[EDIT] added evrything to namespace, also fixed includes
This commit is contained in:
Jasper
@@ -1,58 +1,59 @@
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#include "BackgroundRemover.h"
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#include"opencv2\opencv.hpp"
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/*
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Author: Pierfrancesco Soffritti https://github.com/PierfrancescoSoffritti
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*/
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namespace computervision
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{
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BackgroundRemover::BackgroundRemover(void) {
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background;
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calibrated = false;
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}
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BackgroundRemover::BackgroundRemover(void) {
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background;
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calibrated = false;
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}
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void BackgroundRemover::calibrate(Mat input) {
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cvtColor(input, background, CV_BGR2GRAY);
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calibrated = true;
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}
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void BackgroundRemover::calibrate(Mat input) {
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cvtColor(input, background, CV_BGR2GRAY);
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calibrated = true;
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}
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Mat BackgroundRemover::getForeground(Mat input) {
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Mat foregroundMask = getForegroundMask(input);
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Mat BackgroundRemover::getForeground(Mat input) {
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Mat foregroundMask = getForegroundMask(input);
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//imshow("foregroundMask", foregroundMask);
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//imshow("foregroundMask", foregroundMask);
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Mat foreground;
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input.copyTo(foreground, foregroundMask);
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Mat foreground;
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input.copyTo(foreground, foregroundMask);
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return foreground;
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}
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return foreground;
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}
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Mat BackgroundRemover::getForegroundMask(Mat input) {
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Mat foregroundMask;
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Mat BackgroundRemover::getForegroundMask(Mat input) {
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Mat foregroundMask;
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if (!calibrated) {
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foregroundMask = Mat::zeros(input.size(), CV_8UC1);
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return foregroundMask;
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}
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cvtColor(input, foregroundMask, CV_BGR2GRAY);
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removeBackground(foregroundMask, background);
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if (!calibrated) {
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foregroundMask = Mat::zeros(input.size(), CV_8UC1);
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return foregroundMask;
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}
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cvtColor(input, foregroundMask, CV_BGR2GRAY);
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void BackgroundRemover::removeBackground(Mat input, Mat background) {
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int thresholdOffset = 10;
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removeBackground(foregroundMask, background);
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for (int i = 0; i < input.rows; i++) {
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for (int j = 0; j < input.cols; j++) {
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uchar framePixel = input.at<uchar>(i, j);
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uchar bgPixel = background.at<uchar>(i, j);
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return foregroundMask;
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}
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void BackgroundRemover::removeBackground(Mat input, Mat background) {
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int thresholdOffset = 10;
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for (int i = 0; i < input.rows; i++) {
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for (int j = 0; j < input.cols; j++) {
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uchar framePixel = input.at<uchar>(i, j);
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uchar bgPixel = background.at<uchar>(i, j);
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if (framePixel >= bgPixel - thresholdOffset && framePixel <= bgPixel + thresholdOffset)
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input.at<uchar>(i, j) = 0;
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else
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input.at<uchar>(i, j) = 255;
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if (framePixel >= bgPixel - thresholdOffset && framePixel <= bgPixel + thresholdOffset)
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input.at<uchar>(i, j) = 0;
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else
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input.at<uchar>(i, j) = 255;
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}
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}
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}
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}
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@@ -1,24 +1,25 @@
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#pragma once
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#include<opencv\cv.h>
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#include"opencv2\opencv.hpp"
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#include <opencv2/imgproc\types_c.h>
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/*
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Author: Pierfrancesco Soffritti https://github.com/PierfrancescoSoffritti
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*/
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namespace computervision
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{
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using namespace cv;
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using namespace std;
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class BackgroundRemover {
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public:
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BackgroundRemover(void);
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void calibrate(Mat input);
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Mat BackgroundRemover::getForeground(Mat input);
