Face_ID | This repository can be used to build a FACE ID program | Computer Vision library

by MauryaRitesh Python Version: Current License: MIT

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

kandi X-RAY | Face_ID Summary

Face_ID is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, OpenCV applications. Face_ID has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Face_ID build file is not available. You can download it from GitHub.

To create a simple and reliable Face Recognition program (For this VIDEO) by using Tensorflow in the Python Programming Language.

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Face_ID has a low active ecosystem.

It has 33 star(s) with 23 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 3 have been closed. On average issues are closed in 36 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Face_ID is current.

Quality

Face_ID has 0 bugs and 0 code smells.

Security

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

Face_ID code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Face_ID 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

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

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

Installation instructions are not available. Examples and code snippets are available.

Face_ID saves you 436 person hours of effort in developing the same functionality from scratch.

It has 1031 lines of code, 31 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Create model information
Add input distortions
Creates a list of images in the given directory
Retrieves random bottlenecks
Get or create bottleneck
Get image path
Create bottleneck file
Adds the final training
Create summaries for variable
Returns a list of bottleneck_truths
Cache bottleneck images
Download and extract data from url
Adds JPEG decoding
Reads a Tensor from a file
Creates a tf graph
Adds the evaluation step
Determine if the image should distort
Load a tf graph from a file
Capture the image
Prepare the file system
Save the graph to a file
Load label from file

Get all kandi verified functions for this library.

Face_ID Key Features

No Key Features are available at this moment for Face_ID.

Face_ID Examples and Code Snippets

No Code Snippets are available at this moment for Face_ID.

Community Discussions

Trending Discussions on Face_ID

Rendering images in Django ModelForm instead of __str__ representation

Using Azure Face Api in Python, How to Return a single faceId or a group of FaceIds if the same person is detected in Video Stream?

error C2338: YOU_MIXED_DIFFERENT_NUMERIC_TYPES

std::vector reserve being more costly than expected

Face API Python SDK "Image Size too Small" (PersonGroupPerson add_face_from_stream)

sed recognition response to DynamoDB table using Lambda-python

python cv2 VideoCapture not working on wamp server

0.07s query on MySQL takes 11.68s on MariaDB?

Kairos enroll API returns confidence in the response. What does it mean?

error occur due to numpy in face_recognition

QUESTION

Rendering images in Django ModelForm instead of __str__ representation

Asked 2021-Jan-24 at 19:45

I have the following django models / forms / views / html setup. So I am rendering the InputFileForm in the html and the user should select from dropdown list a face_file that is saved in the Face model (preloaded via fixtures). I would like to have the face_file images to be rendered in the dropdown (alternatively a radio select would be fine as well) instead of the image str names - as it currently looks like the following:

Image of current dropdown

So in short: I would like to have an image rendered in the dropdown instead of the "Face 1", "Face 2",...

Thanks in advance for your help!

...

ANSWER

Answered 2021-Jan-24 at 19:45

You can override label_from_instance - see ModelChoiceField.iterator

See also Overriding the default fields

And than you can do something like following:

Source https://stackoverflow.com/questions/65870673

QUESTION

Using Azure Face Api in Python, How to Return a single faceId or a group of FaceIds if the same person is detected in Video Stream?

Asked 2020-Dec-01 at 01:15

I am using Azure Face APi to detect faces in video stream, but for each detected face Azure returns a unique faceId( which is exactly what the documentation says).

The problem is, Let's say Mr.ABC appears in 20 video frames, 20 unique faceIds gets generated. I want something that Azure Face should return me a single faceId or a group of FaceIds generated particularly for Mr.ABC so that I can know that its the same person that stays in front of camera for x amount of time.

I have read the documentation of Azure Facegrouping and Azure FindSimilar, but didn't understand how can I make it work in case of live video stream.

The code I am using for detecting faces using Azure face is given below:

...

ANSWER

Answered 2020-Nov-30 at 08:26

There is no magic on Face API: you have to process it with 2 steps for each face found.

