I am trying to create a scene inside the Reality Composer to track images.
So I added a cube, an image, and I see this error message
This image lacks the attributes necessary for tracking.
I have tried with different images, same message.
After reading the extensive documentation Apple wrote about the theme (zero lines), I ask you guys what what kind of attributes should the be present on tracking image...
You're right, there's no official documentation on that topic. But you can get to the bottom of the truth experimentally. A reference picture you've imported for image tracking has repetitive pattern. If you change it for a proper image you'll never receive such a message again.
Here are some tips for correct reference image attributes:
Well-discernable pattern
No repetitive pattern (like yours, or stripes, or polka dot)
High-contrast edges of silhouettes on a picture
High-contrast colours (RGB image mustn't be a solid grey if you convert it into B&W)
Moderate size (400 x 400 pix is quite enough)
No white-sheet-image or gradient-only-image
RGB linear color space
72 pixels per inch
No high compression for JPG
Related
I'm trying to figure out how to automatically cut some images like the one below (this is a negative film), basically, I want to remove the blank parts at the top and at the bottom. I'm not looking for complete code for it, I just want to understand a way to do it. The language is not important at this point, but I think this kind of thing normally is accomplished with Python.
I think there are several ways to do that, ranging from simple to complex. You can see the problem as detecting white rectangles or segmenting the image I would say.
I can suggest you opencv (which is available in more than one language, among which python), you can have a look here at the image processing examples
First we need to find the white part, then remove it.
Finding the white part
Thresholding
Let's start with an easy one: thresholding
Thresholding means dividing the image into two parts (usually black and white). You can do that by selecting a threshold (in your case, the threshold would be towards white - or black if you invert the image). By doing so, however, you may also threshold some parts of the images (for example the chickens and the white part above the kid). Do you have information about position of the white stripes? Are they always on top and bottom of the image? If so, you can apply the thresholding operation only on the top 25% and bottom 25% of the image. You would then most likely not "ruin" the image.
Finding the white rectangle
If that does not work or you would like to try something else, you can see the white stripes as rectangles and try to find their contour. You can see how in this tutorial. In this case you do not get a binary image, but a bounding box of the white areas. You most likely find the chickens also in this case, but by looking at the bounding box is easy to understand which one are correct and which one not. You can also check this calculating areas of the bounding box (width * height) and keep only the big ones.
Removing the part
Once you get the binary image (white part and not white part) or the bounding box, you have to crop the image. This also can be done in several ways, but I think the easiest one would be cropping by just selecting the central part of the image. For example, if the image has H pixels vertically, you would keep only the pixel from H1 (the height of the first white space) to H-H2 (where H2 is the height of the second white space). There is no tutorial on cropping, but several questions here on SO, as for example this one.
Additional notes
You could use more advanced segmentation algorithms as the Watershed one or even learn and use advanced techinques as machine learning to do that (here an article), as you can see the rabbit hole is pretty deep in this case. But I believe that would be an overkill and already the easy techniques would give you some results in this case.
Hope this was helpful and have fun!
I'm looking to crop thousands of images that contain text that I'm ultimately hoping to feed to tesseract. Unfortunately, there is a boundary on all the images that I would like to cut away before attempting to transcribe the text (the location of the boundary varies from image to image). An example of the boundary text that I would like to remove appears in the image below --- the black box contains lines of text that I would like to keep (it is blacked out in this case as it is sensitive data).
I would typically use mogrify to pre-process images, but in this case the shifting location prevents me from doing so. Can someone suggest a quick and dirty approach to cropping these images? I've reviewed approaches using Python's PIL, but have not found a solution (I have limited experience).
Due to the unpredictable margin lengths I was forced to use template matching instead. The python example here was very useful. When the image quality is poor, there is some confusion as to where the template may lie and so four regions of interest (ROI), N, S, E, and W are selected from the original image and templates are matched within these regions.
For those interested, I chose a random selection of images from the image set and extracted my templates from this set. I then decide where the cutting edge was based on a majority vote between the templates in each region.
I am doing some image processing of the retina images.. I need to replace the blood vessels with background pixels so that I can focus on other aspects of retina. I could not figure out a way to do this. I am using matlab. any suggestions?
Having worked extensively with retinal images, I can tell you that what you're proposing is a complex problem in itself. Sure, if you just want a crude method, you can use imdilate. But that will affect your entire image, and other structures in the image will change appearance. Something, that is not desirable.
However, if you want to do it properly, you will first need to segment all the blood vessels and create a binary mask. Once you have a binary mask, it's up to you how to fill up the vessel regions. You can either interpolate from the boundaries or calculate a background image and replace the vessel regions with pixels from the background image, etc.
Segmentation of the blood vessels is a challenging problem and you will find a lot of literature concerning that on the internet. Ultimately, you will have to choose how accurate a segmentation you want and build your algorithm accordingly.
imdilate should do what you want, since it replaces each pixel with the maximum of its neighbors. For more detailed suggestions, I'd need to see images.
