face_recognition | simplest facial recognition api for Python and the command | Computer Vision library

by ageitgey Python Version: v1.2.2 License: MIT

kandi X-RAY | face_recognition Summary

face_recognition is a Python library typically used in Artificial Intelligence, Computer Vision applications. face_recognition has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install face_recognition' or download it from GitHub, PyPI.

The world's simplest facial recognition api for Python and the command line

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face_recognition has a medium active ecosystem.
It has 47024 star(s) with 12705 fork(s). There are 1585 watchers for this library.
It had no major release in the last 12 months.
There are 674 open issues and 570 have been closed. On average issues are closed in 105 days. There are 21 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of face_recognition is v1.2.2

face_recognition Support

Best in #Computer Vision

Average in #Computer Vision

face_recognition Support

Best in #Computer Vision

Average in #Computer Vision

quality kandi Quality

face_recognition has 0 bugs and 0 code smells.

face_recognition Quality

Best in #Computer Vision

Average in #Computer Vision

face_recognition Quality

Best in #Computer Vision

Average in #Computer Vision

security Security

face_recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
face_recognition code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

face_recognition Security

Best in #Computer Vision

Average in #Computer Vision

face_recognition Security

Best in #Computer Vision

Average in #Computer Vision

license License

face_recognition is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

face_recognition License

Best in #Computer Vision

Average in #Computer Vision

face_recognition License

Best in #Computer Vision

Average in #Computer Vision

build Reuse

face_recognition releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
face_recognition saves you 458 person hours of effort in developing the same functionality from scratch.
It has 1081 lines of code, 61 functions and 24 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.

face_recognition Reuse

Best in #Computer Vision

Average in #Computer Vision

face_recognition Reuse

Best in #Computer Vision

Average in #Computer Vision

Top functions reviewed by kandi - BETA

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

Batch image locations

Trim CSS to bounding box
Wrapper for _raw_face_detector
Convert a rect to CSS

Return a list of landmarks for a given face image

Wrapper for face detection
Returns a list of face locations for a given face image
Convert css to a rectangle

Process a single frame

Return the next worker id
Return the previous worker id

Test if an image is likely to be similar

Print results

Render an image

Detect face encoding

Compute face encodings

Calculate face locations

Compute the eye aspect ratio

Compare two faces

Trains the model

Scan for known people

Captures a video

Process images in a multiprocessing process

Visualize prediction labels on an image

Predict the face of a given image

Run a test

Get all kandi verified functions for this library.

face_recognition Key Features

Find all the faces that appear in a picture:. Get the locations and outlines of each person's eyes, nose, mouth and chin.

face_recognition Examples and Code Snippets

See all related Code Snippets

Community Discussions

Trending Discussions on face_recognition

cannot append results to lists on multiprocessing
"ERROR: CMake must be installed to build dlib" when installing face_recognition
The called python file won't show again
PyInstaller: How to call "shape_predictor_68_face_landmarks.dat" file with dlib.shape_predictor, while imported it with 'binaries'?
Sorting a tensor list in ascending order
problem in Installing (python Library) face_recognition on windows 10/11
Celery/redis tasks don't always complete - not sure why or how to fit it
is possible to face recognition with mediapipe in python
"No CMAKE_CXX_COMPILER could be found" errror while deploying flask app on gcloud
I am trying to load and split my data however i get TypeError: 'only integer scalar arrays can be converted to a scalar index'

Trending Discussions on face_recognition

QUESTION

cannot append results to lists on multiprocessing

Asked 2022-Mar-18 at 11:30

This below code will generate face encodings using multiprocessing , i can able to print the encoding but the problem is the knownEncodings ,knownNames ,no_faces ,error_in_image all are empty after the execution. I know its due to multiprocessing , but not sure how to mitigate this.

import face_recognition
from imutils import paths
from multiprocessing import Pool
import pickle
import cv2
import os,sys,time

print("[INFO] quantifying faces...")

img_folder_path=sys.argv[1]

image_paths = list(paths.list_images(img_folder_path))

knownEncodings = []
knownNames = []
no_faces = []
error_in_image =[]

def create_encoding(imagePath):
    print("[INFO] processing image...")
    name = imagePath.split(os.path.sep)[-1]
    image = cv2.imread(imagePath)
    if image is None:
        return
    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # detect the (x, y)-coordinates of the bounding boxes
    # corresponding to each face in the input image
    boxes = face_recognition.face_locations(rgb)

