I have grey scale images as shown (it is scanned profile of an actual construction bridge). I would like to identify or detect hyperbolic variations or regions in it (and preferably separate them in the form of boxes as shown in another image). I have tried canny edge detector, texture segmentation and other methods, but they are not as reliable ..i.e., they don't have high degree of accuracy. The problems associated with this are:
a) Sometimes the hyperbolic variations are too close..so I have to stretch the image and adjust contrast.
b) Sometimes the region contrast is too light or heavily distorted.. but I still wanna detect them.. Please suggest a suitable method or code to identify it.. I have attached some sample images. Thank you very much for your suggestions.
sample image:
result needed - sample:
Related
I'm trying to find a way to reliably determine the location of a puzzle piece in an image. The puzzle piece varies in both shape and how easy it is to find it. What algorithm(s) in the opencv module would help me with the task at hand? Or is what I'm trying to do beyond the scope of the module?
Example images below:
Update
The original title was "Detecting obscure shapes with Opencv Python". However I am interested in concepts of image-processing that would solve such a problem: How to find a pasted image inside the bigger image?
Assume the following:
The jigsaw shapes are always of same (rectangle) boundary size (ie: a template-based searching method could work).
The jigsaw shape is not rotated to any angle (ie: there will be straight(-ish) horizontal and vertical lines to find.
The jigsaw shape is always "pasted" into some other "original" image (ie: a paste-detection method could work).
The solution can be OpenCV (as requested by the asker), but the core concepts should be applicable when using any appropriate software (ie: can loop through image pixels to process their values, in order to achieve the described solution).
I myself use JavaScript, but of course I will understand that openCV.calcHist() becomes a histogram function in JS code. I have no problem translating a good explanation into code. I will consider OpenCV code as pseudo-code towards a working idea.
In my opinion the best approach for a canonical answer was suggested in the comments by Christoph, which is training a CNN:
Implement a generator for shapes of puzzle pieces.
Get a large set of natural images from the net.
Generate tons of sample images with puzzle pieces.
Train your model to detect those puzzle pieces.
Histogram of Largest Error
This is a rough concept of a possible algorithm.
The idea comes from an unfounded premise that seems plausible enough.
The premise is that adding the puzzle piece drastically changes the histogram of the image.
Let's assume that the puzzle piece is bounded by a 100px by 100px square.
We are going to use this square as a mask to mask out pixels that are used to calculate the histogram.
The algorithm is to find the placement of the square mask on the image such that the error between the histogram of the masked image and the original image is maximized.
There are many norms to experiment with to measure the error: You could start with the sum over the error components squared.
I'll throw in my own attempt. It fails on the first image, only works fine on the next two images. I am open to other pixel-processing based techniques where possible.
I do not use OpenCV so the process is explained with words (and pictures). It is up to the reader to implement the solution in their own chosen programming language/tool.
Background:
I wondered if there was something inherent in pasted images (something maybe revealed by pixel processing or even by frequency domain analysis, eg: could a Fourier signal analysis help here?).
After some research I came across Error Level Analysis (or ELA). This page has a basic introduction for beginners.
Process: In 7 easy steps, this detects the location of a pasted puzzle piece.
(1) Take a provided cat picture and re-save 3 times as JPEG in this manner:
Save copy #1 as JPEG of quality setting 2.
Reload (to force a decode of) copy #1 then re-save copy #2 as JPEG of quality setting 5.
Reload (to force a decode of) copy #2 then re-save copy #3 as JPEG of quality setting 2.
(2) Do a difference blend-mode with original cat picture as base layer versus the re-saved copy #3 image. Thimage will be black so we increase Levels.
(3) Increase Levels to make the ELA detected area(s) more visible. note: I recommend working in BT.709 or BT.601 grayscale at this point. Not necessary, but it gives "cleaner" results when blurring later on.
(4) Alternate between applying a box blur to the image and also increasing levels, to a point where the islands disappear and a large blob remains..
(5) The blob itself is also emphasized with an increase of levels.
(6) Finally a Gaussian blur is used to smoothen the selection area
(7) Mark the blob area (draw an outline stroke) and compare to input image...
How to detect such cracks that you can see in the attached images? I have tried some OpenCV algorithms like blob detection (cv::SimpleBlobDetector) but couldn't get any results.
It is a cropped image, the full image has some other features as well, so I am not sure thresholding can work because I have to get the bounding box of the detected crack. One way is to assign several (region of interest) ROI and try to detect within that ROI, but this crack doesn't appear at the same location in the image. Any idea?
Can this problem be solved with machine/deep learning (like object detection)? If I train a model with a crack dataset? Because the crack part of the image doesn't have lots of features so I am not sure this method will work. Please guide.
Thanks.
These cracks are difficult to detect because the image is noisy (presumably X-ray) and the contrast poor, so the signal-to-noise ratio is low.
I would try by applying a gaussian filter for denoising, but only in the horizontal direction, to preserve the horizontal edges. Then detection of the horizontal edges.
This is about what a Gabor filter does. You can try different orientations.
Use mathematical morphology operation.
By example Matlab code:
a=imread('in.png');
se=strel( 'disk', 7);
b = imgaussfilt(a,1.3);
c=b-imopen(b,se);
c=3*c;
d=imclearborder(c);
imwrite(d, 'out.png');
i'm interested in some kind of charcoal-filters like the photoshop Photocopy-Filter or the note-paper.
Have someone a paper or some instructions how this filter works?
