I am looking for the very simplest way to rasterise a shpfile in geopandas - the equivalent to arcpy PolygonToRaster_conversion() which does things in one line.
I have found some relatively involved methods eg
https://snorfalorpagus.net/blog/2014/11/09/masking-rasterio-layers-with-vector-features/
is it this complicated? or is there a one line option like arcpy's PolygonToRaster_conversion()
I'm looking for the simplest starting point to get the idea
I've been exploring rasterio to do this, but perhaps there are other ways
I'm only just starting to use Geopandas and would appreciate any pointers
Are you trying to rasterize a set of polygons with unique values in one step? If so, you want to rasterize using that unique value for each polygon, but beware that the last polygon rasterized to a given pixel will "claim" it (i.e., multiple polygons may touch a pixel, but the last one in your list of features will be the value rasterized there).
Or do you want to rasterize each polygon independently (or all polygons at the same time, as if they were a single polygon), so that you can extract out statistics from the raster? Mask may work for this, in a loop over each feature.
The closest you are likely to get to a one-line operation is using rasterio's rio mask or rio rasterize operation. The reason that the example you link to is more involved is that you need to do a few extra things to extract a subset of your original raster. There are now a few extra methods in rasterio that make that a bit easier (docs).
From geopandas, your geometry is in a GeoSeries. I haven't tested this directly, but you may need to call the __geo_interface__ of the series to get back GeoJSON-like shapes that rasterio expects as input.
first of all, I have to say I'm new to the field of computervision and I'm currently facing a problem, I tried to solve with opencv (Java Wrapper) without success.
Basicly I have a picture of a part from a Model taken by a camera (different angles, resoultions, rotations...) and I need to find the position of that part in the model.
Example Picture:
Model Picture:
So one question is: Where should I start/which algorithm should I use?
My first try was to use KeyPoint Matching with SURF as Detector, Descriptor and BF as Matcher.
It worked for about 2 pcitures out of 10. I used the default parameters and tried other detectors, without any improvements. (Maybe it's a question of the right parameters. But how to find out the right parameteres combined with the right algorithm?...)
Two examples:
My second try was to use the color to differentiate the certain elements in the model and to compare the structure with the model itself (In addition to the picture of the model I also have and xml representation of the model..).
Right now I extraxted the color red out of the image, adjusted h,s,v values manually to get the best detection for about 4 pictures, which fails for other pictures.
Two examples:
I also tried to use edge detection (canny, gray, with histogramm Equalization) to detect geometric structures. For some results I could imagine, that it will work, but using the same canny parameters for other pictures "fails". Two examples:
As I said I'm not familiar with computervision and just tried out some algorithms. I'm facing the problem, that I don't know which combination of algorithms and techniques is the best and in addition to that which parameters should I use. Testing it manually seems to be impossible.
Thanks in advance
gemorra
Your initial idea of using SURF features was actually very good, just try to understand how the parameters for this algorithm work and you should be able to register your images. A good starting point for your parameters would be varying only the Hessian treshold, and being fearles while doing so: your features are quite well defined, so try to use tresholds around 2000 and above (increasing in steps of 500-1000 till you get good results is totally ok).
Alternatively you can try to detect your ellipses and calculate an affine warp that normalizes them and run a cross-correlation to register them. This alternative does imply much more work, but is quite fascinating. Some ideas on that normalization using the covariance matrix and its choletsky decomposition here.
I have two binary image like this. I have a data set with lots of picture like at the bottom but with differents signs.
and
I would like to compare them in order to know if it's the same figure or not (especially inside the triangle). I took a look in Sift and Surf feature but it's doesn't work well on this type of picture (it find matchning point whereas the two picture are different,especially inside).
I also hear about SVM but i don't know if i have to implement it for this type of problem.
Do you have an idea ?
Thank you
I think you should not use SURF features on the binary image as you have already discarded a lot of information at that stage with your edge detector.
You could also use the Linear or Circle Hough Transform that in this case could tell you a lot about image differences.
If you wat to find 2 exactly identical images, simply use hash functions like md5.