    # compute the facial embedding for the face
    if len(boxes) != 0:
        boxes = list(boxes[0])
        encodings = face_recognition.face_encodings(image, [boxes])
        for encoding in encodings:  
            knownEncodings.append(encoding)
            knownNames.append(name)
        
    else:
        print("no face found" ,image_paths )
        no_faces.append(image_paths )



# loop over the image paths with multiprocessing
start_time = time.time()

with Pool(8) as pool:
    pool.map(create_encoding, image_paths )


end_time = time.time()
print(end_time - start_time)

# dump the facial encodings + names to disk
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames, "no_faces":no_faces,"error_in_image":error_in_image}

f_name = img_folder_path.replace("/","-")
print(f_name)
f = open(f"encodings_{f_name}.pickle", "wb")
f.write(pickle.dumps(data))
f.close()

ANSWER

Answered 2022-Mar-18 at 10:46

You should not use list cross several processes. You can use multiprocessing.Queue or other Process safe models. How to use multiprocessing queue in Python?

Source https://stackoverflow.com/questions/71525117

QUESTION

"ERROR: CMake must be installed to build dlib" when installing face_recognition

Asked 2022-Mar-01 at 22:50

I'm facing this error while installing face_recognition in a virtualenv with Python 3.8.10 on Ubuntu 20.04.

ERROR: Failed building wheel for face-recognition-models
  Running setup.py clean for face-recognition-models
Failed to build dlib face-recognition-models
Installing collected packages: Click, numpy, dlib, face-recognition-models, face-recognition
    Running setup.py install for dlib ... error
    ERROR: Command errored out with exit status 1:
     command: '/home/badrelden/Desktop/test python sound/venv/bin/python3' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-tai2snq9/dlib/setup.py'"'"'; __file__='"'"'/tmp/pip-install-tai2snq9/dlib/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-bhy0sde6/install-record.txt --single-version-externally-managed --compile --install-headers '/home/badrelden/Desktop/test python sound/venv/include/site/python3.8/dlib'
         cwd: /tmp/pip-install-tai2snq9/dlib/
    Complete output (8 lines):
    running install
    running build
    running build_py
    package init file 'tools/python/dlib/__init__.py' not found (or not a regular file)
    running build_ext
    
    ERROR: CMake must be installed to build dlib
    
    ----------------------------------------
ERROR: Command errored out with exit status 1: '/home/badrelden/Desktop/test python sound/venv/bin/python3' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-tai2snq9/dlib/setup.py'"'"'; __file__='"'"'/tmp/pip-install-tai2snq9/dlib/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-bhy0sde6/install-record.txt --single-version-externally-managed --compile --install-headers '/home/badrelden/Desktop/test python sound/venv/include/site/python3.8/dlib' Check the logs for full command output.

ANSWER

Answered 2022-Mar-01 at 22:49

ERROR: CMake must be installed to build dlib

This is the key part of the error message. You need to install cmake, which can be done by running sudo apt install cmake on Debian-based systems, including Ubuntu. After cmake is installed, you can rerun the pip install command.

Source https://stackoverflow.com/questions/71315443

QUESTION

The called python file won't show again

Asked 2022-Feb-20 at 13:56

I am having problem calling the file online.py.

Code:

# For Face_recognition
from logging import disable
import tkinter
from tkinter.font import BOLD
from cv2 import data
import numpy as np
import face_recognition as fr
import cv2

# SQL-CONNECTOR
import mysql.connector
from mysql.connector import IntegrityError

# For Trainer
import PIL.Image
import os
from tkinter import ttk

# GUI
from tkinter import *
import tkinter as tk
from tkinter import messagebox


# USER INTERFACE
window = tk.Tk()
window.title("FACEREGISTRATION SYSTEM")
window.resizable(0,0)
# bgs = PhotoImage(file = "C://Users//So_Low//Desktop//final_recog//online_reg_img/edited2.png")
# labels1 = Label(window, image = bgs)
# labels1.place(x = 0, y = 0, relwidth= 1, relheight= 1)



# UPPER DESIGN
l1 = tk.Label(window, text= "N O R M I",  font=("Exton Free Trial",35), fg='green')
l1.grid(column = 2, row= 1)



