omero-archiving | OMERO python API to tag images | Computer Vision library
kandi X-RAY | omero-archiving Summary
kandi X-RAY | omero-archiving Summary
OMERO is used to store and manage microscope images. Images are imported, metadata extracted from the images and thumbnails generated. The images can be browsed using OMERO clients and the metadata viewed. The image can be opened and the image pixels viewed. Archiving is used when instant access to the pixel data is no longer required. In this case valuable storage space can be regained by extracting the image data from OMERO and moving it to a low cost storage medium for long term archiving. OMERO provides storage of image pixel data and associated meta-data as separate physical entities, by storing meta- data in cache files and a database. This can be exploited to allow images to be safely archived from OMERO to a remote data archiving system. The OMERO image browsing features using the image metadata remain functional. Attempts to access the pixel data will safely fail; if the pixel data cannot be found then OMERO will wait and the request can be cancelled. This archiving strategy applies to files physically managed by OMERO, that is they have been copied into the OMERO file repository. OMERO version 5 introduced in-place import where files can be imported without being copied into the OMERO managed file repository. This process creates symbolic links from the OMERO managed file repository to the file location. The archiving stratgey is a reverse of the in-place import. The managed files are moved out of the managed repository and symbolic links created from the original location to the archive location. If the archive location has low or zero availability the behaviour of OMERO is identical to the file being missing; it will wait. If the archive location has high availablity then OMERO will function as if the file is in the managed repository. This can be exploited to unarchive images by returning files to the archive location.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of omero-archiving
omero-archiving Key Features
omero-archiving Examples and Code Snippets
Community Discussions
Trending Discussions on Computer Vision
QUESTION
The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.
What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:
...ANSWER
Answered 2022-Mar-25 at 10:26It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.
QUESTION
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
...ANSWER
Answered 2022-Mar-22 at 13:26It appears that the 'visions.application' module was available in v0.7.1
https://github.com/dylan-profiler/visions/tree/v0.7.1/src/visions
But it's no longer available in v0.7.2
https://github.com/dylan-profiler/visions/tree/v0.7.2/src/visions
It also appears that the pandas_profiling project has been updated, the file summary.py no longer tries to do this import.
In summary: use visions version v0.7.1 or upgrade pandas_profiling.
QUESTION
I'm exploring Google Cloud Vision to detect handwriting in text. I see that the model is quite accurate in read handwritten text.
I'm following this guide: https://cloud.google.com/vision/docs/handwriting
Here is my question: is there a way to discover in the responses if the text is handwritten or typed?
A parameter or something in the response useful to classify images?
Here is the request:
...ANSWER
Answered 2022-Mar-01 at 00:36It seems that there's already an open discussion with the Google team to get this Feature Request addressed:
https://issuetracker.google.com/154156890
I would recommend you to comment on the Public issue tracker and indicate that "you are affected to this issue" to gain visibility and push for get this change done.
Other that that I'm unsure is that can be implemented locally.
QUESTION
I want to try out this tutorial and therefore used the code from here in order to calibrate my camera. I use this image:
The only thing I adapted was chessboard_size = (14,9)
so that it matches the corners of my image.
I don't know what I do wrong. I tried multiple chessboard pattern and cameras but still cv2.findChessboardCorners always fails detecting corners.
Any help would be highly appreciated.
ANSWER
Answered 2022-Jan-29 at 23:59Finally I could do it. I had to set chessboard_size = (12,7)
then it worked. I had to count the internal number of horizontal and vertical corners.
QUESTION
I am trying to get the RGB average inside of a non-rectangular multi-edge (closed) contour generated over a face landmark region in the frame (think of it as a face contour) from AVCaptureVideoDataOutput. I currently have the following code,
...ANSWER
Answered 2022-Jan-26 at 02:12If you could make all pixels outside of the contour transparent then you could use CIKmeans
filter with inputCount
equal 1
and the inputExtent
set to the extent of the frame to get the average color of the area inside the contour (the output of the filter will contain 1-pixel image and the color of the pixel is what you are looking for).
Now, to make all pixels transparent outside of the contour, you could do something like this:
- Create a mask image but setting all pixels inside the contour white and black outside (set background to black and fill the path with white).
- Use
CIBlendWithMask
filter where:inputBackgroundImage
is a fully transparent (clear) imageinputImage
is the original frameinputMaskImage
is the mask you created above
The output of that filter will give you the image with all pixels outside the contour fully transparent. And now you can use the CIKMeans
filter with it as described at the beginning.
BTW, if you want to play with every single of the 230 filters out there check this app out: https://apps.apple.com/us/app/filter-magic/id1594986951
UPDATE:CIFilters can only work with CIImages. So the mask image has to be a CIImage as well. One way to do that is to create a CGImage from CAShapeLayer containing the mask and then create CIImage out of it. Here is how the code could look like:
QUESTION
I am actually experimenting with the Vision Framework. I have simply an UIImageView in my Storyboard and my class is from type UIViewController. But when I try to override viewDidAppear(_ animated: Bool) I get the error message: Method does not override any method from its superclass Do anyone know what the issue is? Couldn't find anything that works for me...
