I am pretty new to Objective C and working with Cocoa Framework. I want to read an image and then extract the image data (just pixel data and not the header) and then write the data to a binary file. I am kind of stuck with this, I was going through the methods of NSImage but I couldn't find a suitable one. Can anyone suggest me some other ways of doing this?
Cocoa-wise, the easiest approach is to use the NSBitmapImageRep class. Once initialized with a NSData object, for example, you can access the color value at any coordinate as a NSColor object using the -setColor:atX:y: and -colorAtX:y: methods. Note that if you call these methods in tight loops, you may suffer a performance hit from objc_msg_send. You could consider accessing the raw bitmap data as C array via the -bitmapData method. When dealing with a RGB image, for example, the color values for each channel are stored at offsets of 3.
For example:
color values: [R,G,B][R,G,B][R,G,B]
indices: [0,1,2, 3,4,5, 6,7,8]
To loop through each pixel in the image and extract the RGB components:
unsigned char *bitmapData = [bitmapRep bitmapData];
if ([bitmapRep samplesPerPixel] == 3) {
for (i = 0; i < [image size].width * [image size].height; i++) {
int base = (i * 3);
// these range from 0-255
unsigned char red = bitmapData[base + 0];
unsigned char green = bitmapData[base + 1];
unsigned char blue = bitmapData[base + 2];
}
}
Related
I have an image, read using "cv::imread". I have to flatten it so that I could use CUDA & GPU for my image processing algorithms acceleration.
My problem: When I read my image, I can show it correctly using imshow, however when I flatten it and convert it to a Mat object to be used with imshow, only part of my image is displayed. The size of the output image is also wrong, meaning that some data is really lost. What's the problem with my for loop?
// The problematic part of my code
// The Camera Man gray test image
const char* img_gray_name = "../../Test_Images/cameraman.tiff";
const char* img_blur_name = "../cameraman-blur.tiff";
const char* image_general_name = "cameraman_blur";
cv::Mat img = cv::imread(img_gray_name);
unsigned long int img_gray_size = img.rows * img.cols * sizeof(uchar);
uchar *h_img_in;// input image, converted to a flat array to be
// processed by GPU
h_img_in = (uchar *)malloc(img_gray_size);
//*************** The bug should be here! ***************//
for (int i = 0; i < img.rows; ++i) {
for (int j = 0; j < img.cols; ++j) {
h_img_in[i*img.cols+j] = img.at<uchar>(i, j);
}
}
Mat img_test;
img_test = Mat(cv::Size(img.cols, img.rows), CV_8U, h_img_in);
imwrite(img_blur_name, img_test);
// create image window named "camera man"
cv::namedWindow(image_general_name);
// show the image on window
cv::imshow(image_general_name, img_test);
P.S.: I also tested with a new 2D array instead of 1D h_img_in, result is the same; This means that something goes wrong with my usage of "img.at(i, j)".
Mapbox provides Global elevation data with height data encoded in PNG image. Height is decoded by height = -10000 + ((R * 256 * 256 + G * 256 + B) * 0.1). Details are in https://www.mapbox.com/blog/terrain-rgb/.
I want to import the height data to generate terrains in Unity3D.
Texture2D dem = (Texture2D)AssetDatabase.LoadAssetAtPath("Assets/dem/12/12_3417_1536.png", typeof(Texture2D));
for (int i = 0; i < width; i++)
for (int j = 0; j < height; j++)
{
Color c = dem.GetPixel(i, j);
float R = c.r*255;
float G = c.g*255;
float B = c.b*255;
array[i, j] = -10000 + ((R * 256 * 256 + G * 256 + B) * 0.1f);
}
Here I set a break point and the rgba value of the first pixel is RGBA(0.000, 0.592, 0.718, 1.000). c.r is 0. The height is incorrect as this point represent the height of somewhere on a mountain.
Then I open the image in Photoshop and get RGB of the first pixel: R=1,G=152,B=179.
