kandi X-RAY | MicroscoPy Summary
kandi X-RAY | MicroscoPy Summary
The microscope has a simple operation principle based on changing the magnification and the focus by adjusting the relative distances between a camera, a single objective lens and a sample. Briefly, two linear stages with stepper motors are used to adjust these distances for a continuous and wide magnification range. Four additional stepper motors tilt the camera module and change the X-Y position and rotation of the sample. A uniform light source illuminates the sample either from an angle (reflected light) or from the bottom of the sample (transmitted light). The system can also be used as a digital water contact angle goniometer by taking cross-section images of droplets.
Top functions reviewed by kandi - BETA
- Camera shortcut
- Menu keyboard shortcuts
- Reset the camera
MicroscoPy Key Features
MicroscoPy Examples and Code Snippets
Trending Discussions on MicroscoPy
I have a nested dictionary containing microscopy acquisition metadata that would look like:...
ANSWERAnswered 2022-Feb-16 at 07:41
You can try this to see if it works, if not, i suggest that you explain the results you want, you can use data as an example
I have a Z-stack of 2D confocal microscopy images (2D slices) and I want to segment cells. The Z-stack of 2D images is actually a 3D data. In different slices along the Z-axis, I see same cells do appear in multiple slices. I am interested in cell shape in the XY so I want to preserve the largest cell area from different Z-axis slices. I thought to combine the consecutive 2D slices after converting them to labelled binary images but I am having few issues and I need some help to proceed further.
I have two images
img_b. I first converted them to binary images using
OTSU, then applied some morphological operations and then used
cv2.connectedComponentsWithStats() to obtain labelled objects. After labeling images, I combined them using
cv2.bitwise_or() but it messes up with the labels. You can see this in the attached processed image (cell higlighted by red circles). I see multiple labels for overlapping cell. However, I want to assign one unique label for every combined overlapping object.
What I want at the end is that when I combine two labelled images, I want to assign one single label (a unique value) to the combined overlapping objects and keep the largest cell area by combining both images. Does anyone know how to do it?
Here is the code:...
ANSWERAnswered 2022-Jan-25 at 11:41
Based on the comments from Christoph Rackwitz and beaker, I started to look around for 3D connected components labeling. I found one python library that can handle such things and I installed it and give it a try. It seems to be doing pretty good. It does assign labels in each slice and keeps the labels same for the same cells in different slices. This is exactly what I wanted.
Here is the link to the library that I used to label objects in 3D. https://pypi.org/project/connected-components-3d/
I'm trying to connect a ToupTek XCAM4K8MPA camera to a Linux Ubuntu 20.04 64-bit computer to use with their provided ToupLite software for microscopy image capturing. The camera comes with a USB 3.0 cable, HDMI cable and USB WLAN adapter.
I have managed to set up the ToupTek ToupLite software (Link to manual, Link to download page) which, as far as I understand, comes with the SDK included. I have then connected the camera via USB 3.0 and HDMI cable to the computer, but ToupLite shows No device as in, the camera is not recognized or connected. I have tested the camera by simply connecting it to a computer monitor via HDMI cable and it works, but for capture I would like it to be connected to a computer and the WLAN or Wi-Fi connection solution is not really a good option.
Perhaps someone has some ideas of what I am missing.
Note: Relevant tags include touptek, touplite, microscopy...
ANSWERAnswered 2021-Dec-01 at 09:17
I ended up using the WLAN connection. Made a non-changing IP address for the camera and the ToupLite software recognizes that it is on the local network.
I have a Endote Export File, looking like this:...
ANSWERAnswered 2021-Nov-29 at 05:26
Would you please try the following:
The image is obtained from Nanonis software for a typical STM (Scanning Tunneling Microscopy) topography, so the image pixel is fixed by the settings of software, which is always 512 x 512 pixels. However, for publication purpose, often time we need to correct the image background via a home-built LabVIEW program (doing things like linear background subtraction).
The problem is the exported image after LabVIEW processing is giving us a very low DPI value (I think it's 72 dpi). But for any academia journals, they all kinda have requirement on the image dpi values, typically, at least, 300dpi is required for images in the article we submit. (And we've been trying to solve this by modifying the LabVIEW program, but it's too complicated, and so far no luck...that's why I realized Python might save our day...?)
So I'm wondering, can anyone help me write a Python code that define a function (LabVIEW can only call Python functions) to increase the dpi of an image(png), without altering the image sizes(width & height)?
PIL package? I'm not very familiar with that one unfortunately)
A PNG image sample is here:
ANSWERAnswered 2021-Aug-15 at 08:38
You should try using Image.save from PIL.
I have a microscopy image and need to calculate the area shown in red. The idea is to build a function that returns the area inside the red line on the right photo (float value, X mm²).
Since I have almost no experience in image processing, I don't know how to approach this (maybe silly) problem and so I'm looking for help. Other image examples are pretty similar with just 1 aglomerated "interest area" close to the center.
I'm comfortable coding in python and have used the software ImageJ for some time.
Any python package, software, bibliography, etc. should help.
EDIT: The example in red I made manually just to make people understand what I want. Detecting the "interest area" must be done inside the code....
ANSWERAnswered 2021-Aug-05 at 20:35
I don't know much about this topic, but it seems like a very similar question to this one, which has what look like two great answers:
In the following microscopy image, I extracted the horizontal white line grid using morphological operators in OpenCV. I couldn't completely get rid of the noise which is why there are some white lines in-between. The grid lines need to be parallel to the x-axis. During the microscopic reading process, perfect parallelism cannot be ensured. In this case, the lines are moving slightly upwards from left to right. How can I realign the lines to the x-axis so that they are parallel to the lower and upper edges of the image using OpenCV or any other Python package?
I'm relatively new to OpenCV so if anyone could give me a hint what operations or functions would be helpful to tackle this problem, I'd be grateful.
ANSWERAnswered 2021-May-25 at 09:50
You may fit lines, get the mean angle and rotate the image.
The suggested solution uses the following stages:
- Threshold (binarize) the image.
- Apply closing morphological operation for connecting the lines.
- Find contours.
- Iterate the contours and fit a line for each contour.
Compute the angle of each line, and build a list of angles.
- Compute the mean angle of the angles that are "close to the median angle".
- Rotate the image by the mean angle.
Here is the code:
When I execute a python script (
test2.py) from a running python script (
test.py) I get the following error in
ANSWERAnswered 2021-May-14 at 08:12
I was actually quite close. The problem is, that python imports modules from another python installation due to a wrong path. Modifying the
PYTHONPATH according to "https://stackoverflow.com/a/4453495/5934316" works for my example.
Working with an xml data that was downloaded using GET function from the httr package. The content returned is of type application/xml. An extract is as shown below:...
ANSWERAnswered 2021-May-08 at 14:26
I got it solved
I'm following a simple approach to segment cells (microscopy images) using the Watershed algorithm in Python. I'm happy with the result 90% of the time, but I have two main problems: (i) the markers/contours are really "spiky" and (2) the algorithm sometimes fails when two cells are to close to each other (i.e they are segmented together). Can you give some tips in how to improve it?
Here's the code I'm using and an output image showing my 2 issues....
ANSWERAnswered 2021-Feb-07 at 18:14
In the past, the best approach for me to apply the watershed algorithm is 'only when needed'. It is computationally intensive and not needed for the majority of cells in your image. This is the code I have used with your image:
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You can use MicroscoPy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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