OpenCV3.4的神经网络功能主要提供了以下三种:

  1. ml模块中的多层感知机(Artificial Neural Networks - Multi-Layer Perceptrons),提供了MLP的创建、训练、参数设置等函数。如:

    
    static Ptr< ANN_MLP >   create ()
        Creates empty model. 
    static Ptr< ANN_MLP >   load (const String &filepath)
        Loads and creates a serialized ANN from a file. 
    void    setAnnealFinalT (double val)
    void    setAnnealInitialT (double val)
    void    setAnnealItePerStep (int val)
    virtual void    setBackpropMomentumScale (double val)=0
    virtual void    setBackpropWeightScale (double val)=0
    virtual void    setLayerSizes (InputArray _layer_sizes)=0
    virtual void    setRpropDW0 (double val)=0
    virtual void    setRpropDWMax (double val)=0
    
    enum    ActivationFunctions { 
        IDENTITY = 0, 
        SIGMOID_SYM = 1, 
        GAUSSIAN = 2, 
        RELU = 3, 
        LEAKYRELU = 4 
    }
    
    enum    TrainFlags { 
        UPDATE_WEIGHTS = 1, 
        NO_INPUT_SCALE = 2, 
        NO_OUTPUT_SCALE = 4 
    }
    enum    TrainingMethods { 
        BACKPROP =0, 
        RPROP = 1, 
        ANNEAL = 2 
    }

请参看帮助文档

  1. DNN模块,提供了很多用于创建、加载、训练深度网络和参数设置以及加载TensorFlow、Caffe、Torch模型的方法和类,如:

    class   cv::dnn::BackendNode
        Derivatives of this class encapsulates functions of certain backends.
    class   cv::dnn::BackendWrapper
        Derivatives of this class wraps cv::Mat for different backends and targets.
    class   cv::dnn::Dict
        This class implements name-value dictionary, values are instances of DictValue.
    struct      cv::dnn::DictValue
        This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. 
    class   cv::dnn::Layer
        This interface class allows to build new Layers - are building blocks of networks.
    class   cv::dnn::LayerParams
        This class provides all data needed to initialize layer. 
    class   cv::dnn::Net
        This class allows to create and manipulate comprehensive artificial neural networks.
        Mat     cv::dnn::blobFromImages (const std::vector< Mat > &images, double scalefactor=1.0, Size size=Size(), const Scalar &mean=Scalar(), bool swapRB=true, bool crop=true)
        Creates 4-dimensional blob from series of images. Optionally resizes and crops images from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    void    cv::dnn::NMSBoxes (const std::vector< Rect > &bboxes, const std::vector< float > &scores, const float score_threshold, const float nms_threshold, std::vector< int > &indices, const float eta=1.f, const int top_k=0)
        Performs non maximum suppression given boxes and corresponding scores.
    
    Net     cv::dnn::readNetFromCaffe (const String &prototxt, const String &caffeModel=String())
        Reads a network model stored in Caffe framework's format.
    Net     cv::dnn::readNetFromDarknet (const String &cfgFile, const String &darknetModel=String())
        Reads a network model stored in Darknet model files.
    Net     cv::dnn::readNetFromTensorflow (const String &model, const String &config=String())
        Reads a network model stored in TensorFlow framework's format. 
    Net     cv::dnn::readNetFromTorch (const String &model, bool isBinary=true)

