How can I quantify difference between two images

Question

Here s what I would like to do   I m taking pictures with a webcam at regular intervals   Sort of like a time lapse thing   However  if nothing has really changed  that is  the picture pretty much looks the same  I don t want to store the latest snapshot   I imagine there s some way of quantifying the difference  and I would have to empirically determine a threshold     I m looking for simplicity rather than perfection  I m using python

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Check out how Haar Wavelets are implemented by isk-daemon. You could use it's imgdb C++ code to calculate the difference between images on-the-fly:

isk-daemon is an open source database server capable of adding content-based (visual) image searching to any image related website or software.

This technology allows users of any image-related website or software to sketch on a widget which image they want to find and have the website reply to them the most similar images or simply request for more similar photos at each image detail page.

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you can compute the histogram of both the images and then calculate the Bhattacharyya Coefficient  this is a very fast algorithm and I have used it to detect shot changes in a cricket video  in C using openCV

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A trivial thing to try   Resample both images to small thumbnails  e g  64 x 64  and compare the thumbnails pixel-by-pixel with a certain threshold  If the original images are almost the same  the resampled thumbnails will be very similar or even exactly the same  This method takes care of noise that can occur especially in low-light scenes  It may even be better if you go grayscale

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A trivial thing to try   Resample both images to small thumbnails  e g  64 x 64  and compare the thumbnails pixel-by-pixel with a certain threshold  If the original images are almost the same  the resampled thumbnails will be very similar or even exactly the same  This method takes care of noise that can occur especially in low-light scenes  It may even be better if you go grayscale

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General idea  Option 1  Load both images as arrays  scipy misc imread  and calculate an element-wise  pixel-by-pixel  difference  Calculate the norm of the difference   Option 2  Load both images  Calculate some feature vector for each of them  like a histogram   Calculate distance between feature vectors rather than images   However  there are some decisions to make first   Questions  You should answer these questions first    Are images of the same shape and dimension   If not  you may need to resize or crop them  PIL library will help to do it in Python   If they are taken with the same settings and the same device  they are probably the same  Are images well-aligned   If not  you may want to run cross-correlation first  to find the best alignment first  SciPy has functions to do it   If the camera and the scene are still  the images are likely to be well-aligned  Is exposure of the images always the same   Is lightness contrast the same    If not  you may want to normalize images   But be careful  in some situations this may do more wrong than good  For example  a single bright pixel on a dark background will make the normalized image very different  Is color information important   If you want to notice color changes  you will have a vector of color values per point  rather than a scalar value as in gray-scale image  You need more attention when writing such code  Are there distinct edges in the image  Are they likely to move   If yes  you can apply edge detection algorithm first  e g  calculate gradient with Sobel or Prewitt transform  apply some threshold   then compare edges on the first image to edges on the second  Is there noise in the image   All sensors pollute the image with some amount of noise  Low-cost sensors have more noise  You may wish to apply some noise reduction before you compare images  Blur is the most simple  but not the best  approach here  What kind of changes do you want to notice   This may affect the choice of norm to use for the difference between images   Consider using Manhattan norm  the sum of the absolute values  or zero norm  the number of elements not equal to zero  to measure how much the image has changed  The former will tell you how much the image is off  the latter will tell only how many pixels differ    Example  I assume your images are well-aligned  the same size and shape  possibly with different exposure  For simplicity  I convert them to grayscale even if they are color  RGB  images   You will need these imports   import sys  from scipy misc import imread from scipy linalg import norm from scipy import sum  average   Main function  read two images  convert to grayscale  compare and print results   def main        file1  file2   sys argv 1 1 2        read images as 2D arrays  convert to grayscale for simplicity      img1   to grayscale imread file1  astype float       img2   to grayscale imread file2  astype float         compare     n m  n 0   compare images img1  img2      print  Manhattan norm    n m     per pixel    n m img1 size     print  Zero norm    n 0     per pixel    n 0 1 0 img1 size   How to compare  img1 and img2 are 2D SciPy arrays here   def compare images img1  img2         normalize to compensate for exposure difference  this may be unnecessary       consider disabling it     img1   normalize img1      img2   normalize img2        calculate the difference and its norms     diff   img1 - img2    elementwise for scipy arrays     m norm   sum abs diff      Manhattan norm     z norm   norm diff ravel    0     Zero norm     return  m norm  z norm    If the file is a color image  imread returns a 3D array  average RGB channels  the last array axis  to obtain intensity  No need to do it for grayscale images  e g   pgm    def to grayscale arr        If arr is a color image  3D array   convert it to grayscale  2D array        if len arr shape     3          return average arr  -1     average over the last axis  color channels      else          return arr   Normalization is trivial  you may choose to normalize to  0 1  instead of  0 255   arr is a SciPy array here  so all operations are element-wise   def normalize arr       rng   arr max  -arr min       amin   arr min       return  arr-amin  255 rng   Run the main function   if   name         main         main     Now you can put this all in a script and run against two images  If we compare image to itself  there is no difference     python compare py one jpg one jpg Manhattan norm  0 0   per pixel  0 0 Zero norm  0   per pixel  0 0   If we blur the image and compare to the original  there is some difference     python compare py one jpg one-blurred jpg  Manhattan norm  92605183 67   per pixel  13 4210411116 Zero norm  6900000   per pixel  1 0   P S  Entire compare py script   Update  relevant techniques  As the question is about a video sequence  where frames are likely to be almost the same  and you look for something unusual  I d like to mention some alternative approaches which may be relevant    background subtraction and segmentation  to detect foreground objects  sparse optical flow  to detect motion  comparing histograms or some other statistics instead of images   I strongly recommend taking a look at     Learning OpenCV    book  Chapters 9  Image parts and segmentation  and 10  Tracking and motion   The former teaches to use Background subtraction method  the latter gives some info on optical flow methods  All methods are implemented in OpenCV library  If you use Python  I suggest to use OpenCV   2 3  and its cv2 Python module   The most simple version of the background subtraction    learn the average value    and standard deviation s for every pixel of the background compare current pixel values to the range of    -2s    2s  or    -s    s    More advanced versions make take into account time series for every pixel and handle non-static scenes  like moving trees or grass    The idea of optical flow is to take two or more frames  and assign velocity vector to every pixel  dense optical flow  or to some of them  sparse optical flow   To estimate sparse optical flow  you may use Lucas-Kanade method  it is also implemented in OpenCV   Obviously  if there is a lot of flow  high average over max values of the velocity field   then something is moving in the frame  and subsequent images are more different   Comparing histograms may help to detect sudden changes between consecutive frames  This approach was used in Courbon et al  2010      Similarity of consecutive frames  The distance between two consecutive frames is measured  If it is too high  it means that the second frame is corrupted and thus the image is eliminated  The Kullback   Leibler distance  or mutual entropy  on the histograms of the two frames             where p and q are the histograms of the frames is used  The threshold is fixed on 0 2

