I'm using Arrayfire to make a 2D heat transfer simulation. My dataset is a matrix of temperatures and I want to vizualize it as a heatmap. I need to produce frames of the colored dataset and save it as an image on the disk. So each temperature in my dataset has to be mapped to a color according to a certain color scheme.
I found that you can render the dataset in a window with a colormap using fig():
http://blog.accelereyes.com/blog/2013/07/03/arrayfire-examples-part-7-of-8-pde/
I also found that the colormaps available:
http://arrayfire.org/docs/defines_8h.htm#a553ceda8a1d8946efac3b08e642574ae
My plan so far has been to render the colored dataset using window.image() in a hidden window and then extract an array/image from the result so I can save this result using saveImage(). But I cannot find a way to extract the image rendered by the window.
Is there a better way to do this using the image processing functions? I would like to avoid defining my own color scheme. (i.e. making my own function that maps a temperature to a color)
Related
I'm using colmap. I succeed to visualize a 3D sparse reconstitution from a video.
Now I have some new images from the same scene and I want to (only) localize them. I want the (x,y,z, angles) of the camera.
Following the doc, I used the commands colmap feature_extractor and colmap vocab_tree_matcher.
Everything seemed to get well; the output is
Indexing image [1/23] in 0,022s
...
Indexing image [23/23] in 0,077s
Matching image [1/16] in 0,078s
...
Matching image [16/16] in 0,003s
Elapsed time: 0,043 [minutes]
But now what ?
How do I query the colmap database to get the (x,y,z,angle) of image, say, 12 ?
I want to programmatically get the information.
How can I let resliceimageviewer display a colored image? I know that vtkResliceImageViewer can setup lookup table. But I need to blend two images after map image to color.
I use DICOM reader to read image data. Then I create a lookup table to change image data into RGBA. Next I blend image with a mask to display the region of interst. Everything works fine except vtkResliceImageViewer can’t display color. Can someone help me?
Or does someone know how to create a Lookup table that output origin image?
I have a dataset of a single class (rectangular object) with a size of 130 images. My goal is to detect the object & draw a circle/dot/mark in the centre of the object.
Because the objects are rectangular, my idea is to get the dimensions of the predicted bounding box and take the circle/dot/mark as (width/2, height/2).
However, if I were to do transfer learning, would YOLO be a good choice to detect a single class of objects in a small dataset?
YOLO should be fine. However it is old now. Try YoloV4 for better results.
People have tried transfer learning from FasterRCNN to detect single objects with 300 images and it worked fine. (Link). However 130 images is a bit smaller. Try augmenting images - flipping, rotating etc if you get inferior results.
Use same augmentation for annotation as well while doing translation, rotation, flip augmentations. For example in pytorch, for segmentation, I use:
if random.random()<0.5: # Horizontal Flip
image = T.functional.hflip(image)
mask = T.functional.hflip(mask)
if random.random()<0.25: # Rotation
rotation_angle = random.randrange(-10,11)
image = T.functional.rotate(image,angle = rotation_angle)
mask = T.functional.rotate(mask ,angle = rotation_angle)
For bounding box you will have to create coordinates, x becomes width-x for horizontal flip.
Augmentations where object position is not changing: do not change annotations e.g.: gamma intensity transformation
I have a Kinect and I'm using OpenCV and point cloud library. I would like to project the IR Image onto a 2D plane for forklift pallet detection. How would I do that?
I'm trying to detect the pallet in the forklift here is an image:
Where are the RGB data? You can use it to help with the detection. You do not need to project the image onto any plane to detect a pellet. There are basically 2 ways used for detection
non-deterministic based on neural network, fuzzy logic, machine learning, etc
This approach need a training dataset to recognize the object. Much experience is needed for proper training set and classifier architecture/topology selection. But other then that you do not need to program it... as usually some readily available lib/tool is used just configure and pass the data.
deterministic based on distance or correlation coefficients
I would start with detecting specific features like:
pallet has specific size
pallet has sharp edges and specific geometry shape in depth data
pallet has specific range of colors (yellowish wood +/- lighting and dirt)
Wood has specific texture patterns
So compute some coefficient for each feature how close the object is to real pallet. And then just treshold the distance of all coefficients combined (possibly weighted as some features are more robust).
I do not use the #1 approach so I would go for #2. So combine the RGB and depth data (they have to be matched exactly). Then segmentate the image (based on depth and color). After that for each found object classify if it is pallet ...