# TEXT BOXES
l1 = tk.Label(window, text= "Name",  font=("Bahnschrift",15))
l1.grid(column = 1, row=3)
on1 = tk.Entry(window, width=40, bd=3)
on1.grid(column=2, row=3)

l1 = tk.Label(window, text= "Parent/Visitor",  font=("Bahnschrift",15))
l1.grid(column = 1, row=5)
on2 = tk.Entry(window, width=40, bd=3)
on2.grid(column= 2, row=5)

l1 = tk.Label(window, text= "Relationship",  font=("Bahnschrift",15))
l1.grid(column = 1, row=6)
on3 = tk.Entry(window, width=40, bd=3)
on3.grid(column= 2, row=6)

l1 = tk.Label(window, text= "ID",  font=("Bahnschrift",15))
l1.grid(column = 1, row=7)
on4 = tk.Entry(window, width=19, bd=3, bg="light blue")
on4.grid(column=2, row=7)

l1 = tk.Label(window, text= "Address",  font=("Bahnschrift",15))
l1.grid(column = 1, row=8)
on5 = tk.Entry(window, width=40, bd=3)
on5.grid(column= 2, row=8)

l1 = tk.Label(window, text= "Contact #",  font=("Bahnschrift",15))
l1.grid(column = 1, row=9)
on6 = tk.Entry(window, width=20, bd=3, bg = "light blue")
on6.grid(column= 2, row=9)

def onregister():
    if (on1.get()==""  or on2.get()=="" or on3.get()=="" or on4.get()=="" or on5.get()=="" or on6.get()==""):
        messagebox.showinfo("Result","Please Complete the Provided Details!")
    else:
        databases = mysql.connector.connect(
        host ="localhost",
        user = "userdata",
        password = "",
        database = "facerecog"
        )

        cursors = databases.cursor()
        cursors.execute("SELECT * from record")
        result = cursors.fetchall()


        id= on4.get()
        id = int(id)+1
        id
        sql = "INSERT INTO record(ids, names,course_year,positions,addresses,contact) values(%s ,%s ,%s , %s, %s, %s)"
        val = (id, on1.get(), on2.get(), on3.get(), on5.get(), on6.get())
        try:
            cursors.execute(sql,val)
            databases.commit()
        except IntegrityError:
            messagebox.showinfo("Error","ID Is Already Exist")
            return onregister
        finally:
            cursors.close()
            databases.close()

        on2.delete(0,END)
        on3.delete(0,END)
        on4.delete(0,END)
        on5.delete(0,END)
        on6.delete(0,END)

        DirPath = "C:/Users/So_Low/Desktop/final_recog/online_reg_img/"
        Files = os.listdir(DirPath)

        for File in Files:
            imgPath = os.path.join(DirPath, File)
            image = cv2.imread(imgPath, cv2.IMREAD_GRAYSCALE)
            cv2.imwrite("C:/Users/So_Low/Desktop/final_recog/img/"+str (on1.get())+"." +str(id)+".jpg",image)
            
btns1 = tk.Button(window,text= "Save To Database", font=("Bahnschrift",15),bg="light blue",fg="black",command=onregister)
btns1.grid(column=2,row=40)
l1 = tk.Label(window, text= " ",  font=("Bahnschrift",15))
l1.grid(column = 2, row=35)

window.geometry("500x400")
window.mainloop()

I'm importing it in my other code by calling this function:

def onreg():
    import online
    online
    return online

btn1 = tk.Button(window,text= "Online Register", font=("Bahnschrift",15),bg="light blue",fg="black",command=onreg)
btn1.grid(column=1,row=35)

It shows when I click the button but when I close the called file and click it again it won't open. And there's no error showing. How could I show it when I called it again?

ANSWER

Answered 2022-Feb-20 at 13:56

Modules in Python are not supposed to be run multiple times, they are supposed to be a way of defining variables to be used in other scripts. Therefore importing a module twice does not run it twice; the interpreter stores the variables from the module the first time it is run, and if you try to import it again it will give you the variables it already has stored and not run the file again.

There are a few ways of getting around this; here are two of them:

Put the code in the module into a function, and import that function:

# online.py (module):
def online():
    ...  # code to be run

# Registers.py (main script):
import online  # at start of file
...  # some code
...  # get rid of the onreg() function, it is not necessary
btn1 = tk.Button(..., command=online.online)  # Line where button is created
...  # rest of code

Alternatively, if this is not possible for whatever reason, use the importlib library to reload() the module which will run it again:

# online.py (module) stays the same

# Registers.py (main script):
import importlib  # at start of file
online_module = None  # at start of file, just after imports, call this variable whatever you want
...  # some code
def onreg():  # redo this function
    global online_module
    try:
        importlib.reload(online_module)
    except TypeError:
        import online_module
...  # rest of code stays the same

Source https://stackoverflow.com/questions/71193516

QUESTION

Sorting a tensor list in ascending order

Asked 2021-Dec-05 at 21:29

I am working on a facial comparison app that will give me the closest n number of faces to my target face.

