Face_Recognizer | face recognition application | Computer Vision library

by chengstone Python Version: Current License: No License

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

kandi X-RAY | Face_Recognizer Summary

Face_Recognizer is a Python library typically used in Artificial Intelligence, Computer Vision applications. Face_Recognizer has no bugs, it has no vulnerabilities and it has low support. However Face_Recognizer build file is not available. You can download it from GitHub.

face recognition application

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Quality

Security

License

Reuse

Support

Face_Recognizer has a low active ecosystem.

It has 55 star(s) with 30 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Face_Recognizer is current.

Quality

Face_Recognizer has 0 bugs and 0 code smells.

Security

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

Face_Recognizer code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Face_Recognizer does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

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

Face_Recognizer saves you 1907 person hours of effort in developing the same functionality from scratch.

It has 4203 lines of code, 250 functions and 36 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Bulk detect face detection
Compute the NMS of a box
Generate a bounding box for a given image
Create bounding box
Start find face
Batch feature extraction
Find the face of the given image
Detects the face of the image
Feature extraction
Check if file exists
Detect the face of the image
Build the convolution layer
Build the model
Train the model
Find face in img
Process the image
Calculate the ROC curve
Calculate the value for a given threshold
Creates input pipeline
Detect the face of a given image
Stores the revision info in src_path
Setup the module
Create TMNN layer
Load a model
Setup the network
Creates a forward layer
Parse command line arguments
Setup neural network

Get all kandi verified functions for this library.

Face_Recognizer Key Features

No Key Features are available at this moment for Face_Recognizer.

Face_Recognizer Examples and Code Snippets

No Code Snippets are available at this moment for Face_Recognizer.

Community Discussions

Trending Discussions on Face_Recognizer

Tensor Tensor("flatten/Reshape:0", shape=(?, 2622), dtype=float32) is not an element of this graph

QUESTION

Tensor Tensor("flatten/Reshape:0", shape=(?, 2622), dtype=float32) is not an element of this graph

Asked 2021-May-24 at 09:55

Hello StackOverFlow Team: I built a model based on (Vgg_Face_Model) with weights loaded (vgg_face_weights.h5). Note that I use tensorflow-gpu = 2.1.0 , and keras=2.3.1 , with Anaconda 3 create it as interpreter and used with pycharm But the code shows an error in the part :

...

ANSWER

Answered 2021-May-24 at 09:55

from tensorflow.python.keras.backend import set_session
sess = tf.Session()

#This is a global session and graph
graph = tf.get_default_graph()
set_session(sess)


#now where you are calling the model
global sess
global graph
with graph.as_default():
    set_session(sess)
    input_descriptor = [model.predict(face), img]

Source https://stackoverflow.com/questions/67659336

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

Vulnerabilities

No vulnerabilities reported

Install Face_Recognizer

You can download it from GitHub.
You can use Face_Recognizer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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