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I think you could simply compute the euclidean distance  i e  sqrt sum of squares of differences  pixel by pixel   between the luminance of the two images  and consider them equal if this falls under some empirical threshold  And you would better do it wrapping a C function

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I apologize if this is too late to reply  but since I ve been doing something similar I thought I could contribute somehow   Maybe with OpenCV you could use template matching  Assuming you re using a webcam as you said    Simplify the images  thresholding maybe   Apply template matching and check the max val with minMaxLoc   Tip  max val  or min val depending on the method used  will give you numbers  large numbers  To get the difference in percentage  use template matching with the same image -- the result will be your 100    Pseudo code to exemplify   previous screenshot       current screenshot          simplify both images somehow    get the 100  corresponding value res   matchTemplate previous screenshot  previous screenshot  TM CCOEFF     hundred p val         minMaxLoc res     hundred p val is now the 100   res   matchTemplate previous screenshot  current screenshot  TM CCOEFF     max val         minMaxLoc res   difference percentage   max val   hundred p val    the tolerance is now up to you   Hope it helps

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You can compare two images using functions from PIL    import Image import ImageChops  im1   Image open  splash png   im2   Image open  splash2 png    diff   ImageChops difference im2  im1    The diff object is an image in which every pixel is the result of the subtraction of the color values of that pixel in the second image from the first image  Using the diff image you can do several things  The simplest one is the diff getbbox   function  It will tell you the minimal rectangle that contains all the changes between your two images   You can probably implement approximations of the other stuff mentioned here using functions from PIL as well

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Two popular and relatively simple methods are   a  the Euclidean distance already suggested  or  b  normalized cross-correlation   Normalized cross-correlation tends to be noticeably more robust to lighting changes than simple cross-correlation   Wikipedia gives a formula for the normalized cross-correlation   More sophisticated methods exist too  but they require quite a bit more work   Using numpy-like syntax    dist euclidean   sqrt sum  i1 - i2  2     i1 size  dist manhattan   sum abs i1 - i2     i1 size  dist ncc   sum   i1 - mean i1      i2 - mean i2            i1 size - 1    stdev i1    stdev i2      assuming that i1 and i2 are 2D grayscale image arrays

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A simple solution   Encode the image as a jpeg and look for a substantial change in filesize   I ve implemented something similar with video thumbnails  and had a lot of success and scalability

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There s a simple and fast solution using numpy by calculating mean squared error   before   np array get picture    while True      now   np array get picture        MSE   np mean  now - before   2       if  MSE  gt  threshold          break      before   now

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Here is a function I wrote  which takes 2 images  filepaths  as arguments and returns the average difference between the two images  pixels  components  This worked pretty well for me to determine visually  quot equal quot  images  when they re not    equal    I found 8 to be a good limit to determine if images are essentially the same    Images must have the same dimensions if you add no preprocessing to this   from PIL import Image  def imagesDifference  imageA  imageB        A   list Image open imageA  r r   convert r RGB   getdata        B   list Image open imageB  r r   convert r RGB   getdata        if  len A     len B    return -1     diff          for i in range 0  len A            diff     abs A i  0  - B i  0    abs A i  1  - B i  1    abs A i  2  - B i  2        return  sum diff    len diff

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A simple solution   Encode the image as a jpeg and look for a substantial change in filesize   I ve implemented something similar with video thumbnails  and had a lot of success and scalability

