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Image comparison algorithm
So basically i need to write a program that checks whether 2 images are the same or not. Consider the following 2 images:
http://i221.photobucket.com/albums/dd298/ramdeen32/starry_night.jpg
http://i221.photobucket.com/albums/dd298/ramdeen32/starry_night2.jpg
Well they are both the same images but how do i check to see if these images are the same. I am only limited to the media functions. All i can think of right now is the width height scaling and compare the RGB for each pixel but wouldnt the color be different?
Im completely lost on this one, any help is appreciated.
*Note this has to be in python and use the (media library)
Wow - that is a massive question, and one that has a vast number of possible solutions. I'm afraid I'm not a python expert, but I thought your question was interesting - so I wanted to propose a method that I would implement if I were posed with this problem.
Obviously, the two images you posted are actually very different - so you will need to consider 'how much different is the same', especially when working with images and considering different image formats and compression etc.
Anyway, for a solution that allows for a given difference in colour values (but not for pixels to be in the wrong places), I would do something like the following;
Pick two images.
Rescale the largest image to the exact same height and width as the first (even distorting the image if necessary).
Possibly grayscale the images to make the next steps simpler, without losing much in the way of effectiveness. Actually, possibly running edge detection here could work too.
Go through each pixel in both images and store the difference in either each of the RGB channels, or just the difference in grayscale intensity. You would end up with an array the size of the image noting the difference between the pixel intensities on the two images.
Now, I don't know the exact values, but you would probably then find that if you iterate over the array you could see whether the difference between each pixel in the two images is the same (or nearly the same) across all of the pixels. Perhaps iterate over the array once to find the average difference between the pixel intensities in the two images, then iterate over the image again to see if 90% of the differences fall within a certain threshold (5% difference?).
Just an idea. Of course, there might be some nice functions that I'm not aware of to make this easy, but I wouldn't hold my breath!
ImageMagick has Python bindings and a comparison function. It should do most of the work for you, but I've never used it in Python.
I think step 2 of John Wordsworths answer may be one of the hardest - here you are dealing with a stretched copy of the image but do you also allow rotated, cropped or in other ways distorted images? If so you are going to need a feature matching algorithm, such as used in Hugin or other panorama creation software. This will find matching features, distort to fit and then you can do the other stages of comparing. Ideally you want to recognise Van Gogh's painting from photos, even photos on mugs! It's easy for a human to do this, for a computer it needs rather more complex maths.
Related
I have a collection of images taken with different lens (same distorsion) and I want to see if theres a difference in the colour.
I think the best way to compare is simple average, for which I have:
import cv2
import numpy
myimg = cv2.imread('image.jpg')
avg_color_per_row = numpy.average(myimg, axis=0)
avg_color = numpy.average(avg_color_per_row, axis=0)
print(avg_color)
From: How to find the average colour of an image in Python with OpenCV?
I am struggling to find out if I should be using squared values of rgb and how i would do this in python. Any help highly appreciated & any advice on other ways to compare colour of identical images. I am new here so pls advise on any protocol ive missed out on too. Thank you.
That is a very broad question. And it depends on what you call "difference of color".
Are your images supposed to be strictly (to the pixel) aligned? Your comparison by rows seems to indicate so. But the rest of the text makes it very unrealistic (in fact, even in industrial computer vision, for example to control defects on parts, in very controlled environment, strictly positioned parts and cameras, this is unrealistic).
Are differences of luminosity considered difference of color?
Are the images contents supposed to be identical?
...
Very generally speaking, if what you are trying to assess is color impact of a lense, on an otherwise very similar image (same lighting, same object), I would just compute histograms of colors and compare them.
Forgive me but I'm new in OpenCV.
I would like to delete the common background in 3 images, where there is a landscape and a man.
I tried some subtraction codes but I can't solve the problem.
I would like output each image only with the man and without landscape
Are there in OpenCV Algorithms what do this do? (then without any manual operation so no markers or other)
I tried this python code CV - Extract differences between two images
but not works because in my case i don't have an image with only background (without man).
I thinks that good solution should to Compare all the images and save those "points" that are the same at least in an image.
In this way I can extrapolate a background (which we call "Result.jpg") and finally analyze each image and cut those portions that are also present in "Result.jpg".
You say it's a good idea? Do you have other simplest ideas?
Without semantic segmentation, you can't do that.
