object_detector_app | Real-Time Object Recognition App with Tensorflow and OpenCV | Computer Vision library

by datitran Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | object_detector_app Summary

object_detector_app is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow, OpenCV applications. object_detector_app has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However object_detector_app build file is not available. You can download it from GitHub.

Real-Time Object Recognition App with Tensorflow and OpenCV

Support

Quality

Security

License

Reuse

Support

object_detector_app has a medium active ecosystem.

It has 1273 star(s) with 745 fork(s). There are 93 watchers for this library.

It had no major release in the last 6 months.

There are 17 open issues and 32 have been closed. On average issues are closed in 45 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of object_detector_app is current.

Quality

object_detector_app has 0 bugs and 0 code smells.

Security

object_detector_app has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

object_detector_app code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

object_detector_app is licensed under the MIT License. This license is Permissive.

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

Reuse

object_detector_app releases are not available. You will need to build from source code and install.

object_detector_app has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed object_detector_app and discovered the below as its top functions. This is intended to give you an instant insight into object_detector_app implemented functionality, and help decide if they suit your requirements.

Build a preprocessing step
Build a regular expression
Builds the params for a batch norm
Return activation function
Batch non - max suppression
Calculate a multiclass regression using multiclass
Generate multi resolution feature map for multi resolution
Gets a depth function based on depth multiplier
Train the model
Create input queue
Performs a multi - class multi - class suppression problem
Matches the similarity matrix
Postprocessing
Refine a list of boxes
Computes the loss of the prediction
Batches the given targets
Visualize boxes and labels
Evaluate a tensorflow model
Evaluate detection results using Pascal VOC
Repeated checkpoint run
Generate image
Convert a dict to a tf example
Predict a second - stage box classification
Preprocess inputs
Random crop
Flip an image

Get all kandi verified functions for this library.

object_detector_app Key Features

No Key Features are available at this moment for object_detector_app.

object_detector_app Examples and Code Snippets

No Code Snippets are available at this moment for object_detector_app.

Community Discussions

Trending Discussions on object_detector_app

ssd_mobilenet_v2_coco can't detect custom trained objects after exporting inference graph

QUESTION

ssd_mobilenet_v2_coco can't detect custom trained objects after exporting inference graph

Asked 2020-Jan-04 at 09:14

I'm trying to detect marigolds on a field using the tensorflow api. I have already tried it with the faster_rcnn_inception model which worked pretty well but i'm planning to run the detection on raspi4 and for that it's too heavy.

So I tried ssd_mobilenet_v2_coco. In training everything worked fine and it could detect almost every flower, but when i try to use the exported inference graph on the same image, it doesn't detect anything.

Detected Flowers in evaluation during training from tensorboard

Code used for inference:

...

ANSWER

Answered 2020-Jan-04 at 09:14

You are not converting BGR to RGB. OpenCV reads image in BGR format and tensorflow is trained in RGB format. So, just do the following:

Source https://stackoverflow.com/questions/59569482

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

Vulnerabilities

No vulnerabilities reported

Install object_detector_app

conda env create -f environment.yml
python object_detection_app.py / python object_detection_multithreading.py Optional arguments (default value): Device index of the camera --source=0 Width of the frames in the video stream --width=480 Height of the frames in the video stream --height=360 Number of workers --num-workers=2 Size of the queue --queue-size=5 Get video from HLS stream rather than webcam '--stream-input=http://somertmpserver.com/hls/live.m3u8' Send stream to livestreaming server '--stream-output=--stream=http://somertmpserver.com/hls/live.m3u8'

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 .

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