I'm trying to OCR an image in order to read the text on it. Original image is of 300 dpi.But all the ROI parts that are used as input parameters for Tesseract call are 96 dpi. I want to find out when is the quality drop happens. It will be possible if there is a way to calculate the DPI value of each vectors that are passed to Tesseract. Tesseract requires minimum 300 DPI for OCR.
Any idea?
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I have a black area around my image and I want to create a mask using OpenCV C++ that selects just this black area so that I can paint it later. How can i do that without affecting the image itself?
I tried to convert the image to grayscale and then using threshold to convert it to binary, but it affects my image since the result contains black pixels from inside the image.
Another Question : if i want to crop the image instead of paint it, how can i do it??
Thanks in advance,
I would solve the problem like this:
Inverse-binarize the image with a threshold of 1 (i.e. all pixels with the value 0 are set to 1, all others to 0)
use cv::findContours to find white segments
remove segments that don't touch image borders
use cv::drawContours to draw the remaining segments to a mask.
There is probably a more efficient solution in terms of runtime efficiency, but you should be able to prototype my solution quite quickly.
I read dicom images with ITK using itk::ImageSeriesReader and itk::GDCMImageIO after reading i flip the images with itk::FlipImageFilter (to get right orientation of the images) and convert the itkImageData to vtkImageData using itk::ImageToVTKImageFilter. I visualization images with VTK using vtkResliceImageViewer in QVTKWidget2.
I set:
(vtkResliceImageViewer)m_imageViewer[i]->SetColorWindow(windowWidthTAGvalue[0028|1051]);
(vtkResliceImageViewer)m_imageViewer[i]->SetColorLevel(windowCenterTAGvalue[0028|1050]);
and i set following blac&white LookUpTable:
vtkLookupTable* lutbw = vtkLookupTable::New();
lutbw->SetTableRange(0,1000);
lutbw->SetSaturationRange(0,0);
lutbw->SetHueRange(0,0);
lutbw->SetValueRange(0,1);
lutbw->Build();
And images shown into my software compared with the same images shown into other software are much darker, i can not get the same effect as other DICOM viewers
My software images are right other software image is left also when i use some other LookUpTable in this example Flow i can not get the same effect (2nd row images) my image on right is much darker then other.
What i am missing why my images are darker what can i do? i was research a lot into dicom and ikt/vtk can not find good solution any help is appreciate.
Please check the values for Rescale Slope (0028,1053) and Rescale Intercept(0028,1052) and apply the Modality LUT transformation before applying the Window level.
Your dataset may have VOI LUT Function (0028,1056) attribute value of "SIGMOID" instead of "LINEAR".
I extracted the image data from one of your DICOM file (brain_009.dcm) and looked at the histogram of the image data. It looks like, the minimum value stored in the image is 0 and maximum value is 960 regardless of interpreting the data is signed or unsigned. Also, the Window Width (0028:1051) has an invalid value of “0” and you cannot use that for displaying the image.
So your default display could set the Window Width to 960 and Window Center to half the window width plus the minimum value.
I want to denoise a video using OpenCV and C++. I found on the OpenCV doc site this:
fastNlMeansDenoising(contourImage,contourImage2);
Every time a new frame is loaded, my program should denoise the current frame (contourImage) and write it to contourImage2.
But if I run the code, it returns 0 and exits. What am I doing wrong or is there an alternative way to denoise an image? (It should be fast, because I am processing a video)
while you are using c++ you are not providing the full argument try this that way.
cv::fastNlMeansDenoisingColored(contourImage, contourImage2, 10, 10,7, 21);
// This is Original Function to be used.
cv::fastNlMeansDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) → dst
Parameters:
src – Input 8-bit 1-channel, 2-channel or 3-channel image.
dst – Output image with the same size and type as src .
templateWindowSize – Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels.
searchWindowSize – Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater.
searchWindowsSize - greater denoising time. Recommended value 21 pixels.
h – Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
I have an image as shown in the inset. I sampled it in Adobe Photoshop using the blue color as the image shows. The sampled image is shown in gray-scale on the left.
I know that openCV provides a similar method to sample images that is the inRange() function. How can I find out the range of HSV values that Adobe checked for to sample my image. Since the resultant image is pretty much what I want and I am not able to determine the range myself It would be a great help if some one could guide me for the same.
You can convert your image in HSV with cv::cvtColor(...) here the documentation
Then accordingly to Wikipedia the blue is near to 240° of the HUE channel of your image.
You can set something like maxHue = 270 and a minHue = 180 or other values to scan your image.
Maybe you should set a minSaturation and a minValue to avoid the black and white.
To find the best ranges you can link them with some sliders in a Qt GUI and change them until you get the same result as photoshop...
Could you please tell me how to what are ranges for Hue, Saturation and Value indices for intense red?
I try to use this values for color tracking and I couldn't find a specific answer via Google.
you can map any color to OpenCV HSV. Actually opencv use 1800 hue cylinder while ideally it is 360, on the orher hand MS paint use 2400 cyllinder.
So to get OpenCV HSV value, simply open MS paint, open mixer, and read the value of HSV, now to map this value into OpenCV HSV multiply it with 180/240.
the range to value for saturation and value is 00-1800
You are the only one who can answer this question, since we don't know your criteria for "intense red". Collect as many samples as you can, some of which you consider intense red and some which are close but just miss the cut. Convert them all to HSL. Study the pattern.
You might put together a small app that has sliders for the H, S, and L parameters and displays a block of color corresponding to the settings. That will tell you your limits very quickly.