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class BackgroundRemover {
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public:
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BackgroundRemover(void);
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void calibrate(Mat input);
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Mat getForeground(Mat input);
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private:
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Mat background;
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bool calibrated = false;
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private:
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Mat background;
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bool calibrated = false;
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Mat getForegroundMask(Mat input);
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void BackgroundRemover::removeBackground(Mat input, Mat background);
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};
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Mat getForegroundMask(Mat input);
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void removeBackground(Mat input, Mat background);
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};
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}
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@@ -1,51 +1,53 @@
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#include "FaceDetector.h"
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#include"opencv2\opencv.hpp"
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/*
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Author: Pierfrancesco Soffritti https://github.com/PierfrancescoSoffritti
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*/
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namespace computervision
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{
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Rect getFaceRect(Mat input);
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Rect getFaceRect(Mat input);
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String faceClassifierFileName = "../res/haarcascade_frontalface_alt.xml";
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CascadeClassifier faceCascadeClassifier;
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String faceClassifierFileName = "../res/haarcascade_frontalface_alt.xml";
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CascadeClassifier faceCascadeClassifier;
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FaceDetector::FaceDetector(void) {
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if (!faceCascadeClassifier.load(faceClassifierFileName))
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throw runtime_error("can't load file " + faceClassifierFileName);
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}
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void FaceDetector::removeFaces(Mat input, Mat output) {
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vector<Rect> faces;
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Mat frameGray;
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cvtColor(input, frameGray, CV_BGR2GRAY);
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equalizeHist(frameGray, frameGray);
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faceCascadeClassifier.detectMultiScale(frameGray, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(120, 120));
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for (size_t i = 0; i < faces.size(); i++) {
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rectangle(
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output,
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Point(faces[i].x, faces[i].y),
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Point(faces[i].x + faces[i].width, faces[i].y + faces[i].height),
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Scalar(0, 0, 0),
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-1
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);
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FaceDetector::FaceDetector(void) {
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if (!faceCascadeClassifier.load(faceClassifierFileName))
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throw runtime_error("can't load file " + faceClassifierFileName);
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}
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}
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Rect getFaceRect(Mat input) {
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vector<Rect> faceRectangles;
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Mat inputGray;
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void FaceDetector::removeFaces(Mat input, Mat output) {
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vector<Rect> faces;
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Mat frameGray;
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cvtColor(input, inputGray, CV_BGR2GRAY);
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equalizeHist(inputGray, inputGray);
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cvtColor(input, frameGray, CV_BGR2GRAY);
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equalizeHist(frameGray, frameGray);
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faceCascadeClassifier.detectMultiScale(inputGray, faceRectangles, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(120, 120));
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faceCascadeClassifier.detectMultiScale(frameGray, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(120, 120));
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if (faceRectangles.size() > 0)
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return faceRectangles[0];
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else
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return Rect(0, 0, 1, 1);
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for (size_t i = 0; i < faces.size(); i++) {
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rectangle(
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output,
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Point(faces[i].x, faces[i].y),
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Point(faces[i].x + faces[i].width, faces[i].y + faces[i].height),
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Scalar(0, 0, 0),
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-1
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);
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}
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}
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Rect getFaceRect(Mat input) {
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vector<Rect> faceRectangles;
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Mat inputGray;
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cvtColor(input, inputGray, CV_BGR2GRAY);
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equalizeHist(inputGray, inputGray);
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faceCascadeClassifier.detectMultiScale(inputGray, faceRectangles, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(120, 120));
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if (faceRectangles.size() > 0)
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return faceRectangles[0];
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else
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return Rect(0, 0, 1, 1);
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}
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}
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@@ -1,7 +1,7 @@
