OpenCV 개발(2)-신경 망 사용 예시
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
}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)다음은 예 시 를 드 리 겠 습 니 다.1.MLP 기반 식별.이 프로그램 은 4 가지 동물 데 이 터 를 인공 적 으로 생 성하 여 MLP 네트워크 트 레이 닝 모델 을 통 해 테스트 데이터 유형 을 측정 한다.
#exam1.py
import cv2
import numpy as np
from random import randint
# MLP ， 、 、 。
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 ))
#
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]
# （ ）
def record(sample, classification):
return (np.array([sample], dtype=np.float32), np.array([classification], dtype=np.float32))
# 5000
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()))
# MLP
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)
#
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
#
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.DN 기반 식별.이 프로그램 은 미리 훈련 된 caffe 모델 을 불 러 와 카메라 에서 얻 은 이미지 에서 얼굴 을 감지 한다.
import numpy as np
import argparse
import cv2 as cv
# ImportError， PYTHONPATH Python 。
# ， （ ）。
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)')
# DNN
from cv2 import dnn
inWidth = 300
inHeight = 300
confThreshold = 0.5
# opencv3.4\sources\samples\dnn\face_detector ， OpenCV3.4
prototxt = 'face_detector/deploy.prototxt'
# caffe ， opencv3.4\sources\samples\dnn\face_detector
caffemodel = 'face_detector/res10_300x300_ssd_iter_140000.caffemodel'
# caffe
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]
# ， ，
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:
break이 내용에 흥미가 있습니까?
현재 기사가 여러분의 문제를 해결하지 못하는 경우 AI 엔진은 머신러닝 분석(스마트 모델이 방금 만들어져 부정확한 경우가 있을 수 있음)을 통해 가장 유사한 기사를 추천합니다:
Visual Studio 2017에서 OpenCV 템플릿 프로젝트 만들기・Windows 7 Professional 64bit ・Visual Studio 2017 Version 15.9.14 · OpenCV 3.4.1 OpenCV의 도입 방법 등은 아래를 참조하십시오. Visual Stu...
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