guys. Just for you know, I'm new to this environment, so if I do something wrong, feel free to warn me.
So, I was trying to create a basic algorithm for downsampling an image by an ancient technique. I'm new to OpenCV as well.
#include "opencv2/core/utility.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <stdio.h>
#include <iostream>
using namespace std;
using namespace cv;
void filter2(const Mat &image, Mat &out)
{
// convert and split
Mat imgf;
image.convertTo(imgf, CV_32F, 1.0 / 255.0);
Mat chn[3];
split(imgf, chn);
Mat newchn[3];
// Resampling by 2
for (int dim = 0; dim < 3; dim++) {
Mat dummy2;
Mat dummy = chn[dim];
for (int i = 0; i < chn[dim].rows/2; i++) {
for (int j = 0; j < chn[dim].cols/2; j++) {
dummy2.at<double>(i,j) = dummy.at<double>(i*2,j*2);
}
}
dummy2 = newchn[dim];
}
// merge and convert
merge(newchn, 3, imgf);
imgf.convertTo(out, CV_8U, 255.0);
}
int main(int agra, char** argv) {
String fn = "eva_green.png";
if (agra > 1) fn = argv[1];
Mat image = imread(fn);
if (image.empty())
{
cerr << "No image";
return 1;
}
Mat result;
filter2(image, result);
imshow("org", image);
imshow("res", result);
waitKey(0);
return 0;
}
The "filter2" function does the job. What is wrong with my code?
Related
When running this code with the same image and mask I occasionally get values that differ previously. For example
Normal/Expected Results
Abnormal Results
As can be seen in the two results I somehow receive negative RGB values. The Abnormal Results show up around every 5-8 runs for the exact same image with the exact same mask.
What do I need to change to prevent the occasional abnormal result?
#include <opencv2/opencv.hpp>
#include <iostream>
#include <fstream>
#include <stdio.h>
#include <string>
using namespace cv;
using namespace std;
float get_color(Mat img,Mat &mask){
Mat img1 = img;
Mat hist;
int dims = 1;
int histSize = 255;
float hranges[] = { 0, 255 };
const float *ranges = {hranges};
calcHist(&img1,1,0,mask,hist, dims, &histSize, &ranges ,true ,false);
int sum=0;
for(int i = 0;i<256;i++){
sum += hist.at<float>(i,0);
}
Mat weights = hist/sum;
float hist_avg=0.0;
for(int i = 0;i<256;i++){
hist_avg += i*weights.at<float>(i,0);
}
return hist_avg;
}
int main(int argc, char** argv){
Mat aa = imread("/Users/dnguyen/Desktop/snapshot207042/pic.jpg", CV_LOAD_IMAGE_COLOR);
vector<Mat> bgr;
split(aa, bgr);
Mat b = bgr[0];
Mat g = bgr[1];
Mat r = bgr[2];
for(unsigned int i=1;i<23;i++){
stringstream ss;
ss << i;
string str = ss.str();
string file_name = "/Users/dnguyen/Desktop/okay/data/card_masks/"+str+"_mask.png";
Mat mask = imread(file_name,0);
Mat cc;
threshold(mask,cc,90,255,THRESH_BINARY);
float b_avg = get_color(b, cc);
float g_avg = get_color(g, cc);
float r_avg = get_color(r, cc);
cout << b_avg << ","<< g_avg << "," << r_avg << endl;
}
}
I am using otsu threshold on an image.
Here is the input image :
Here is the output :
Here is the code I am using:
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>
#include <iostream>
#include <string>
#include <math.h>
using namespace std;
using namespace cv;
int main(int argc, char const *argv[]) {
title("Text Extractor");
string win_name = "textextractor";
Mat img_a;
img_a = imread("../input/test_c.jpg");
Mat img_a_gray;
cvtColor(img_a, img_a_gray, CV_BGR2GRAY);
Mat img_a_blur;
GaussianBlur(img_a_gray, img_a_blur, Size(3, 3), 0, 0);
Mat img_a_thres;
// adaptiveThreshold(img_a_blur, img_a_thres, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 5, 4);
threshold(img_a_blur, img_a_thres, 0, 255, THRESH_OTSU);
namedWindow(win_name + "_a", CV_WINDOW_AUTOSIZE);
imshow(win_name + "_a", img_a_thres);
imwrite("../output/output_a.jpg", img_a_thres);
waitKey(0);
return 0;
}
The problem is that output has a black region on the bottom and on the left. What can I do to minimize/remove this ?