I write a test program in C#.
System.Drawing.Bitmap bitmap = new System.Drawing.Bitmap("12_3417_1536.png");
Color a = bitmap.GetPixel(0, 0);
It shows Color a is (R,G,B,A)=(1,147,249,255)
Here is the image I test:
https://api.mapbox.com/v4/mapbox.terrain-rgb/12/3417/1536.pngraw?access_token=pk.eyJ1Ijoib2xlb3RpZ2VyIiwiYSI6ImZ2cllZQ3cifQ.2yDE9wUcfO_BLiinccfOKg
Why I got different RGBA value with different method? Which one is correct?
According to the comments below, different read order and compressed data in unity may result in different value of the rgba of pixel at (0,0).
Now I want to focus on----How to convert the rgba(0~1) to RGBA(0~255)?
r_ps=r_unity*255? But how can I explain r=0 in unity and r=1 in PS of pixel at (0,0)
?
Try disabling compression from the texture's import settings in Unity (No compression). Alternatively, if you fetch the data at runtime, you can use Texture.LoadBytes() to avoid compression artifacts.
I will assume you are using the same picture and that there aren't two 12_3417_1536.png files in separate folders.
Each of these functions has a different concept of which pixel is at (0,0). Not sure what you mean by "first" pixel when you tested with photoshop, but Texture coordinates in unity start at lower left corner.
When I tested the lower left corner pixel using paint, I got the same value as you did with photoshop. However, if you test the upper left corner, you get (1,147,249,255) which is the result bitmap.GetPixel returns.
The unity values that you're getting seem to be way off. Try calling dem.GetPixel(0,0) so that you're sure you're analyzing the simplest case.
Bitmap is constructed by pixel data(purely pixel data). The construction was done by properly setting the bitmap parameters like hieght,width, bitcount etc. Bitmap is actually constructed with CreateDIBsection. And the bitmap is loaded onto a CStatic object having Bitmap as property.
Image is getting displayed with proper width and content. But only difference is the content color is colored instead of scale of gray. For eg image is a white H letter on black Bground, instead of displaying it as whitish, say a blue colored H letter is displayed. Similar color changes applies for different images. Also, sometimes junk colored data appears deviating from original content of image apart from just the color change.
Bitmap is a 16 bit bitmap.
Please see below for code used for creating BitMap.
HDC is device context of CStatic variable in which the created bitmap is loaded;
I directly set the BitMap returned by below function to this variable using setbitmap function. CStatic varibale has also BitMap as one of its property. See below for function used to create bitmap.
Function parameter definitions.
PixMapHeight = number of rows in pixel matrix.
PixMapWidth = number of columns in pixel matrix.
BitsPerPixel = The bits stored for one pixel.
pPixMapBits = Void pointer to pixel array.(raw pixel data only! 16 bit per pixel).
DoBitmapFromPixels(HDC Hdc, UINT PixMapWidth, UINT PixMapHeight, UINT BitsPerPixel, LPVOID pPixMapBits)
BITMAPINFO *bmpInfo = (BITMAPINFO *)malloc(sizeof(BITMAPINFOHEADER) + sizeof(RGBQUAD) * 256);
BITMAPINFOHEADER &bmpInfoHeader(bmpInfo->bmiHeader);
bmpInfoHeader.biSize = sizeof(BITMAPINFOHEADER);
LONG lBmpSize = PixMapWidth * PixMapHeight * (BitsPerPixel / 8);
bmpInfoHeader.biWidth = PixMapWidth;
bmpInfoHeader.biHeight = -(static_cast<int>(PixMapHeight));
bmpInfoHeader.biPlanes = 1;
bmpInfoHeader.biBitCount = BitsPerPixel;
bmpInfoHeader.biCompression = BI_RGB;
bmpInfoHeader.biSizeImage = 0;
bmpInfoHeader.biClrUsed = 0;
bmpInfoHeader.biClrImportant = 0;
void *pPixelPtr = NULL;
HBITMAP hBitMap = CreateDIBSection(Hdc, bmpInfo, DIB_RGB_COLORS, &pPixelPtr, NULL, 0);
if (pPixMapBits != NULL)
{
BYTE* pbBits = (BYTE*)pPixMapBits;
BYTE *Pix = (BYTE *)pPixelPtr;
memcpy(Pix, ((BYTE*)pbBits + (lBmpSize * (CurrentFrame - 1))), lBmpSize);
}
free(bmpInfo);
return hBitMap;
The supposed output is the figure in the left of attached file. But I am getting a blue toned image as in right(never mind the scaling and exact match issue, put the image to depict the problem).