I have done this with dlib/face_recognition as it uses numpy arrays, however i am now trying to do the same thing with facenet/pytorch and running into an issue because it uses tensors.

I have created a database of embeddings and I am giving the function one picture to compare to them. What i would like is for it to sort the list from lowest distances to highest, and give me the lowest 5 results or so.

here is the code I am working on that is doing the comparison. at this point i am feeding it a photo and asking it to compare against the embedding database.

def face_match(img_path, data_path): # img_path= location of photo, data_path= location of data.pt 
    # getting embedding matrix of the given img
    img_path = (os.getcwd()+'/1.jpg')
    img = Image.open(img_path)
    face = mtcnn(img) # returns cropped face and probability
    emb = resnet(face.unsqueeze(0)).detach() # detech is to make required gradient false

    saved_data = torch.load('data.pt') # loading data.pt file
    embedding_list = saved_data[0] # getting embedding data
    name_list = saved_data[1] # getting list of names
    dist_list = [] # list of matched distances, minimum distance is used to identify the person
    
    for idx, emb_db in enumerate(embedding_list):
        dist = torch.dist(emb, emb_db)
        dist_list.append(dist)
    
    namestodistance = list(zip(name_list,dist_list))
    
    print(namestodistance)

face_match('1.jpg', 'data.pt')

This results in giving me all the names and their distance from the target photo in alphabetical order of the names, in the form of (Adam Smith, tensor(1.2123432)), Brian Smith, tensor(0.6545464) etc. If the 'tensor' wasn't part of every entry I think it would be no problem to sort it. I don't quite understand why its being appended to the entries. I can cut this down to the best 5 by adding [0:5] at the end of dist_list, but I can't figure out how to sort the list, I think the problem is the word tensor being in every entry.

I have tried for idx, emb_db in enumerate(embedding_list): dist = torch.dist(emb, emb_db) sorteddist = torch.sort(dist) but for whatever reason this only returns one distance value, and it isn't the smallest one.

idx_min = dist_list.index(min(dist_list)), this works fine in giving me the lowest value and then matching it to a name using namelist[idx_min], therefore giving the best match, but I would like the best 5 matches in order as opposed to just the best match.

Anyone able to solve this ?

ANSWER

Answered 2021-Dec-05 at 16:43

Unfortunately I cannot test your code, but to me it seems like you are operation on a python list of tuples. You can sort that by using a key:

namestodistance = [('Alice', .1), ('Bob', .3), ('Carrie', .2)]
names_top = sorted(namestodistance, key=lambda x: x[1])
print(names_top[:2])

Of course you have to modify the anonymous function in key to return a sortable value instead of e.g. a torch.tensor.

This can be done by using key = lambda x: x[1].item().

Edit: To answer the question that crept up in the comments, we can refactor our code a little. Namely

namestodistance = list(map(lambda x: (x[0], x[1].item()), namestodistance)
names_top = sorted(namestodistance, key=lambda x: x[1])
print(names_top[:2])

Source https://stackoverflow.com/questions/70232894

QUESTION

problem in Installing (python Library) face_recognition on windows 10/11

Asked 2021-Nov-26 at 05:09

I am using python 3.9.9 pip 21.3.1

I am trying to install face_recognition

so I installed CMake then when I tried to install dlib using command pip install dlib I got the following

Collecting dlib
  Using cached dlib-19.22.1.tar.gz (7.4 MB)
  Prep

    aring metadata (setup.py) ... done
    Building wheels for collected packag

es: dlib
  Building wheel for dlib (setup.py) ... error
  ERROR: Command errored out with exit status 1:
   command: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-78dld3fg\\dlib_098968d181654cc09e283aff4801a4b1\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-78dld3fg\\dlib_098968d181654cc09e283aff4801a4b1\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\aenas\AppData\Local\Temp\pip-wheel-hjnpx8xk'
       cwd: C:\Users\aenas\AppData\Local\Temp\pip-install-78dld3fg\dlib_098968d181654cc09e283aff4801a4b1\
  Complete output (8 lines):
  running bdist_wheel
  running build
  running build_py
  package init file 'tools\python\dlib\__init__.py' not found (or not a regular file)
  running build_ext