Edit:
I tried equalizeHist() and I am getting this:
Will try out breaking image into pieces and working them separately.
Sorry, my bad. The previous one is using adaptive filtering. Using Otsu I get this:
There is no change in otsu's output :/
Edit 2: Completed the Feng Tan algorithm, it gives better results but text looses clarity.
Code:
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/photo/photo.hpp"
#include <stdlib.h>
#include <stdio.h>
#include <iostream>
#include <string>
#include <math.h>
using namespace std;
using namespace cv;
int main(int argc, char const *argv[]) {
string win_name = "textextractor";
Mat img_c;
img_c = imread("../input/sample.jpg");
Mat img_c_gray;
cvtColor(img_c, img_c_gray, CV_BGR2GRAY);
Mat img_c_bin = Mat::zeros(img_c_gray.rows, img_c_gray.cols, CV_8UC1);
int s_win = 17;
int l_win = 35;
double min_tau = 10;
Rect roi_s = Rect(-s_win/2, -s_win/2, s_win, s_win);
Rect roi_l = Rect(-l_win/2, -l_win/2, l_win, l_win);
Rect img_c_roi = Rect(0, 0, img_c_gray.cols, img_c_gray.rows);
for (size_t r = 0; r < img_c_gray.rows; r++) {
for (size_t c = 0; c < img_c_gray.cols; c++) {
double pthres = 255;
Rect sROI = roi_s + Point(c, r);
sROI = sROI & img_c_roi;
if(sROI.width == 0 || sROI.height == 0) {
continue;
}
Rect lROI = roi_l + Point(c, r);
lROI = lROI & img_c_roi;
if(lROI.width == 0 || lROI.height == 0) {
continue;
}
Mat sROI_gray = img_c_gray(sROI);
Mat lROI_gray = img_c_gray(lROI);
double s_stdDev = 0;
double l_stdDev = 0;
double s_mean = 0;
double l_mean = 0;
double l_min = DBL_MAX;
for (size_t r = 0; r < sROI_gray.rows; r++) {
for (size_t c = 0; c < sROI_gray.cols; c++) {
s_mean += sROI_gray.at<unsigned char>(r, c);
}
}
s_mean = s_mean / static_cast<double> (sROI_gray.cols * sROI_gray.rows);
for (size_t r = 0; r < sROI_gray.rows; r++) {
for (size_t c = 0; c < sROI_gray.cols; c++) {
double diff = sROI_gray.at<unsigned char> (r, c) - s_mean;
s_stdDev += diff * diff;
}
}
s_stdDev = sqrt(s_stdDev / static_cast<int> (sROI_gray.cols * sROI_gray.rows));
for (size_t r = 0; r < lROI_gray.rows; r++) {
for (size_t c = 0; c < lROI_gray.cols; c++) {
l_mean += lROI_gray.at<unsigned char> (c, r);
if(lROI_gray.at<unsigned char> (r, c) < l_min) {
l_min = lROI_gray.at<unsigned char> (r, c);
}
}
}
l_mean = l_mean / static_cast<double> (lROI_gray.cols * lROI_gray.rows);
for (size_t r = 0; r < lROI_gray.rows; r++) {
for (size_t c = 0; c < lROI_gray.cols; c++) {
double diff = lROI_gray.at<unsigned char> (r, c) - l_mean;
l_stdDev += diff * diff;
}
}
l_stdDev = sqrt(l_stdDev / static_cast<double> (lROI_gray.cols * lROI_gray.rows));
double tau = ((s_mean - l_min) * (1 - s_stdDev / l_stdDev)) / 2.0;
if(tau < min_tau) {
tau = min_tau;
}
double threshold = s_mean - tau;
unsigned char pixel_val = img_c_gray.at<unsigned char>(r, c);
if(pixel_val >= threshold) {
img_c_bin.at<unsigned char> (r, c) = 255;
} else {
img_c_bin.at<unsigned char> (r, c) = 0;
}
}
}
namedWindow(win_name + "_c", CV_WINDOW_AUTOSIZE);
imshow(win_name + "_c", img_c_bin);
imwrite("../output/output_c.jpg", img_c_bin);
waitKey(0);
return 0;
}
Output:
This is what I was able to obtain after some trial and run. Initially I median blurred the original image. Then I applied adpative threshold to the blurred image.