...ANSWER
Answered 2022-Jan-21 at 19:37This is my complete code:
QUESTION
I'm using Vision Framework to detecting faces with iPhone's front camera. My code looks like
...ANSWER
Answered 2021-Dec-23 at 14:33For some reason, remove
QUESTION
I would like to read Japanese characters from a scanned image using swift's Vision framework. However, when I attempt to set the recognition language of VNRecognizeTextRequest
to Japanese using
request.recognitionLanguages = ["ja", "en"]
the output of my program becomes nonsensical roman letters. For each image of japanese text there is unexpected recognized text output. However, when set to other languages such as Chinese or German the text output is as expected. What could be causing the unexpected output seemingly peculiar to Japanese?
I am building from the github project here.
...ANSWER
Answered 2021-Oct-12 at 23:37As they said in WWDC 2019 video, Text Recognition in Vision Framework:
First, a prerequisite, you need to check the languages that are supported by language-based correction...
Look at supportedRecognitionLanguages
for VNRecognizeTextRequestRevision2
for “accurate” recognition, and it would appear that the supported languages are:
QUESTION
For my research project I'm trying to distinguish between hydra plant (the larger amoeba looking oranges things) and their brine shrimp feed (the smaller orange specks) so that we can automate the cleaning of petri dishes using a pipetting machine. An example of a snap image from the machine of the petri dish looks like so:
I have so far applied a circle mask and an orange color space mask to create a cleaned up image so that it's mostly just the shrimp and hydra.
There is some residual light artifacts left in the filtered image, but I have to bite the cost or else I lose the resolution of the very thin hydra such as in the top left of the original image.
I was hoping to box and label the larger hydra plants but couldn't find much applicable literature for differentiating between large and small objects of similar attributes in an image, to achieve my goal.
I don't want to approach this using ML because I don't have the manpower or a large enough dataset to make a good training set, so I would truly appreciate some easier vision processing tools. I can afford to lose out on the skinny hydra, just if I can know of a simpler way to identify the more turgid, healthy hydra from the already cleaned up image that would be great.
I have seen some content about using openCV findCountours
? Am I on the right track?
Attached is the code I have so you know what datatypes I'm working with.
...ANSWER
Answered 2021-Oct-12 at 10:58You are on the right track, but I have to be honest. Without DeepLearning you will get good results but not perfect.
That's what I managed to get using contours:
Code:
QUESTION
Assume you have a binary buffer or file which represents a 2-dimensional image.
How can you convert the binary data into a IMAQ image for further processing using LabVIEW?
...ANSWER
Answered 2021-Sep-30 at 13:54For LabVIEW users who have the NI vision library installed, there are VIs that allow for the image data of an IMAQ image to be copied from a 2D array.
For single-channel images (U8
, U16
, I16
, float
) the VI is
Vision and Motion >> Vision Utilites >> Pixel Manipulation >> IMAQ ArrayToImage.vi
For multichannel images (RGB
etc) the VI is
Vision and Motion >> Vision Utilites >> Color Utilities >> IMAQ ArrayColorToImage.vi
Example 1
An example of using the IMAQ ArrayToImage.vi
is shown in the snippet below where U16
data is read from a binary file and written to a Greyscale U16
type IMAQ image
. Please note, if the file has been created by other software than LabVIEW then it is likely that it will have to be read in little-endian format which is specified for the Read From Binary File.vi
Example 2
A similar process can be used when some driver DLL call is used to get the image data as a buffer. For example, if the driver has a function capture(unsigned short * buffer)
then the following technique could be employed where a correctly sized array is initialized before the function call using the initialize array
primitive.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install omero-archiving
Copy the tagging scripts into your OMERO installation: OMERO_DIST/lib/scripts
Update your list of installed scripts by examining the list of scripts in OMERO.insight or OMERO.web, or by running the following command: path/to/bin/omero script list
Copy the archiving scripts into a known location. This will be added to the path of the task executing the archiving scripts. [path]/tools/bin
Copy the library functions used by the scripts into a known location. This will be added to the python path of the task executing the archiving scripts. [path]/tools/lib
Update the details stored in the library script. [path]/tools/lib/gdsc/omero.py
Create the directory structure used by the archiving job workflow. cd /path/to/OMERO mkdir Archive cd Archive && mkdir Job log cd Job && mkdir New Approved Declined Running Error Finished
Configure the archiving control script to run the archiving process. This should set up the environment variables to add the OMERO python libraries and the archiving libraries to the python path. It should then run the archiving scripts. An example is provided as omero_to_arkivum.sh.
Test the localhost can relay e-mails via a SMTP server. Use the send_mail.py script to test the system is correctly configured. send_mail.py user@somewhere.com
Test the script by running the archiving control script. The scripts currently log message to standard output. The archiving scripts create a PID file to prevent two instances executing concurrently. This prevents corruption of the /Archive directory state. Testing can be performed by running the script manually on the server.
The script can be executed in a scheduled task. On a unix based system this can be done in a cron job and the output captured: # Run the script to copy all tagged OMERO images to Arkivum # Daily processing @4:10am 10 4 * * * omero [path]/tools/bin/omero_to_arkivum.sh >> /var/omero/arkivum.log 2>&1
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page