Because all you can compute is where two images differ, and this does not give you the silhouette of the person, but an overlapping of two silhouettes. You'll never know the exact outline.
Im trying to remove the differences between two frames and keep the non-chaning graphics. Would probably repeat the same process with more frames to get more accurate results. My idea is to simplify the frames removing things that won't need to simplify the rest of the process that will do after.
The different frames are coming from the same video so no need to deal with different sizes, orientation, etc. If the same graphic its in another frame but with a different orientation or scale, I would like to also remove it. For example:
Image 1
Image 2
Result (more or less, I suppose that will be uglier but containing a similar information)
One of the problems of this idea is that the source video, even if they are computer generated graphics, is compressed so its not that easy to identify if a change on the tonality of a pixel its actually a change or not.
Im ideally not looking at a pixel level and given the differences in saturation applied by the compression probably is not possible. Im looking for unchaged "objects" in the image. I want to extract the information layer shown on top of whats happening behind it.
During the last couple of days I have tried to achieve it in a Python script by using OpenCV with all kinds of combinations of absdiffs, subtracts, thresholds, equalizeHists, canny but so far haven't found the right implementation and would appreciate any guidance. How would you achieve it?
Im ideally not looking at a pixel level and given the differences in saturation applied by the compression probably is not possible. Im looking for unchaged "objects" in the image. I want to extract the information layer shown on top of whats happening behind it.
This will be extremely hard. You would need to employ proper CV and if you're not an expert in that field, you'll have really hard time.
How about this, forgetting about tooling and libs, you have two images, ie. two equally sized sequences of RGB pixels. Image A and Image B, and the output image R. Allocate output image R of the same size as A or B.
Run a single loop for every pixel, read pixel a and from A and pixel b from B. You get a 3-element (RGB) vector. Find distance between the two vectors, eg. magnitude of a vector (b-a), if this is less than some tolerance, write either a or b to the same offset into result image R. If not, write some default (background) color to R.
You can most likely do this with some HW accelerated way using OpenCV or some other library, but that's up to you to find a tool that does what you want.
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Compare Images in Python
(4 answers)
Closed 1 year ago.
Im using python to check in hundred of websites if a web banner in GIF format exist.
I have a folder with gif files as examples and Im compairing the gif files from each web sites with my own examples files.
I use filecmp but I found that many webs compress the gif files so even the files are visually identical the filecmp wont detect as same.
Is there any python library to detect if two gif files or vídeos are similar even if the resolución has changed?
Comparing two images for similarity is a general image processing problem so the solution you develop can be as simple or complex as you want it to be. In your specific case, you'll need a method for making two images the same size and a method for comparing the images.
First, you'll probably want to convert the images to RGB or grayscale arrays for comparison.
I would suggest reducing the size of the larger image to the size of the smaller image. That is less likely to introduce artifacts than increasing the size of the smaller image. Resizing can be accomplished with the Python Pillow library.
Image.resize(size, resample=None, box=None, reducing_gap=None)
https://pillow.readthedocs.io/en/stable/reference/Image.html
The resampling method may have some small effect on the similarity measure. However, you'll probably be fine just using resample = NEAREST.
After making sure the images are the same size, they must be compared. One could compare them using mean squared error (MSE) or structural similarity (SSIM). Luckily, SSIM is already implemented in scikit-image.
from skimage.metrics import structural_similarity as ssim
s = ssim(imageA, imageB)
https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/?_ga=2.129476107.608689084.1632687977-376301729.1627364626
In your case, MSE might work just as well. However, if the average brightness of the image had been changed by some kind of process, you'd want to first subtract that from each image.
If resizing is the only issue, that should be it. If, however, the images may have been flipped, rotated, or cropped, additional steps might be necessary.
Given two images - one a cropped (but not scaled) portion of the other, how can I find the crop parameters (i.e.: the x and y offsets and width/height)? The idea is to crop one image (screenshot) by hand, and then crop a lot more at the same points.
Ideally via imagemagick, but I am happy with any pseudo-code solution, or with Perl, Python, JavaScript (in order of preference)
I have thought of a brute-force approach (find the first pixel which is the same color, check the next, keep going until different, or move to the next). Before I go down this barabarous (and probably slow) route, I'd like to check for better ones.
Template matching can be used for the identification of smaller image within a larger image.
The following resource might be helpful. Please check it out
https://docs.opencv.org/4.5.2/d4/dc6/tutorial_py_template_matching.html