This is what I got:
1. Adaptive Threshold using Gaussian filter:
2. Adaptive Threshold using Mean filter:
From here on you can carry out a series of morphological operations that best suits your final image. :)
You should try using CLAHE.
I tried it on MATLAB using:
Ia = imread('FHXTJ.jpg');
I = rgb2gray(Ia);
A = adapthisteq(I, 'clipLimit', 0.02, 'Distribution', 'rayleigh');
Result:
Note: You can apply thresholding on this image. Otsu should work fine now.
I want to use the fitLine function to come up with a line to draw on my source image src_crop. I load the frame in my main() and call the drawLine().
But the code aborts with the following error :
Code:
#include "stdafx.h"
#include <fstream>
#include <iostream>
#include <vector>
#include <stdlib.h>
#include <stdio.h>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace std;
using namespace cv;
/// Global variables
Mat src_gray;
Mat src_crop;
Mat dst, detected_edges;
int edgeThresh = 1;
int lowThreshold = 27;
int const max_lowThreshold = 100;
int ratio = 3;
int kernel_size = 3;
char* window_name = "Edge Map";
int i,j;
void drawLine(int, void*)
{
vector<Vec4f> outline;
vector<Point2f> ssline;
int flag2 = 0;
/// Reduce noise with a kernel 3x3
blur(src_gray, detected_edges, Size(3, 3));
/// Canny detector
Canny(detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size);
/// Using Canny's output as a mask, we display our result
dst.create(detected_edges.size(), detected_edges.type());
dst = Scalar::all(0);
src_crop.copyTo(dst, detected_edges);
//namedWindow("Detected Edges", CV_WINDOW_AUTOSIZE);
//imshow("Detected Edges", detected_edges);
cvtColor(dst, dst, CV_BGR2GRAY);
for (j = 0; j < dst.cols; j++)
{
for (i = 0; i < dst.rows; i++)
{
if (Scalar(dst.at<uchar>(i,j)).val[0] >= 90)
{
//cout << "Hi";
flag2 = 1;
break;
}
}
if (flag2 == 1)
break;
}
int k = j;
int l = i;
for (j = k; j < dst.cols; j++)
{
Point2f ss = Point2f(l,j);
ssline.push_back(ss);
}
fitLine(ssline, outline, CV_DIST_L1, 0, 0.01, 0.01);
//imshow("Result", src_crop);
}
int main(int argc, char** argv)
{
/// Load an image
src = imread(s);
if (!src.data)
{
return -1;
}
/// Create a matrix of the same type and size as src (for dst)
//dst.create(src.size(), src.type());
src_crop = src;
/// Convert the image to grayscale
cvtColor(src_crop, src_gray, CV_BGR2GRAY);
/// Create a window
namedWindow(window_name, CV_WINDOW_AUTOSIZE);
/// Create a Trackbar for user to enter threshold
createTrackbar("Min Threshold:", window_name, &lowThreshold, max_lowThreshold, drawLine);
/// Show the image
drawLine(0, 0);
if (waitKey(30) >= 0) break;
return 0;
}
The code stops working at the point fitLine() is called. This I found by testing the code with printf statements.
Can anyone kindly help me solve the issue?
Aside the fact the your code won't compile, the issue is that you're passing to fitLine the parameter outline as a vector<Vec4f>, while it should be a Vec4f.
Change outline declaration as:
Vec4f outline;
Hello I'm trying to find characters on this image.
This is my image after some preprocessing I recieved this image.
Now I'm trying to do connected component labeling to find blobs. however I get a lot of small blobs too.
#include <iostream>
#include <vector>
#include <stdio.h>
#include <stdlib.h>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;
void FindBlobs(const Mat &binary, vector < vector<Point2i> > &blobs);
int main(int argc, char **argv)
{
Mat img = imread("adaptive.png", 0);
if(!img.data) {
cout << "File not found" << endl;
return -1;
}
namedWindow("binary");
namedWindow("labelled");
Mat output = Mat::zeros(img.size(), CV_8UC3);
Mat binary;
vector < vector<Point2i > > blobs;
threshold(img, binary, 0, 1, THRESH_BINARY_INV);
FindBlobs(binary, blobs);
// Randomy color the blobs
for(size_t i=0; i < blobs.size(); i++) {
unsigned char r = 255 * (rand()/(1.0 + RAND_MAX));
unsigned char g = 255 * (rand()/(1.0 + RAND_MAX));
unsigned char b = 255 * (rand()/(1.0 + RAND_MAX));
for(size_t j=0; j < blobs[i].size(); j++) {
int x = blobs[i][j].x;
int y = blobs[i][j].y;
output.at<Vec3b>(y,x)[0] = b;//Vec3b RGB color order
output.at<Vec3b>(y,x)[1] = g;
output.at<Vec3b>(y,x)[2] = r;
}
}
imshow("binary", img);
imshow("labelled", output);
waitKey(0);
return 0;
}
void FindBlobs(const Mat &binary, vector < vector<Point2i> > &blobs)
{
blobs.clear();
Mat label_image;
binary.convertTo(label_image, CV_32SC1);
int label_count = 2; // starts at 2 because 0,1 are used already
for(int y=0; y < label_image.rows; y++) {
int *row = (int*)label_image.ptr(y);
for(int x=0; x < label_image.cols; x++) {
if(row[x] != 1) {
continue;
}
Rect rect;
floodFill(label_image, Point(x,y), label_count, &rect, 0, 0, 4);
vector <Point2i> blob;
for(int i=rect.y; i < (rect.y+rect.height); i++) {
int *row2 = (int*)label_image.ptr(i);
for(int j=rect.x; j < (rect.x+rect.width); j++) {
if(row2[j] != label_count) {
continue;
}
blob.push_back(Point2i(j,i));
}
}
blobs.push_back(blob);
label_count++;
}
}
}
so with this algorithm I recieve blobs
but when I do
if(blobs.size()>50) {
blob.push_back(Point2i(j,i));
}
I recieve black screen. however when I try to
if(blob.size()<50){
blob.push_back(Point2i(j,i));
}
I recieve small blobs what can be the actual problem here ?
Guess you want to store those "big" blobs?
If so, change the following code
blobs.push_back(blob);
label_count++;
to this:
if(blob.size() > 50){
blobs.push_back(blob);
}
label_count++;
And you can receive picture like this:
The Hog based multi scale detector is not able to detect any of the faces or even any human. According to the peopledetect.cpp in OpenCV examples, I am trying to detect the people but not getting what I am missing here. Following the code that I am trying:
#include "cvaux.h"
#include "highgui.h"
#include <stdio.h>
#include <iostream>
#include <string.h>
#include <ctype.h>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
Mat img;
FILE* f = 0;
char _filename[1024];
if( argc == 1 )
{
printf("Usage: peopledetect (<image_filename>\n");
return 0;
}
img = imread(argv[1]);
if( img.data )
{
strcpy(_filename, argv[1]);
}
HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
for(;;)
{
char* filename = _filename;
printf("%s:\n", filename);
if(!img.data)
continue;
fflush(stdout);
vector<Rect> found, found_filtered;
double t = (double)getTickCount();
hog.detectMultiScale(img, found, 0, Size(8,8), Size(32,32), 1.05, 2);
cout<<found.size()<<endl; // To check how many faces detected.
t = (double)getTickCount() - t;
printf("tdetection time = %gms\n",t*1000./cv::getTickFrequency());
size_t i, j;
for( i = 0; i < found.size(); i++ )
{
Rect r = found[i];cout<<i<<endl;
for( j = 0; j < found.size(); j++ )
if( j != i && (r & found[j]) == r)
break;
if( j == found.size() )
found_filtered.push_back(r);
cout<<found_filtered.size()<<endl;
}
imshow("people detector", img);
int c = waitKey(0) & 255;
}
return 0;
}
Any picture is not being detected for anything. What might be the issue? Am I not linking something ?