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#pragma once
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#include<opencv\cv.h>
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#include"opencv2\opencv.hpp"
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#include <opencv2\imgproc\types_c.h>
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#include <opencv2/objdetect/objdetect_c.h>
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/*
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Author: Pierfrancesco Soffritti https://github.com/PierfrancescoSoffritti
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*/
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@@ -9,8 +9,11 @@
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using namespace cv;
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using namespace std;
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class FaceDetector {
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public:
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FaceDetector(void);
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void removeFaces(Mat input, Mat output);
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};
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namespace computervision
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{
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class FaceDetector {
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public:
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FaceDetector(void);
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void removeFaces(Mat input, Mat output);
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};
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}
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@@ -12,277 +12,280 @@
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#define BOUNDING_RECT_FINGER_SIZE_SCALING 0.3
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#define BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING 0.05
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FingerCount::FingerCount(void) {
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color_blue = Scalar(255, 0, 0);
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color_green = Scalar(0, 255, 0);
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color_red = Scalar(0, 0, 255);
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color_black = Scalar(0, 0, 0);
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color_white = Scalar(255, 255, 255);
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color_yellow = Scalar(0, 255, 255);
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color_purple = Scalar(255, 0, 255);
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}
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Mat FingerCount::findFingersCount(Mat input_image, Mat frame) {
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Mat contours_image = Mat::zeros(input_image.size(), CV_8UC3);
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// check if the source image is good
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if (input_image.empty())
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return contours_image;
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// we work only on the 1 channel result, since this function is called inside a loop we are not sure that this is always the case
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if (input_image.channels() != 1)
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return contours_image;
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vector<vector<Point>> contours;
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vector<Vec4i> hierarchy;
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findContours(input_image, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
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// we need at least one contour to work
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if (contours.size() <= 0)
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return contours_image;
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// find the biggest contour (let's suppose it's our hand)
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int biggest_contour_index = -1;
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double biggest_area = 0.0;
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for (int i = 0; i < contours.size(); i++) {
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double area = contourArea(contours[i], false);
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if (area > biggest_area) {
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biggest_area = area;
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biggest_contour_index = i;
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}
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namespace computervision
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{
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FingerCount::FingerCount(void) {
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color_blue = Scalar(255, 0, 0);
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color_green = Scalar(0, 255, 0);
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color_red = Scalar(0, 0, 255);
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color_black = Scalar(0, 0, 0);
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color_white = Scalar(255, 255, 255);
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color_yellow = Scalar(0, 255, 255);
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color_purple = Scalar(255, 0, 255);
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}
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if (biggest_contour_index < 0)
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return contours_image;
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Mat FingerCount::findFingersCount(Mat input_image, Mat frame) {
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Mat contours_image = Mat::zeros(input_image.size(), CV_8UC3);
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// find the convex hull object for each contour and the defects, two different data structure are needed by the OpenCV api
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vector<Point> hull_points;
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vector<int> hull_ints;
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// check if the source image is good
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if (input_image.empty())
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return contours_image;
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// for drawing the convex hull and for finding the bounding rectangle
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convexHull(Mat(contours[biggest_contour_index]), hull_points, true);
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// we work only on the 1 channel result, since this function is called inside a loop we are not sure that this is always the case
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if (input_image.channels() != 1)
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return contours_image;
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// for finding the defects
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convexHull(Mat(contours[biggest_contour_index]), hull_ints, false);
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vector<vector<Point>> contours;
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vector<Vec4i> hierarchy;
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// we need at least 3 points to find the defects
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vector<Vec4i> defects;
