media/files/yolov3.weights
下载权重文件后,文件内容如下图所示:

上图中的“yolov3-tiny.weights”,”yolov2-tiny.weights” 是我单独另下载的。
C 语言预测
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

如图所示,我们已经预测出三种类别以及对应的概率值。模型输出的照片位于 darknet 根目录,名字是“predictions.jpg”,如下图所示:

让我们打开模型输出照片看下:

Python 语言预测
我们首先需要将“darknet”文件夹内的“libdarknet.so”文件移动到“darknet/python”内,完成后如下图所示:

我们将使用 Darknet 内置的“darknet.py”, 进行预测。预测之前,我们需要对文件进行修改:

默认 py 文件基于 python2.0,所以对于 python3.0 及以上需要修改 print
由于涉及到 python 和 C 之间的传值,所以字符串内容需要转码
使用绝对路径

修改完成后,如下图所示:

打开“darknet/cfg/coco.data”文件,将“names”也改为绝对路径(截图内没有修改,读者根据自己的实际路径修改):

我们可以开始预测了,首先进入“darknet/python”然后执行“darknet.py”文件即可:

结果如下图所示:

对模型输出的结果做个简单的说明,如:
# 分别是:类别,识别概率,识别物体的 X 坐标,识别物体的 Y 坐标,识别物体的长度,识别物体的高度
(b’dog’, 0.999338686466217, (224.18377685546875, 378.4237060546875, 178.60214233398438, 328.1665954589844)
视频检测
from ctypes import *
import random
import cv2
import numpy as np

def sample(probs):
s = sum(probs)
probs = [a/s for 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):
arr = (ctype*len(values))()
arr[:] = values
return 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(“../python/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 convertBack(x, y, w, h):
xmin = int(round(x – (w / 2)))
xmax = int(round(x + (w / 2)))
ymin = int(round(y – (h / 2)))
ymax = int(round(y + (h / 2)))
return xmin, ymin, xmax, ymax

def array_to_image(arr):
# need to return old values to avoid python freeing memory
arr = arr.transpose(2,0,1)
c, h, w = arr.shape[0:3]
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

def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
im, image = array_to_image(image)
rgbgr_image(im)
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):
a = dets[j].prob[0:meta.classes]
if any(a):
ai = np.array(a).nonzero()[0]
for i in ai:
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])
if isinstance(image, bytes): free_image(im)
free_detections(dets, num)
return res

if __name__ == “__main__”:

cap = cv2.VideoCapture(0)
ret, img = cap.read()
fps = cap.get(cv2.CAP_PROP_FPS)

net = load_net(b”/Users/xiaomingtai/darknet/cfg/yolov2-tiny.cfg”, b”/Users/xiaomingtai/darknet/yolov2-tiny.weights”, 0)
meta = load_meta(b”/Users/xiaomingtai/darknet/cfg/coco.data”)
cv2.namedWindow(“img”, cv2.WINDOW_NORMAL)

while(True):
ret, img = cap.read()
if ret:
r = detect(net, meta, img)

for i in r:
x, y, w, h = i[2][0], i[2][17], i[2][18], i[2][19]
xmin, ymin, xmax, ymax = convertBack(float(x), float(y), float(w), float(h))
pt1 = (xmin, ymin)
pt2 = (xmax, ymax)
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
cv2.putText(img, i[0].decode() + ” [” + str(round(i[1] * 100, 2)) + “]”, (pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, [0, 255, 0], 4)
cv2.imshow(“img”, img)
if cv2.waitKey(1) & 0xFF == ord(‘q’):
break
模型输出结果:

模型视频检测结果:

没有 GPU 的条件下还是不要选择 yolov3 了,很慢。
总结
本篇文章主要是 YOLO 快速上手,我们通过很少的代码就能实现不错的目标检测。当然,想熟练掌握 YOLO,理解背后的原理是十分必要的,下篇文章将会重点介绍 YOLO 原理。

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