I am writing code in GEF4/Zest to represent train runs (connections) from stations to stations (nodes), as depicted in the attached image. This image is built with GEF4, but my needs are a little more, as below, that I am still looking for the solution.
We need to draw the train run X, the only manual drawn thick black line, to connect Station1 to Station2 but via Station3 (train makes a stop at this station), currently an edge/connection is only made by 2 nodes, is that possible to have a "via node" for an edge/connection in GEF4/Zest?
Is there any snippet GEF4 code to print the whole view (including the invisible zones due to zoom) to PDF? (printing to image is already OK).
Thanks in advance for your suggestions/helps!
Cheers
Dan
Related
I'm kind of new to this image classification stuff so this is a somewhat high-level question. I was wondering if it's possible to train an image classifier (i.e using just TF/Keras or one of the many image recognition libraries and APIs) to identify whether an object is in an object. For example:
Output: A square
Output: A circle
Output: A circle in a square
Output: A square in a circle in a square
Output: A square in a circle and a square in a square
...and so on
If it's possible, what's the best way to go about it? Do I have to train the model to recognize all the variations example by example (which is unfavorable as there are far too many potential examples), or is there some better way? Thanks :)
You can do it by using simpler computer vision techniques instead of going for machine learning.
For example, if you use OpenCV, it has an inbuilt function called findContours, which returns a hierarchy.
Example:
The matrix on top shows how each shape is related to other, according to -
[Next, Previous, First_Child, Parent]
For instance, contours 2 and 4 (circle and rectangle) are at the same level. Hence in the matrix, the next of the second row is 4. You can construct a tree like this to get the output as you desired. You just need to make sure that the inner and outer contours of single shape are not counted as two separate ones which I didn't do here so it shows 5,7 in the output.
I want to remove background and get deer as a foreground image.
This is my source image captured by trail camera:
This is what I want to get. This output image can be a binary image or RGB.
I worked on it and try many methods to get solution but every time it failed at specific point. So please first understand what is my exact problem.
Image are captured by a trail camera and camera is motion detector. when deer come in front of camera it capture image.
Scene mode change with respect to weather changing or day and night etc. So I can't use frame difference or some thing like this.
Segmentation may be not work correctly because Foreground (deer) and Background have same color in many cases.
If anyone still have any ambiguity in my question then please first ask me to clear and then answer, it will be appreciated.
Thanks in advance.
Here's what I would do:
As was commented to your question, you can detect the dear and then perform grabcut to segment it from the picture.
To detect the dear, I would couple a classifier with a sliding window approach. That would mean that you'll have a classifier that given a patch (can be a large patch) in the image, output's a score of how much that patch is similar to a dear. The sliding window approach means that you loop on the window size and then loop on the window location. For each position of the window in the image, you should apply the classifier on that window and get a score of how much that window "looks like" a dear. Once you've done that, threshold all the scores to get the "best windows", i.e. the windows that are most similar to a dear. The rational behind this is that if we a dear is present at some location in the image, the classifier will output a high score at all windows that are close/overlap with the actual dear location. We would like to merge all that locations to a single location. That can be done by applying the functions groupRectangles from OpenCV:
http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#grouprectangles
Take a look at some face detection example from OpenCV, it basically does the same (sliding window + classifier) where the classifier is a Haar cascade.
Now, I didn't mention what that "dear classifier" can be. You can use HOG+SVM (which are both included in OpenCV) or use a much powerful approach of running a deep convulutional neural network (deep CNN). Luckily, you don't need to train a deep CNN. You can use the following packages with their "off the shelf" ImageNet networks (which are very powerful and might even be able to identify a dear without further training):
Decaf- which can be used only for research purposes:
https://github.com/UCB-ICSI-Vision-Group/decaf-release/
Or Caffe - which is BSD licensed:
http://caffe.berkeleyvision.org/
There are other packages of which you can read about here:
http://deeplearning.net/software_links/
The most common ones are Theano, Cuda ConvNet's and OverFeat (but that's really opinion based, you should chose the best package from the list that I linked to).
The "off the shelf" ImageNet network were trained on roughly 10M images from 1000 categories. If those categories contain "dear", that you can just use them as is. If not, you can use them to extract features (as a 4096 dimensional vector in the case of Decaf) and train a classifier on positive and negative images to build a "dear classifier".
Now, once you detected the dear, meaning you have a bounding box around it, you can apply grabcut:
http://docs.opencv.org/trunk/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.html
You'll need an initial scribble on the dear to perform grabcu. You can just take a horizontal line in the middle of the bounding box and hope that it will be on the dear's torso. More elaborate approaches would be to find the symmetry axis of the dear and use that as a scribble, but you would have to google, research an implement some method to extract symmetry axis from the image.
That's about it. Not straightforward, but so is the problem.
Please let me know if you have any questions.
Try OpenCV Background Substraction with Mixture of Gaussians models. They should be adaptable enough for your scenes. Of course, the final performance will depend on the scenario, but it is worth trying.