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I had a similar problem at work  I was rewriting our image transform endpoint and I wanted to check that the new version was producing the same or nearly the same output as the old version  So I wrote this   https   github com nicolashahn diffimg  Which operates on images of the same size  and at a per-pixel level  measures the difference in values at each channel  R  G  B   A   takes the average difference of those channels  and then averages the difference over all pixels  and returns a ratio   For example  with a 10x10 image of white pixels  and the same image but one pixel has changed to red  the difference at that pixel is 1 3 or 0 33     RGB 0 0 0 vs 255 0 0  and at all other pixels is 0  With 100 pixels total  0 33    100   a  0 33  difference in image   I believe this would work perfectly for OP s project  I realize this is a very old post now  but posting for future StackOverflowers who also want to compare images in python

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There s a simple and fast solution using numpy by calculating mean squared error   before   np array get picture    while True      now   np array get picture        MSE   np mean  now - before   2       if  MSE  gt  threshold          break      before   now

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A trivial thing to try   Resample both images to small thumbnails  e g  64 x 64  and compare the thumbnails pixel-by-pixel with a certain threshold  If the original images are almost the same  the resampled thumbnails will be very similar or even exactly the same  This method takes care of noise that can occur especially in low-light scenes  It may even be better if you go grayscale

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Have you seen the Algorithm for finding similar images question  Check it out to see suggestions   I would suggest a wavelet transformation of your frames  I ve written a C extension for that using Haar transformation   then  comparing the indexes of the largest  proportionally  wavelet factors between the two pictures  you should get a numerical similarity approximation

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I am addressing specifically the question of how to compute if they are  different enough    I assume you can figure out how to subtract the pixels one by one   First  I would take a bunch of images with nothing changing  and find out the maximum amount that any pixel changes just because of variations in the capture  noise in the imaging system  JPEG compression artifacts  and moment-to-moment changes in lighting   Perhaps you ll find that 1 or 2 bit differences are to be expected even when nothing moves   Then for the  real  test  you want a criterion like this    same if up to P pixels differ by no more than E    So  perhaps  if E   0 02  P   1000  that would mean  approximately  that it would be  different  if any single pixel changes by more than  5 units  assuming 8-bit images   or if more than 1000 pixels had any errors at all   This is intended mainly as a good  triage  technique to quickly identify images that are close enough to not need further examination   The images that  fail  may then more to a more elaborate expensive technique that wouldn t have false positives if the camera shook bit  for example  or was more robust to lighting changes   I run an open source project  OpenImageIO  that contains a utility called  idiff  that compares differences with thresholds like this  even more elaborate  actually    Even if you don t want to use this software  you may want to look at the source to see how we did it   It s used commercially quite a bit and this thresholding technique was developed so that we could have a test suite for rendering and image processing software  with  reference images  that might have small differences from platform-to-platform or as we made minor tweaks to tha algorithms  so we wanted a  match within tolerance  operation

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import os from PIL import Image from PIL import ImageFile import imagehash     just use to the size diferent picture def compare image img file1  img file2       if img file1    img file2          return True     fp1   open img file1   rb       fp2   open img file2   rb        img1   Image open fp1      img2   Image open fp2       ImageFile LOAD TRUNCATED IMAGES   True     b   img1    img2      fp1 close       fp2 close        return b       through picturu hash to compare def get hash dict dir       hash dict          image quantity   0     for       files in os walk dir           for i  fileName in enumerate files               with open dir   fileName   rb   as fp                  hash dict dir   fileName    imagehash average hash Image open fp                   image quantity    1      return hash dict  image quantity  def compare image with hash image file name 1  image file name 2  max dif 0        quot  quot  quot      max dif  The maximum hash difference is allowed  the smaller and more accurate  the minimum is 0      recommend to use      quot  quot  quot      ImageFile LOAD TRUNCATED IMAGES   True     hash 1   None     hash 2   None     with open image file name 1   rb   as fp          hash 1   imagehash average hash Image open fp       with open image file name 2   rb   as fp          hash 2   imagehash average hash Image open fp       dif   hash 1 - hash 2     if dif  lt  0          dif   -dif     if dif  lt   max dif          return True     else          return False   def compare image dir with hash dir 1  dir 2  max dif 0        quot  quot  quot      max dif  The maximum hash difference is allowed  the smaller and more accurate  the minimum is 0        quot  quot  quot      ImageFile LOAD TRUNCATED IMAGES   True     hash dict 1  image quantity 1   get hash dict dir 1      hash dict 2  image quantity 2   get hash dict dir 2       if image quantity 1  gt  image quantity 2          tmp   image quantity 1         image quantity 1   image quantity 2         image quantity 2   tmp          tmp   hash dict 1         hash dict 1   hash dict 2         hash dict 2   tmp      result dict           for k in hash dict 1 keys            result dict k    None      for dif i in range 0  max dif   1           have none   False          for k 1 in result dict keys                if result dict get k 1  is None                  have none   True          if not have none              return result dict          for k 1  v 1 in hash dict 1 items                for k 2  v 2 in hash dict 2 items                    sub    v 1 - v 2                  if sub  lt  0                      sub   -sub                 if sub    dif i and result dict get k 1  is None                      result dict k 1    k 2                     break     return result dict   def main        print compare image  image1  815 jpg    image2  5 jpg        print compare image with hash  image1  815 jpg    image2  5 jpg   7       r   compare image dir with hash  image1      image2     10      for k in r keys            print k  r get k     if   name         main         main     output  False True image2 5 jpg     image1 815 jpg image2 6 jpg     image1 819 jpg image2 7 jpg     image1 900 jpg image2 8 jpg     image1 998 jpg image2 9 jpg     image1 1012 jpg   the example pictures   815 jpg    5 jpg

