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Add Java and Python code for cascade classifier and HDR tutorials.

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This commit is contained in:
catree
2018-06-07 20:14:16 +02:00
parent 1187a7fa34
commit afa5b0cc93
13 changed files with 557 additions and 100 deletions
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samples/cpp/tutorial_code/objectDetection/objectDetection.cpp
+39 -24
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@@ -2,7 +2,7 @@
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <stdio.h>
#include <iostream>
using namespace std;
using namespace cv;
@@ -11,48 +11,63 @@ using namespace cv;
void detectAndDisplay( Mat frame );
/** Global variables */
String face_cascade_name, eyes_cascade_name;
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
String window_name = "Capture - Face detection";
/** @function main */
int main( int argc, const char** argv )
{
CommandLineParser parser(argc, argv,
"{help h||}"
"{face_cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|}"
"{eyes_cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}");
"{help h||}"
"{face_cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|Path to face cascade.}"
"{eyes_cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|Path to eyes cascade.}"
"{camera|0|Camera device number.}");
parser.about( "\nThis program demonstrates using the cv::CascadeClassifier class to detect objects (Face + eyes) in a video stream.\n"
"You can use Haar or LBP features.\n\n" );
parser.printMessage();
face_cascade_name = parser.get<String>("face_cascade");
eyes_cascade_name = parser.get<String>("eyes_cascade");
VideoCapture capture;
Mat frame;
String face_cascade_name = parser.get<String>("face_cascade");
String eyes_cascade_name = parser.get<String>("eyes_cascade");
//-- 1. Load the cascades
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading face cascade\n"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading eyes cascade\n"); return -1; };
if( !face_cascade.load( face_cascade_name ) )
{
cout << "--(!)Error loading face cascade\n";
return -1;
};
if( !eyes_cascade.load( eyes_cascade_name ) )
{
cout << "--(!)Error loading eyes cascade\n";
return -1;
};
int camera_device = parser.get<int>("camera");
VideoCapture capture;
//-- 2. Read the video stream
capture.open( 0 );
if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture\n"); return -1; }
capture.open( camera_device );
if ( ! capture.isOpened() )
{
cout << "--(!)Error opening video capture\n";
return -1;
}
Mat frame;
while ( capture.read(frame) )
{
if( frame.empty() )
{
printf(" --(!) No captured frame -- Break!");
cout << "--(!) No captured frame -- Break!\n";
break;
}
//-- 3. Apply the classifier to the frame
detectAndDisplay( frame );
if( waitKey(10) == 27 ) { break; } // escape
if( waitKey(10) == 27 )
{
break; // escape
}
}
return 0;
}
@@ -60,33 +75,33 @@ int main( int argc, const char** argv )
/** @function detectAndDisplay */
void detectAndDisplay( Mat frame )
{
std::vector<Rect> faces;
Mat frame_gray;
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(60, 60) );
std::vector<Rect> faces;
face_cascade.detectMultiScale( frame_gray, faces );
for ( size_t i = 0; i < faces.size(); i++ )
{
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2 ), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2 ), 0, 0, 360, Scalar( 255, 0, 255 ), 4 );
Mat faceROI = frame_gray( faces[i] );
std::vector<Rect> eyes;
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
std::vector<Rect> eyes;
eyes_cascade.detectMultiScale( faceROI, eyes );
for ( size_t j = 0; j < eyes.size(); j++ )
{
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4 );
}
}
//-- Show what you got
imshow( window_name, frame );
imshow( "Capture - Face detection", frame );
}
samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp
+20 -5
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@@ -1,6 +1,7 @@
#include <opencv2/photo.hpp>
#include "opencv2/photo.hpp"
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include "opencv2/highgui.hpp"
#include <vector>
#include <iostream>
#include <fstream>
@@ -10,38 +11,52 @@ using namespace std;
void loadExposureSeq(String, vector<Mat>&, vector<float>&);
int main(int, char**argv)
int main(int argc, char**argv)
{
CommandLineParser parser( argc, argv, "{@input | | Input directory that contains images and exposure times. }" );
//! [Load images and exposure times]
vector<Mat> images;
vector<float> times;
loadExposureSeq(argv[1], images, times);
loadExposureSeq(parser.get<String>( "@input" ), images, times);
//! [Load images and exposure times]
//! [Estimate camera response]
Mat response;
Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec();
calibrate->process(images, response, times);
//! [Estimate camera response]
//! [Make HDR image]
Mat hdr;