Since you just want to separate the background from the foreground I think you do not need to recognize the deer. You need to recognize an object in motion in the scene. You just need to separate what is static in a significant interval of time (background) from what is not static: the deer.
There are algorithms that combine multiple frames from the same scene in order to determine the background, like THIS ONE.
You mentioned that the scene mode changes with respect to weather changing or day and night considering photos of different deers.
You could implement a solution when motion is detected, instead of taking a single photo, it could take a few ones with some interval of time.
This interval has to be long as to get the deer in different positions or out of the scene and at the same time short enough to not be much affected by scene variations. Perhaps you need to deal with some brightness variation, but I think it is feasible to determine the background using these frames and finally segment the deer in the "motion frame".
I'd like to program a detection of a rectangular sheet of paper which doesn't absolutely need to be perfectly straight on each side as I may take a picture of it "in the air" which means the single sides of the paper might get distorted a bit.
The app (iOs and android) CamScanner does this very very good and Im wondering how this might be implemented. First of all I thought of doing:
smoothing / noise reduction
Edge detection (canny etc) OR thresholding (global / adaptive)
Hough Transformation
Detecting lines (only vertically / horizontally allowed)
Calculate the intercept point of 4 found lines
But this gives me much problems with different types of images.
And I'm wondering if there's maybe a better approach in directly detecting a rectangular-like shape in an image and if so, if maybe camscanner does implement it like this as well!?
Here are some images taken in CamScanner.
These ones are detected quite nicely even though in a) the side is distorted (but the corner still gets shown in the overlay but doesnt really fit the corner of the white paper) and in b) the background is pretty close to the actual paper but it still gets recognized correctly:
It even gets the rotated pictures correctly:
And when Im inserting some testing errors, it fails but at least detects some of the contour, but always try to detect it as a rectangle:
And here it fails completely:
I suppose in the last three examples, if it would do hough transformation, it could have detected at least two of the four sides of the rectangle.
Any ideas and tips?
Thanks a lot in advance
OpenCV framework may help your problem. Also, you can look to this document for the Android platform.
The full source code is available on Github.
If someone scans their right hand pressed against the glass of a scanner, the result would look like this:
(without the orange and white annotations). How could we determine someone's 2D:4D ratio from an image of their hand?
You've already tagged this opencv which is great - I'd highly recommend taking a look at openFrameworks and the openCV addon, as the basic examples there will give you some great starting blocks for this.
The general approach to this I would take is to first distill the image to light and dark areas, detect the edges of the hand and fingers, and then simplify your data until you have lines representing the edges and tips of the fingers. Finally, take the lower inseam between 2nd and 3rd finger, stopping at the tip of the 2nd, and the inseam of the 3rd and 4th, stopping at the tip of the 4th, which should give you your 2D:4D ratio.
First, you'll need to process your images to get to black and white images openCV can easily handle. You may have to play with various thresholds to get both the outline of the hand and the inseams of the fingers to be detected. (You may even need two passes to detect both the outline and inseams)
While there are many approaches to feature detection, OpenCV will generally return arrays of "blobs" detected. With the right thresholds, I believe you would be able to reliably and simply find contiguous horizontal blobs (or nearly contiguous, allowing for some distance between nearby blobs) for the inseams of each finger.
A simple algorithm for detecting the inseams would be to walk through the detected blobs starting from the top left and proceeding left-to-right through the image, as if reading a page. Assemble an array of detected horizontal lines from the blobs in your image, and play with various image processing thresholds, minimum accepted line length, and distance allowances between detected blobs which you still consider part of the same line until you're satisfied you're detecting the finger edges well.
Once you have detected the horizontal lines, you can process the blobs again, looking for the vertical lines that represent the tips of the fingers (stopping when you hit the previously detected horizontal lines)
Finally, find the lines which represent the correct inseams, measure them until they intersect with the appropriate fingertips, and you should have your ratio!
Interesting question. I'd go about it this way:
First, binarize the image by Otsu's thresholding. Then find the skeleton of the image using a Medial-Axis Transform (MAT). This would mean doing a distance transform on the image, then using adaptive thresholding to get the local maxima in the distance transform. This gives a rough and ready skeleton of your image. Sample code from here.
The obtained hand-skeleton may be slightly disconnected, in which case use the OpenCV morphology "CLOSE" (not "open") function can connect it into a single skeleton. Then checking convexity defects of the resulting hand should give an estimate.
so i came across this question which says:
You need to identify a moving (red) cricket ball against a green (grass) background. Assuming that both a colour image and an optic flow field are available, briefly describe how you would address this task using K-means segmentation.
my answer would be- define 2 clusters as there are two colours. since you have the optic flow, you can identify the cricket ball, and use clusters within that ball to come up with the mean of each cluster. then start k-means.
do you think my explanation makes sense? if so, i dont get why we need to use k-means algorithm to segment an image whose optic flow we already know (hence can segment to begin with). thoughts? thanks for any help!
you can use k-means to find the average velocity of all the objects (both grass and ball) on the basis of the flows. hence the scene can be divided into moving objects (ball) and non-moving objects (grass).. that would be my guess...