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I’m trying to make sky detection, I already try on opencv blue color detection, it didn’t work well because of clouds and also because of different color depends on time, the final try was the most useful method, I used a canny detection that need adjusted by user, then I filled the sky with white pixels, and other with black.
The question is,
Is it possible to make sky detection automatically without interface from the user ?!
I can give a recommendation from an AI perspective. Read to the end for a non-AI recommendation.
Actually, there does exist a "simple" way given that you are willing to annotate about a few thousand images manually. You can have some of your coworkers/classmates to help you out with this. I worked with the YOLO-V3 program which does give you a decent GUI to manually annotate your images. YOLO-V3 however only works with bounding boxes, so my next suggestion will work with identifying the whole sky and segmenting the image pixel by pixel.
But if you already have an annotated dataset, There's a neural network architecture called Mask RCNN which overlays your given image with a mask of any color you choose over a given object or setting you to indicate. This one, from my experience, does take a LOT of annotated data to train on for a decent result. But for something as generalizable as a sky-detector and overlay, it should work well with only 1-3k annotated pictures. If you chose to go down this route, here is an article that describes how you can make your own annotated pictures.
Non-AI recommendations:
using the blue color index in the RGB tuple, play around with what thresholds you can use for each color, and you can then do some random sampling of these points and go from there.
But seriously, based on everything I researched on this, looking at other people's repositories, it seems the best method is via AI. Here's an example. The reason is detecting a sky takes a lot of spacial "awareness". For example, how will the computer discriminate between the sky and an ocean? Both are blue. But you can see from the waves that it is an ocean. Basic spatial reasoning can really be done by AI or a crap ton of manual coding.
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I have a stock of photos, of a spinning disk of varying angles. I wish to find the edge of the top of the disk. The top is colored in a distinct black color in comparison to the rest of the photo.
A
B
I first tried using canny edge detection which does a decent job, but also identifies the bottom half of the disk, which I wish to avoid.
Next idea was to use the distinct black color- perhaps by dividing the photo into domains characterized by colors\intensities- and by choosing the largest domain\most black domain or some other parameter, perhaps to isolate that black circle, and only then to use canny edge detector.
Is there any existing function that can divide a greyscale image into domains? Transferring from matlab to python, so I'm new to it's syntax and functions.
Thanks
The Canny disaster goes on !
People playing with image processing keep willing to rely on edge detection when they have beautifully segmenting scenes. With a careful selection of a binarization threshold, you can extract the ellipse as a single piece.
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I have a couple of standard ways of detecting a modified image such as
Luminance gradient
Copy move detection
Metadata Extraction
Histogram analysis
ELA(Error level analysis)
Quantization matrix analysis
Thumbnail analysis
are there any other standard ways of detecting a modified image?
Tried out
Finding the EXIF of the image to check the created and modified date and check for modification. I also had some rules for EXIF camera make and make note validation along with checking for the software used such as photoshop, Shotwell, etc.
Was able to segment the image and use SLIC(simple linear iterative clustering) to find out the similar cluster regions in an image
Find the largest contour with less pixel inconsistency with luminance gradient to mark that as a potential modified region
Largest contour with ELA as a potential modified region
Check for inconsistencies in histogram graph and mark it as a potential editted image.
Here are my questions
Are there any standard logics to verify the image with metadata such as using the created and modified dates, the camera make or maker note, etc. As these details are not consistent for any given image.
Finding out the least pixel inconsistency contour in the Luminance gradient would always give me an image that is modified?
If the histogram graph has a regular interval fluctuation could it be considered a modified image?
How could I use Quantization matrices to find out image anomalies
what is the best way to compare the thumbnail image to the original image to check for inconsistencies?
The answer to this question needs more detailed so, I will give some references to the subject itself and I will share with you the code of every part of your question :
You need to use exif to verify the image with metadata
For Anomaly Detection in Images see here
To compare the thumbnail image to the original image read this. where it showed you how to compare two images using Python.
References :
ccse.kfupm.edu.sa
github.com/redaelli
github.com/Ghirensics
www.amazon.com/Learning
books.google.com.tw
hal.inria.fr/
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Let's say we pick an image of space. Let's assume there is big star at 40 light years away and a smaller star 20 light years away. But when we look at the image both would look same size. How can we identify which is bigger and which is smaller by size and how far is it from us.
I want to do image processing on these images.
Keywords: Astronomy, space telescope, stars machine learning, opencv, python.