Ptr<MergeDebevec> merge_debevec = createMergeDebevec();
merge_debevec->process(images, hdr, times, response);
//! [Make HDR image]
//! [Tonemap HDR image]
Mat ldr;
Ptr<TonemapDurand> tonemap = createTonemapDurand(2.2f);
tonemap->process(hdr, ldr);
//! [Tonemap HDR image]
//! [Perform exposure fusion]
Mat fusion;
Ptr<MergeMertens> merge_mertens = createMergeMertens();
merge_mertens->process(images, fusion);
//! [Perform exposure fusion]
//! [Write results]
imwrite("fusion.png", fusion * 255);
imwrite("ldr.png", ldr * 255);
imwrite("hdr.hdr", hdr);
//! [Write results]
return 0;
}
void loadExposureSeq(String path, vector<Mat>& images, vector<float>& times)
{
path = path + std::string("/");
path = path + "/";
ifstream list_file((path + "list.txt").c_str());
string name;
float val;
samples/java/tutorial_code/objectDetection/cascade_classifier/ObjectDetectionDemo.java
+98
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@@ -0,0 +1,98 @@
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.highgui.HighGui;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.videoio.VideoCapture;
class ObjectDetection {
public void detectAndDisplay(Mat frame, CascadeClassifier faceCascade, CascadeClassifier eyesCascade) {
Mat frameGray = new Mat();
Imgproc.cvtColor(frame, frameGray, Imgproc.COLOR_BGR2GRAY);
Imgproc.equalizeHist(frameGray, frameGray);
// -- Detect faces
MatOfRect faces = new MatOfRect();
faceCascade.detectMultiScale(frameGray, faces);
List<Rect> listOfFaces = faces.toList();
for (Rect face : listOfFaces) {
Point center = new Point(face.x + face.width / 2, face.y + face.height / 2);
Imgproc.ellipse(frame, center, new Size(face.width / 2, face.height / 2), 0, 0, 360,
new Scalar(255, 0, 255));
Mat faceROI = frameGray.submat(face);
// -- In each face, detect eyes
MatOfRect eyes = new MatOfRect();
eyesCascade.detectMultiScale(faceROI, eyes);
List<Rect> listOfEyes = eyes.toList();
for (Rect eye : listOfEyes) {
Point eyeCenter = new Point(face.x + eye.x + eye.width / 2, face.y + eye.y + eye.height / 2);
int radius = (int) Math.round((eye.width + eye.height) * 0.25);
Imgproc.circle(frame, eyeCenter, radius, new Scalar(255, 0, 0), 4);
}
}
//-- Show what you got
HighGui.imshow("Capture - Face detection", frame );
}
public void run(String[] args) {
String filenameFaceCascade = args.length > 2 ? args[0] : "../../data/haarcascades/haarcascade_frontalface_alt.xml";
String filenameEyesCascade = args.length > 2 ? args[1] : "../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml";
int cameraDevice = args.length > 2 ? Integer.parseInt(args[2]) : 0;
CascadeClassifier faceCascade = new CascadeClassifier();
CascadeClassifier eyesCascade = new CascadeClassifier();
if (!faceCascade.load(filenameFaceCascade)) {
System.err.println("--(!)Error loading face cascade: " + filenameFaceCascade);
System.exit(0);
}
if (!eyesCascade.load(filenameEyesCascade)) {
System.err.println("--(!)Error loading eyes cascade: " + filenameEyesCascade);
System.exit(0);
}
VideoCapture capture = new VideoCapture(cameraDevice);
if (!capture.isOpened()) {
System.err.println("--(!)Error opening video capture");
System.exit(0);
}
Mat frame = new Mat();
while (capture.read(frame)) {
if (frame.empty()) {
System.err.println("--(!) No captured frame -- Break!");
break;
}
//-- 3. Apply the classifier to the frame
detectAndDisplay(frame, faceCascade, eyesCascade);
if (HighGui.waitKey(10) == 27) {
break;// escape
}
}
System.exit(0);
}
}
public class ObjectDetectionDemo {
public static void main(String[] args) {
// Load the native OpenCV library
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
new ObjectDetection().run(args);
}
}
samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java
+102
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@@ -0,0 +1,102 @@
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.photo.CalibrateDebevec;
import org.opencv.photo.MergeDebevec;
import org.opencv.photo.MergeMertens;
import org.opencv.photo.Photo;
import org.opencv.photo.TonemapDurand;
class HDRImaging {
public void loadExposureSeq(String path, List<Mat> images, List<Float> times) {
path += "/";
List<String> lines;
try {
lines = Files.readAllLines(Paths.get(path + "list.txt"));
for (String line : lines) {
String[] splitStr = line.split("\\s+");
if (splitStr.length == 2) {
String name = splitStr[0];
Mat img = Imgcodecs.imread(path + name);
images.add(img);
float val = Float.parseFloat(splitStr[1]);
times.add(1/ val);
}
}
} catch (IOException e) {
e.printStackTrace();
}
}
public void run(String[] args) {
String path = args.length > 0 ? args[0] : "";
if (path.isEmpty()) {
System.out.println("Path is empty. Use the directory that contains images and exposure times.");
System.exit(0);
}
//! [Load images and exposure times]
List<Mat> images = new ArrayList<>();
List<Float> times = new ArrayList<>();
loadExposureSeq(path, images, times);