  ERROR: CMake must be installed to build dlib

  ----------------------------------------
  ERROR: Failed building wheel for dlib
  Running setup.py clean for dlib
Failed to build dlib
Installing collected packages: dlib
    Running setup.py install for dlib ... error
    ERROR: Command errored out with exit status 1:
     command: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-78dld3fg\\dlib_098968d181654cc09e283aff4801a4b1\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-78dld3fg\\dlib_098968d181654cc09e283aff4801a4b1\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\aenas\AppData\Local\Temp\pip-record-zbjc8ulw\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\Users\aenas\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\Include\dlib'
         cwd: C:\Users\aenas\AppData\Local\Temp\pip-install-78dld3fg\dlib_098968d181654cc09e283aff4801a4b1\
    Complete output (8 lines):
    running install
    running build
    running build_py
    package init file 'tools\python\dlib\__init__.py' not found (or not a regular file)
    running build_ext

    ERROR: CMake must be installed to build dlib

    ----------------------------------------
ERROR: Command errored out with exit status 1: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-78dld3fg\\dlib_098968d181654cc09e283aff4801a4b1\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-78dld3fg\\dlib_098968d181654cc09e283aff4801a4b1\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\aenas\AppData\Local\Temp\pip-record-zbjc8ulw\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\Users\aenas\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\Include\dlib' Check the logs for full command output.**

and when I tried to install face_recognition using command pip install face_recognition I got the following:

Collecting face_recognition
  Using cached face_recognition-1.3.0-py2.py3-none-any.whl (15 kB)
Requirement already satisfied: face-recognition-models>=0.3.0 in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from face_recognition) (0.3.0)
Collecting dlib>=19.7
  Using cached dlib-19.22.1.tar.gz (7.4 MB)
  Preparing metadata (setup.py) ... done
Collecting Click>=6.0
  Using cached click-8.0.3-py3-none-any.whl (97 kB)
Requirement already satisfied: numpy in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from face_recognition) (1.21.4)
Requirement already satisfied: Pillow in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from face_recognition) (8.4.0)
Requirement already satisfied: colorama in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from Click>=6.0->face_recognition) (0.4.4)
Building wheels for collected packages: dlib
  Building wheel for dlib (setup.py) ... error
  ERROR: Command errored out with exit status 1:
   command: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\aenas\AppData\Local\Temp\pip-wheel-elyfs9nv'
       cwd: C:\Users\aenas\AppData\Local\Temp\pip-install-cy9bzkon\dlib_8e45a10b6067402ab676a29d5bd742c8\
  Complete output (8 lines):
  running bdist_wheel
  running build
  running build_py
  package init file 'tools\python\dlib\__init__.py' not found (or not a regular file)
  running build_ext

  ERROR: CMake must be installed to build dlib

  ----------------------------------------
  ERROR: Failed building wheel for dlib
  Running setup.py clean for dlib
Failed to build dlib
Installing collected packages: dlib, Click, face-recognition
    Running setup.py install for dlib ... error
    ERROR: Command errored out with exit status 1:
     command: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\aenas\AppData\Local\Temp\pip-record-oojov4ij\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\Users\aenas\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\Include\dlib'
         cwd: C:\Users\aenas\AppData\Local\Temp\pip-install-cy9bzkon\dlib_8e45a10b6067402ab676a29d5bd742c8\
    Complete output (8 lines):
    running install
    running build
    running build_py
    package init file 'tools\python\dlib\__init__.py' not found (or not a regular file)
    running build_ext

    ERROR: CMake must be installed to build dlib

    ----------------------------------------
ERROR: Command errored out with exit status 1: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\aenas\AppData\Local\Temp\pip-record-oojov4ij\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\Users\aenas\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\Include\dlib' Check the logs for full command output.

