I need to copy images from 'Asset' folder in Windows 10 which has background images automatically downloaded. Some of these images will never be displayed and at some point deleted. To make sure I have seen all the new images before they are deleted I have created a Python script that copy these images into a different folder. To efficient I need a way to compare two images those that only the new ones are copied. All I need to do is to have a function that takes two images compare them with a simple approach to be sure that the two images are not visually identical. A simple test would be to take an image file copy it and compare the copy and the original, in which case the function should be able to tell that those are the same images.
How can I compare two images in python? I need simple and efficient way to do it. Several answers I have read are a bit complicated.
I encountered a similar problem before. I used PIL.Image.tobytes() to convert the image to a byte object, then call hash() on the byte object and compared the hash values.
Compare two images in python
Option 1:
Use ImageChops module and it contains a number of arithmetical image operations, called channel operations (“chops”). These can be used for various purposes, including special effects, image compositions, algorithmic painting, and more.
Example:
ImageChops.difference(image1, image2) ⇒ image
Returns the absolute value of the difference between the two images.
out = abs(image1 - image2)
Option 2:
Scikit-image is an image processing toolbox for SciPy.
In scikit-image, please use the compare_ssim to Compute the mean structural similarity index between two images.
References:
Python Compare Two Images
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I am trying to build up an algorithm to detect some objects and track them over time. My input data is a tif multi-stack file, which I read as a np array. I apply a U-Net model to create a binary mask and then identify the coordinates of single objects using scipy.
Up to here everything kind of works but I just cannot get my head around the tracking. I have a dictionary where keys are the frame numbers and values are lists of tuples. Each tuple contain the coordinates of each object.
Now I have to link the objects together, which on paper seems pretty simple. I was hoping there was a function or a package to do so (ideally something similar to trackMate or M2track on ImageJ), but I cannot find anything like that. I am considering writing my own nearest neighbor tool but I'd like to know whether there is a less painful way (and also, I would like to consider also more advanced metrics).
The other option I considered is using cv2, but this would require converting the data in a format cv2 likes, which will significantly slow down the code. In addition, I would like to keep the data as close as possible to the original input, so no cv2 for me.
I solved it using trackpy.
http://soft-matter.github.io/trackpy/v0.5.0/
trackpy properly reads multistack tiff files (OpenCv can't).
for my school project, I need to find images in a large dataset. I'm working with python and opencv. Until now, I've managed to find an exact match of an image in the dataset but it takes a lot of time even though I had 20 images for the test code. So, I've searched few pages of google and I've tried the code on these pages
image hashing
building an image hashing search engine
feature matching
Also, I've been thinking to search through the hashed dataset, save their paths, then find the best feature matching image among them. But most of the time, my narrowed down working area is so much different than what is my query image.
The image hashing is really great. It looks like what I need but there is a problem: I need to find an exact match, not similar photos. So, I'm asking you guys, if you have any suggestion or a piece of code might help or improve the reference code that I've linked, can you share it with me? I'd be really happy to try or research what you guys send or suggest.
opencv is probably the wrong tool for this. The algorithms there are geared towards finding similar matches, not exact ones. The general idea is to use machine learning to teach the code to recognize what a car looks like so it can detect cars in videos, even when the color or form changes (driving in the shadow, different make, etc).
I've found two approaches work well when trying to build an image database.
Use a normal hash algorithm like SHA-256 plus maybe some metadata (file or image size) to find matches
Resize the image down to 4x4 or even 2x2. Use the pixel RGB values as "hash".
The first approach is to reduce the image to a number. You can then put the number in a look up table. When searching for the image, apply the same hashing algorithm to the image you're looking for. Use the new number to look in the table. If it's there, you have a match.
Note: In all cases, hashing can produce the same number for different pictures. So you have to compare all the pixels of two pictures to make sure it's really an exact match. That's why it sometimes helps to add information like the picture size (in pixels, not file size in bytes).
The second approach allows to find pictures which very similar to the eye but in fact slightly different. Imagine cropping off a single pixel column on the left or tilting the image by 0.01°. To you, the image will be the same but for a computer, they will by totally different. The second approach tries to average small changes out. The cost here is that you will get more collisions, especially for B&W pictures.
Finding exact image matches using hash functions can be done with the undouble library (Disclaimer: I am also the author). It works using a multi-step process of pre-processing the images (grayscaling, normalizing, and scaling), computing the image hash, and the grouping of images based on a threshold value.
I work with a huge library of components (step files) that are currently used in various products. My goal is to identify parts with great similarity in order to unify them. At the moment I can think of two solutions:
Compare certain properties of the 3D data with a suitable python library. E.g. identify parts with similar volume and dimensions.
Convert step files to JPG and compare the images with one of the many image processing libraries.
Both have their pitfalls.
Is there a library that can handle step files or do you know a better way to solve the problem?
You are underestimating the complexity of this project. Once the STEP geometry is loaded, taking dimensions on it (apart from bounding box extents) can be really cumbersome. Very different parts can have the same volume and comparing bitmaps you completely ignore the hidden part of the geometry.
Here is the effect I am trying to achieve - Imagine a user submits an image, then a python script to cycle through each JPEG/PNG for a similar image in the current working directory.
Close to how Google image search works (when you submit your image and it returns similar ones). Should I use PIL or OpenCV?
Preferably using Python3.4 by the way, but Python 2.7 is fine.
Wilson
I mean, why not use both? It's trivial to convert PIL images into OpenCV images and vice-versa, and both have niche functions that can make your life easier. Pair them up with sklearn and numpy, and you're cooking with gas.
I created the undouble library in Python which seems a match for your issue.
It uses Hash functions to detect (near-)identical images in for example a directory. It works using a multi-step process of pre-processing the images (grayscaling, normalizing, and scaling), computing the image hash, and the grouping of images based on a threshold value.
I have a set of images in a folder, where each image either has a square shape or a triangle shape on a white background (like this and this). I would like to separate those images into different folders (note that I don't care about detecting whether image is a square/triangle etc. I just want to separate those two).
I am planning to use more complex shapes in the future (e.g. pentagons, or other non-geometric shapes) so I am looking for an unsupervised approach. But the main task will always be clustering a set of images into different folders.
What is the easiest/best way to do it? I looked at image clustering algorithms, but they do clustering of colors/shapes inside the image. In my case I simply want to separate those image files based on the shapes that have.
Any pointers/help is appreciated.
You can follow this method:
1. Create a look-up tables with shape you are using in the images
2. Do template matching on the images stored in a single folder
3. According to the result of template matching just store them in different folders
4. You can create folders beforehand and just replace the strings in program according to the usage.
I hope this helps
It's really going to depend on what your data set looks like (e.g., what your shape images look like), and how robust you want your solution to be. The tricky part is going to be extracting features from each shape image the produce a clustering result that you're satisfied with. A few ideas:
You could compute SIFT features for each images and then cluster the images based on those features: http://en.wikipedia.org/wiki/Scale-invariant_feature_transform
If you don't want to go the SIFT route, you could try something like HOG: http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
A somewhat more naive approach - If the shapes are always the same scale, and the background color is fixed you could get rid of the background cluster the images based on shape area (e.g., number of pixels taken up by the shape).