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Have you seen the Algorithm for finding similar images question  Check it out to see suggestions   I would suggest a wavelet transformation of your frames  I ve written a C extension for that using Haar transformation   then  comparing the indexes of the largest  proportionally  wavelet factors between the two pictures  you should get a numerical similarity approximation

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I think you could simply compute the euclidean distance  i e  sqrt sum of squares of differences  pixel by pixel   between the luminance of the two images  and consider them equal if this falls under some empirical threshold  And you would better do it wrapping a C function

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A somewhat more principled approach is to use a global descriptor to compare images  such as GIST or CENTRIST  A hash function  as described here  also provides a similar solution

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I have been having a lot of luck with jpg images taken with the same camera on a tripod by  1  simplifying greatly  like going from 3000 pixels wide to 100 pixels wide or even fewer   2  flattening each jpg array into a single vector  3  pairwise correlating sequential images with a simple correlate algorithm to get correlation coefficient  4  squaring correlation coefficient to get r-square  i e fraction of variability in one image explained by variation in the next   5  generally in my application if r-square  lt  0 9  I say the two images are different and something happened in between   This is robust and fast in my implementation  Mathematica 7   It s worth playing around with the part of the image you are interested in and focussing on that by cropping all images to that little area  otherwise a distant-from-the-camera but important change will be missed    I don t know how to use Python  but am sure it does correlations  too  no

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Check out how Haar Wavelets are implemented by isk-daemon. You could use it's imgdb C++ code to calculate the difference between images on-the-fly:

isk-daemon is an open source database server capable of adding content-based (visual) image searching to any image related website or software.

This technology allows users of any image-related website or software to sketch on a widget which image they want to find and have the website reply to them the most similar images or simply request for more similar photos at each image detail page.

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There are many metrics out there for evaluating whether two images look like how much they look like   I will not go into any code here  because I think it should be a scientific problem  other than a technical problem   Generally  the question is related to human s perception on images  so each algorithm has its support on human visual system traits   Classic approaches are   Visible differences predictor  an algorithm for the assessment of image fidelity  https   www spiedigitallibrary org conference-proceedings-of-spie 1666 0000 Visible-differences-predictor--an-algorithm-for-the-assessment-of 10 1117 12 135952 short SSO 1   Image Quality Assessment  From Error Visibility to Structural Similarity  http   www cns nyu edu pub lcv wang03-reprint pdf   FSIM  A Feature Similarity Index for Image Quality Assessment  https   www4 comp polyu edu hk  cslzhang IQA TIP IQA FSIM pdf   Among them  SSIM  Image Quality Assessment  From Error Visibility to Structural Similarity   is the easiest to calculate and its overhead is also small  as reported in another paper  Image Quality Assessment Based on Gradient Similarity   https   www semanticscholar org paper Image-Quality-Assessment-Based-on-Gradient-Liu-Lin 2b819bef80c02d5d4cb56f27b202535e119df988    There are many more other approaches  Take a look at Google Scholar and search for something like  visual difference    image quality assessment   etc  if you are interested really care about the art

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Two popular and relatively simple methods are   a  the Euclidean distance already suggested  or  b  normalized cross-correlation   Normalized cross-correlation tends to be noticeably more robust to lighting changes than simple cross-correlation   Wikipedia gives a formula for the normalized cross-correlation   More sophisticated methods exist too  but they require quite a bit more work   Using numpy-like syntax    dist euclidean   sqrt sum  i1 - i2  2     i1 size  dist manhattan   sum abs i1 - i2     i1 size  dist ncc   sum   i1 - mean i1      i2 - mean i2            i1 size - 1    stdev i1    stdev i2      assuming that i1 and i2 are 2D grayscale image arrays

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What about calculating the Manhattan Distance of the two images  That gives you n n values  Then you could do something like an row average to reduce to n values and a function over that to get one single value

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There are many metrics out there for evaluating whether two images look like how much they look like   I will not go into any code here  because I think it should be a scientific problem  other than a technical problem   Generally  the question is related to human s perception on images  so each algorithm has its support on human visual system traits   Classic approaches are   Visible differences predictor  an algorithm for the assessment of image fidelity  https   www spiedigitallibrary org conference-proceedings-of-spie 1666 0000 Visible-differences-predictor--an-algorithm-for-the-assessment-of 10 1117 12 135952 short SSO 1   Image Quality Assessment  From Error Visibility to Structural Similarity  http   www cns nyu edu pub lcv wang03-reprint pdf   FSIM  A Feature Similarity Index for Image Quality Assessment  https   www4 comp polyu edu hk  cslzhang IQA TIP IQA FSIM pdf   Among them  SSIM  Image Quality Assessment  From Error Visibility to Structural Similarity   is the easiest to calculate and its overhead is also small  as reported in another paper  Image Quality Assessment Based on Gradient Similarity   https   www semanticscholar org paper Image-Quality-Assessment-Based-on-Gradient-Liu-Lin 2b819bef80c02d5d4cb56f27b202535e119df988    There are many more other approaches  Take a look at Google Scholar and search for something like  visual difference    image quality assessment   etc  if you are interested really care about the art