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if (hull_ints.size() > 3)
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convexityDefects(Mat(contours[biggest_contour_index]), hull_ints, defects);
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else
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return contours_image;
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findContours(input_image, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
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// we bound the convex hull
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Rect bounding_rectangle = boundingRect(Mat(hull_points));
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// we need at least one contour to work
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if (contours.size() <= 0)
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return contours_image;
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// we find the center of the bounding rectangle, this should approximately also be the center of the hand
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Point center_bounding_rect(
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(bounding_rectangle.tl().x + bounding_rectangle.br().x) / 2,
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(bounding_rectangle.tl().y + bounding_rectangle.br().y) / 2
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);
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// find the biggest contour (let's suppose it's our hand)
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int biggest_contour_index = -1;
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double biggest_area = 0.0;
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// we separate the defects keeping only the ones of intrest
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vector<Point> start_points;
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vector<Point> far_points;
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for (int i = 0; i < defects.size(); i++) {
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start_points.push_back(contours[biggest_contour_index][defects[i].val[0]]);
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// filtering the far point based on the distance from the center of the bounding rectangle
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if (findPointsDistance(contours[biggest_contour_index][defects[i].val[2]], center_bounding_rect) < bounding_rectangle.height * BOUNDING_RECT_FINGER_SIZE_SCALING)
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far_points.push_back(contours[biggest_contour_index][defects[i].val[2]]);
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}
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// we compact them on their medians
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vector<Point> filtered_start_points = compactOnNeighborhoodMedian(start_points, bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING);
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vector<Point> filtered_far_points = compactOnNeighborhoodMedian(far_points, bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING);
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// now we try to find the fingers
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vector<Point> filtered_finger_points;
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if (filtered_far_points.size() > 1) {
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vector<Point> finger_points;
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for (int i = 0; i < filtered_start_points.size(); i++) {
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vector<Point> closest_points = findClosestOnX(filtered_far_points, filtered_start_points[i]);
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if (isFinger(closest_points[0], filtered_start_points[i], closest_points[1], LIMIT_ANGLE_INF, LIMIT_ANGLE_SUP, center_bounding_rect, bounding_rectangle.height * BOUNDING_RECT_FINGER_SIZE_SCALING))
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finger_points.push_back(filtered_start_points[i]);
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for (int i = 0; i < contours.size(); i++) {
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double area = contourArea(contours[i], false);
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if (area > biggest_area) {
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biggest_area = area;
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biggest_contour_index = i;
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}
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}
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if (finger_points.size() > 0) {
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if (biggest_contour_index < 0)
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return contours_image;
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// we have at most five fingers usually :)
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while (finger_points.size() > 5)
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finger_points.pop_back();
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// find the convex hull object for each contour and the defects, two different data structure are needed by the OpenCV api
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vector<Point> hull_points;
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vector<int> hull_ints;
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// filter out the points too close to each other
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for (int i = 0; i < finger_points.size() - 1; i++) {
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if (findPointsDistanceOnX(finger_points[i], finger_points[i + 1]) > bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING * 1.5)
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filtered_finger_points.push_back(finger_points[i]);
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// for drawing the convex hull and for finding the bounding rectangle
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convexHull(Mat(contours[biggest_contour_index]), hull_points, true);
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// for finding the defects
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convexHull(Mat(contours[biggest_contour_index]), hull_ints, false);
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// we need at least 3 points to find the defects
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vector<Vec4i> defects;
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if (hull_ints.size() > 3)
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convexityDefects(Mat(contours[biggest_contour_index]), hull_ints, defects);
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else
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return contours_image;
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// we bound the convex hull
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Rect bounding_rectangle = boundingRect(Mat(hull_points));
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// we find the center of the bounding rectangle, this should approximately also be the center of the hand
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Point center_bounding_rect(
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(bounding_rectangle.tl().x + bounding_rectangle.br().x) / 2,
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(bounding_rectangle.tl().y + bounding_rectangle.br().y) / 2
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);
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// we separate the defects keeping only the ones of intrest
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vector<Point> start_points;