Though I have tried many solution but none of them solved my problem

ANSWER

Answered 2021-Nov-20 at 08:45

Fortunately, I solved the problem. These are the steps to download face_recognition Library:

1- install python:

in the command prompt write python and click Enter, this will open Microsoft store for you, "click download" or "install" to download and install python. "to check if it is downloaded or not type python in the command prompt

2- install pip if you do not have it:

go to: https://pypi.org/project/pip-download/ click download files. Now open the downloaded file, then click right click with the mouse and choose "open in windows terminal". now type the command python get-pip.py. now your pip should be installed, type pip in the terminal to check if it is installed

3-Install Visual Studio tools For C++

go to: https://visualstudio.microsoft.com/ and download the community version of VS. after downloading install it and choose the following options in the installation page:
image1 image2

and choose install

4- installing cmake

Enter the command pip install cmake and wait fo it to be installed.

5- Add cmake to the Path variable

follow the instructions in the following link to know how to add something to your Path variable: https://helpdeskgeek.com/windows-10/add-windows-path-environment-variable/

6- Install dlib

write the following command in the command prompt: pip install dlib

7- install face_recognition

write the following command in the command prompt: pip install face_recognition

8- start your project in PyCharm

click File click Sittings in the search type "interpreter" choose "python interpreter" choose "show all" click the "+" sign choose system Interpreter choose the interpreter that its path starts with the "users" directory

That is the solution I used to fix my problem

Source https://stackoverflow.com/questions/70001837

QUESTION

Celery/redis tasks don't always complete - not sure why or how to fit it

Asked 2021-Nov-25 at 05:54

I am running celery v 4.0.3/redis v 4.09 in a django v 3.0.1 application (Python v 3.6.9). I am also using face_recognition in a celery task find_faces to find faces in the images I have uploaded to the application, among other image processing celery tasks. There are no issues processing five or fewer image files in that all the image processing celery tasks complete successfully.

When I have the image process tasks (including find_faces) iterate over 100 images there are 10-30 images where the find_faces task does not complete. When I use flower v0.9.7 to take a look at the celery tasks, I see that the find_faces task status is "started" for those images that did not complete. All the other images have find_faces task status as "success". The status of these "started" tasks never changes, and there are no errors or exceptions reported. I can then run the image processing tasks, including the find_faces task, on each of these images individually, and the task status is "success". These results do not change if I run celery as a daemon or locally, or if I run the django app using wsgi and apache or runserver. Flower also reports that retries = 0 for all my tasks.

I have CELERYD_TASK_SOFT_TIME_LIMIT = 60 set globally in the django app, and max_retries=5 for the find_faces task.

@app.task(bind=True, max_retries=5)
def find_faces_task(self, document_id, use_cuda=settings.USE_CUDA):
    logger.debug("find_faces_task START")
    try:
        temp_face = None
        from memorabilia.models import TaskStatus, Document      
        args = "document_id=%s, use_cuda=%s" % (document_id, use_cuda)
        ts = TaskStatus(document_id_id=document_id, task_id=self.request.id, task_name='find_faces_task', task_args=args, task_status=TaskStatus.PENDING)
        ts.save()
        import time
        time_start = time.time()
        # Check if we already have the faces for this document
        from biometric_identification.models import Face
        if len(Face.objects.filter(document_id=document_id)) != 0:
            # This document has already been scanned, so need to remove it and rescan
            # Have to manually delete each object per django docs to insure the 
            # model delete method is run to update the metadata.
            logger.debug("Document %s has already been scanned" % document_id)
            faces = Face.objects.filter(document_id=document_id)
            for face in faces:
                face.delete()
                logger.debug("Deleted face=%s" % face.tag_value.value)
        document = Document.objects.get(document_id=document_id)
        image_file = document.get_default_image_file(settings.DEFAULT_DISPLAY_IMAGE)
        image_path = image_file.path
        time_start_looking = time.time()
        temp_file = open(image_path, 'rb')
        temp_image = Image.open(temp_file)
        logger.debug("fred.mode=%s" % fred.mode)
        width, height = temp_image.size
        image = face_recognition.load_image_file(temp_file)
        # Get the coordinates of each face
        if use_cuda:
            # With CUDA installed
            logger.debug("Using CUDA for face recognition")
            face_locations = face_recognition.face_locations(image, model="cnn", number_of_times_to_upsample=0) 
        else:
            # without CUDA installed
            logger.debug("NOT using CUDA for face recognition")
            face_locations = face_recognition.face_locations(image, model="hog", number_of_times_to_upsample=2)
        time_find_faces = time.time()
        # Get the face encodings for each face in the picture    
        face_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations) 
        logger.debug("Found %s face locations and %s encodings" % (len(face_locations), len(face_encodings)))
        time_face_encodings = time.time()
        # Save the faces found in the database
        for location, encoding in zip(face_locations, face_encodings):
            # Create the new Face object and load in the document, encoding, and location of a face found
            # Locations seem to be of the form (y,x)
            from memorabilia.models import MetaData, MetaDataValue
            tag_type_people = MetaDataValue.objects.filter(metadata_id=MetaData.objects.filter(name='Tag_types')[0].metadata_id, value='People')[0]
            tag_value_unknown = MetaDataValue.objects.filter(metadata_id=MetaData.objects.filter(name='Unknown')[0].metadata_id, value='Unknown')[0]
            new_face = Face(document=document, face_encoding=numpy_to_json(encoding), face_location=location, image_size={'width': width, "height":height}, tag_type=tag_type_people, tag_value=tag_value_unknown)         
            # save the newly found Face object
            new_face.save()
            logger.debug("Saved new_face %s" % new_face.face_file) 
        time_end = time.time()
        logger.debug("total time = {}".format(time_end - time_start))
        logger.debug("time to find faces = {}".format(time_find_faces - time_start_looking))
        logger.debug("time to find encodings = {}".format(time_face_encodings - time_find_faces))
        ts.task_status = TaskStatus.SUCCESS
        ts.comment = "Found %s faces" % len(face_encodings)
        return document_id
    except Exception as e:
        logger.exception("Hit an exception in find_faces_task %s" % str(e))
        ts.task_status = TaskStatus.ERROR
        ts.comment = "An exception while finding faces: %s" % repr(e)
    finally:
        logger.debug("Finally clause in find-faces_task")
        if temp_image:
            temp_image.close()
        if temp_file:
            temp_file.close()
        ts.save(update_fields=['task_status', 'comment'])
        logger.debug("find_faces_task END")

