This post will be helpful in learning OpenCV using Python programming. Here I will show how to implement OpenCV functions and apply them in various aspects using some great examples.Bicubic Interpolation - Computerphile
Then the output will be visualized along with the comparisons. We will also discuss the basic of image processing and provide the detail explanation related to the OpenCV functions. The goal is to make you understand how to resize images in OpenCV. There are many interpolation algorithms in opencv and we will try to learn some of them with examples. Your email address will not be published.
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This site uses Akismet to reduce spam. Learn how your comment data is processed. CUBIC - better but slow cv2. CUBIC - better but slow. Resize image based on image widht and height. Author Recent Posts. Technology Related Blog at Life2Coding. Feel free to contact us for your any kind of technical problems.
We are here to help you. Latest posts by Life2Coding see all. Leave a Reply Cancel reply Your email address will not be published. Either dsize or both fx and fy must be non-zero.Bilinear interpolation is linear interpolation in 2 dimensions, and is typically used for image scaling and for 2D finite element analysis.
BiLinear from the golang. File:J bilinear interpolant. Let n mean shape function, C mean constants, i mean interpolant, and the three digits meaning dimensionality, number of corners, and in base 36 the number of nodes we construct various linear and quadratic interpolants in 1, 2, and 3 dimensions as. This uses the polynomial expansion described in wikipedia, and draws the same example as illustrated in that page with a different pallette.
It's not particularly fast - about ms for a x surface on an arbitrary machine. Create account Log in. Toggle navigation. Page Discussion Edit History. Bilinear interpolation From Rosetta Code. Jump to: navigationsearch. This task has been flagged for clarification. Code on this page in its current state may be flagged incorrect once this task has been clarified.
Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I want to resize image with bilinear interpolation. I found new intensity value but I do not know how can I use it. The code is below which is I written. Orginal image is here. That is output accorting to written code. Learn more. Asked 2 years, 6 months ago.
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Bilinear interpolation on images stored as Python Numpy ndarray
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Question feed.What is the best way to scale a 2D image array? For instance, suppose I have an image of x bytes, with each byte being a pixel. Each pixel is a grayscale level from 0 to I would like t…. What is the best image downscaling algorithm quality-wise? I want to find out which algorithm is the best that can be used for downsizing a raster picture.
With best I mean the one that gives the nicest-looking results. I know of bicubic, but is there somethi…. In my case, the quad is not rectangular, but it should at least be convex. Interpolation algorithms when downscaling. Im trying to understand downscaling. I can see how interpolation algorithms such as bicubic and nearest neighbour can be used when when upscaling, to "fill in the blanks" between the old, known points….
What's the best re-sampling algorithm I can use to divide an image into half its original size. Speed is of primary importance but it shouldn't degrade quality too bad. I'm basically trying to generat…. How to perform bilinear interpolation in Python. I would like to perform blinear interpolation using python.
I have a unit right triangle and a value at each of the 3 vertices. I need to interpolate to find the value at a point inside the triangle. Hours of searching have turned up nothing that actually tell…. Piecewise regression with R: plotting the segments.
I have 54 points. They represent offer and demand for products. I would like to show there is a break point in the offer. First, I sort the x-axis offer and remove the values that appears twice. I am looking for how to resample a numpy array representing image data at a new size, preferably having a choice of the interpolation method nearest, bilinear, etc.
I know there is scipy. How to get the image pixel at real locations in opencv? I want to retrieve the rgb of a pixel in the image. But the location is not integer location but real values x,y.If you are working in image processing and using Python as a prototyping script language to test algorithms, you might have noticed that all the libs providing fast image interpolation methods to either sub-sample or over-sample work in 8 bits unsigned integers uint8. This is quite annoying if you are working with floating point images.
PIL supports floating point interpolationbut only for one layer, thus forget about RGB, and scipy. The drawback of uint8 is it cannot store negative values, which is important if you are doing wavelets-style frequency separation. Of course, there are tricks like adding and normalizing, but tricks are bad in general, and the floating point arithmetic capabilities of modern CPU make any integer arithmetic speed-up unworthy of the rounding.
The other drawback, that people often forget, is working in uint8 always assumes that your pixels are non-linearly encoded with an OETF Opto-electrical transfer functionimproperly called a gammathat is meant to alleviate quantization errors in lower bits hence lower lightswhich produce banding in smooth gradients in low lights.
Which implies working in float because uint8 without OETF will fail. So, we need a good and fast way to interpolate float RGB images. Among all the interpolation methods, the bilinear is the most efficient of the ok-ish.
Notice that the C takes the number of channels as a parameter, because the function is inlined later, while the Python sets 3 channels. We are not done yet. Life is too short for unoptimised code. This is the reason why you want to use Python libs only as an API of wrappers for not-so-nice-looking loop operations written in C and compiled. What Numpy actually is. So you want to have nice-looking Python code at C speed? Here, since our loop is almost pure C no fancy pythonic featuresthere is very little to change.
Cython seems like a good intent started without clearly writing down the specifications before starting to fiddle. I let you google the args, but basically we turn off every Python thingy Global Interpreter Lock, noticeably, which prevents true multiprocess execution in Pythonand turn on parallelization, vectorization and loop reordering.