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vector<Point> far_points;
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for (int i = 0; i < defects.size(); i++) {
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start_points.push_back(contours[biggest_contour_index][defects[i].val[0]]);
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// filtering the far point based on the distance from the center of the bounding rectangle
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if (findPointsDistance(contours[biggest_contour_index][defects[i].val[2]], center_bounding_rect) < bounding_rectangle.height * BOUNDING_RECT_FINGER_SIZE_SCALING)
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far_points.push_back(contours[biggest_contour_index][defects[i].val[2]]);
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}
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// we compact them on their medians
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vector<Point> filtered_start_points = compactOnNeighborhoodMedian(start_points, bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING);
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vector<Point> filtered_far_points = compactOnNeighborhoodMedian(far_points, bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING);
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// now we try to find the fingers
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vector<Point> filtered_finger_points;
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if (filtered_far_points.size() > 1) {
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vector<Point> finger_points;
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for (int i = 0; i < filtered_start_points.size(); i++) {
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vector<Point> closest_points = findClosestOnX(filtered_far_points, filtered_start_points[i]);
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|
||||
if (isFinger(closest_points[0], filtered_start_points[i], closest_points[1], LIMIT_ANGLE_INF, LIMIT_ANGLE_SUP, center_bounding_rect, bounding_rectangle.height * BOUNDING_RECT_FINGER_SIZE_SCALING))
|
||||
finger_points.push_back(filtered_start_points[i]);
|
||||
}
|
||||
|
||||
if (finger_points.size() > 2) {
|
||||
if (findPointsDistanceOnX(finger_points[0], finger_points[finger_points.size() - 1]) > bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING * 1.5)
|
||||
if (finger_points.size() > 0) {
|
||||
|
||||
// we have at most five fingers usually :)
|
||||
while (finger_points.size() > 5)
|
||||
finger_points.pop_back();
|
||||
|
||||
// filter out the points too close to each other
|
||||
for (int i = 0; i < finger_points.size() - 1; i++) {
|
||||
if (findPointsDistanceOnX(finger_points[i], finger_points[i + 1]) > bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING * 1.5)
|
||||
filtered_finger_points.push_back(finger_points[i]);
|
||||
}
|
||||
|
||||
if (finger_points.size() > 2) {
|
||||
if (findPointsDistanceOnX(finger_points[0], finger_points[finger_points.size() - 1]) > bounding_rectangle.height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING * 1.5)
|
||||
filtered_finger_points.push_back(finger_points[finger_points.size() - 1]);
|
||||
}
|
||||
else
|
||||
filtered_finger_points.push_back(finger_points[finger_points.size() - 1]);
|
||||
}
|
||||
}
|
||||
|
||||
// we draw what found on the returned image
|
||||
drawContours(contours_image, contours, biggest_contour_index, color_green, 2, 8, hierarchy);
|
||||
polylines(contours_image, hull_points, true, color_blue);
|
||||
rectangle(contours_image, bounding_rectangle.tl(), bounding_rectangle.br(), color_red, 2, 8, 0);
|
||||
circle(contours_image, center_bounding_rect, 5, color_purple, 2, 8);
|
||||
drawVectorPoints(contours_image, filtered_start_points, color_blue, true);
|
||||
drawVectorPoints(contours_image, filtered_far_points, color_red, true);
|
||||
drawVectorPoints(contours_image, filtered_finger_points, color_yellow, false);
|
||||
putText(contours_image, to_string(filtered_finger_points.size()), center_bounding_rect, FONT_HERSHEY_PLAIN, 3, color_purple);
|
||||
|
||||
// and on the starting frame
|
||||
drawContours(frame, contours, biggest_contour_index, color_green, 2, 8, hierarchy);
|
||||
circle(frame, center_bounding_rect, 5, color_purple, 2, 8);
|
||||
drawVectorPoints(frame, filtered_finger_points, color_yellow, false);
|
||||
putText(frame, to_string(filtered_finger_points.size()), center_bounding_rect, FONT_HERSHEY_PLAIN, 3, color_purple);
|
||||
|
||||
return contours_image;
|
||||
}
|
||||
|
||||
double FingerCount::findPointsDistance(Point a, Point b) {
|
||||
Point difference = a - b;
|
||||
return sqrt(difference.ddot(difference));
|
||||
}
|
||||
|
||||
vector<Point> FingerCount::compactOnNeighborhoodMedian(vector<Point> points, double max_neighbor_distance) {
|
||||
vector<Point> median_points;
|
||||
|
||||
if (points.size() == 0)
|
||||
return median_points;
|
||||
|
||||
if (max_neighbor_distance <= 0)
|
||||
return median_points;
|
||||
|
||||
// we start with the first point
|
||||
Point reference = points[0];
|
||||
Point median = points[0];
|
||||
|
||||
for (int i = 1; i < points.size(); i++) {
|
||||
if (findPointsDistance(reference, points[i]) > max_neighbor_distance) {
|
||||
|
||||
// the point is not in range, we save the median
|
||||
median_points.push_back(median);
|
||||
|
||||
// we swap the reference
|
||||
reference = points[i];
|
||||
median = points[i];
|
||||
}
|
||||
else
|
||||
filtered_finger_points.push_back(finger_points[finger_points.size() - 1]);
|
||||
median = (points[i] + median) / 2;
|
||||
}
|
||||
|
||||
// last median
|
||||
median_points.push_back(median);
|
||||
|
||||
return median_points;
|
||||
}
|
||||
|
||||
// we draw what found on the returned image
|
||||
drawContours(contours_image, contours, biggest_contour_index, color_green, 2, 8, hierarchy);
|
||||
polylines(contours_image, hull_points, true, color_blue);
|
||||
rectangle(contours_image, bounding_rectangle.tl(), bounding_rectangle.br(), color_red, 2, 8, 0);
|
||||
circle(contours_image, center_bounding_rect, 5, color_purple, 2, 8);
|
||||
drawVectorPoints(contours_image, filtered_start_points, color_blue, true);
|
||||
drawVectorPoints(contours_image, filtered_far_points, color_red, true);
|
||||
drawVectorPoints(contours_image, filtered_finger_points, color_yellow, false);
|
||||
putText(contours_image, to_string(filtered_finger_points.size()), center_bounding_rect, FONT_HERSHEY_PLAIN, 3, color_purple);
|
||||
|
||||
// and on the starting frame
|
||||
drawContours(frame, contours, biggest_contour_index, color_green, 2, 8, hierarchy);
|
||||
circle(frame, center_bounding_rect, 5, color_purple, 2, 8);
|
||||
drawVectorPoints(frame, filtered_finger_points, color_yellow, false);
|
||||
putText(frame, to_string(filtered_finger_points.size()), center_bounding_rect, FONT_HERSHEY_PLAIN, 3, color_purple);
|
||||
|
||||
return contours_image;
|
||||
}
|
||||
|
||||
double FingerCount::findPointsDistance(Point a, Point b) {
|
||||
Point difference = a - b;
|
||||
return sqrt(difference.ddot(difference));
|
||||
}
|
||||
|
||||
vector<Point> FingerCount::compactOnNeighborhoodMedian(vector<Point> points, double max_neighbor_distance) {
|
||||
vector<Point> median_points;
|
||||
|
||||
if (points.size() == 0)
|
||||
return median_points;
|
||||
|
||||
if (max_neighbor_distance <= 0)
|
||||
return median_points;
|
||||
|
||||
// we start with the first point
|
||||
Point reference = points[0];
|
||||
Point median = points[0];
|
||||
|
||||
for (int i = 1; i < points.size(); i++) {
|
||||
if (findPointsDistance(reference, points[i]) > max_neighbor_distance) {
|
||||
|
||||
// the point is not in range, we save the median
|
||||
median_points.push_back(median);
|
||||
|
||||
// we swap the reference
|
||||
reference = points[i];
|
||||
median = points[i];
|
||||
}
|
