I'm a newbie to the GIS world but after few weeks of hard searching/trying I feel stuck.
I am building an app to analyse landcover to assess terrain roughness. All the logic will come later but now I try to design the architecture.
A sample raster image is under the link:
Africa landcover 20m resolution - small sample
a visualization of the problem
Goal:
- read raster file (BigTIFF/GeoTIFF, ...around 6GB) from a cloud storage, like AWS S3
- use a javascript library to process the file, like node-gdal, geotiff.js, etc.
- apply a vector polygon to the raster and count the different pixels within the polygon. THis will be a "circle" with a radius of 3km for instance. Also make some histograms, so to see which pixels are dominant within a quadrant or 1/8 of the area.
- do some maths in JS with the data (about this I have no concerns)
- visualize the raster image on a map including the vector polygon
- push the code to production for multi users.
My skillset and limited experience is with the following as also my preferences to solve the challange:
Node + Express (Javascript)
Node-gdal library
PostgreSQL
Heroku for production
MApbox for visulaizing the maps
So far I have got the following issues when finding the right architecture or functioning codes:
node-gdal library can read only from the file system
geotiff.js can do the job but less documentation and I cannot see how to handle the specific tasks later
PostGIS should be very powerfull but a bit cumbersome to setup. Furthermore I am not sure if it is worth for a single tiff raster to
be feeded
Rasterio in Python can do very nice jobs and I managed in a Jupyter Notebook but I have no experience with Flask or others. Prefer Node JS
Turf.js can do a lot but more for vectors, I could not find modules for raster analysis
Thank you for your suggestions.
Related
image stitching with a reference image.
I have multiple images of subject(bone), the images are of different sections of the subject as on a 3x3 matrix. I would like to stitch them together but the problem is they don't have any common feature, as the subject was cut into these sections using a saw. What i have is the image of subject before cutting and want to use it as a guide to stitch the images of sections together.
I have tried using Fiji imagej and searched the web for an alternative. imageJ can only do the job if it has common feature between images to work with. can someone point to some code in python or matlab that can do this or any software that could help.
'[Reference image][1] section (11) section (12) section (13) section (21) section (23) section (31)'
' [1]: https://i.stack.imgur.com/wQr09.jpg
I'm not able to add more than 8 links due to SO's policy. There are two more remaining, I'll add them soon. And the "section (22)" i.e centre position in the 3X3 matrix is empty.
Solutions for image processing needs like this vary wildly depending on whether you need a script to use just a few times, a software tool you'll use for a few weeks, or what could become lab automation software.
This seems to be a problem more of image matching rather than image stitching. By image matching I mean you need to find out how a subimage such as the bone section at (row 2, column 1) would match what is labeled as "4," the center left section, in the reference bone image.
The basic process:
Load your reference image as a 2D array (first converted to grayscale)
Load your first sample image of a subsection of bone.
Use an algorithm such as SIFT to determine the location, orientation, and scale to fit the bone subsection image onto the reference image.
Apply the fit criteria (x,y,rotation,scale) to the bone subsection image, transform it, and past it into a black image the same size as the reference image.
Continue the process above to fit all subsections.
(Optional) With all bone subsections fitted in place, perform additional image processing operations to improve the fit, fill in gaps, etc.
From your sample images it appears that the reference and the bone section images area taken using different lighting, sometimes with the flat portion of the bone slightly tilted relative to the camera's optical axis, etc., all of which makes the image match more difficult.
SIFT is an algorithm that could help here. Note that "scale invariant" is part of the algorithm name.
https://en.wikipedia.org/wiki/Scale-invariant_feature_transform
Given all that, your reference image and bone subsection images appear to be taken under very different circumstances, and that makes solving the problem harder than it needs to be. You'll have an easier time overall if you can control the conditions under which images are captured.
Capture all images with the same camera, with the same lighting, at roughly the same distance
For lighting, use something like a high-frequency diffuse fluorescent
Use the same background for every image (e.g. matte black)
Making this image match a robust process means paying attention to the physical setup as well as creating your image processing algorithm.
If you need a good reference for traditional image processing techniques, find a copy of Digital Image Processing by Gonzalez and Woods. Some time spent with that book will give you better answers faster than learning image processing piecemeal online.
For practical image processing that addresses real-world concerns for implementing even simple image processing algorithms, look for Machine Vision by Davies.
I would strongly urge that you NOT look into machine learning, or try to find an answer in a more advanced image processing textbook until you run into a roadblock with more traditional methods.