What I suggest is to use "Find similar":

at the beginning, create a "FaceList"
then process your video:
- Face detect on each frame
- For each face found, use find similar operation on the face list created. If there is no match (with a sufficient confidence), add the face to the facelist.

At the end, your facelist will contain all the different people found on the video.

For your realtime use-case, don't use "Identify" operation with PersonGroup / LargePersonGroup (the choice between those 2 depends on the size of the group), because you will be stuck by the need of training on the group. Example, you would be doing the following:

Step 1, 1 time: generate the PersonGroup / LargePersonGroup for this execution
Step 2, N times (for each image where you want to identify the face):
- Step 2a: face detect
- Step 2b: face "identify" on each detected face based on the PersonGroup / LargePersonGroup
- Step 2c: for each unidentified face, add it to the PersonGroup / LargePersonGroup.

Here the issue is the fact that after 2c, you have to train your group again. Even if it is not so long, it cannot be used in real time as it will be too long.

Source https://stackoverflow.com/questions/65040892

QUESTION

error C2338: YOU_MIXED_DIFFERENT_NUMERIC_TYPES

Asked 2020-Oct-02 at 16:38

I'm receiving this error at line eigen/src/Core/AssignEvaluator.h(834) of Eigen library:

error C2338: YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY

Based on the compiler logs I think the errors is triggered by this line in the code:

...

ANSWER

Answered 2020-Oct-02 at 16:35

Type mismatch between Vec3d and Vec3f was the error cause.

Source https://stackoverflow.com/questions/64174792

QUESTION

std::vector reserve being more costly than expected

Asked 2020-Sep-24 at 16:20

So I need to manually handle the memory allocated by an std::vector for efficiency purposes. And I noticed that my program was slower than expected, so I added this idiom everywhere in my code base:

...

ANSWER

Answered 2020-Sep-24 at 16:13

You're not reimplementing reserve, you're reimplementing the resize operation when capacity is exhausted. Problem is, if you do this every time you insert an item, you're resizing every time, making every operation O(n) (as it has to move all the items from the original backing storage to the new, larger, storage). If you ran this every time, building from an empty vector, you'd see a pattern of:

Source https://stackoverflow.com/questions/64050059

QUESTION

Face API Python SDK "Image Size too Small" (PersonGroupPerson add_face_from_stream)

Asked 2019-Dec-17 at 03:19

First things first, the documentation here says "JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size is from 1KB to 6MB."

I am sending a .jpg that is ~1.4 MB In my search, others who had this issue were custom forming packets and ran into issues chunk transfering images. however unlike the others I am not forming my own API call, just passing a jpg to the python sdk. What is going wrong/what am I missing?

The error is:

...

ANSWER

Answered 2019-Dec-17 at 03:19

I run your code on my side and got the same error .Seems there is something wrong with image param in code :

Source https://stackoverflow.com/questions/59350034

QUESTION

sed recognition response to DynamoDB table using Lambda-python

Asked 2019-Feb-22 at 10:53

I am using Lambda to detect faces and would like to send the response to a Dynamotable. This is the code I am using:

...

ANSWER

Answered 2019-Feb-22 at 10:53

When you create a Table in DynamoDB, you must specify, at least, a Partition Key. Go to your DynamoDB table and grab your partition key. Once you have it, you can create a new object that contains this partition key with some value on it and the object you want to pass itself. The partition key is always a MUST upon creating a new Item in a DynamoDB table.

Your JSON object should look like this:

Source https://stackoverflow.com/questions/54824911

QUESTION

python cv2 VideoCapture not working on wamp server

Asked 2018-Oct-24 at 15:25

Background - I have python and required scripts installed on my desktop.
I am developing a face recognition WebApp.
It is working fine from Command Line but when I try to run it from localhost on wampserver, the webcam lights get on but no webcam window appears and the page starts loading for unlimited time.

Here is the code for data training

...

ANSWER

Answered 2018-Jun-20 at 04:45

I solved this problem I replaced

Source https://stackoverflow.com/questions/50919120

QUESTION

0.07s query on MySQL takes 11.68s on MariaDB?

