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I would like to know whether it is possible to carry out such an operation, that one picture is placed into another one at exact coordinates, automatedly.
I have a set consisting of 20 pairs of images of fixed size. I need to merge the pairs by placing first image of a pair to a fixed position on the second image.
I might need to do this mergers several times in future, so I am interested if there is a chance to write a scrypt in Python/Perl or any other simple language to handle this task. I am now using GIMP scripts, but most of the work still needs to be carried out manually...
Anyway, if this is not an option, I am asking someone to be so kind and briefly explain why is it so.
Thanks!
a lot of recepies ImageMagick v6 Examples -- Compositing Images. And you need perl-magick.
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I am trying to implement some kind of text detection algorithm and I want to separate the image into regions where each region contains different font size.
As in this image, for example:
Is there any easy way to implement it using python and/or opencv? if so, how?
I did tried googling it but could not find anything useful..
Thanks.
This is an interesting question. There are a few steps that you need to take in order to achieve your goals. I hope you are sufficiently informed of basic computer vision algorithm (knowledge in openCV function helps) to understand the steps i am suggesting.
Group all the words together using morphological dilation process.
Use openCV findcountour function to label all the blobs. This will give you the width and height information of each blob as well.
Here is the tricky part, now that you have data on each blob, try to run a clustering algorithm on the data with the location(x,y) and geometry(width,height) as your features.
Once you cluster them correctly, its a matter of finding the leftmost, rightmost, topmost and bottom data to draw the bounding rect.
I hope this will provide you enough information to start you work. Its is not detailed but i think its enough to guide you.
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I am trying to find out how to estimate position and orientation of an object using stereo camera images.
Assume a description of an object is given either by its edges and edge-relations, or points, or other features. The images are taken from above by a set of two cameras that have a fixed position relative to each other (like Microsoft Kinect gives you).
With Python's skimage toolkit I am able to recognise the similarites in the picture without telling if the searched object is in the images, but that is as far as I get.
What I want to do more is to segment the known object from the background and to be able to tell its position relatively to the cameras (e.g. by comparing its calculated position with the position of a known mark on the floor or something similar which I don't think will be too hard).
I know Python pretty well and have some experience in basic image processing. If this problem is already solved in OpenCV, I can work with that as well.
Thank you in advance for any help you give, either by naming keywords to improve my search, links, or by sharing your experience in this field!
To illustrate my problem: You have a bunch of same kind (shape+color) lego bricks laying in a chaotic manner, e.g. they are overlaying completely/partially or not at all and have an arbitrary orientation. The floor underneath is of the same color as the bricks. Cameras look straight down. Task is to find as many as bricks as possible and tell there location.
edit: added illustration
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I need to create a code that will take 24 scale values and save them in a txt. file, i then want to be able to recall these values and put them back into the 24 scales. can someone point me in the right direction.
Thanks
First read this :
http://docs.python.org/2/library/stdtypes.html#bltin-file-objects
then write some code and come back with concrete questions if necessary.
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I have a large sparse matrix (lil) implemented in Python with scipy, which comprises of Users on one axis, and songs they played on the other. So each row is a linked list of the songs that user has played.
I need to split each row ( user's songs ) so i have training and test data, but I'm unsure of how to do this. Sorry, im new to python, any help would be greatly appreciated.
I think you need sklearn.cross_validation.train_test_split
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I know in c++ it already exist
#include <list>
Now I am curious to know if it exist in python also.
You can also take a look at llist python package, which provides some useful features that deque does not. There are not only doubly linked lists, but also single linked lists data structure in that package. IMHO, one of the biggest advantages of this package is the ability to store a reference to the llist elements.
It appears that collections.deque is a doubly-linked-list library in Python. According to the documentation, it should have approximately O(1) cost when appending or popping from the head or the tail, as well as O(n) for regular inserts (which matches what we'd expect from a linked list).
API: http://docs.python.org/2/library/collections.html#collections.deque
Source: https://stackoverflow.com/a/282238/2441252