在OpenCV中使用YOLOv3进行物体检测

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软硬件环境

ubuntu 18.04 64bit
NVIDIA GTX 1070Ti 8G
anaconda with python 3.6
opencv 3.4.3
cuda 9.0
YOLO v3

前言

下图是近年来物体检测领域算法的演化，YOLO 是目前公认的比较准确的物体检测算法，已经发展到了第三个版本。关于 darknet (实现YOLO检测的开源项目)的基本情况，参考之前的博文 xugaoxiang.com/2019/12/16/…，里面有比较详细的阐述。

state-of-the-art-in-object-detection

准备工作

下载 YOLO 检测需要用到的配置文件、weights 模型文件及物体类型 class 文件

wget https://pjreddie.com/media/files/yolov3.weights
wget https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg?raw=true -O ./yolov3.cfg
wget https://github.com/pjreddie/darknet/blob/master/data/coco.names?raw=true -O ./coco.names

YOLO的基本原理

一般来讲，物体检测由两部分组成，物体定位( object locator )和物体识别( object recognizer )。下面以图片为例来讲 YOLOv3 的实现原理

将图片分割成 13x13 大小的网格单元, 以一张 416x416 像素大小的图片为例，会有1024个网格单元，模型会在每个 cell 上预测 bounding box
每个 cell 有可能会被预测出多个 bounding box，大部分 bounding box 最后都会被清除，因为它们的相似度太低。这里有个算法叫Non-Maximum Suppression，翻译过来叫非极大值抑制，可参考论文Efficient-Non-Maximum-Suppression，NMS 算法是用来提取相似度最高的

python代码

opencv 3.4.2 及以上的版本已经支持 darknet，同时也支持使用其他常见深度学习框架的模型，如 torch、tensorflow、caffe 等。

# -*- coding: utf-8 -*-
# @time    : 18-10-26 下午4:47
# @author  : xugaoxiang
# @email   : xugx.ai@gmail.com
# @website : https://xugaoxiang.com
# @file    : opencv_yolov3.py
# @software: PyCharm

# Usage example: python3 opencv_yolov3.py --image=test.png

import sys
import cv2
import argparse
import numpy as np
import os.path

# 参数初始化
# 相似度阈值
confThreshold = 0.5  # Confidence threshold

# NMS算法阈值
nmsThreshold = 0.4

# 输入图片的宽和高
inpWidth = 416  
inpHeight = 416

parser = argparse.ArgumentParser(description = 'Object detection using YOLOv3 in opencv')
parser.add_argument('--image', help = 'Path to image file.')
args = parser.parse_args()

# 导入物体类别class文件，默认支持80种
classesFile = "coco.names"
classes = None
with open(classesFile, 'rt') as f :
	classes = f.read().rstrip('\n').split('\n')

# yolo v3的配置及weights文件
modelConfiguration = "yolov3.cfg"
modelWeights = "yolov3.weights"

# opencv读取外部模型
net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
# 这里使用CPU，如果想使用GPU的话，参数是DNN_TARGET_OPENCL,　但是当前版本只支持interl GPU，如果是其它GPU的话，会自动切换到CPU模式
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

# Get the names of the output layers
def getOutputsNames(net) :
	# Get the names of all the layers in the network
	layersNames = net.getLayerNames()
	# Get the names of the output layers, i.e. the layers with unconnected outputs
	return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]


# 画bounding box
def drawPred(classId, conf, left, top, right, bottom) :
	# Draw a bounding box.
	cv2.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
	
	label = '%.2f' % conf
	
	# Get the label for the class name and its confidence
	if classes :
		assert (classId < len(classes))
		label = '%s:%s' % (classes[classId], label)
	
	# Display the label at the top of the bounding box
	labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
	top = max(top, labelSize[1])
	cv2.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine),
	             (255, 255, 255), cv2.FILLED)
	cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 0), 1)


# 使用NMS算法，丢弃低相似度的bounding box
def postprocess(frame, outs) :
	frameHeight = frame.shape[0]
	frameWidth = frame.shape[1]
	
	classIds = []
	confidences = []
	boxes = []
	# Scan through all the bounding boxes output from the network and keep only the
	# ones with high confidence scores. Assign the box's class label as the class with the highest score.
	classIds = []
	confidences = []
	boxes = []
	for out in outs :
		for detection in out :
			scores = detection[5 :]
			classId = np.argmax(scores)
			confidence = scores[classId]
			if confidence > confThreshold :
				center_x = int(detection[0] * frameWidth)
				center_y = int(detection[1] * frameHeight)
				width = int(detection[2] * frameWidth)
				height = int(detection[3] * frameHeight)
				left = int(center_x - width / 2)
				top = int(center_y - height / 2)
				classIds.append(classId)
				confidences.append(float(confidence))
				boxes.append([left, top, width, height])
	
	# Perform non maximum suppression to eliminate redundant overlapping boxes with
	# lower confidences.
	indices = cv2.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
	for i in indices :
		i = i[0]
		box = boxes[i]
		left = box[0]
		top = box[1]
		width = box[2]
		height = box[3]
		drawPred(classIds[i], confidences[i], left, top, left + width, top + height)


# Process inputs
winName = 'Deep learning object detection in OpenCV'
cv2.namedWindow(winName, cv2.WINDOW_NORMAL)

if (args.image) :

	if not os.path.isfile(args.image) :
		print('Input image file {} does not exist.'.format(args.image))
		sys.exit(1)
	frame = cv2.imread(args.image, cv2.IMREAD_ANYCOLOR)
	outputFile = args.image[:-4] + '_yolov3_out.png'
	
	# Create a 4D blob from a frame.
	blob = cv2.dnn.blobFromImage(frame, 1 / 255, (inpWidth, inpHeight), [0, 0, 0], 1, crop = False)

	# Sets the input to the network
	net.setInput(blob)

	# Runs the forward pass to get output of the output layers
	outs = net.forward(getOutputsNames(net))

	# Remove the bounding boxes with low confidence
	postprocess(frame, outs)
	
	cv2.imshow(winName, frame)
	cv2.imwrite(outputFile, frame)
	cv2.destroyAllWindows()

测试程序输出

opencv_yolov3_bird

opencv_yolov3_person

在OpenCV中使用YOLOv3进行物体检测

软硬件环境

前言

准备工作

YOLO的基本原理

python代码

参考资料