Asked 2018-Aug-28 at 04:44

There is different hardware involved (MySQL is on my laptop, MariaDB on the server) but usually the difference is at most 2x not 166x!

The tables contain the same data on each instance (18,000 rows in _cache_card and 157,000 rows in card_legality).

THE QUERY ...

ANSWER

Answered 2018-Aug-25 at 07:44

I would add index:

Source https://stackoverflow.com/questions/52014839

QUESTION

Kairos enroll API returns confidence in the response. What does it mean?

Asked 2018-May-23 at 13:13

I am exploring Kairos Facial Recognition APIs. The API /enroll is used for uploading an image to Kairos for a subject_id. I noticed that the response of enroll API contains a confidence score. The image is treated as a new image. What does this confidence mean? When you verify an image, in that case the confidence score is important. But while uploading an image, why does the API return a confidence?

I assume, the API compares the image to the images uploaded before for that subject_id and returns the confidence. Is this the case or is it something else?

API Documentation: API_docs.

Here is a sample response for reference:

...

ANSWER

Answered 2018-May-23 at 13:13

Yes, this isn't clear from the documentation.

For /recognize and /verify the confidence % represents how similar the face sent in with the request is to the the face being compared against.

For /detect and /enroll the confidence represents how confident the engine is that it found a face. Usually you will see 98-99 percent range for those values.

Disclosure: Kairos.com CTO

Source https://stackoverflow.com/questions/50290012

QUESTION

error occur due to numpy in face_recognition

Asked 2018-Mar-20 at 02:22

import face_recognition
import cv2
import os, os.path
import numpy as np 

count = 0 

def finding_members():
    finding_members.list= os.listdir('/Users/apple/Desktop/face_id/train_models')
    print(finding_members.list)
    video_capture = cv2.VideoCapture(0)
    count = 0 
    count1 = 0
    encoding_name = str()
    face_encoding = []
    length_of_encoding = 0
    length_of_encodings = 0
    name_face_encodings = []
    names = []




    for i in finding_members.list:
        findDot = i.find('.')
        finding_members.name = i[0:findDot]
        if ((len(finding_members.name)) > 1):
            names.append(finding_members.name)

        print(names)

    for i in finding_members.list:

        dit = "train_models/"+i
        print(dit)
        if (i != ".DS_Store"):
            images = face_recognition.load_image_file(dit)
            findDot = i.find('.')
            encoding_name = i[0:findDot]
            if (length_of_encodings >= 0):
                name_face_encodings.append(face_encoding)





            face_encoding = [face_recognition.face_encodings(images)[0]]
            length_of_encoding = len(face_encoding)

            #face_encoding.update(face_encoding)
            print(len(name_face_encodings))

            count = count+1
            print (count)

    while True:
        #cv2.imshow('Video', frame)
        ret, frame = video_capture.read()
        cv2.imshow('video1',frame)
        face_locations = face_recognition.face_locations(frame)
        face_encodings = face_recognition.face_encodings(frame, face_locations)
        #for 
        for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
            for paru in names:
                print(len(name_face_encodings))
                print(count1)
                local_encoding = name_face_encodings[count1]

                match = face_recognition.compare_faces([local_encoding], face_encoding)
                count1 = count1 + 1
                name = "Unknown"
                print(count1)
                print(names[count1])
                if (match ==True):
                    name = names[count1]

                cv2.rectangle(frame, (left, top), (right, bottom), (100, 40, 100), 2)
                font = cv2.FONT_HERSHEY_DUPLEX
                cv2.putText(frame, name, (left + 6, top - 6), font, 1.0, (255, 255, 255), 1)

        print(names)
        #print(len())
        cv2.imshow('Video', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    video_capture.release()
    cv2.destroyAllWindows()




finding_members()
print(finding_members.list)
print(finding_members.name)

...

ANSWER

Answered 2018-Mar-20 at 02:22

If I understand you correctly,

The frame video_captured is BGR, so frame convert it to RGB.

Source https://stackoverflow.com/questions/47487300

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

Vulnerabilities

No vulnerabilities reported

Install Face_ID

You can download it from GitHub.
You can use Face_ID 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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