The objective is to display the person on a different background (aka background removal).
I'm using the Kinect with Microsoft's Beta Kinect SDK to do so. With help of the depth, the background is filtered and we get only the image of the person.
This is pretty simple to do, and we can find the code that does that everywhere on the Internet. However, the depth signal is noisy, and we get pixels which do not belong to the person that are displayed.
I applied an edge detector to see if it was useful, and I currently get this:
Here's another without edge detection:
My question is: Which way can I get rid of these noisy white pixels around the person?
I tried morphological operations, but some parts of the body are erased and still leave white pixels behind.
The algorithm doesn't need to be real-time, I can just apply it when I press a 'Save image' button.
Edit 1:
I just tried to do background substraction with the closest frames on the shape border. The single pixels you see are flickering, which means it is noise and I can get easily get rid of them.
Edit 2:
The project is now over, and here's what we did: manual calibration of the Kinect by using the OpenNI driver, which provides directly the infrared image. The result is really good, but each calibration is specific to each Kinect.
Then, we applied a little transparency on the borders, and the result looks really nice! I can't provide pictures, however.
Your problem isn't just the noisy white pixels. You're missing significant parts of the person as well, e.g. part of his right hand. I'd recommend being more conservative with your thresholding of the depth data (allow more false positives). This would give you more noisy pixels, but at least you'd have the person in their entirety.
To get rid of the noisy pixels, I can think of a couple of things:
Feather the outer pixels (reduce them in intensity/increase their transparency if you're using an alpha channel)
Smooth the image, perform the edge detection on the smoothed image, then use these edges with your original sharp image.
Do some skin region detection to mark parts that definitely belong to a person. See skin detection in the YUV color space? and Skin Color Detection
For clothes, work with the hue and saturation image. If you know the color of the t-shirt (or that at least that it's not a neutral color), then this will stand out easily. If you don't know this information, then it may be worth building up a model of the person using the other frames (if there's a big gray blob that's moving around in your video, chances are that your subject is wearing a gray shirt)
The approaches aren't mutually exclusive so it may be worth trying to do them in combination. If I think of anything else, I'll post back here.
If there is no other way of resolving the jitter on the edges you could always try anti-alias as post-process.
I am trying to teach my camera to be a scanner: I take pictures of printed text and then convert them to bitmaps (and then to djvu and OCR'ed). I need to compute a threshold for which pixels should be white and which black, but I'm stymied by uneven illumination. For example if the pixels in the center are dark enough, I'm likely to wind up with a bunch of black pixels in the corners.
What I would like to do, under relatively simple assumptions, is compensate for uneven illumination before thresholding. More precisely:
Assume one or two light sources, maybe one with gradual change in light intensity across the surface (ambient light) and another with an inverse square (direct light).
Assume that the white parts of the paper all have the same reflectivity/albedo/whatever.
Find some algorithm to estimate degree of illumination at each pixel, and from that recover the reflectivity of each pixel.
From a pixel's reflectivity, classify it white or black
I have no idea how to write an algorithm to do this. I don't want to fall back on least-squares fitting since I'd somehow like to ignore the dark pixels when estimating illumination. I also don't know if the algorithm will work.
All helpful advice will be upvoted!
EDIT: I've definitely considered chopping the image into pieces that are large enough so they still look like "text on a white background" but small enough so that illumination of a single piece is more or less even. I think if I then interpolate the thresholds so that there's no discontinuity across sub-image boundaries, I will probably get something halfway decent. This is a good suggestion, and I will have to give it a try, but it still leaves me with the problem of where to draw the line between white and black. More thoughts?
EDIT: Here are some screen dumps from GIMP showing different histograms and the "best" threshold value (chosen by hand) for each histogram. In two of the three a single threshold for the whole image is good enough. In the third, however, the upper left corner really needs a different threshold:
I'm not sure if you still need a solution after all this time, but if you still do. A few years ago I and my team photographed about 250,000 pages with a camera and converted them to (almost black and white ) grey scale images which we then DjVued ( also make pdfs of).
(See The catalogue and complete collection of photographic facsimiles of the 1144 paper transcripts of the French Institute of Pondicherry.)
We also ran into the problem of uneven illumination. We came up with a simple unsophisticated solution which worked very well in practice. This solution should also work to create black and white images rather than grey scale (as I'll describe).
The camera and lighting setup
a) We taped an empty picture frame to the top of a table to keep our pages in the exact same position.
b) We put a camera on a tripod also on top of the table above and pointing down at the taped picture frame and on a bar about a foot wide attached to the external flash holder on top of the camera we attached two "modelling lights". These can be purchased at any good camera shop. They are designed to provide even illumination. The camera was shaded from the lights by putting small cardboard box around each modelling light. We photographed in greyscale which we then further processed. (Our pages were old browned paper with blue ink writing so your case should be simpler).
Processing of the images
We used the free software package irfanview.