And also it will be very helpful if I know how RGB values are stored in 16 bits!
You never actually said what format pPixMapBits is in, but I'm guessing that it contains 16-bit values where 0 represents black, 32768 represents gray, and 65535 represents white.
You are creating a BITMAPINFOHEADER with bitBitCount = 16 and biCompression = BI_RGB. According to the documentation, if you set the fields that way, then:
Each WORD in the bitmap array represents a single pixel. The relative intensities of red, green, and blue are represented with five bits for each color component. The value for blue is in the least significant five bits, followed by five bits each for green and red. The most significant bit is not used.
This is not the same format as your source data, and you are doing no conversion, so you get junk. Note that the bitmap format you chose is capable of representing only 2^5 = 32 shades of gray, not 65536, so you will suffer loss of quality during the conversion.
I am trying to access the image colors in a QImage.
The method that I found most in docs is based on the scanline function...
I tried and it worked... on RGB32 images. I had surprising - and unpleasant results when using the exact method to get color data for 8 bit indexed or monochrome images.
This was my code:
// note RGBTriple is a struct containing unsigned R, G, B
// rgbImage.pixels is a RGBTriple* array
RGBTriple* pTriple = rgbImage.pixels;
for (int y = 0; y < source.height(); y++)
{
const unsigned char* pScanLine = source.scanLine(y);
for (int x = 0; x < source.width(); x++)
{
QRgb* color = (QRgb*)pScanLine;
pTriple->R = qRed(*color);
pTriple->G = qGreen(*color);
pTriple->B = qBlue(*color);
++pTriple;
pScanLine += 4;
}
}
Running the same code with images 8bit indexed or monochrome, I got errors in creating getting colors. The documentation says that scanline is aligned to multiples of 32b - but since that is a multiple of 8 and 2 I didn't think it would be a problem.
Once I found out that I am not getting correct results for all types of input images, I changed it to
RGBTriple* pTriple = rgbImage.pixels;
for (int y = 0; y < source.height(); y++)
{
for (int x = 0; x < source.width(); x++)
{
pTriple->R = qRed(source.pixel(x, y));
pTriple->G = qGreen(source.pixel(x, y));
pTriple->B = qBlue(source.pixel(x, y));
++pTriple;
}
}
Works perfectly... I wonder if it is slower or will have other unexpected behavior ? After all, I am using the pixel() function - even on indexed images - to get color information, which actually should be stored differently... that seems like it should fail...
Is there a way to make the first version, using scanline, work for other image types ?
Why does it seem like using scanline to get the data is the preferred method ?
I tried and it worked... on RGB32 images. I had surprising - and
unpleasant results when using the exact method to get color data for 8
bit indexed or monochrome images.
You should not be surprised because the indexed and monochrome images are different formats. The first code snippet you posted is based on the knowledge on how RGB32 (and RGB32 only) is layed out in memory.
Think about it. In a monochrome image R=G=B. So only one channel need to be saved in memory.