The first time you will call the function, it will be compiled for the input types you use it for. Of course, the compilation takes some time, but the compiled code is cached, so if you call the function several times, it will only be computed once.
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Code Review Stack Exchange is a question and answer site for peer programmer code reviews. It only takes a minute to sign up. I have written a bilinear interpolant, which is working moderately well except that is painfuly slow.
How can rewrite the code to make it faster? Using opencv directly isn't a valid answer. After some work I have got here, almost no for loops except for a single list comprehension. I have added functions as hinted on a comment MCVEs are not expected here, but I will leave there for history. This code has some helper functions and I really think that there is a lot of room for improvement, I just don't know how to do.
Almost got there except for two things:. First pixel  is weirdly set as 0. Some rounding issues in values when comparing to OpenCV's result. Tried to use np. Left as is using the option that gives the smallest error using opencv's output as reference. Converting types there and back again. Don't know what is happening and where the error may be, since the vector space transfomation should result in the correct orientation.
I could use np. But I treat the first two columns differently than the last two columns. Can np. Any better approaches? I would like to share some observations about your main concerns given at the end of the question. Let's start from the back:. From what I can see, the "trick" here is to bring the points into a homogenous coordinate system and back. I'm also relatively sure that there has to be a better way other than itertools. As you rightfully suspected, you could use np.
If you look at its documentationyou'll see, that you can pass array-like arguments as upper and lower bounds. Please verify this yourself, for what I've seen it always delivers the same results as your original implementation. While talking about the 5th point, I already presented a first step towards reducing the amount of transpositions. Rewriting it to make use of the full power of numpy will help as a first step:.
This implementation creates a numpy array of the right size and then fills its 1st and 3rd column with the floored coordinates and the 2nd and 4th column with the ceiled coordinates of the points.
Again, this should be fully compatible with your original implementation, but without all the transpositions and converting back and forth between Python and numpy. Indeed, the list comprehension here seems to be the biggest bottleneck of the code.
Since I wasn't fully able to decipher all the cryptic variable names in your code and not had so much time at hand to really wrap my head around the problem, I took the lazy approach and threw the just-in-time compiler numba i. This is the code I ended up with:. As you can see, I had to make some changes to use numba's faster nopython mode. The biggest change is that you cannot use zip Splitting the code up in two functions was likely not necessary, but I like to do it nevertheless to keep numba-specific modifications contained to a small scope.
But what are the benefits of these extra hoops you now have to jump through? Not bad, isn't it? Sign up to join this community.The following problem appeared in an assignment in the Princeton course COS The problem description is taken from the course itself.
Write a program that plots a Sierpinski triangle, as illustrated below. Then develop a program that plots a recursive patterns of your own design. The pattern was described by Polish mathematician Waclaw Sierpinski inbut has appeared in Italian art since the 13th century. Though the Sierpinski triangle looks complex, it can be generated with a short recursive program.
We need to think recursively: our function should draw one black triangle pointed downwards and then call itself recursively 3 times with an appropriate stopping condition. When writing our program, we should exercise modular design. Here, the number of self-similar pieces is 4 or 25 and the scaling factor is 2 or 5.
We can also apply this definition directly to the set of white points in Sierpinski triangle. Its dimension is fractional—more than a line segment, but less than a square! With Euclidean geometry, the dimension is always an integer; with fractal geometry, it can be something in between. Drawing a tree recursively, as described here :. The next problem appeared in an assignment in the Cornell course CS To interpolate values on a 2D grid, we can use the 2D analogue of linear interpolation: bilinear interpolation.
However, this forward warping procedure has several problems. Rotation by 45 degrees around the center of the image.
To do this, we need to simply call transform image three times, once for each channel, then put the results together into a single image. Next figures and animations show some results on an RGB image. The next figure shows the transform functions from here :. There is a problem with our interpolation method above: it is not very good at shrinking images, due to aliasing. Down-sampled Image with Bilinear Interpolation.
The problem is that a single pixel in the output image corresponds to about 2. To overcome this problem, we will create a data structure that will let us approximately average over any possible square regions of pixels in the input image: an image stack. An image stack is a 3D matrix that we can think of as, not surprisingly, a stack of images, one on top of the other.
The top image in the cube will be the original input image. Images further down the stack will be the input image with progressively larger amounts of blur.
Before we use the stack, we must write a function to create it, which takes as input a grayscale image and a number of levels in the stack, and returns a 3D matrix stack corresponding to the stack.
Every other image in the stack will be a blurred version of the previous image. A good blur kernel to use is:. Thus, if we down-sample the image by a factor of k, we want to sample pixels from level k of the stack. Now, what happens if we down-sample the image by a fractional factor, such as 3.
Unfortunately, there is no level 3. Fortunately, we have a tool to solve this problem: interpolation. We now potentially need to sample a value at position row,col,k of the image stack, where all three coordinates are fractional. We therefore something more powerful than bilinear interpolation: trilinear interpolation!
This sounds complicated, but we can write this in terms of our existing functions.