apc-vision-toolbox | Princeton Vision Toolbox for the Amazon Picking Challenge | Computer Vision library

by andyzeng C++ Version: Current License: BSD-2-Clause

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | apc-vision-toolbox Summary

apc-vision-toolbox is a C++ library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow, OpenCV applications. apc-vision-toolbox has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

MIT-Princeton Vision Toolbox for the Amazon Picking Challenge 2016 - RGB-D ConvNet-based object segmentation and 6D object pose estimation.

Support

Quality

Security

License

Reuse

Support

apc-vision-toolbox has a low active ecosystem.

It has 300 star(s) with 140 fork(s). There are 27 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 14 have been closed. On average issues are closed in 27 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of apc-vision-toolbox is current.

Quality

apc-vision-toolbox has no bugs reported.

Security

apc-vision-toolbox has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

apc-vision-toolbox is licensed under the BSD-2-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

apc-vision-toolbox releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of apc-vision-toolbox

Get all kandi verified functions for this library.

apc-vision-toolbox Key Features

No Key Features are available at this moment for apc-vision-toolbox.

apc-vision-toolbox Examples and Code Snippets

No Code Snippets are available at this moment for apc-vision-toolbox.

Community Discussions

Trending Discussions on Computer Vision

Image similarity in swift

When using pandas_profiling: "ModuleNotFoundError: No module named 'visions.application'"

Classify handwritten text using Google Cloud Vision

cv2 findChessboardCorners does not detect corners

Fastest way to get the RGB average inside of a non-rectangular contour in the CMSampleBuffer

UIViewController can't override method from it's superclass

X and Y-axis swapped in Vision Framework Swift

Swift's Vision framework not recognizing Japanese characters

Boxing large objects in image containing both large and small objects of similar color and in high density from a picture

Create a LabVIEW IMAQ image from a binary buffer/file with and without NI Vision

QUESTION

Image similarity in swift

Asked 2022-Mar-25 at 11:42

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 10:26

It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.

Source https://stackoverflow.com/questions/71615277

QUESTION

When using pandas_profiling: "ModuleNotFoundError: No module named 'visions.application'"

Asked 2022-Mar-22 at 13:26

import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport

...

ANSWER

Answered 2022-Mar-22 at 13:26

It 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.

Source https://stackoverflow.com/questions/71568414

QUESTION

Classify handwritten text using Google Cloud Vision

Asked 2022-Mar-01 at 00:36

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 00:36

It 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.

Source https://stackoverflow.com/questions/71296897

QUESTION

cv2 findChessboardCorners does not detect corners

Asked 2022-Jan-29 at 23:59

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 23:59

Finally 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.

Source https://stackoverflow.com/questions/70907902

QUESTION

Fastest way to get the RGB average inside of a non-rectangular contour in the CMSampleBuffer

Asked 2022-Jan-26 at 02:12

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 02:12

If 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) image
- inputImage is the original frame
- inputMaskImage 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:

Source https://stackoverflow.com/questions/70344336

QUESTION

UIViewController can't override method from it's superclass

Asked 2022-Jan-21 at 19:37

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 19:37

This is my complete code:

Source https://stackoverflow.com/questions/70804364

QUESTION

X and Y-axis swapped in Vision Framework Swift

Asked 2021-Dec-23 at 14:33

I'm using Vision Framework to detecting faces with iPhone's front camera. My code looks like

...

ANSWER

Answered 2021-Dec-23 at 14:33

For some reason, remove

Source https://stackoverflow.com/questions/70463081

QUESTION

Swift's Vision framework not recognizing Japanese characters

Asked 2021-Oct-12 at 23:37

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 23:37

As 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:

Source https://stackoverflow.com/questions/69546997

QUESTION

Boxing large objects in image containing both large and small objects of similar color and in high density from a picture

Asked 2021-Oct-12 at 10:58

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 10:58

You 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:

Source https://stackoverflow.com/questions/69503515

QUESTION

Create a LabVIEW IMAQ image from a binary buffer/file with and without NI Vision

Asked 2021-Sep-30 at 13:54

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 13:54

With NI Vision

For 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.

Source https://stackoverflow.com/questions/69380393

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install apc-vision-toolbox

Download our segmentation training dataset
Navigate to directory convnet-training/
Specify training dataset filepath in APCData layer of network architecture in models/train_shelf_color.json
Navigate to models/weights/ and run bash script ./download_weights.sh to download VGG pre-trained weights on ImageNet (see Marvin for more pre-trained weights)
Navigate to convnet-training/ and run in terminal ./compile.sh to compile Marvin.
Run in terminal ./marvin train models/rgb-fcn/train_shelf_color.json models/weights/vgg16_imagenet_half.marvin to train a segmentation model on RGB-D data with objects in the shelf (for objects in the tote, use network architecture models/rgb-fcn/train_shelf_color.json).
Download our 'Shelf & Tote' benchmark dataset from here and extract its contents to apc-vision-toolbox/data/benchmark (e.g. apc-vision-toolbox/data/benchmark/office, `apc-vision-toolbox/data/benchmark/warehouse', etc.)
In evaluation/getError.m, change the variable benchmarkPath to point to the filepath of your benchmark dataset directory
We have provided our vision system's predictions in a saved Matlab .mat file evaluation/predictions.mat. To compute the accuracy of these predictions against the ground truth labels of the 'Shelf & Tote' benchmark dataset, run evaluation/getError.m

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: