Accessing certain pixel RGB value in openCV - c++

I have searched internet and stackoverflow thoroughly, but I haven't found answer to my question:
How can I get/set (both) RGB value of certain (given by x,y coordinates) pixel in OpenCV? What's important-I'm writing in C++, the image is stored in cv::Mat variable. I know there is an IplImage() operator, but IplImage is not very comfortable in use-as far as I know it comes from C API.
Yes, I'm aware that there was already this Pixel access in OpenCV 2.2 thread, but it was only about black and white bitmaps.
EDIT:
Thank you very much for all your answers. I see there are many ways to get/set RGB value of pixel. I got one more idea from my close friend-thanks Benny! It's very simple and effective. I think it's a matter of taste which one you choose.
Mat image;
(...)
Point3_<uchar>* p = image.ptr<Point3_<uchar> >(y,x);
And then you can read/write RGB values with:
p->x //B
p->y //G
p->z //R

Try the following:
cv::Mat image = ...do some stuff...;
image.at<cv::Vec3b>(y,x); gives you the RGB (it might be ordered as BGR) vector of type cv::Vec3b
image.at<cv::Vec3b>(y,x)[0] = newval[0];
image.at<cv::Vec3b>(y,x)[1] = newval[1];
image.at<cv::Vec3b>(y,x)[2] = newval[2];

The low-level way would be to access the matrix data directly. In an RGB image (which I believe OpenCV typically stores as BGR), and assuming your cv::Mat variable is called frame, you could get the blue value at location (x, y) (from the top left) this way:
frame.data[frame.channels()*(frame.cols*y + x)];
Likewise, to get B, G, and R:
uchar b = frame.data[frame.channels()*(frame.cols*y + x) + 0];
uchar g = frame.data[frame.channels()*(frame.cols*y + x) + 1];
uchar r = frame.data[frame.channels()*(frame.cols*y + x) + 2];
Note that this code assumes the stride is equal to the width of the image.

A piece of code is easier for people who have such problem. I share my code and you can use it directly. Please note that OpenCV store pixels as BGR.
cv::Mat vImage_;
if(src_)
{
cv::Vec3f vec_;
for(int i = 0; i < vHeight_; i++)
for(int j = 0; j < vWidth_; j++)
{
vec_ = cv::Vec3f((*src_)[0]/255.0, (*src_)[1]/255.0, (*src_)[2]/255.0);//Please note that OpenCV store pixels as BGR.
vImage_.at<cv::Vec3f>(vHeight_-1-i, j) = vec_;
++src_;
}
}
if(! vImage_.data ) // Check for invalid input
printf("failed to read image by OpenCV.");
else
{
cv::namedWindow( windowName_, CV_WINDOW_AUTOSIZE);
cv::imshow( windowName_, vImage_); // Show the image.
}

The current version allows the cv::Mat::at function to handle 3 dimensions. So for a Mat object m, m.at<uchar>(0,0,0) should work.

uchar * value = img2.data; //Pointer to the first pixel data ,it's return array in all values
int r = 2;
for (size_t i = 0; i < img2.cols* (img2.rows * img2.channels()); i++)
{
if (r > 2) r = 0;
if (r == 0) value[i] = 0;
if (r == 1)value[i] = 0;
if (r == 2)value[i] = 255;
r++;
}

const double pi = boost::math::constants::pi<double>();
cv::Mat distance2ellipse(cv::Mat image, cv::RotatedRect ellipse){
float distance = 2.0f;
float angle = ellipse.angle;
cv::Point ellipse_center = ellipse.center;
float major_axis = ellipse.size.width/2;
float minor_axis = ellipse.size.height/2;
cv::Point pixel;
float a,b,c,d;
for(int x = 0; x < image.cols; x++)
{
for(int y = 0; y < image.rows; y++)
{
auto u = cos(angle*pi/180)*(x-ellipse_center.x) + sin(angle*pi/180)*(y-ellipse_center.y);
auto v = -sin(angle*pi/180)*(x-ellipse_center.x) + cos(angle*pi/180)*(y-ellipse_center.y);
distance = (u/major_axis)*(u/major_axis) + (v/minor_axis)*(v/minor_axis);
if(distance<=1)
{
image.at<cv::Vec3b>(y,x)[1] = 255;
}
}
}
return image;
}

Related

How to convert image storage order from channel-height-width to height-width-channel?