//! [Load images and exposure times]
//! [Estimate camera response]
Mat response = new Mat();
CalibrateDebevec calibrate = Photo.createCalibrateDebevec();
Mat matTimes = new Mat(times.size(), 1, CvType.CV_32F);
float[] arrayTimes = new float[(int) (matTimes.total()*matTimes.channels())];
for (int i = 0; i < times.size(); i++) {
arrayTimes[i] = times.get(i);
}
matTimes.put(0, 0, arrayTimes);
calibrate.process(images, response, matTimes);
//! [Estimate camera response]
//! [Make HDR image]
Mat hdr = new Mat();
MergeDebevec mergeDebevec = Photo.createMergeDebevec();
mergeDebevec.process(images, hdr, matTimes);
//! [Make HDR image]
//! [Tonemap HDR image]
Mat ldr = new Mat();
TonemapDurand tonemap = Photo.createTonemapDurand();
tonemap.process(hdr, ldr);
//! [Tonemap HDR image]
//! [Perform exposure fusion]
Mat fusion = new Mat();
MergeMertens mergeMertens = Photo.createMergeMertens();
mergeMertens.process(images, fusion);
//! [Perform exposure fusion]
//! [Write results]
fusion = fusion.mul(fusion, 255);
ldr = ldr.mul(ldr, 255);
Imgcodecs.imwrite("fusion.png", fusion);
Imgcodecs.imwrite("ldr.png", ldr);
Imgcodecs.imwrite("hdr.hdr", hdr);
//! [Write results]
System.exit(0);
}
}
public class HDRImagingDemo {
public static void main(String[] args) {
// Load the native OpenCV library
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
new HDRImaging().run(args);
}
}
samples/python/tutorial_code/objectDetection/cascade_classifier/objectDetection.py
+61
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@@ -0,0 +1,61 @@
from __future__ import print_function
import cv2 as cv
import argparse
def detectAndDisplay(frame):
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
frame_gray = cv.equalizeHist(frame_gray)
#-- Detect faces
faces = face_cascade.detectMultiScale(frame_gray)
for (x,y,w,h) in faces:
center = (x + w//2, y + h//2)
frame = cv.ellipse(frame, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)
faceROI = frame_gray[y:y+h,x:x+w]
#-- In each face, detect eyes
eyes = eyes_cascade.detectMultiScale(faceROI)
for (x2,y2,w2,h2) in eyes:
eye_center = (x + x2 + w2//2, y + y2 + h2//2)
radius = int(round((w2 + h2)*0.25))
frame = cv.circle(frame, eye_center, radius, (255, 0, 0 ), 4)
cv.imshow('Capture - Face detection', frame)
parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
parser.add_argument('--face_cascade', help='Path to face cascade.', default='../../data/haarcascades/haarcascade_frontalface_alt.xml')
parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
parser.add_argument('--camera', help='Camera devide number.', type=int, default=0)
args = parser.parse_args()
face_cascade_name = args.face_cascade
eyes_cascade_name = args.eyes_cascade
face_cascade = cv.CascadeClassifier()
eyes_cascade = cv.CascadeClassifier()
#-- 1. Load the cascades
if not face_cascade.load(face_cascade_name):
print('--(!)Error loading face cascade')
exit(0)
if not eyes_cascade.load(eyes_cascade_name):
print('--(!)Error loading eyes cascade')
exit(0)
camera_device = args.camera
#-- 2. Read the video stream
cap = cv.VideoCapture(camera_device)
if not cap.isOpened:
print('--(!)Error opening video capture')
exit(0)
while True:
ret, frame = cap.read()
if frame is None:
print('--(!) No captured frame -- Break!')
break
detectAndDisplay(frame)
if cv.waitKey(10) == 27:
break
samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py
+56
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@@ -0,0 +1,56 @@
from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse
import os
def loadExposureSeq(path):
images = []
times = []
with open(os.path.join(path, 'list.txt')) as f:
content = f.readlines()
for line in content:
tokens = line.split()
images.append(cv.imread(os.path.join(path, tokens[0])))
times.append(1 / float(tokens[1]))
return images, np.asarray(times, dtype=np.float32)
parser = argparse.ArgumentParser(description='Code for High Dynamic Range Imaging tutorial.')
parser.add_argument('--input', type=str, help='Path to the directory that contains images and exposure times.')
args = parser.parse_args()
if not args.input:
parser.print_help()
exit(0)
## [Load images and exposure times]
images, times = loadExposureSeq(args.input)
## [Load images and exposure times]
## [Estimate camera response]
calibrate = cv.createCalibrateDebevec()
response = calibrate.process(images, times)
## [Estimate camera response]
## [Make HDR image]
merge_debevec = cv.createMergeDebevec()
hdr = merge_debevec.process(images, times, response)
## [Make HDR image]
## [Tonemap HDR image]
tonemap = cv.createTonemapDurand(2.2)
ldr = tonemap.process(hdr)
## [Tonemap HDR image]
## [Perform exposure fusion]
merge_mertens = cv.createMergeMertens()
fusion = merge_mertens.process(images)
## [Perform exposure fusion]
## [Write results]
cv.imwrite('fusion.png', fusion * 255)
cv.imwrite('ldr.png', ldr * 255)
cv.imwrite('hdr.hdr', hdr)
## [Write results]