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I had a similar problem at work  I was rewriting our image transform endpoint and I wanted to check that the new version was producing the same or nearly the same output as the old version  So I wrote this   https   github com nicolashahn diffimg  Which operates on images of the same size  and at a per-pixel level  measures the difference in values at each channel  R  G  B   A   takes the average difference of those channels  and then averages the difference over all pixels  and returns a ratio   For example  with a 10x10 image of white pixels  and the same image but one pixel has changed to red  the difference at that pixel is 1 3 or 0 33     RGB 0 0 0 vs 255 0 0  and at all other pixels is 0  With 100 pixels total  0 33    100   a  0 33  difference in image   I believe this would work perfectly for OP s project  I realize this is a very old post now  but posting for future StackOverflowers who also want to compare images in python

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A somewhat more principled approach is to use a global descriptor to compare images  such as GIST or CENTRIST  A hash function  as described here  also provides a similar solution

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Earth movers distance might be exactly what you need  It might be abit heavy to implement in real time though

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A trivial thing to try   Resample both images to small thumbnails  e g  64 x 64  and compare the thumbnails pixel-by-pixel with a certain threshold  If the original images are almost the same  the resampled thumbnails will be very similar or even exactly the same  This method takes care of noise that can occur especially in low-light scenes  It may even be better if you go grayscale

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Earth movers distance might be exactly what you need  It might be abit heavy to implement in real time though

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You can compare two images using functions from PIL    import Image import ImageChops  im1   Image open  splash png   im2   Image open  splash2 png    diff   ImageChops difference im2  im1    The diff object is an image in which every pixel is the result of the subtraction of the color values of that pixel in the second image from the first image  Using the diff image you can do several things  The simplest one is the diff getbbox   function  It will tell you the minimal rectangle that contains all the changes between your two images   You can probably implement approximations of the other stuff mentioned here using functions from PIL as well

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Earth movers distance might be exactly what you need  It might be abit heavy to implement in real time though

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What about calculating the Manhattan Distance of the two images  That gives you n n values  Then you could do something like an row average to reduce to n values and a function over that to get one single value

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import os from PIL import Image from PIL import ImageFile import imagehash     just use to the size diferent picture def compare image img file1  img file2       if img file1    img file2          return True     fp1   open img file1   rb       fp2   open img file2   rb        img1   Image open fp1      img2   Image open fp2       ImageFile LOAD TRUNCATED IMAGES   True     b   img1    img2      fp1 close       fp2 close        return b       through picturu hash to compare def get hash dict dir       hash dict          image quantity   0     for       files in os walk dir           for i  fileName in enumerate files               with open dir   fileName   rb   as fp                  hash dict dir   fileName    imagehash average hash Image open fp                   image quantity    1      return hash dict  image quantity  def compare image with hash image file name 1  image file name 2  max dif 0        quot  quot  quot      max dif  The maximum hash difference is allowed  the smaller and more accurate  the minimum is 0      recommend to use      quot  quot  quot      ImageFile LOAD TRUNCATED IMAGES   True     hash 1   None     hash 2   None     with open image file name 1   rb   as fp          hash 1   imagehash average hash Image open fp       with open image file name 2   rb   as fp          hash 2   imagehash average hash Image open fp       dif   hash 1 - hash 2     if dif  lt  0          dif   -dif     if dif  lt   max dif          return True     else          return False   def compare image dir with hash dir 1  dir 2  max dif 0        quot  quot  quot      max dif  The maximum hash difference is allowed  the smaller and more accurate  the minimum is 0        quot  quot  quot      ImageFile LOAD TRUNCATED IMAGES   True     hash dict 1  image quantity 1   get hash dict dir 1      hash dict 2  image quantity 2   get hash dict dir 2       if image quantity 1  gt  image quantity 2          tmp   image quantity 1         image quantity 1   image quantity 2         image quantity 2   tmp          tmp   hash dict 1         hash dict 1   hash dict 2         hash dict 2   tmp      result dict           for k in hash dict 1 keys            result dict k    None      for dif i in range 0  max dif   1           have none   False          for k 1 in result dict keys                if result dict get k 1  is None                  have none   True          if not have none              return result dict          for k 1  v 1 in hash dict 1 items                for k 2  v 2 in hash dict 2 items                    sub    v 1 - v 2                  if sub  lt  0                      sub   -sub                 if sub    dif i and result dict get k 1  is None                      result dict k 1    k 2                     break     return result dict   def main        print compare image  image1  815 jpg    image2  5 jpg        print compare image with hash  image1  815 jpg    image2  5 jpg   7       r   compare image dir with hash  image1      image2     10      for k in r keys            print k  r get k     if   name         main         main     output  False True image2 5 jpg     image1 815 jpg image2 6 jpg     image1 819 jpg image2 7 jpg     image1 900 jpg image2 8 jpg     image1 998 jpg image2 9 jpg     image1 1012 jpg   the example pictures   815 jpg    5 jpg

