geeup | Simple CLI for Google Earth Engine Uploads | Computer Vision library
kandi X-RAY | geeup Summary
kandi X-RAY | geeup Summary
This tool came of the simple need to handle batch uploads of both image assets to collections but also thanks to the new table feature the possibility of batch uploading shapefiles into a folder. Though a lot of these tools including batch image uploader is part of my other project geeadd which also includes additional features to add to the python CLI, this tool was designed to be minimal so as to allow the user to simply query their quota, upload images or tables and also to query ongoing tasks and delete assets. I am hoping this tool with a simple objective proves useful to a few users of Google Earth Engine.
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Top functions reviewed by kandi - BETA
- Check for the geeup version
- Compares two strings
- Validate metatdata from a csv file
- Create an upload
- Upload a single image
- Get Google Auth session
- Find the remaining assets that are in the local assets
- Load metadata from a csv file
- Checks if a cookie is valid
- Create an image collection
- Upload a file to GID
- Gets the upload url
- Get asset names from a collection
- Tab up the given parser
- Create a tabup from a dirc file
- Get authentication session
geeup Key Features
geeup Examples and Code Snippets
Lines of Code : 17License : Permissive (Apache-2.0)
Copygeeup upload -h
usage: geeup upload [-h] --source SOURCE --dest DEST -m METADATA [--nodata NODATA] [--pyramids PYRAMIDS] [-u USER]
optional arguments:
-h, --help show this help message and exit
Required named arguments.:
--source SOU geeup tabup -h
usage: geeup tabup [-h] --source SOURCE --dest DEST [-u USER] [--x X] [--y Y]
optional arguments:
-h, --help show this help message and exit
Required named arguments.:
--source SOURCE Path to the directory with z usage: geeup getmeta [-h] --input INPUT --metadata METADATA
optional arguments:
-h, --help show this help message and exit
Required named arguments.:
--input INPUT Path to the input directory with all raster files
--metadata META 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
CIBlendWithMaskfilter where:inputBackgroundImageis a fully transparent (clear) imageinputImageis the original frameinputMaskImageis 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 geeup
Shapely and a few other libraries are notoriously difficult to install on windows machines so follow the steps mentioned here before installing porder. You can download and install shapely and other libraries from the Unofficial Wheel files from here download depending on the python version you have. Do this only once you have install GDAL. I would recommend the steps mentioned above to get the GDAL properly installed. However I am including instructions to using a precompiled version of GDAL similar to the other libraries on windows. You can test to see if you have gdal by simply running. in your command prompt. If you get a read out and not an error message you are good to go. If you don't have gdal try Option 1,2 or 3 in that order and that will install gdal along with the other libraries.
As usual, to print help:. To obtain help for specific functionality, simply call it with help switch, e.g.: geeup zipshape -h. If you didn't install geeup, then you can run it just by going to geeup directory and running python geeup.py [arguments go here].
This method was added since v0.4.6 and uses a third party chrome extension to simply code all cookies. This step is now the only stable method for uploads and has to be completed before any upload process. The chrome extension is simply the first one I found and is no way related to the project and as such I do not extend any support or warranty for it.
Open a brand browser window while you are copying cookies (do not use an incognito window as GEE does not load all cookies needed), if you have multiple GEE accounts open on the same browser the cookies being copied may create some read issues at GEE end.
Clear cookies and make sure you are copying cookies from code.earthengine.google in a fresh browser instance if upload fails with a Unable to read error.
Make sure you save the cookie for the same account which you initiliazed using earthengine authenticate
For Bash the cannonical mode will allow you to only paste upto 4095 characters and as such geeup cookie_setup might seem to fail for this use the following steps
Disable cannonical mode by typing stty -icanon in terminal
Then run geeup cookie_setup
Once done reenable cannonical mode by typing stty icanon in terminal
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