[Edit1]
Your colored image does not correspond to depth data. The aligned gray-scale has poor quality and the depth data image is also very poor. Is the depth data processed somehow (loosing precision)? If you look at your data from different sides:
You can see how poor it is so I doubt you can use depth data for detection at all...
PS. I used my Align already captured rgb and depth images for the visualization.
The only thing left is the colored image and detect areas with matching color only. Then detect the features and classify. The color of your pallet in the image is almost white. Here HSV reduced colors to basic 16 colors (too lazy to segmentate)
You should obtain range of colors of the pallets possible by your setup to ease up the detection. Then check those objects for the features like size, shape,area,circumference...
[Edit2]
So I would start with Image preprocessing:
convert to HSV
treshold only pixels close to pallet color
I chose (H=40,S=18,V>100) as a pallet color. My HSV ranges are <0,255> per channel so Hue angle difference can be only <-180deg,+180deg> max which corresponds to <-128,+128> in my ranges.
remove too thin areas
Just scan all Horizontal an Vertical lines count consequent set pixels and if too small size recolor them to black...
This is the result:
On the left the original image (downsized so it fits to this page), In the middle is the color treshold result and last is the filtering out of small areas. You can play with tresholds and pallet color to change behavior to suite your needs.
Here C++ code:
int tr_d=10; // min size of pallet [pixels[
int h,s,v,x,y,xx;
color c;
pic1=pic0;
pic1.pf=_pf_rgba;
pic2.resize(pic1.xs*3,pic1.ys); xx=0;
pic2.bmp->Canvas->Draw(xx,0,pic0.bmp); xx+=pic1.xs;
// [color selection]
for (y=0;y<pic1.ys;y++)
for (x=0;x<pic1.xs;x++)
{
// get color from image
c=pic0.p[y][x];
rgb2hsv(c);
// distance to white-yellowish color in HSV (H=40,S=18,V>100)
h=c.db[picture::_h]-40;
s=c.db[picture::_s]-18;
v=c.db[picture::_v];
// hue is cyclic angular so use only shorter angle
if (h<-128) h+=256;
if (h>+128) h-=256;
// abs value
if (h< 0) h=-h;
if (s< 0) s=-s;
// treshold close colors
c.dd=0;
if (h<25)
if (s<25)
if (v>100)
c.dd=0x00FFFFFF;
pic1.p[y][x]=c;
}
pic2.bmp->Canvas->Draw(xx,0,pic1.bmp); xx+=pic1.xs;
// [remove too thin areas]
for (y=0;y<pic1.ys;y++)
for (x=0;x<pic1.xs;)
{
for ( ;x<pic1.xs;x++) if ( pic1.p[y][x].dd) break; // find set pixel
for (h=x;x<pic1.xs;x++) if (!pic1.p[y][x].dd) break; // find unset pixel
if (x-h<tr_d) for (;h<x;h++) pic1.p[y][h].dd=0; // if too small size recolor to zero
}
for (x=0;x<pic1.xs;x++)
for (y=0;y<pic1.ys;)
{
for ( ;y<pic1.ys;y++) if ( pic1.p[y][x].dd) break; // find set pixel
for (h=y;y<pic1.ys;y++) if (!pic1.p[y][x].dd) break; // find unset pixel
if (y-h<tr_d) for (;h<y;h++) pic1.p[h][x].dd=0; // if too small size recolor to zero
}
pic2.bmp->Canvas->Draw(xx,0,pic1.bmp); xx+=pic1.xs;
See how to extract the borders of an image (OCT/retinal scan image) for the description of picture and color. Or look at any of my DIP/CV tagged answers. Now the code is well commented and straightforward but just need to add:
You can ignore pic2 stuff it is just the image posted above so I do not need to manually print screen and merge the subresult in paint... To improve robustness you should add enhancing of dynamic range (so the tresholds have the same conditions for any input images). Also you should compare to more then just single color (if more wood types of pallet is present).
Now you should segmentate or label the areas
loop through entire image
find first pixel set with the pallet color
flood fill the area with some distinct ID color different from set pallet color
I use black 0x00000000 space and white 0x00FFFFFF as pallete pixel color. So use ID={1,2,3,4,5...}. Also remember number of filled pixels (that is your area) so you do not need to compute it again. You can also compute bounding box directly while filling.
compute and compare features
You need to experiment with more then one image. To find out what properties are good for detection. I would go for circumference length vs area ratio. and or bounding box size... The circumference can be extracted by simply selecting all pixels with proper ID color neighboring black pixel.
See also similar Fracture detection in hand using image proccessing
Good luck and have fun ...
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.