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I apologize if this is too late to reply  but since I ve been doing something similar I thought I could contribute somehow   Maybe with OpenCV you could use template matching  Assuming you re using a webcam as you said    Simplify the images  thresholding maybe   Apply template matching and check the max val with minMaxLoc   Tip  max val  or min val depending on the method used  will give you numbers  large numbers  To get the difference in percentage  use template matching with the same image -- the result will be your 100    Pseudo code to exemplify   previous screenshot       current screenshot          simplify both images somehow    get the 100  corresponding value res   matchTemplate previous screenshot  previous screenshot  TM CCOEFF     hundred p val         minMaxLoc res     hundred p val is now the 100   res   matchTemplate previous screenshot  current screenshot  TM CCOEFF     max val         minMaxLoc res   difference percentage   max val   hundred p val    the tolerance is now up to you   Hope it helps

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I think you could simply compute the euclidean distance  i e  sqrt sum of squares of differences  pixel by pixel   between the luminance of the two images  and consider them equal if this falls under some empirical threshold  And you would better do it wrapping a C function

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Have you seen the Algorithm for finding similar images question  Check it out to see suggestions   I would suggest a wavelet transformation of your frames  I ve written a C extension for that using Haar transformation   then  comparing the indexes of the largest  proportionally  wavelet factors between the two pictures  you should get a numerical similarity approximation

User · Answer

What about calculating the Manhattan Distance of the two images  That gives you n n values  Then you could do something like an row average to reduce to n values and a function over that to get one single value

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General idea  Option 1  Load both images as arrays  scipy misc imread  and calculate an element-wise  pixel-by-pixel  difference  Calculate the norm of the difference   Option 2  Load both images  Calculate some feature vector for each of them  like a histogram   Calculate distance between feature vectors rather than images   However  there are some decisions to make first   Questions  You should answer these questions first    Are images of the same shape and dimension   If not  you may need to resize or crop them  PIL library will help to do it in Python   If they are taken with the same settings and the same device  they are probably the same  Are images well-aligned   If not  you may want to run cross-correlation first  to find the best alignment first  SciPy has functions to do it   If the camera and the scene are still  the images are likely to be well-aligned  Is exposure of the images always the same   Is lightness contrast the same    If not  you may want to normalize images   But be careful  in some situations this may do more wrong than good  For example  a single bright pixel on a dark background will make the normalized image very different  Is color information important   If you want to notice color changes  you will have a vector of color values per point  rather than a scalar value as in gray-scale image  You need more attention when writing such code  Are there distinct edges in the image  Are they likely to move   If yes  you can apply edge detection algorithm first  e g  calculate gradient with Sobel or Prewitt transform  apply some threshold   then compare edges on the first image to edges on the second  Is there noise in the image   All sensors pollute the image with some amount of noise  Low-cost sensors have more noise  You may wish to apply some noise reduction before you compare images  Blur is the most simple  but not the best  approach here  What kind of changes do you want to notice   This may affect the choice of norm to use for the difference between images   Consider using Manhattan norm  the sum of the absolute values  or zero norm  the number of elements not equal to zero  to measure how much the image has changed  The former will tell you how much the image is off  the latter will tell only how many pixels differ    Example  I assume your images are well-aligned  the same size and shape  possibly with different exposure  For simplicity  I convert them to grayscale even if they are color  RGB  images   You will need these imports   import sys  from scipy misc import imread from scipy linalg import norm from scipy import sum  average   Main function  read two images  convert to grayscale  compare and print results   def main        file1  file2   sys argv 1 1 2        read images as 2D arrays  convert to grayscale for simplicity      img1   to grayscale imread file1  astype float       img2   to grayscale imread file2  astype float         compare     n m  n 0   compare images img1  img2      print  Manhattan norm    n m     per pixel    n m img1 size     print  Zero norm    n 0     per pixel    n 0 1 0 img1 size   How to compare  img1 and img2 are 2D SciPy arrays here   def compare images img1  img2         normalize to compensate for exposure difference  this may be unnecessary       consider disabling it     img1   normalize img1      img2   normalize img2        calculate the difference and its norms     diff   img1 - img2    elementwise for scipy arrays     m norm   sum abs diff      Manhattan norm     z norm   norm diff ravel    0     Zero norm     return  m norm  z norm    If the file is a color image  imread returns a 3D array  average RGB channels  the last array axis  to obtain intensity  No need to do it for grayscale images  e g   pgm    def to grayscale arr        If arr is a color image  3D array   convert it to grayscale  2D array        if len arr shape     3          return average arr  -1     average over the last axis  color channels      else          return arr   Normalization is trivial  you may choose to normalize to  0 1  instead of  0 255   arr is a SciPy array here  so all operations are element-wise   def normalize arr       rng   arr max  -arr min       amin   arr min       return  arr-amin  255 rng   Run the main function   if   name         main         main     Now you can put this all in a script and run against two images  If we compare image to itself  there is no difference     python compare py one jpg one jpg Manhattan norm  0 0   per pixel  0 0 Zero norm  0   per pixel  0 0   If we blur the image and compare to the original  there is some difference     python compare py one jpg one-blurred jpg  Manhattan norm  92605183 67   per pixel  13 4210411116 Zero norm  6900000   per pixel  1 0   P S  Entire compare py script   Update  relevant techniques  As the question is about a video sequence  where frames are likely to be almost the same  and you look for something unusual  I d like to mention some alternative approaches which may be relevant    background subtraction and segmentation  to detect foreground objects  sparse optical flow  to detect motion  comparing histograms or some other statistics instead of images   I strongly recommend taking a look at     Learning OpenCV    book  Chapters 9  Image parts and segmentation  and 10  Tracking and motion   The former teaches to use Background subtraction method  the latter gives some info on optical flow methods  All methods are implemented in OpenCV library  If you use Python  I suggest to use OpenCV   2 3  and its cv2 Python module   The most simple version of the background subtraction    learn the average value    and standard deviation s for every pixel of the background compare current pixel values to the range of    -2s    2s  or    -s    s    More advanced versions make take into account time series for every pixel and handle non-static scenes  like moving trees or grass    The idea of optical flow is to take two or more frames  and assign velocity vector to every pixel  dense optical flow  or to some of them  sparse optical flow   To estimate sparse optical flow  you may use Lucas-Kanade method  it is also implemented in OpenCV   Obviously  if there is a lot of flow  high average over max values of the velocity field   then something is moving in the frame  and subsequent images are more different   Comparing histograms may help to detect sudden changes between consecutive frames  This approach was used in Courbon et al  2010      Similarity of consecutive frames  The distance between two consecutive frames is measured  If it is too high  it means that the second frame is corrupted and thus the image is eliminated  The Kullback   Leibler distance  or mutual entropy  on the histograms of the two frames             where p and q are the histograms of the frames is used  The threshold is fixed on 0 2