In best case i want to create the following:
input:
Output:
greetings
I think it's a process akin to pan-sharpening. I could get a quite similar image in gimp by:
Converting to gray
Duplicating into two layers
Lightly blurring one layer
Edge-detecting in the other layer with a DOG filter with large radius
Compositing the two layers, playing a bit with the transparency.
What this is doing is converting the color picture into a 0-1 bitmap picture.
They typically use a threshold function which returns 1 (white) for some values and 0 (black) for some other.
One simple function would be transform the image from color to gray-scale, and then select a shade of gray above which everything is white, and below it everything is black. The actual threshold you use could be made adaptive depending on the brightness of the picture (you want a certain percentage of pixels to be white).
It can also be adaptive based on the context within the picture (i.e. a dark area may still have some white pixels to show local contrast). The trees behind the house are not all black because the filtering is sensitive to the average darkness of the region.
Also note that the area close to the light gap in the tree has a cluster of dark pixels, because of its relative darkness. The edges of the home, the bench are also highlighted. There is an edge detection element at play.
I do not know exactly what effect you gave an example of but there are a variety that are similar to it. As VSOverFlow pointed out, thresholding an image would result in something very similar to that though I do not think it is what is being used. Open cv has a function for this, its documentation can be found here. You may also want to look into Otsu's method for thresholding.
Again as VSOverFlow pointed out, there is an edge detection element at play as well. You may want to investigate the Sobel and Prewitt filters. Those are 3 simple options that will give you something similar to the image you provided. Perhaps you could threshold the result from the Prewitt filter? I have no knowledge of how Photoshop implements its filters. If none of these options are close enough to what you are looking for I would recommend looking for information on the specific implementations of those filters in photoshop.
My aim is to detect the vein pattern in leaves which characterize various species of plants
I have already done the following:
Original image:
After Adaptive thresholding:
However the veins aren't that clear and get distorted , Is there any way i could get a better output
EDIT:
I tried color thresholding my results are still unsatisfactory i get the following image
Please help
The fact that its a JPEG image is going to give the "block" artifacts, which in the example you posted causes most square areas around the veins to have lots of noise, so ideally work on an image that's not been through lossy compression. If that's not possible then try filtering the image to remove some of the noise.
The veins you are wanting to extract have a different colour from the background, leaf and shadow so some sort of colour based threshold might be a good idea. There was a recent S.O. question with some code that might help here.
After that some sort of adaptive normalisation would help increase the contrast before you threshold it.
[edit]
Maybe thresholding isn't an intermediate step that you want to do. I made the following by filtering to remove jpeg artifacts, doing some CMYK channel math (more cyan and black) then applying adaptive equalisation. I'm pretty sure you could then go on to produce (subpixel maybe) edge points using image gradients and non-maxima supression, and maybe use the brightness at each point and the properties of the vein structure (mostly joining at a tangent) to join the points into lines.
In the past I made good experiences with the Edge detecting algorithm difference of Gaussian. Which basically works like this:
You blur the image twice with the gaussian blurr algorithm but with differenct blur radii.
Then you calculate the difference between both images.
Pixel with same color beneath each other will creating a same blured color.
Pixel with different colors beneath each other wil reate a gradient which is depending on the blur radius. For bigger radius the gradient will stretch more far. For smaller ones it wont.
So basically this is bandpass filter. If the selected radii are to small a vain vill create 2 "parallel" lines. But since the veins of leaves are small compared with the extends of the Image you mostly find radii, where a vein results in 1 line.
Here I added th processed picture.
Steps I did on this picture:
desaturate (grayscaled)
difference of Gaussian. Here I blured the first Image with a radius of 10px and the second image with a radius of 2px. The result you can see below.
This is only a quickly created result. I would guess that by optimizing the parametes, you can even get better ones.
This sounds like something I did back in college with neural networks. The neural network stuff is a bit hard so I won't go there. Anyways, patterns are perfect candidates for the 2D Fourier transform! Here is a possible scheme:
You have training data and input data
Your data is represented as a the 2D Fourier transform
If your database is large you should run PCA on the transform results to convert a 2D spectrogram to a 1D spectrogram
Compare the hamming distance by testing the spectrum (after PCA) of 1 image with all of the images in your dataset.
You should expect ~70% recognition with such primitive methods as long as the images are of approximately the same rotation. If the images are not of the same rotation.you may have to use SIFT. To get better recognition you will need more intelligent training sets such as a Hidden Markov Model or a neural net. The truth is to getting good results for this kind of problem may be quite a lot of work.
Check out: https://theiszm.wordpress.com/2010/07/20/7-properties-of-the-2d-fourier-transform/
I've been reading a lot about information transformations/distortions on here and elsewhere, and they seem to fall into two categories: distortion of the pixels of the image while maintaining the original boundaries, or transformations like rotation, scaling, etc. What I would like to do is pretty different.
I would like to warp a rectangular image into a polygon. In particular, I want to warp an image into each one of the 50 United States. Simple mapping of the state and then cropping out parts of the image that don't fit in is not acceptable. These images have borders, and then someone's face contained inside of them. I did find this really cool paper on content-aware image resizing (paper), and while it would let me keep the focus of the images (the face) undistorted, it still maps to 4 corners. For my initial test, I don't care about warping the faces too.
Does anyone have suggestions? Research papers, code, Wikipedia pages, GIMP plugins, software tools, etc welcome.
See if multipoint distortion in ImageMagick can work for you.