||||
else
|
||||
median = (points[i] + median) / 2;
|
||||
double FingerCount::findAngle(Point a, Point b, Point c) {
|
||||
double ab = findPointsDistance(a, b);
|
||||
double bc = findPointsDistance(b, c);
|
||||
double ac = findPointsDistance(a, c);
|
||||
return acos((ab * ab + bc * bc - ac * ac) / (2 * ab * bc)) * 180 / CV_PI;
|
||||
}
|
||||
|
||||
// last median
|
||||
median_points.push_back(median);
|
||||
bool FingerCount::isFinger(Point a, Point b, Point c, double limit_angle_inf, double limit_angle_sup, Point palm_center, double min_distance_from_palm) {
|
||||
double angle = findAngle(a, b, c);
|
||||
if (angle > limit_angle_sup || angle < limit_angle_inf)
|
||||
return false;
|
||||
|
||||
return median_points;
|
||||
}
|
||||
// the finger point sohould not be under the two far points
|
||||
int delta_y_1 = b.y - a.y;
|
||||
int delta_y_2 = b.y - c.y;
|
||||
if (delta_y_1 > 0 && delta_y_2 > 0)
|
||||
return false;
|
||||
|
||||
double FingerCount::findAngle(Point a, Point b, Point c) {
|
||||
double ab = findPointsDistance(a, b);
|
||||
double bc = findPointsDistance(b, c);
|
||||
double ac = findPointsDistance(a, c);
|
||||
return acos((ab * ab + bc * bc - ac * ac) / (2 * ab * bc)) * 180 / CV_PI;
|
||||
}
|
||||
// the two far points should not be both under the center of the hand
|
||||
int delta_y_3 = palm_center.y - a.y;
|
||||
int delta_y_4 = palm_center.y - c.y;
|
||||
if (delta_y_3 < 0 && delta_y_4 < 0)
|
||||
return false;
|
||||
|
||||
bool FingerCount::isFinger(Point a, Point b, Point c, double limit_angle_inf, double limit_angle_sup, Point palm_center, double min_distance_from_palm) {
|
||||
double angle = findAngle(a, b, c);
|
||||
if (angle > limit_angle_sup || angle < limit_angle_inf)
|
||||
return false;
|
||||
double distance_from_palm = findPointsDistance(b, palm_center);
|
||||
if (distance_from_palm < min_distance_from_palm)
|
||||
return false;
|
||||
|
||||
// the finger point sohould not be under the two far points
|
||||
int delta_y_1 = b.y - a.y;
|
||||
int delta_y_2 = b.y - c.y;
|
||||
if (delta_y_1 > 0 && delta_y_2 > 0)
|
||||
return false;
|
||||
// this should be the case when no fingers are up
|
||||
double distance_from_palm_far_1 = findPointsDistance(a, palm_center);
|
||||
double distance_from_palm_far_2 = findPointsDistance(c, palm_center);
|
||||
if (distance_from_palm_far_1 < min_distance_from_palm / 4 || distance_from_palm_far_2 < min_distance_from_palm / 4)
|
||||
return false;
|
||||
|
||||
// the two far points should not be both under the center of the hand
|
||||
int delta_y_3 = palm_center.y - a.y;
|
||||
int delta_y_4 = palm_center.y - c.y;
|
||||
if (delta_y_3 < 0 && delta_y_4 < 0)
|
||||
return false;
|
||||
return true;
|
||||
}
|
||||
|
||||
double distance_from_palm = findPointsDistance(b, palm_center);
|
||||
if (distance_from_palm < min_distance_from_palm)
|
||||
return false;
|
||||
vector<Point> FingerCount::findClosestOnX(vector<Point> points, Point pivot) {
|
||||
vector<Point> to_return(2);
|
||||
|
||||
// this should be the case when no fingers are up
|
||||
double distance_from_palm_far_1 = findPointsDistance(a, palm_center);
|
||||
double distance_from_palm_far_2 = findPointsDistance(c, palm_center);
|
||||
if (distance_from_palm_far_1 < min_distance_from_palm / 4 || distance_from_palm_far_2 < min_distance_from_palm / 4)
|
||||
return false;
|
||||
if (points.size() == 0)
|
||||
return to_return;
|
||||
|
||||
return true;
|
||||
}
|
||||
double distance_x_1 = DBL_MAX;
|
||||
double distance_1 = DBL_MAX;
|
||||
double distance_x_2 = DBL_MAX;
|
||||
double distance_2 = DBL_MAX;
|
||||
int index_found = 0;
|
||||
|
||||
vector<Point> FingerCount::findClosestOnX(vector<Point> points, Point pivot) {
|
||||
vector<Point> to_return(2);
|
||||
for (int i = 0; i < points.size(); i++) {
|
||||
double distance_x = findPointsDistanceOnX(pivot, points[i]);
|
||||
double distance = findPointsDistance(pivot, points[i]);
|
||||
|
||||
if (distance_x < distance_x_1 && distance_x != 0 && distance <= distance_1) {
|
||||
distance_x_1 = distance_x;
|
||||
distance_1 = distance;
|
||||
index_found = i;
|
||||
}
|
||||
}
|
||||
|
||||
to_return[0] = points[index_found];
|
||||
|
||||
for (int i = 0; i < points.size(); i++) {
|
||||
double distance_x = findPointsDistanceOnX(pivot, points[i]);
|
||||
double distance = findPointsDistance(pivot, points[i]);
|
||||
|
||||
if (distance_x < distance_x_2 && distance_x != 0 && distance <= distance_2 && distance_x != distance_x_1) {
|
||||
distance_x_2 = distance_x;
|
||||
distance_2 = distance;
|
||||
index_found = i;
|
||||
}
|
||||
}
|
||||
|
||||
to_return[1] = points[index_found];
|
||||
|
||||
if (points.size() == 0)
|
||||
return to_return;
|
||||
|
||||
double distance_x_1 = DBL_MAX;
|
||||
double distance_1 = DBL_MAX;
|
||||
double distance_x_2 = DBL_MAX;
|
||||
double distance_2 = DBL_MAX;
|
||||
int index_found = 0;
|
||||
|
||||
for (int i = 0; i < points.size(); i++) {
|
||||
double distance_x = findPointsDistanceOnX(pivot, points[i]);
|
||||
double distance = findPointsDistance(pivot, points[i]);
|
||||
|
||||
if (distance_x < distance_x_1 && distance_x != 0 && distance <= distance_1) {
|
||||
distance_x_1 = distance_x;
|
||||
distance_1 = distance;
|
||||
index_found = i;
|
||||
}
|
||||
}
|
||||
|
||||
to_return[0] = points[index_found];
|
||||
double FingerCount::findPointsDistanceOnX(Point a, Point b) {
|
||||
double to_return = 0.0;
|
||||
|
||||
for (int i = 0; i < points.size(); i++) {
|
||||
double distance_x = findPointsDistanceOnX(pivot, points[i]);
|
||||
double distance = findPointsDistance(pivot, points[i]);
|
||||
if (a.x > b.x)
|
||||
to_return = a.x - b.x;
|
||||
else
|
||||
to_return = b.x - a.x;
|
||||
|
||||
if (distance_x < distance_x_2 && distance_x != 0 && distance <= distance_2 && distance_x != distance_x_1) {
|
||||
distance_x_2 = distance_x;
|
||||
distance_2 = distance;
|
||||
index_found = i;
|
||||
}
|
||||
return to_return;
|
||||
}
|
||||
|
||||
to_return[1] = points[index_found];
|
||||
|
||||
return to_return;
|
||||
}
|
||||
|
||||
double FingerCount::findPointsDistanceOnX(Point a, Point b) {
|
||||
double to_return = 0.0;
|
||||
|
||||
if (a.x > b.x)
|
||||
to_return = a.x - b.x;
|
||||
else
|
||||
to_return = b.x - a.x;
|
||||
|
||||
return to_return;
|
||||
}
|
||||
|
||||
void FingerCount::drawVectorPoints(Mat image, vector<Point> points, Scalar color, bool with_numbers) {
|
||||
for (int i = 0; i < points.size(); i++) {
|
||||
circle(image, points[i], 5, color, 2, 8);
|
||||
if (with_numbers)
|
||||
putText(image, to_string(i), points[i], FONT_HERSHEY_PLAIN, 3, color);
|
||||
void FingerCount::drawVectorPoints(Mat image, vector<Point> points, Scalar color, bool with_numbers) {
|
||||
for (int i = 0; i < points.size(); i++) {
|
||||
circle(image, points[i], 5, color, 2, 8);
|
||||
if (with_numbers)
|
||||
putText(image, to_string(i), points[i], FONT_HERSHEY_PLAIN, 3, color);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,32 +1,36 @@
|
||||
#pragma once
|
||||
|
||||
#include "opencv/cv.h"
|
||||
#include "opencv2/core.hpp"
|
||||
#include <opencv2/imgproc/types_c.h>
|
||||
|
||||
/*
|
||||
Author: Nicol<6F> Castellazzi https://github.com/nicast
|
||||
*/
|
||||
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
namespace computervision
|
||||
{
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
|
||||
class FingerCount {
|
||||
public:
|
||||
FingerCount(void);
|
||||
Mat findFingersCount(Mat input_image, Mat frame);
|
||||
class FingerCount {
|
||||
public:
|
||||
FingerCount(void);
|
||||
Mat findFingersCount(Mat input_image, Mat frame);
|
||||
|
||||
private:
|
||||
Scalar color_blue;
|
||||
Scalar color_green;
|
||||
Scalar color_red;
|
||||
Scalar color_black;
|
||||
Scalar color_white;
|
||||
Scalar color_yellow;
|
||||
Scalar color_purple;
|
||||
double findPointsDistance(Point a, Point b);
|
||||