I'm looking for a python 3 module that can generate a visual representation of a graph. Ideally I would give it a list of nodes and a list of connection, as well as some small amount of data associated with those things, and it would open a window (but an image saved on disk is fine) showing said nodes connected as specified. I don't want to specify the positions of the nodes, instead I'd like the software to arrange them in a way that minimizes edge crossings at least approximately.
Is there any such module? All I've been able to find are plotters and such...
If there is none, an easy-to-learn graphics module would do: I have never done any graphics things.
You can take a look at networkx. It offers the possibility to draw graphs with matplotlib
GOAL: I have to create a 3d model of a machine part. I have about 25 images of the same thing taken from different angles.
Progress: I am able to extract the coordinates for a label that is on the machine for most of the images.
Problem: but I have no idea how to proceed. I have read a bit about aero-triangulation, but I couldn't figure out how to implement it. I would really appreciate it, if you could guide me in the right direction.
It would be really helpful, if you could provide your solutions using python and opencv.
Edit: sorry but I cannot upload the code for this one as it is confidential. don't blame me please I am just an intern. Although I can tell that I cropped a template of the label from an image and then used Sift to match that template on all the images to get the coordinates of the label.
If you want to implement things yourself with OpenCV, I would command looking at SIFT (or SURF) features, RANSAC and the epipolar constraint. I believe the OpenCV cookbook describe those. Warning: math involved. And I don't know how to do dense mapping in OpenCV.
I know the GUI program "VisualSFM" that can automatically recreate 3D model from images. It uses SFM and other command line utilities behind the scenes. Since everything is opensource, you could create a python wrapper around the actual libraries (I found https://github.com/mapillary/OpenSfM asking Google). VisualSFM prints the command it calls, so a hacky way could be to call the same commands from python.
If it is a simple shape and you don't want to automate it, it could be faster to model it yourself (and the result could look better). In 1.5 week I managed to learn the basics of blender and to model a guitar necklace: https://youtu.be/BCGKsh51TNA . And I would now be able to do it in less than 1h. How long are you ready to invest to find a solution with OpenCV?
I am an undergraduate student working with detecting defects on a surface of an object, in a given digital image using image processing technique. I am planning on using OpenCV library to get image processing functions. Currently I am trying to decide on which defect detection algorithm to use, in order to detect defects. This is one of my very first projects related to this field, so it will be appreciated if I can get some help related to this issue. The reference image with a defect (missing teeth in the gear), which I am currently working with is uploaded as a link below ("defective gear image").
defective gear image
Get the convex hull of a gear (which is a polygon) and shrink is slightly so that it crosses the teeth. Make sure that the centroid of the gear is the fixed point.
Then sample the pixels along the hull, preferably using equidistant points (divide the perimeter by a multiple of the number of teeth). The unwrapped profile will be a dashed line, with missing dashes corresponding to missing teeth, and the problem is reduced to 1D.
You can also try a polar unwarping, making the outline straight, but you will need an accurate location of the center.
I am doing some studies on eye vascularization - my project contains a machine which can detect the different blood vessels in the retinal membrane at the back of the eye. What I am looking for is a possibility to segment the picture and analyze each segmentation on it`s own. The Segmentation consist of six squares wich I want to analyze separately on the density of white pixels.
I would be very thankful for every kind of input, I am pretty new in the programming world an I actually just have a bare concept on how it should work.
Thanks and Cheerio
Sam
Concept DrawOCTA PICTURE
You could probably accomplish this by using numpy to load the image and split it into sections. You could then analyze the sections using scikit-image or opencv (though this could be difficult to get working. To view the image, you can either save it to a file using numpy, or use matplotlib to open it in a new window.
First of all, please note that in image processing "segmentation" describes the process of grouping neighbouring pixels by context.
https://en.wikipedia.org/wiki/Image_segmentation
What you want to do can be done in various ways.
The most common way is by using ROIs or AOIs (region/area of interest). That's basically some geometric shape like a rectangle, circle, polygon or similar defined in image coordinates.
The image processing is then restricted to only process pixels within that region. So you don't slice your image into pieces but you restrict your evaluation to specific areas.
Another way, like you suggested is to cut the image into pieces and process them one by one. Those sub-images are usually created using ROIs.
A third option which is rather limited but sufficient for simple tasks like yours is accessing pixels directly using coordinate offsets and several nested loops.
Just google "python image processing" in combination with "library" "roi" "cropping" "sliding window" "subimage" "tiles" "slicing" and you'll get tons of information...