If your goal is to obtain an rgb image inside rgbImage.pixels use QImage::convertToFormat() :
QImage source;
QImage dest = source.convertToFormat( QImage::Format_RGB888 );
memcpy(rgbImage.pixels, dest.bits(),dest.byteCount () );
I'm trying to detect a object using cvblob. So I use cvRenderBlob() method. Program compiled successfully but when at the run time it is returning an unhandled exception. When I break it, the arrow is pointed out to CvLabel *labels = (CvLabel *)imgLabel->imageData + imgLabel_offset + (blob->miny * stepLbl); statement in the cvRenderBlob() method definition of the cvblob.cpp file. But if I use cvRenderBlobs() method it's working fine. I need to detect only one blob that is the largest one. Some one please help me to handle this exception.
Here is my VC++ code,
CvCapture* capture = 0;
IplImage* frame = 0;
int key = 0;
CvBlobs blobs;
CvBlob *blob;
capture = cvCaptureFromCAM(0);
if (!capture) {
printf("Could not initialize capturing....\n");
return 1;
}
int screenx = GetSystemMetrics(SM_CXSCREEN);
int screeny = GetSystemMetrics(SM_CYSCREEN);
while (key!='q') {
frame = cvQueryFrame(capture);
if (!frame) break;
IplImage* imgHSV = cvCreateImage(cvGetSize(frame), 8, 3);
cvCvtColor(frame, imgHSV, CV_BGR2HSV);
IplImage* imgThreshed = cvCreateImage(cvGetSize(frame), 8, 1);
cvInRangeS(imgHSV, cvScalar(61, 156, 205),cvScalar(161, 256, 305), imgThreshed); // for light blue color
IplImage* imgThresh = imgThreshed;
cvSmooth(imgThresh, imgThresh, CV_GAUSSIAN, 9, 9);
cvNamedWindow("Thresh");
cvShowImage("Thresh", imgThresh);
IplImage* labelImg = cvCreateImage(cvGetSize(imgHSV), IPL_DEPTH_LABEL, 1);
unsigned int result = cvLabel(imgThresh, labelImg, blobs);
blob = blobs[cvGreaterBlob(blobs)];
cvRenderBlob(labelImg, blob, frame, frame);
/*cvRenderBlobs(labelImg, blobs, frame, frame);*/
/*cvFilterByArea(blobs, 60, 500);*/
cvFilterByLabel(blobs, cvGreaterBlob(blobs));
cvNamedWindow("Video");
cvShowImage("Video", frame);
key = cvWaitKey(1);
}
cvDestroyWindow("Thresh");
cvDestroyWindow("Video");
cvReleaseCapture(&capture);
First off, I'd like to point out that you are actually using the regular c syntax. C++ uses the class Mat. I've been working on some blob extraction based on green objects in the picture. Once thresholded properly, which means we have a "binary" image, background/foreground. I use
findContours() //this function expects quite a bit, read documentation
Descriped more clearly in the documentation on structural analysis. It will give you the contour of all the blobs in the image. In a vector which is handling another vector, which is handling points in the image; like so
vector<vector<Point>> contours;
I too need to find the biggest blob, and though my approach can be faulty to some extend, I won't need it to be different. I use
minAreaRect() // expects a set of points (contained by the vector or mat classes
Descriped also under structural analysis
Then access the size of the rect
int sizeOfObject = 0;
int idxBiggestObject = 0; //will track the biggest object
if(contours.size() != 0) //only runs code if there is any blobs / contours in the image
{
for (int i = 0; i < contours.size(); i++) // runs i times where i is the amount of "blobs" in the image.
{
myVector = minAreaRect(contours[i])
if(myVector.size.area > sizeOfObject)
{
sizeOfObject = myVector.size.area; //saves area to compare with further blobs
idxBiggestObject = i; //saves index, so you know which is biggest, alternatively, .push_back into another vector
}
}
}
So okay, we really only measure a rotated bounding box, but in most cases it will do. I hope that you will either switch to c++ syntax, or get some inspiration from the basic algorithm.
Enjoy.