findimagedupes is the usual command-line program for that on Linux and other Unix platforms.
It looks for similarity among all files. You can filter the output to retain only information about one file if you wish, I don't think that makes a huge difference in performance (the slow part is scanning all files).
To scan PNG and JPEG files under a certain ...
This answer won't be as good as I usually like (due to lack of knowledge on my part) but I think it should work well for you.
What I would suggest is ImageMagick's Compare function. It is command line only but it outputs an 2 (error), 0 (similar) or 1 (disimilar) as well as image difference map - there are a few different types that it can output - see the ...
This is the sort of thing that you can knock up quite quickly using OpenCV and Python.
Free (both gratis and FLOSS)
Cross platform: Windows, Linux, Android and Mac OS
Motion Detection algorithms built in, (including being able to set thresholds).
VideoWriter class to save your results
Active user community.
Just download and install: Python, OpenCV, the ...
Here's a Python 3 program that distinguishes object pixels from non-object pixels by simply thresholding the red channel at 128.
from PIL import Image
orig = Image.open(sys.argv)
width = orig.width
height = orig.height
# The workspace will be a list of lists of bools, where True
# means an object pixel.
workspace = list(orig.getdata())
Just found this today on my search for something similar.
(I've been looking for this for years usually once a year. happened to be today)
imgSeek is a photo collection manager and viewer with content-based
search and many other features. The query is expressed either as a
rough sketch painted by the user or ...
There is an example of using python to do this visually for you here but to reproduce it:
Done on windows - Yes but could be on any of a number of platforms
from PIL import Image
from PIL import ImageChops
a = Image.open("pict1.jpg")
b = Image.open("pict2.jpg")
diff = ImageChops.difference (a, b)
RED = ('red')
RL = Image.new('RGB', diff....
Try search by image browser-based OS-independent tool (Windows, Linux, Mac etc.), which I developed. The limitation is the browser type (works best for Chrome and Firefox, some browsers do not support folder selection or too slow parsing the file directory), the file read speed, type of images (browser-readable only), and available memory. But if you have a ...
A little DIY but you can do this sort of thing with python plus either OpenCV or Numpy - in either case the approach is the same:
Generate a finger print of the image that you are searching for by something along the lines of:
Reduce to grayscale
resize to a fixed size, e.g. 64x64
possibly generate a histogram of the intensities
Use os.walk to find files ...
I recommend geeqie, you can install via package manager such as sudo apt install geeqie.
As you can see, it able to group by Pikachu's ears even though the images quite different. Note that this directory has ~3759 images and take ~5 minutes to complete.
How to use:
cd to desired directory. Alternatively you can open geeqie first and insert the path later....
What you are asking for is Image Classification software and it is available as open source as a part of deep learning with image net. There is quite a lot of software to install so before you read any further you need to be aware that recognising portions of an image takes time - you will not be able to roll over a fresh image and have things recognised in ...
The best Windows tool I can find for this is Visipics http://www.visipics.info/index.php?title=Main_Page
It basically uses ImageMagick to fingerprint images, with a slider to pick out the similarity values.
However, it seems to only do bulk comparisons (so you can't specify one file to look for, only whole folders).
[Disclaimer: i work for Moodstocks]
You should have a look at the Moodstocks SDK. It fits most of your requirements:
it's robust to lightning and perspective changes,
it tolerates partial occlusion, for example by fingers,
it require no training at all: you just upload (index) an image to make it instantly recognizable,
it's extremely fast: the image ...
Most sexually explicit images usually come from/are hosted off of sexually explicit websites. E.g. Porn Websites which are crawled and appear in search engines such as Google Images. Because of this I would recommend using a web filter such as K9webprotection.
It's free, can be used for personal use and is available on Android, IOS, Windows and Mac OS.
I am using a program that works well in Windows only. It is called "Media Detective" and is available here
It is only a scanner of files and internet history and does not have real time protection. It scans pictures and videos and attempts to detect the "Skin Color". If too high a percentage of skin color is present it flags the picture/video and assembles ...
This is not really an answer but in case nothing better comes up, it could give an answer in the future :-)
There is our own online application, VIRaL, that allows for much more major changes like
major change of viewpoint (wide-baseline matching), resulting e.g. in major scale change or arbitrary orientation;
major occlusion, or partial matching to the ...
There is a quite simple to follow set of instructions of how to do what you are asking using OpenCV and python, (actually targeted on Live Detection but you can use any video source), available at PyImageSearch in two blog posts: motion detection and home surveillance.
The blog posts show how to use OpenCV to use a video source such as a Raspberry Pi camera,...
That is a easy set of specifications: OpenCV should - with a little effort do the job nicely:
Free - both Gratis & Open Source
Things like face detection & blink detection are well within the scope
Altium 3000 Nano - OpenCV is cross platform,
older versions, V2.x are still available that were written in pure C and the source code is still available,...
I haven't done this yet, but you can use tensorflow.js and one of the many pretrained Models that are available for it (e.g. "vgg16") . This Vgg16 model was trained on a database of ~10 million Images, and certainly there are lots of flowers among these. (This image collection is called IMAGENET by the way)
Then you can use that Vgg16 model file (it's a ...
I would recommend using MATLAB for this kind of analysis. The documentation in MATLAB is superb, and they already have tutorials for doing just this sort of thing. MATLAB also allows you to do transfer learning, so you can take an already existing deep neural network and fine tune its weights in order to make your model applicable to your use case without ...
You should find some useful information in the article at https://autottblog.wordpress.com/programming-the-car/opencv/ which links to a repo.
Also for anything OpenCV and Deep Learning I strongly recommend reading Adrians blog at pyimagesearch as it gives you a really good grounding in both.
If you have a static positioned webcam, you can use Linux as an os with the program "motion" - it detects changes in the webcam view, and then can run a script when motion is detected. Lots of other options like sensitivity level, watching only a certain area of the image for change, how long change must be taking place from reference frame for it to be ...
On the Tensorflow Github site there are pretrained models, some of which are based on Image collections. I think with some digging you'll find more.
Here is one available as a NodeJS model: MobileNet - Classify images with labels from the ImageNet database.
`npm i @tensorflow-models/mobilenet`
I haven't used it personally though. A while back, at a ...
If you're looking to develop an application to handle this, I would recommend checking out the LEADTOOLS Multimedia SDK (Windows-only) to implement this type of task. It supports C, C++, .NET and will work with other languages that support COM objects. This SDK also includes a DirectShow Motion Detection filter. When the filter detects motion, it invokes a ...
You can run your video through a motion detection & tracking process using python & OpenCV as in this tutorial.
Both are Free, Gratis & Open Source.
Both are cross platform and will run on Windows, OS-X, Linux or even Raspberry Pi platforms.
The referenced tutorial uses just 90 lines of code & no compiler.
The tutorial does not give an ...
You need to grab some, or all of the screen, then pass it to OpenCV for processing - isolating a specific icon against a background can be done relatively easily, just remember to search your image for an area that matches a smaller sub-region of the icon due to anti-aliasing. Spotting anything that looks like an icon would require machine learning which is ...
What you are looking for is called an image classifier - these are very advanced AI projects that require lots of training and as Google recently and notoriously found tend to be error prone.
You can potentially build your own using python, OpenCV and SciKit-Learn and it will be callable from the command line and should do exactly what you need but expect ...
I've developed a software product which is currently in Beta, and it is currently free. You choose folder sets (the root folder of a set of folders, or on its own) and the software will find similar images across the sets, as long as they are very similar (almost the same image). The only thing is that it takes time the first time to analyze all your images (...