I would like to know how to convert an image stored as a 1D std::vector<float> from CHW format (Channel, Height, Width) to HWC format (Height, Width, Channel) in C++. The format change is needed due to requirements of a neural network.
I used OpenCV to read and show the image as below:
cv::namedWindow("Screenshot", cv::WINDOW_AUTOSIZE );
cv::imshow("Screenshot", rgbImage);
Then I converted the cv::Mat rgbImage to a 1D std::vector<float> in format CHW:
size_t channels = 3;
std::vector<float> data(channels*ROS_IMAGE_HEIGHT*ROS_IMAGE_WIDTH);
for(size_t j=0; j<ROS_IMAGE_HEIGHT; j++){
for(size_t k=0; k<ROS_IMAGE_WIDTH; k++){
cv::Vec3b intensity = rgbImage.at<cv::Vec3b>(j, k);
for(size_t i=0; i<channels; i++){
data[i*ROS_IMAGE_HEIGHT*ROS_IMAGE_WIDTH + j*ROS_IMAGE_HEIGHT + k] = (float) intensity[i];
}
}
}
Now I want to convert the format of std::vector<float> data to HWC. How can I do this?
I found some description of the "CHW" and "HWC" formats here.
If the storage order is HWC, it means that
Each sample is stored as a column-major matrix (height, width) of float[numChannels] (r00, g00, b00, r10, g10, b10, r01, g01, b01, r11, g11, b11).
Thus a pixel (x, y, c) is found using
xStride = channels;
yStride = channels * width;
cStride = 1;
data[x*xStride + y*yStride + c*cStride]
If the storage order is CHW, it means that each channel is a different plane. A pixel (x, y, c) is found using
xStride = 1;
yStride = width;
cStride = width * height;
data[x*xStride + y*yStride + c*cStride]
Note that in the code in the question, data[i*ROS_IMAGE_HEIGHT*ROS_IMAGE_WIDTH + j*ROS_IMAGE_HEIGHT + k] is incorrect, j is the y-coordinate and should be multiplied by ROS_IMAGE_WIDTH.
The code in the question can be modified to yield a std::vector in the HWC format by replacing the line in the innermost loop by:
data[i + j*ROS_IMAGE_WIDTH*channels + k*channels] = (float) intensity[i];

How do I pass an OpenCV Mat into a C++ Tensorflow graph?

In Tensorflow C++ I can load an image file into the graph using
tensorflow::Node* file_reader = tensorflow::ops::ReadFile(tensorflow::ops::Const(IMAGE_FILE_NAME, b.opts()),b.opts().WithName(input_name));
tensorflow::Node* image_reader = tensorflow::ops::DecodePng(file_reader, b.opts().WithAttr("channels", 3).WithName("png_reader"));
tensorflow::Node* float_caster = tensorflow::ops::Cast(image_reader, tensorflow::DT_FLOAT, b.opts().WithName("float_caster"));
tensorflow::Node* dims_expander = tensorflow::ops::ExpandDims(float_caster, tensorflow::ops::Const(0, b.opts()), b.opts());
tensorflow::Node* resized = tensorflow::ops::ResizeBilinear(dims_expander, tensorflow::ops::Const({input_height, input_width},b.opts().WithName("size")),b.opts());
For an embedded application I would like to instead pass an OpenCV Mat into this graph.
How would I convert the Mat to a tensor that could be used as input to tensorflow::ops::Cast or tensorflow::ops::ExpandDims?
It's not directly from a CvMat, but you can see an example of how to initialize a Tensor from an in-memory array in the TensorFlow Android example:
https://github.com/tensorflow/tensorflow/blob/0.6.0/tensorflow/examples/android/jni/tensorflow_jni.cc#L173
You would start off by creating a new tensorflow::Tensor object, with something like this (all code untested):
tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT,
tensorflow::TensorShape({1, height, width, depth}));
This creates a Tensor object with float values, with a batch size of 1, and a size of widthxheight, and with depth channels. For example a 128 wide by 64 high image with 3 channels would pass in a shape of {1, 64, 128, 3}. The batch size is just used when you need to pass in multiple images in a single call, and for simple uses you can leave it as 1.
Then you would get the underlying array behind the tensor using a line like this:
auto input_tensor_mapped = input_tensor.tensor<float, 4>();
The input_tensor_mapped object is an interface to the data in your newly-created tensor, and you can then copy your own data into it. Here I'm assuming you've set source_data as a pointer to your source data, for example:
const float* source_data = some_structure.imageData;
You can then loop through your data and copy it over:
for (int y = 0; y < height; ++y) {
const float* source_row = source_data + (y * width * depth);
for (int x = 0; x < width; ++x) {
const float* source_pixel = source_row + (x * depth);
for (int c = 0; c < depth; ++c) {
const float* source_value = source_pixel + c;
input_tensor_mapped(0, y, x, c) = *source_value;
}
}
}
There are obvious opportunities to optimize this naive approach, and I don't have sample code on hand to show how to deal with the OpenCV side of getting the source data, but hopefully this is helpful to get you started.
Here is complete example to read and feed:
Mat image;
image = imread("flowers.jpg", CV_LOAD_IMAGE_COLOR);
cv::resize(image, image, cv::Size(input_height, input_width), 0, 0, CV_INTER_CUBIC);
int depth = 3;
tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT,
tensorflow::TensorShape({1, image.rows, image.cols, depth}));
for (int y = 0; y < image.rows; y++) {
for (int x = 0; x < image.cols; x++) {
Vec3b pixel = image.at<Vec3b>(y, x);
input_tensor_mapped(0, y, x, 0) = pixel.val[2]; //R
input_tensor_mapped(0, y, x, 1) = pixel.val[1]; //G
input_tensor_mapped(0, y, x, 2) = pixel.val[0]; //B
}
}
auto result = Sub(root.WithOpName("subtract_mean"), input_tensor, {input_mean});
ClientSession session(root);
TF_CHECK_OK(session.Run({result}, out_tensors));
I had tried to run inception model on the opencv Mat file and following code worked for me https://gist.github.com/kyrs/9adf86366e9e4f04addb. Although there are some issue with integration of opencv and tensorflow. Code worked without any issue for .png files but failed to load .jpg and .jpeg. You can follow this for more info https://github.com/tensorflow/tensorflow/issues/1924
Tensor convertMatToTensor(Mat &input)
{
int height = input.rows;
int width = input.cols;
int depth = input.channels();
Tensor imgTensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({height, width, depth}));
float* p = imgTensor.flat<float>().data();
Mat outputImg(height, width, CV_32FC3, p);
input.convertTo(outputImg, CV_32FC3);
return imgTensor;
}