This software has a batch mode which can simultaneously do color correction, change the bit depth and crop the images. We would take the photograph of a page and then in interactive mode adjust the brightness, contrast and gamma settings till it was close to black and white. (We used greyscale but by setting the bit depth to 2 you will get black and white when you batch process all the pages.)
After determining the best color correction we then interactively cropped a single image and noted the cropping settings. We then set all these settings in the batch mode window and processed the pages for one book.
Creating DjVu images.
We used the free DjVu Solo 3.1 to create the DjVu images. This has several modes to create the DjVu images. The mode which creates black and white images didn't work well for us for photographs, but the "photo" mode did.
We didn't OCR (since the images were handwritten Sanskrit) but as long as the letters are evenly illuminated I think your OCR software should ignore big black areas like between a two page spread. But you can always get rid of the black between a two page spread or at the edges by cropping the pages twices once for the left hand pages and once for the right hand pages and the irfanview software will allow you to cleverly number your pages so you can then remerge the pages in the correct order. I.e rename your pages something like page-xxxA for lefthand pages and page-xxxB for righthand pages and the pages will then sort correctly on name.
If you still need a solution I hope some of the above is useful to you.
i would recommend calibrating the camera. considering that your lighting setup is fixed (that is the lights do not move between pictures), and your camera is grayscale (not color).
take a picture of a white sheet of paper which covers the whole workable area of your "scanner". store this picture, it tells what is white paper for each pixel. now, when you take take a picture of a document to scan, you can reload your "white reference picture" and even the illumination before performing a threshold.
let's call the white reference REF, the picture DOC, the even illumination picture EVEN, and the maximum value of a pixel MAX (for 8bit imaging, it is 255). for each pixel:
EVEN = DOC * (MAX/REF)
notes:
beware of the parenthesis: most image processing library uses the image pixel type for performing computation on pixel values and a simple multiplication will overload your pixel. eventually, write the loop yourself and use a 32 bit integer for intermediate computations.
the white reference image can be smoothed before being used in the process. any smoothing or blurring filter will do, and don't hesitate to apply it aggressively.
the MAX value in the formula above represents the target pixel value in the resulting image. using the maximum pixel value targets a bright white, but you can adjust this value to target a lighter gray.
Well. Usually the image processing I do is highly time sensitive, so a complex algorithm like the one you're seeking wouldn't work. But . . . have you considered chopping the image up into smaller pieces, and re-scaling each sub-image? That should make the 'dark' pixels stand out fairly well even in an image of variable lighting conditions (I am assuming here that you are talking about a standard mostly-white page with dark text.)
Its a cheat, but a lot easier than the 'right' way you're suggesting.
This might be horrendously slow, but what I'd recommend is to break the scanned surface into quarters/16ths and re-color them so that the average grayscale level is similar across the page. (Might break if you have pages with large margins though)
I assume that you are taking images of (relatively) small black letters on a white background.
One approach could be to "remove" the small black objects, while keeping the illumination variations of the background. This gives an estimate of how the image is illuminated, which can be used for normalizing the original image. It is often enough to subtract the illumination estimate from the original image and then do a threshold based segmentation.
This approach is based on gray scale morphological filters, and could be implemented in matlab like below:
img = imread('filename.png');
illumination = imclose(img, strel('disk', 10));
imgCorrected = img - illumination;
thresholdValue = graythresh(imgCorrected);
bw = imgCorrected > thresholdValue;
For an example with real images take a look at this guide from mathworks. For further reading about the use of morphological image analysis this book by Pierre Soille can be recommended.
Two algorithms come to my mind:
High-pass to alleviate the low-frequency illumination gradient
Local threshold with an appropriate radius
Adaptive thresholding is the keyword. Quote from a 2003 article by R.
Fisher, S. Perkins, A. Walker, and E. Wolfart: “This more sophisticated version
of thresholding can accommodate changing lighting conditions in the image, e.g.
those occurring as a result of a strong illumination gradient or shadows.”
ImageMagick's -lat option can do it, for example:
convert -lat 50x50-2000 input.jpg output.jpg
input.jpg
output.jpg
You could try using an edge detection filter, then a floodfill algorithm, to distinguish the background from the foreground. Interpolate the floodfilled region to determine the local illumination; you may also be able to modify the floodfill algorithm to use the local background value to jump across lines and fill boxes and so forth.
You could also try a Threshold Hysteresis with a rate of change control. Here is the link to the normal Threshold Hysteresis. Set the first threshold to a typical white value. Set the second threshold to less than the lowest white value in the corners.
The difference is that you want to check the difference between pixels for all values in between the first and second threshold. Ideally if the difference is positive, then act normally. But if it is negative, you only want to threshold if the difference is small.
This will be able to compensate for lighting variations, but will ignore the large changes between the background and the text.
Why don't you use simple opening and closing operations?
Try this, just lool at the results:
src - cource image
src - open(src)
close(src) - src
and look at the close - src result
using different window size, you will get backgound of the image.
I think this helps.