vector<Point> compactOnNeighborhoodMedian(vector<Point> points, double max_neighbor_distance);
|
||||
double findAngle(Point a, Point b, Point c);
|
||||
bool isFinger(Point a, Point b, Point c, double limit_angle_inf, double limit_angle_sup, cv::Point palm_center, double distance_from_palm_tollerance);
|
||||
vector<Point> findClosestOnX(vector<Point> points, Point pivot);
|
||||
double findPointsDistanceOnX(Point a, Point b);
|
||||
void drawVectorPoints(Mat image, vector<Point> points, Scalar color, bool with_numbers);
|
||||
};
|
||||
private:
|
||||
Scalar color_blue;
|
||||
Scalar color_green;
|
||||
Scalar color_red;
|
||||
Scalar color_black;
|
||||
Scalar color_white;
|
||||
Scalar color_yellow;
|
||||
Scalar color_purple;
|
||||
double findPointsDistance(Point a, Point b);
|
||||
vector<Point> compactOnNeighborhoodMedian(vector<Point> points, double max_neighbor_distance);
|
||||
double findAngle(Point a, Point b, Point c);
|
||||
bool isFinger(Point a, Point b, Point c, double limit_angle_inf, double limit_angle_sup, cv::Point palm_center, double distance_from_palm_tollerance);
|
||||
vector<Point> findClosestOnX(vector<Point> points, Point pivot);
|
||||
double findPointsDistanceOnX(Point a, Point b);
|
||||
void drawVectorPoints(Mat image, vector<Point> points, Scalar color, bool with_numbers);
|
||||
};
|
||||
}
|
||||
@@ -1,6 +1,13 @@
|
||||
#include "opencv2/opencv.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/videoio.hpp"
|
||||
#include <opencv2/highgui.hpp>
|
||||
#include <opencv2/video.hpp>
|
||||
|
||||
#include "ObjectDetection.h"
|
||||
#include "ObjectDetection.h"
|
||||
//#include "BackgroundRemover.h"
|
||||
#include "BackgroundRemover.h"
|
||||
#include "SkinDetector.h"
|
||||
#include "FaceDetector.h"
|
||||
#include "FingerCount.h"
|
||||
@@ -8,10 +15,11 @@
|
||||
namespace computervision
|
||||
{
|
||||
cv::VideoCapture cap(0);
|
||||
|
||||
cv::Mat img, imgGray, img2, img2Gray, img3, img4;
|
||||
|
||||
Mat frame, frameOut, handMask, foreground, fingerCountDebug;
|
||||
//BackgroundRemover backgroundRemover;
|
||||
BackgroundRemover backgroundRemover;
|
||||
SkinDetector skinDetector;
|
||||
FaceDetector faceDetector;
|
||||
FingerCount fingerCount;
|
||||
@@ -21,15 +29,24 @@ namespace computervision
|
||||
{
|
||||
}
|
||||
|
||||
void ObjectDetection::Init()
|
||||
bool ObjectDetection::Init()
|
||||
{
|
||||
if (!cap.isOpened()) {
|
||||
cout << "Can't find camera!" << endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
cap.read(frame);
|
||||
frameOut = frame.clone();
|
||||
|
||||
skinDetector.drawSkinColorSampler(frameOut);
|
||||
|
||||
foreground = backgroundRemover.getForeground(frame);
|
||||
|
||||
faceDetector.removeFaces(frame, foreground);
|
||||
handMask = skinDetector.getSkinMask(foreground);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void ObjectDetection::readWebcam()
|
||||
|
||||
@@ -17,6 +17,7 @@ namespace computervision
|
||||
|
||||
public:
|
||||
ObjectDetection();
|
||||
bool Init();
|
||||
void readWebcam();
|
||||
void showWebcam();
|
||||
void calculateDifference();
|
||||
|
||||
@@ -1,103 +1,105 @@
|
||||
#include "SkinDetector.h"
|
||||
#include"opencv2\opencv.hpp"
|
||||
|
||||
/*
|
||||
Author: Pierfrancesco Soffritti https://github.com/PierfrancescoSoffritti
|
||||
*/
|
||||
|
||||
SkinDetector::SkinDetector(void) {
|
||||
hLowThreshold = 0;
|
||||
hHighThreshold = 0;
|
||||
sLowThreshold = 0;
|
||||
sHighThreshold = 0;
|
||||
vLowThreshold = 0;
|
||||
vHighThreshold = 0;
|
||||
namespace computervision
|
||||
{
|
||||
SkinDetector::SkinDetector(void) {
|
||||
hLowThreshold = 0;
|
||||
hHighThreshold = 0;
|
||||
sLowThreshold = 0;
|
||||
sHighThreshold = 0;
|
||||
vLowThreshold = 0;
|
||||
vHighThreshold = 0;
|
||||
|
||||
calibrated = false;
|
||||
calibrated = false;
|
||||
|
||||
skinColorSamplerRectangle1, skinColorSamplerRectangle2;
|
||||
}
|
||||
skinColorSamplerRectangle1, skinColorSamplerRectangle2;
|
||||
}
|
||||
|
||||
void SkinDetector::drawSkinColorSampler(Mat input) {
|
||||
int frameWidth = input.size().width, frameHeight = input.size().height;
|
||||
void SkinDetector::drawSkinColorSampler(Mat input) {
|
||||
int frameWidth = input.size().width, frameHeight = input.size().height;
|
||||
|
||||
int rectangleSize = 20;
|
||||
Scalar rectangleColor = Scalar(255, 0, 255);
|
||||
int rectangleSize = 20;
|
||||
Scalar rectangleColor = Scalar(255, 0, 255);
|
||||
|
||||
skinColorSamplerRectangle1 = Rect(frameWidth / 5, frameHeight / 2, rectangleSize, rectangleSize);
|
||||
skinColorSamplerRectangle2 = Rect(frameWidth / 5, frameHeight / 3, rectangleSize, rectangleSize);
|
||||
skinColorSamplerRectangle1 = Rect(frameWidth / 5, frameHeight / 2, rectangleSize, rectangleSize);
|
||||
skinColorSamplerRectangle2 = Rect(frameWidth / 5, frameHeight / 3, rectangleSize, rectangleSize);
|
||||
|
||||
rectangle(
|
||||
input,
|
||||
skinColorSamplerRectangle1,
|
||||
rectangleColor
|
||||
);
|
||||
rectangle(
|
||||
input,
|
||||
skinColorSamplerRectangle1,
|
||||
rectangleColor
|
||||
);
|
||||
|
||||
rectangle(
|
||||
input,
|
||||
skinColorSamplerRectangle2,
|
||||
rectangleColor
|
||||
);
|
||||
}
|
||||
rectangle(
|
||||
input,
|
||||
skinColorSamplerRectangle2,
|
||||
rectangleColor
|
||||
);
|
||||
}
|
||||
|
||||
void SkinDetector::calibrate(Mat input) {
|
||||
void SkinDetector::calibrate(Mat input) {
|
||||
|
||||
Mat hsvInput;
|
||||
cvtColor(input, hsvInput, CV_BGR2HSV);
|
||||
Mat hsvInput;
|
||||
cvtColor(input, hsvInput, CV_BGR2HSV);
|
||||
|
||||
Mat sample1 = Mat(hsvInput, skinColorSamplerRectangle1);
|
||||
Mat sample2 = Mat(hsvInput, skinColorSamplerRectangle2);
|
||||
Mat sample1 = Mat(hsvInput, skinColorSamplerRectangle1);
|
||||
Mat sample2 = Mat(hsvInput, skinColorSamplerRectangle2);
|
||||
|
||||
calculateThresholds(sample1, sample2);
|
||||
calculateThresholds(sample1, sample2);
|
||||
|
||||
calibrated = true;
|
||||
}
|
||||
calibrated = true;
|
||||
}
|
||||
|
||||
void SkinDetector::calculateThresholds(Mat sample1, Mat sample2) {
|
||||
int offsetLowThreshold = 80;
|
||||
int offsetHighThreshold = 30;
|
||||
void SkinDetector::calculateThresholds(Mat sample1, Mat sample2) {
|
||||
int offsetLowThreshold = 80;
|
||||
int offsetHighThreshold = 30;
|
||||
|
||||
Scalar hsvMeansSample1 = mean(sample1);
|
||||
Scalar hsvMeansSample2 = mean(sample2);
|
||||
Scalar hsvMeansSample1 = mean(sample1);
|
||||
Scalar hsvMeansSample2 = mean(sample2);
|
||||
|
||||
hLowThreshold = min(hsvMeansSample1[0], hsvMeansSample2[0]) - offsetLowThreshold;
|
||||
hHighThreshold = max(hsvMeansSample1[0], hsvMeansSample2[0]) + offsetHighThreshold;
|
||||
hLowThreshold = min(hsvMeansSample1[0], hsvMeansSample2[0]) - offsetLowThreshold;
|
||||
hHighThreshold = max(hsvMeansSample1[0], hsvMeansSample2[0]) + offsetHighThreshold;
|
||||
|
||||
sLowThreshold = min(hsvMeansSample1[1], hsvMeansSample2[1]) - offsetLowThreshold;
|
||||
sHighThreshold = max(hsvMeansSample1[1], hsvMeansSample2[1]) + offsetHighThreshold;
|
||||
sLowThreshold = min(hsvMeansSample1[1], hsvMeansSample2[1]) - offsetLowThreshold;
|
||||
sHighThreshold = max(hsvMeansSample1[1], hsvMeansSample2[1]) + offsetHighThreshold;
|
||||
|
||||
// the V channel shouldn't be used. By ignorint it, shadows on the hand wouldn't interfire with segmentation.
|
||||
// Unfortunately there's a bug somewhere and not using the V channel causes some problem. This shouldn't be too hard to fix.
|
||||
vLowThreshold = min(hsvMeansSample1[2], hsvMeansSample2[2]) - offsetLowThreshold;
|
||||
vHighThreshold = max(hsvMeansSample1[2], hsvMeansSample2[2]) + offsetHighThreshold;
|
||||
//vLowThreshold = 0;
|
||||
//vHighThreshold = 255;
|
||||
}
|
||||
// the V channel shouldn't be used. By ignorint it, shadows on the hand wouldn't interfire with segmentation.
|
||||
// Unfortunately there's a bug somewhere and not using the V channel causes some problem. This shouldn't be too hard to fix.