Convert cv::Mat to openni::VideoFrameRef

I have a kinect streaming data into a cv::Mat. I am trying to get some example code running that uses OpenNI.
Can I convert my Mat into an OpenNI format image somehow?
I just need the depth image, and after fighting with OpenNI for a long time, have given up on installing it.
I am using OpenCV 3, Visual Studio 2013, Kinect v2 for Windows.
The relevant code is:
void CDifodoCamera::loadFrame()
{
//Read the newest frame
openni::VideoFrameRef framed; //I assume I need to replace this with my Mat...
depth_ch.readFrame(&framed);
const int height = framed.getHeight();
const int width = framed.getWidth();
//Store the depth values
const openni::DepthPixel* pDepthRow = (const openni::DepthPixel*)framed.getData();
int rowSize = framed.getStrideInBytes() / sizeof(openni::DepthPixel);
for (int yc = height-1; yc >= 0; --yc)
{
const openni::DepthPixel* pDepth = pDepthRow;
for (int xc = width-1; xc >= 0; --xc, ++pDepth)
{
if (*pDepth < 4500.f)
depth_wf(yc,xc) = 0.001f*(*pDepth);
else
depth_wf(yc,xc) = 0.f;
}
pDepthRow += rowSize;
}
}
First you need to understand how your data is coming... If it is already in cv::Mat you should be receiving two images, one for the RGB information that usually is a 3 channel uchar cv::Mat and another image for the depth information that usually it is saved in a 16 bit representation in milimeters (you can not save float mat as images, but you can as yml/xml files using opencv).
Assuming you want to read and process the image that contains the depth information, you can change your code to:
void CDifodoCamera::loadFrame()
{
//Read the newest frame
//the depth image should be png since it is the one which supports 16 bits and it must have the ANYDEPTH flag
cv::Mat depth_im = cv::imread("img_name.png",CV_LOAD_IMAGE_ANYDEPTH);
const int height = depth_im.rows;
const int width = depth_im.cols;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
if (depth_im<unsigned short>(y,x) < 4500)
depth_wf(y,x) = 0.001f * (float)depth_im<unsigned short>(y,x);
else
depth_wf(y,x) = 0.f;
}
}
}
I hope this helps you. If you have any question just ask :)