But if you want to find related ( not exatcly identical) images, you are running in trouble ;). look for artificial neural network libs...
It looks a very simple question.
There are many lines available as their two endpoints.
The question is how to discretize them into a matrix. Then the matrix can be used for image processing purposes.
At the following figure example lines (yellow) and their corresponding pixelated demonstrations are shown.
A piece of code in any language would be of great help and strongly recommended and of course is in advance appreciated.
Note that performance and accuracy are very important factors.
Also as demonstrated each point of line must have only one pixel (i.e., element of matrix) associated.
The easiest way is to use Bresenham's algorithm.
Sometimes two image files may be different on a file level, but a human would consider them perceptively identical. Given that, now suppose you have a huge database of images, and you wish to know if a human would think some image X is present in the database or not. If all images had a perceptive hash / fingerprint, then one could hash image X and it would be a simple matter to see if it is in the database or not.
I know there is research around this issue, and some algorithms exist, but is there any tool, like a UNIX command line tool or a library I could use to compute such a hash without implementing some algorithm from scratch?
edit: relevant code from findimagedupes, using ImageMagick
try $image->Sample("160x160!");
try $image->Modulate(saturation=>-100);
try $image->Blur(radius=>3,sigma=>99);
try $image->Normalize();
try $image->Equalize();
try $image->Sample("16x16");
try $image->Threshold();
try $image->Set(magick=>'mono');
($blob) = $image->ImageToBlob();
edit: Warning! ImageMagick $image object seems to contain information about the creation time of an image file that was read in. This means that the blob you get will be different even for the same image, if it was retrieved at a different time. To make sure the fingerprint stays the same, use $image->getImageSignature() as the last step.
findimagedupes is pretty good. You can run "findimagedupes -v fingerprint images" to let it print "perceptive hash", for example.
Cross-correlation or phase correlation will tell you if the images are the same, even with noise, degradation, and horizontal or vertical offsets. Using the FFT-based methods will make it much faster than the algorithm described in the question.
The usual algorithm doesn't work for images that are not the same scale or rotation, though. You could pre-rotate or pre-scale them, but that's really processor intensive. Apparently you can also do the correlation in a log-polar space and it will be invariant to rotation, translation, and scale, but I don't know the details well enough to explain that.
MATLAB example: Registering an Image Using Normalized Cross-Correlation
Wikipedia calls this "phase correlation" and also describes making it scale- and rotation-invariant:
The method can be extended to determine rotation and scaling differences between two images by first converting the images to log-polar coordinates. Due to properties of the Fourier transform, the rotation and scaling parameters can be determined in a manner invariant to translation.
Colour histogram is good for the same image that has been resized, resampled etc.
If you want to match different people's photos of the same landmark it's trickier - look at haar classifiers. Opencv is a great free library for image processing.
I don't know the algorithm behind it, but Microsoft Live Image Search just added this capability. Picasa also has the ability to identify faces in images, and groups faces that look similar. Most of the time, it's the same person.
Some machine learning technology like a support vector machine, neural network, naive Bayes classifier or Bayesian network would be best at this type of problem. I've written one each of the first three to classify handwritten digits, which is essentially image pattern recognition.
resize the image to a 1x1 pixle... if they are exact, there is a small probability they are the same picture...
now resize it to a 2x2 pixle image, if all 4 pixles are exact, there is a larger probability they are exact...
then 3x3, if all 9 pixles are exact... good chance etc.
then 4x4, if all 16 pixles are exact,... better chance.
etc...
doing it this way, you can make efficiency improvments... if the 1x1 pixel grid is off by a lot, why bother checking 2x2 grid? etc.
If you have lots of images, a color histogram could be used to get rough closeness of images before doing a full image comparison of each image against each other one (i.e. O(n^2)).
There is DPEG, "The" Duplicate Media Manager, but its code is not open. It's a very old tool - I remember using it in 2003.
You could use diff to see if they are REALLY different.. I guess it will remove lots of useless comparison. Then, for the algorithm, I would use a probabilistic approach.. what are the chances that they look the same.. I'd based that on the amount of rgb in each pixel. You could also find some other metrics such as luminosity and stuff like that.