|
||||
vLowThreshold = min(hsvMeansSample1[2], hsvMeansSample2[2]) - offsetLowThreshold;
|
||||
vHighThreshold = max(hsvMeansSample1[2], hsvMeansSample2[2]) + offsetHighThreshold;
|
||||
//vLowThreshold = 0;
|
||||
//vHighThreshold = 255;
|
||||
}
|
||||
|
||||
Mat SkinDetector::getSkinMask(Mat input) {
|
||||
Mat skinMask;
|
||||
Mat SkinDetector::getSkinMask(Mat input) {
|
||||
Mat skinMask;
|
||||
|
||||
if (!calibrated) {
|
||||
skinMask = Mat::zeros(input.size(), CV_8UC1);
|
||||
return skinMask;
|
||||
}
|
||||
|
||||
Mat hsvInput;
|
||||
cvtColor(input, hsvInput, CV_BGR2HSV);
|
||||
|
||||
inRange(
|
||||
hsvInput,
|
||||
Scalar(hLowThreshold, sLowThreshold, vLowThreshold),
|
||||
Scalar(hHighThreshold, sHighThreshold, vHighThreshold),
|
||||
skinMask);
|
||||
|
||||
performOpening(skinMask, MORPH_ELLIPSE, { 3, 3 });
|
||||
dilate(skinMask, skinMask, Mat(), Point(-1, -1), 3);
|
||||
|
||||
if (!calibrated) {
|
||||
skinMask = Mat::zeros(input.size(), CV_8UC1);
|
||||
return skinMask;
|
||||
}
|
||||
|
||||
Mat hsvInput;
|
||||
cvtColor(input, hsvInput, CV_BGR2HSV);
|
||||
|
||||
inRange(
|
||||
hsvInput,
|
||||
Scalar(hLowThreshold, sLowThreshold, vLowThreshold),
|
||||
Scalar(hHighThreshold, sHighThreshold, vHighThreshold),
|
||||
skinMask);
|
||||
|
||||
performOpening(skinMask, MORPH_ELLIPSE, { 3, 3 });
|
||||
dilate(skinMask, skinMask, Mat(), Point(-1, -1), 3);
|
||||
|
||||
return skinMask;
|
||||
}
|
||||
|
||||
void SkinDetector::performOpening(Mat binaryImage, int kernelShape, Point kernelSize) {
|
||||
Mat structuringElement = getStructuringElement(kernelShape, kernelSize);
|
||||
morphologyEx(binaryImage, binaryImage, MORPH_OPEN, structuringElement);
|
||||
void SkinDetector::performOpening(Mat binaryImage, int kernelShape, Point kernelSize) {
|
||||
Mat structuringElement = getStructuringElement(kernelShape, kernelSize);
|
||||
morphologyEx(binaryImage, binaryImage, MORPH_OPEN, structuringElement);
|
||||
}
|
||||
}
|
||||
@@ -1,34 +1,39 @@
|
||||
#pragma once
|
||||
|
||||
#include<opencv\cv.h>
|
||||
|
||||
#include<opencv2\core.hpp>
|
||||
#include <opencv2/imgcodecs.hpp>
|
||||
#include <opencv2/imgproc.hpp>
|
||||
#include <opencv2/imgproc/types_c.h>
|
||||
/*
|
||||
Author: Pierfrancesco Soffritti https://github.com/PierfrancescoSoffritti
|
||||
*/
|
||||
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
namespace computervision
|
||||
{
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
|
||||
class SkinDetector {
|
||||
public:
|
||||
SkinDetector(void);
|
||||
class SkinDetector {
|
||||
public:
|
||||
SkinDetector(void);
|
||||
|
||||
void drawSkinColorSampler(Mat input);
|
||||
void calibrate(Mat input);
|
||||
Mat getSkinMask(Mat input);
|
||||
void drawSkinColorSampler(Mat input);
|
||||
void calibrate(Mat input);
|
||||
Mat getSkinMask(Mat input);
|
||||
|
||||
private:
|
||||
int hLowThreshold = 0;
|
||||
int hHighThreshold = 0;
|
||||
int sLowThreshold = 0;
|
||||
int sHighThreshold = 0;
|
||||
int vLowThreshold = 0;
|
||||
int vHighThreshold = 0;
|
||||
private:
|
||||
int hLowThreshold = 0;
|
||||
int hHighThreshold = 0;
|
||||
int sLowThreshold = 0;
|
||||
int sHighThreshold = 0;
|
||||
int vLowThreshold = 0;
|
||||
int vHighThreshold = 0;
|
||||
|
||||
bool calibrated = false;
|
||||
bool calibrated = false;
|
||||
|
||||
Rect skinColorSamplerRectangle1, skinColorSamplerRectangle2;
|
||||
Rect skinColorSamplerRectangle1, skinColorSamplerRectangle2;
|
||||
|
||||
void calculateThresholds(Mat sample1, Mat sample2);
|
||||
void SkinDetector::performOpening(Mat binaryImage, int structuralElementShapde, Point structuralElementSize);
|
||||
};
|
||||
void calculateThresholds(Mat sample1, Mat sample2);
|
||||
void performOpening(Mat binaryImage, int structuralElementShapde, Point structuralElementSize);
|
||||
};
|
||||
}
|
||||
@@ -37,7 +37,7 @@
|
||||
<ItemGroup>
|
||||
<ClInclude Include="src\computervision\FaceDetector.h" />
|
||||
<ClInclude Include="src\computervision\FingerCount.h" />
|
||||
<ClInclude Include="src\computervision\Header.h" />
|
||||
<ClInclude Include="src\computervision\BackgroundRemover.h" />
|
||||
<ClInclude Include="src\computervision\SkinDetector.h" />
|
||||
<ClInclude Include="src\computervision\ObjectDetection.h" />
|
||||
<ClInclude Include="src\entities\camera.h" />
|
||||
|
||||
@@ -101,7 +101,7 @@
|
||||
<ClInclude Include="src\computervision\FaceDetector.h">
|
||||
<Filter>Header Files</Filter>
|
||||
</ClInclude>
|
||||
<ClInclude Include="src\computervision\Header.h">
|
||||
<ClInclude Include="src\computervision\BackgroundRemover.h">
|
||||
<Filter>Header Files</Filter>
|
||||
</ClInclude>
|
||||
</ItemGroup>
|
||||
|
||||
Reference in New Issue
Block a user