    参看帮助文档

  2. 第三方深度网络工具,详情请查看帮助文档。

下面给出示例。
1.基于MLP的识别。该程序人工生成四类动物数据,通过MLP网络训练模型并检测测试数据类型。

    #exam1.py 
    import cv2
    import numpy as np
    from random import randint
    #创建MLP网络,并设置训练方法、激活函数、层大小和迭代终止条件。
    animals_net = cv2.ml.ANN_MLP_create()
    animals_net.setTrainMethod(cv2.ml.ANN_MLP_RPROP | cv2.ml.ANN_MLP_UPDATE_WEIGHTS)
    animals_net.setActivationFunction(cv2.ml.ANN_MLP_SIGMOID_SYM)
    animals_net.setLayerSizes(np.array([3, 6, 4]))
    animals_net.setTermCriteria(( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 ))
    #生成四类动物数据及类标记
    def dog_sample():
        return [randint(10, 20), 1, randint(38, 42)]
    def dog_class():
        return [1, 0, 0, 0]
    def condor_sample():
        return [randint(3,10), randint(3,5), 0]
    def condor_class():
        return [0, 1, 0, 0]
    def dolphin_sample():
        return [randint(30, 190), randint(5, 15), randint(80, 100)]
    def dolphin_class():
        return [0, 0, 1, 0]
    def dragon_sample():
        return [randint(1200, 1800), randint(30, 40), randint(160, 180)]
    def dragon_class():
        return [0, 0, 0, 1]
    #将动物数据和类标记组成一个记录(样本)
    def record(sample, classification):
        return (np.array([sample], dtype=np.float32), np.array([classification], dtype=np.float32))
    #获取5000个样本数据
    records = []
    RECORDS = 5000
    for x in range(0, RECORDS):
        records.append(record(dog_sample(), dog_class()))
        records.append(record(condor_sample(), condor_class()))
        records.append(record(dolphin_sample(), dolphin_class()))
        records.append(record(dragon_sample(), dragon_class()))
    #训练MLP网络
    EPOCHS = 2
    for e in range(0, EPOCHS):
        print("Epoch %d:" % e)
        for t, c in records:
            animals_net.train(t, cv2.ml.ROW_SAMPLE, c)
    #预测测试样本类别
    TESTS = 100
    dog_results = 0
    for x in range(0, TESTS):
        clas = int(animals_net.predict(np.array([dog_sample()], dtype=np.float32))[0])
        print("class: %d" % clas)
        if (clas) == 0:
            dog_results += 1
    condor_results = 0
    for x in range(0, TESTS):
        clas = int(animals_net.predict(np.array([condor_sample()], dtype=np.float32))[0])
        print("class: %d" % clas)
        if (clas) == 1:
            condor_results += 1
    dolphin_results = 0
    for x in range(0, TESTS):
        clas = int(animals_net.predict(np.array([dolphin_sample()], dtype=np.float32))[0])
        print("class: %d" % clas)
        if (clas) == 2:
            dolphin_results += 1
    dragon_results = 0
    for x in range(0, TESTS):
        clas = int(animals_net.predict(np.array([dragon_sample()], dtype=np.float32))[0])
        print("class: %d" % clas)
        if (clas) == 3:
            dragon_results += 1
    #输出测试准确率
    print("Dog accuracy: %f%%" % (dog_results))
    print("condor accuracy: %f%%" % (condor_results))
    print("dolphin accuracy: %f%%" % (dolphin_results))
    print("dragon accuracy: %f%%" % (dragon_results))

2.基于DNN的识别。该程序加载预先训练的caffe模型在摄像头获取的图像上检测人脸。

import numpy as np
import argparse
import cv2 as cv
#若出现ImportError,请配置环境变量PYTHONPATH为Python可执行文件的地址。
#若不能解决,请更新相关包(或卸载后重新安装)。
try:
    import cv2 as cv
except ImportError:
    raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
#导入DNN模块
from cv2 import dnn
inWidth = 300
inHeight = 300
confThreshold = 0.5
#该文件包含在opencv3.4\sources\samples\dnn\face_detector目录中,该目录的上级目录为OpenCV3.4的下载或安装目录
prototxt = 'face_detector/deploy.prototxt'
#该caffe模型文件需先下载,请参看opencv3.4\sources\samples\dnn\face_detector目录中的文本文件
caffemodel = 'face_detector/res10_300x300_ssd_iter_140000.caffemodel'
#加载caffe模型并从摄像头获取图像
if __name__ == '__main__':
    net = dnn.readNetFromCaffe(prototxt, caffemodel)
    cap = cv.VideoCapture(0)
    while True:
        ret, frame = cap.read()
        cols = frame.shape[1]
        rows = frame.shape[0]
                #将获取的图像设置为网络输入,设置网络传播方向,检测人脸
        net.setInput(dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (104.0, 177.0, 123.0), False, False))
        detections = net.forward()
        perf_stats = net.getPerfProfile()
        print('Inference time, ms: %.2f' % (perf_stats[0] / cv.getTickFrequency() * 1000))
        for i in range(detections.shape[2]):
            confidence = detections[0, 0, i, 2]
            if confidence > confThreshold:
                xLeftBottom = int(detections[0, 0, i, 3] * cols)
                yLeftBottom = int(detections[0, 0, i, 4] * rows)
                xRightTop = int(detections[0, 0, i, 5] * cols)
                yRightTop = int(detections[0, 0, i, 6] * rows)
                cv.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),
                             (0, 255, 0))
                label = "face: %.4f" % confidence
                labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
                cv.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
                                    (xLeftBottom + labelSize[0], yLeftBottom + baseLine),
                                    (255, 255, 255), cv.FILLED)
                cv.putText(frame, label, (xLeftBottom, yLeftBottom),
                           cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
        cv.imshow("detections", frame)
        if cv.waitKey(1) != -1:
            break