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you can compute the histogram of both the images and then calculate the Bhattacharyya Coefficient  this is a very fast algorithm and I have used it to detect shot changes in a cricket video  in C using openCV

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I am addressing specifically the question of how to compute if they are  different enough    I assume you can figure out how to subtract the pixels one by one   First  I would take a bunch of images with nothing changing  and find out the maximum amount that any pixel changes just because of variations in the capture  noise in the imaging system  JPEG compression artifacts  and moment-to-moment changes in lighting   Perhaps you ll find that 1 or 2 bit differences are to be expected even when nothing moves   Then for the  real  test  you want a criterion like this    same if up to P pixels differ by no more than E    So  perhaps  if E   0 02  P   1000  that would mean  approximately  that it would be  different  if any single pixel changes by more than  5 units  assuming 8-bit images   or if more than 1000 pixels had any errors at all   This is intended mainly as a good  triage  technique to quickly identify images that are close enough to not need further examination   The images that  fail  may then more to a more elaborate expensive technique that wouldn t have false positives if the camera shook bit  for example  or was more robust to lighting changes   I run an open source project  OpenImageIO  that contains a utility called  idiff  that compares differences with thresholds like this  even more elaborate  actually    Even if you don t want to use this software  you may want to look at the source to see how we did it   It s used commercially quite a bit and this thresholding technique was developed so that we could have a test suite for rendering and image processing software  with  reference images  that might have small differences from platform-to-platform or as we made minor tweaks to tha algorithms  so we wanted a  match within tolerance  operation

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A simple solution   Encode the image as a jpeg and look for a substantial change in filesize   I ve implemented something similar with video thumbnails  and had a lot of success and scalability

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You can compare two images using functions from PIL    import Image import ImageChops  im1   Image open  splash png   im2   Image open  splash2 png    diff   ImageChops difference im2  im1    The diff object is an image in which every pixel is the result of the subtraction of the color values of that pixel in the second image from the first image  Using the diff image you can do several things  The simplest one is the diff getbbox   function  It will tell you the minimal rectangle that contains all the changes between your two images   You can probably implement approximations of the other stuff mentioned here using functions from PIL as well

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Most of the answers given won t deal with lighting levels   I would first normalize the image to a standard light level before doing the comparison

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Two popular and relatively simple methods are   a  the Euclidean distance already suggested  or  b  normalized cross-correlation   Normalized cross-correlation tends to be noticeably more robust to lighting changes than simple cross-correlation   Wikipedia gives a formula for the normalized cross-correlation   More sophisticated methods exist too  but they require quite a bit more work   Using numpy-like syntax    dist euclidean   sqrt sum  i1 - i2  2     i1 size  dist manhattan   sum abs i1 - i2     i1 size  dist ncc   sum   i1 - mean i1      i2 - mean i2            i1 size - 1    stdev i1    stdev i2      assuming that i1 and i2 are 2D grayscale image arrays

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Another nice  simple way to measure the similarity between two images   import sys from skimage measure import compare ssim from skimage transform import resize from scipy ndimage import imread    get two images - resize both to 1024 x 1024 img a   resize imread sys argv 1     2  10  2  10   img b   resize imread sys argv 2     2  10  2  10      score   -1 1  measure of the structural similarity between the images score  diff   compare ssim img a  img b  full True  print score    If others are interested in a more powerful way to compare image similarity  I put together a tutorial and web app for measuring and visualizing similar images using Tensorflow

