edge-tpu-servers | Object and face recognition using Google 's edge TPU | Computer Vision library
kandi X-RAY | edge-tpu-servers Summary
kandi X-RAY | edge-tpu-servers Summary
Object and face recognition using Google's edge TPU
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Top functions reviewed by kandi - BETA
- Detect face detection of images
- Resize image to square shape
- Compute face classification
- Compute the variance of an image
- Detect objects in the given images
- Detect faces using TPU engine
- Return the best XGBoost estimator
- Finds the best svm estimator
- Generate a dlib encodings using dlib
- Read label file
edge-tpu-servers Key Features
edge-tpu-servers 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
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 edge-tpu-servers
Using the Get Started Guide, flash the Dev Board with the latest software image from Google.
The Dev Board has a modest 8GB on-board eMMC. You need to insert a MicroSD card (at least 32 GB) into the Dev Board to have enough space to install the software in the next steps. The SD card should be auto-mounted so on power-up and reboots the board can operate unattended. I mounted the SD card at /media/mendel. My corresponding /etc/fstab entry for the SD card is shown below.
Create a swap file.
Install zerorpc.
Install OpenCV.
Install scikit-learn.
Install XGBoost.
Install dlib.
Install face_recognition.
Disable and remove swap.
Create a directory called tpu-servers in /media/mendel on the Coral dev board.
Copy detect_server_tpu.py, config.json, encode_faces.py, train.py, in this directory to /media/mendel/tpu-servers/ on the Coral dev board.
Create the face image data set in /media/mendel/tpu-servers/ needed to train the svm- or xgb-based face classifier per the steps in the face-det-rec README or just copy the images to this directory if you created them before.
Download the face embedding dnn model nn4.v2.t7 from OpenFace to the /media/mendel/tpu-servers directory. You can skip this step if you aren't going to use OpenCV to generate the facial embeddings. Currently this does not work well since face alignment isn't implemented. The default facial embedding method used in the project currently is dlib (which does face alignment before generating the embeddings).
Download the tpu face recognition dnn model MobileNet SSD v2 (Faces) from Google Coral to the /media/mendel/tpu-servers directory.
Download both the MobileNet SSD v2 (COCO) tpu object detection dnn model and label file from Google Coral to the /media/mendel/tpu-servers directory.
Run the face encoder program, encode_faces.py, using the images copied above. This will create a pickle file containing the face embeddings used to train the svm- and xgb-based face classifiers. The program can run tpu-, dlib- and openCV-based face detectors and dlib- and OpenCV-based facial embedders. Its currently configured to use tpu-based face detection and dlib-based facial embeddings which gives the best results vs compute.
Run the face classifier training program, train.py. This will create three pickle files - one for the svm model, one for the xgb model and one for the model labels. The program will optimize the model hyperparameters and then evaluate the model, including thier F1 scores which should be close to 1.0 for good classifier performance. The optimal hyperparameters and model statistics are output after training is completed.
Mount a local or remote image store on the Dev Board so the server can find the images and process them. The store should be auto-mounted using sshfs at startup which is done by an entry in /etc/fstab. Below is an example of a using a remote store at lindo@192.168.1.4:/nvr.
Edit the config.json to suit your installation. The configuration parameters are documented in the detect_server_tpu.py code.
Use systemd to run the server as a Linux service. Edit detect-tpu.service to suit your configuration and copy the file to /lib/systemd/system/detect-tpu.service. Then enable and start the service:
Test the entire setup by editing detect_servers_test.py with paths to test images and running that program.
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