How to detect white blobs using OpenCV

I paint a picture to test:
And I want to know how much blobs I have in the black circle and what is the size of each blobs (all blobs are ~white).
For example, in this case I have 12 spots:
I know how to found white pixels and it easy to verify sequence from left:
int whitePixels = 0;
for (int i = 0; i < height; ++i)
{
uchar * pixel = image.ptr<uchar>(i);
for (int j = 0; j < width; ++j)
{
if (j>0 && pixel[j-1]==0) // to group pixels for one spot
whitePixels++;
}
}
but it's clear that this code is not good enough (blobs can be diagonally, etc.).
So, the bottom line, I need help: how can I define the blobs?
Thank you
Following code finds bounding rects (blobs) for all white spots.
Remark: if we can assume white spots are really white (namely have values 255 in grayscaled image), you can use this snippet. Consider putting it in some class to avoid passing uncecessary params to function Traverse. Although it works. The idea is based on DFS. Apart from the gryscaled image, we have ids matrix to assign and remember which pixel belongs to which blob (all pixels having the same id belong to the same blob).
void Traverse(int xs, int ys, cv::Mat &ids,cv::Mat &image, int blobID, cv::Point &leftTop, cv::Point &rightBottom) {
std::stack<cv::Point> S;
S.push(cv::Point(xs,ys));
while (!S.empty()) {
cv::Point u = S.top();
S.pop();
int x = u.x;
int y = u.y;
if (image.at<unsigned char>(y,x) == 0 || ids.at<unsigned char>(y,x) > 0)
continue;
ids.at<unsigned char>(y,x) = blobID;
if (x < leftTop.x)
leftTop.x = x;
if (x > rightBottom.x)
rightBottom.x = x;
if (y < leftTop.y)
leftTop.y = y;
if (y > rightBottom.y)
rightBottom.y = y;
if (x > 0)
S.push(cv::Point(x-1,y));
if (x < ids.cols-1)
S.push(cv::Point(x+1,y));
if (y > 0)
S.push(cv::Point(x,y-1));
if (y < ids.rows-1)
S.push(cv::Point(x,y+1));
}
}
int FindBlobs(cv::Mat &image, std::vector<cv::Rect> &out, float minArea) {
cv::Mat ids = cv::Mat::zeros(image.rows, image.cols,CV_8UC1);
cv::Mat thresholded;
cv::cvtColor(image, thresholded, CV_RGB2GRAY);
const int thresholdLevel = 130;
cv::threshold(thresholded, thresholded, thresholdLevel, 255, CV_THRESH_BINARY);
int blobId = 1;
for (int x = 0;x<ids.cols;x++)
for (int y=0;y<ids.rows;y++){
if (thresholded.at<unsigned char>(y,x) > 0 && ids.at<unsigned char>(y,x) == 0) {
cv::Point leftTop(ids.cols-1, ids.rows-1), rightBottom(0,0);
Traverse(x,y,ids, thresholded,blobId++, leftTop, rightBottom);
cv::Rect r(leftTop, rightBottom);
if (r.area() > minArea)
out.push_back(r);
}
}
return blobId;
}
EDIT: I fixed a bug, lowered threshold level and now the output is given below. I think it is a good start point.
EDIT2: I get rid of recursion in Traverse(). In bigger images recursion caused Stackoverflow.

How to access image Data from a RGB image (3channel image) in opencv

I am trying to take the imageData of image in this where w= width of image and h = height of image
for (int i = x; i < x+h; i++) //height of frame pixels
{
for (int j = y; j < y+w; j++)//width of frame pixels
{
int pos = i * w * Channels + j; //channels is 3 as rgb
// if any data exists
if (data->imageData[pos]>0) //Taking data (here is the problem how to take)
{
xPos += j;
yPos += i;
nPix++;
}
}
}
jeff7 gives you a link to a very old version of OpenCV. OpenCV 2.0 has a new C++ wrapper that is much better than the C++ wrapper mentioned in the link. I recommend that you read the C++ reference of OpenCV for information on how to access individual pixels.
Another thing to note is: you should have the outer loop being the loop in y-direction (vertical) and the inner loop be the loop in x-direction. OpenCV is in C/C++ and it stores the values in row major.
See good explanation here on multiple methods for accessing pixels in an IplImage in OpenCV.
From the code you've posted your problem lies in your position variable, you'd want something like int pos = i*w*Channels + j*Channels, then you can access the RGB pixels at
unsigned char r = data->imageData[pos];
unsigned char g = data->imageData[pos+1];
unsigned char b = data->imageData[pos+2];
(assuming RGB, but on some platforms I think it can be stored BGR).
uchar* colorImgPtr;
for(int i=0; i<colorImg->width; i++){
for(int j=0; j<colorImg->height; j++){
colorImgPtr = (uchar *)(colorImg->imageData) + (j*colorImg->widthStep + i-colorImg->nChannels)
for(int channel = 0; channel < colorImg->nChannels; channel++){
//colorImgPtr[channel] here you have each value for each pixel for each channel
}
}
}
There are quite a few methods to do this (the link provided by jeff7 is very useful).
My preferred method to access image data is the cvPtr2D method. You'll want something like:
for(int x = 0; x < width; ++x)
{
for(int y = 0; y < height; ++y)
{
uchar* ptr = cvPtr2D(img, y, x, NULL);
// blue channel can now be accessed with ptr[0]
// green channel can now be accessed with ptr[1]
// red channel can now be accessed with ptr[2]
}
}
(img is an IplImage* in the above code)
Not sure if this is the most efficient way of doing this etc. but I find it the easiest and simplest way of doing it.
You can find documentation for this method here.