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Here is a function I wrote  which takes 2 images  filepaths  as arguments and returns the average difference between the two images  pixels  components  This worked pretty well for me to determine visually  quot equal quot  images  when they re not    equal    I found 8 to be a good limit to determine if images are essentially the same    Images must have the same dimensions if you add no preprocessing to this   from PIL import Image  def imagesDifference  imageA  imageB        A   list Image open imageA  r r   convert r RGB   getdata        B   list Image open imageB  r r   convert r RGB   getdata        if  len A     len B    return -1     diff          for i in range 0  len A            diff     abs A i  0  - B i  0    abs A i  1  - B i  1    abs A i  2  - B i  2        return  sum diff    len diff

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You can compare two images using functions from PIL    import Image import ImageChops  im1   Image open  splash png   im2   Image open  splash2 png    diff   ImageChops difference im2  im1    The diff object is an image in which every pixel is the result of the subtraction of the color values of that pixel in the second image from the first image  Using the diff image you can do several things  The simplest one is the diff getbbox   function  It will tell you the minimal rectangle that contains all the changes between your two images   You can probably implement approximations of the other stuff mentioned here using functions from PIL as well

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Two popular and relatively simple methods are   a  the Euclidean distance already suggested  or  b  normalized cross-correlation   Normalized cross-correlation tends to be noticeably more robust to lighting changes than simple cross-correlation   Wikipedia gives a formula for the normalized cross-correlation   More sophisticated methods exist too  but they require quite a bit more work   Using numpy-like syntax    dist euclidean   sqrt sum  i1 - i2  2     i1 size  dist manhattan   sum abs i1 - i2     i1 size  dist ncc   sum   i1 - mean i1      i2 - mean i2            i1 size - 1    stdev i1    stdev i2      assuming that i1 and i2 are 2D grayscale image arrays

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Most of the answers given won t deal with lighting levels   I would first normalize the image to a standard light level before doing the comparison

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I had the same problem and wrote a simple python module which compares two same-size images using pillow s ImageChops to create a black white diff image and sums up the histogram values   You can get either this score directly  or a percentage value compared to a full black vs  white diff   It also contains a simple is equal function  with the possibility to supply a fuzzy-threshold under  and including  the image passes as equal   The approach is not very elaborate  but maybe is of use for other out there struggling with the same issue   https   pypi python org pypi imgcompare

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Most of the answers given won t deal with lighting levels   I would first normalize the image to a standard light level before doing the comparison

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What about calculating the Manhattan Distance of the two images  That gives you n n values  Then you could do something like an row average to reduce to n values and a function over that to get one single value

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Have you seen the Algorithm for finding similar images question  Check it out to see suggestions   I would suggest a wavelet transformation of your frames  I ve written a C extension for that using Haar transformation   then  comparing the indexes of the largest  proportionally  wavelet factors between the two pictures  you should get a numerical similarity approximation

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A simple solution   Encode the image as a jpeg and look for a substantial change in filesize   I ve implemented something similar with video thumbnails  and had a lot of success and scalability

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I have been having a lot of luck with jpg images taken with the same camera on a tripod by  1  simplifying greatly  like going from 3000 pixels wide to 100 pixels wide or even fewer   2  flattening each jpg array into a single vector  3  pairwise correlating sequential images with a simple correlate algorithm to get correlation coefficient  4  squaring correlation coefficient to get r-square  i e fraction of variability in one image explained by variation in the next   5  generally in my application if r-square  lt  0 9  I say the two images are different and something happened in between   This is robust and fast in my implementation  Mathematica 7   It s worth playing around with the part of the image you are interested in and focussing on that by cropping all images to that little area  otherwise a distant-from-the-camera but important change will be missed    I don t know how to use Python  but am sure it does correlations  too  no

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Most of the answers given won t deal with lighting levels   I would first normalize the image to a standard light level before doing the comparison

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I think you could simply compute the euclidean distance  i e  sqrt sum of squares of differences  pixel by pixel   between the luminance of the two images  and consider them equal if this falls under some empirical threshold  And you would better do it wrapping a C function

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I had the same problem and wrote a simple python module which compares two same-size images using pillow s ImageChops to create a black white diff image and sums up the histogram values   You can get either this score directly  or a percentage value compared to a full black vs  white diff   It also contains a simple is equal function  with the possibility to supply a fuzzy-threshold under  and including  the image passes as equal   The approach is not very elaborate  but maybe is of use for other out there struggling with the same issue   https   pypi python org pypi imgcompare

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Earth movers distance might be exactly what you need  It might be abit heavy to implement in real time though

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Another nice  simple way to measure the similarity between two images   import sys from skimage measure import compare ssim from skimage transform import resize from scipy ndimage import imread    get two images - resize both to 1024 x 1024 img a   resize imread sys argv 1     2  10  2  10   img b   resize imread sys argv 2     2  10  2  10      score   -1 1  measure of the structural similarity between the images score  diff   compare ssim img a  img b  full True  print score    If others are interested in a more powerful way to compare image similarity  I put together a tutorial and web app for measuring and visualizing similar images using Tensorflow

[python] How can I quantify difference between two images?

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