Short answer: It is impossible.
Long answer: You need additional information, such as light emission from each star and received by the camera (if they are equal, the closer star will be brighter). Using only size in pixels you cannot determine even the relative distance between them as they may differ in the size and distance to the observer (the star with radius r and distance d is projected exactly the same as the star with radius 2r and distance 2d).
Unfortunately, it is not straightforward to infer the distance from the size by just looking at your image. You could make guesstimates on the stars' relative distance by comparing their colors (aka temperatures). However, this requires you to have accurate UBV measurements (quite some other filter systems also work).
If you know (or can identify) the stars in your image, you could look up any relevant information on public databases.
Types of measurements that allow you measure the distance to a star are their parallax, their color, or in some cases it can be inferred from their variability.
You can't tell from the image alone, you need to know the apparent and absolute brightness of each star, their temperature, spectrum, and other characteristics to know which star is the brightest in compare to the others.
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I have some images i'm using for face recognition.
Some of the images are very dark.
I don't want to use Histogram equalisation on all the images only on the dark ones.
How can i determine if an image is dark?
I'm using opencv in python.
I would like to understand the theory and the implementation.
Thanks
To determin if an image is dark, simply calculate the average intensity and judge it.
The problem for the recognition although is not that the image is dark, but that it has a low contrast. A bright image with the same contrast would yield the same bad results.
Histogram equalization is a method that is used to improve images for human vision. Humans have difficulties to distinguish between very similar intensity values. A problem that a computer does not have, unless your algorithm is somehow made to mimic human vision with all its flaws.
A low contrast image bears little information. There is no image enhancement algorithm in the world that will add any further information.
I won't get into too much detail about image characterization. You'll find plenty of resources online or in text books.
A simple measure would be to calculate the standard deviation of image regions you are interested in.
You can try using CLAHE instead of the using histogram equalization. I am not sure if it will work for all your images (if you post some images I can try), however, conceptually, it should work better than "global" histogram equalization.
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How can I segment cells from an image taken on a microscope, along the lines of what was done here in Matlab?
http://blogs.mathworks.com/steve/2006/06/02/cell-segmentation/
Also, if I take multiple image in different fluorescent channels (after staining the cells with some antibody/maker), how can I automatically quantitate the fraction of cells positive for each marker? Has anyone done something like this in Python? Or is there a library in Python that can be used to do this?
You can do this in Python using the OpenCV library.
In particular, you'll be interested in the following features:
histogram stretching (cv.EqualizeHist). This is missing from the current Python API, but if you download the latest SVN release of OpenCV, you can use it. This part is for display purposes only, not required to get the same result
image thresholding
morphological operations such as erode (also dilate, open, close, etc)
determine the outline of a blob in a binary image using cv.FindContours -- see this question. It's using C, not Python, but the APIs are virtually the same so you can learn a lot from there
watershed segmentation (use cv.Watershed -- it exists, but for some reason I can't find it in the manual)
With that in mind, here's how I would use OpenCV to get the same results as in the matlab article:
Threshold the image using an empirically determined threshold (or Ohtsu's method)
Apply dilation to the image to fill in the gaps. Optionally, blur the image prior to the previous thresholding step -- that will also remove small "holes"
Determine outlines using cv.FindContours
Optionally, paint the contours
Using the blob information, iterate over each blob in the original image and apply a separate threshold for each blob to separate the cell nuclei (this is what their imextendedmax operation is doing)
Optionally, paint in the nuclei
Apply the watershed transform
I haven't tried any of this (sorry, don't have the time now), so I can't show you any code yet. However, based on my experience with OpenCV, I'm confident that everything up to step 7 will work well. I've never used OpenCV's watershed transform before but I can't see a reason for it not to work here.
Try going through the steps I have shown and let us know if you have any problems. Be sure to post your source as that way more people will be able to help you.
Finally, to answer your question about staining cells and quantifying their presence, it's quite easy knowing the dyes that you are using. For example, to determine the cells stained with red dye, you'd extract the red channel from the image and examine areas of high intensity (perhaps by thresholding).
Have you read the tutorial on pythonvision.org?
http://pythonvision.org/basic-tutorial
It is very similar to what you are looking for.
And just to add one more: cellprofiler.org (open source cell image analysis software, in python)
You may also find this library useful:
https://github.com/luispedro/pymorph/
I found it easier to get moving with than the OpenCV library.