The find_faces task is called as part of a larger chain of tasks that manipulate the images. Each image file goes through this chain, where step_1 and step_2 are chords for different image processing steps:

step_1 = chord( group( clean ), chordfinisher.si() ) # clean creates different image sizes
step_2 = chord( group( jobs ), chordfinisher.si() )  # jobs include find_faces
transaction.on_commit(lambda: chain(step_1, step_2, faces_2, ocr_job, change_state_task.si(document_id, 'ready')).delay())

@app.task
def chordfinisher( *args, **kwargs ):
    return "OK"

The images are large, so it can take up to 30 seconds for the find_faces task to complete. I thought the CELERYD_TASK_SOFT_TIME_LIMIT = 60 would take care of this long processing time.

I am by no means a celery expert, so I assume there is a celery setting or option that I need to enable to make sure the find_faces task completes all the time. I just don't know what that would be.

ANSWER

Answered 2021-Nov-25 at 05:54

After some more research, I can up with this suggestion from Lewis Carroll, in this post "Beware the oom-killer, my son! The jaws that bite, the claws that catch!", and this post Chaining Chords produces enormously big messages causing OOM on workers, and this post WorkerLostError: Worker exited prematurely: exitcode 155.

It seems my celery workers may have been running out of memory, as I did find traces of the dreaded oomkiller in my syslogs. I reconfigured my tasks to just be in a chain (removed all the groups and chords) so each task is run individually in sequence for each image, and the tasks all completed successfully, no matter how many images I processed.

Source https://stackoverflow.com/questions/70074740

QUESTION

"No CMAKE_CXX_COMPILER could be found" errror while deploying flask app on gcloud

Asked 2021-Nov-07 at 14:13

I have a flask application that I'm deploying on google cloud run. The app using a library 'face_recognition' that requires Cmake. I'm installing the CMake by running a command in DockerFile but getting an error. I don't know what it mean.

Here is my Dockerfile

# Use the official lightweight Python image.
# https://hub.docker.com/_/python
FROM python:3.9-slim

# Allow statements and log messages to immediately appear in the Knative logs
ENV PYTHONUNBUFFERED True

# Copy local code to the container image.
ENV APP_HOME /app
WORKDIR $APP_HOME
COPY . ./

# Install production dependencies.
RUN apt-get update && apt-get install -y cmake
RUN pip install --no-cache-dir -r requirements.txt
RUN pip install gunicorn

# Run the web service on container startup. Here we use the gunicorn
# webserver, with one worker process and 8 threads.
# For environments with multiple CPU cores, increase the number of workers
# to be equal to the cores available.
# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling.
CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 0 main:app

Here is the error

CMake Error at CMakeLists.txt:14 (project):
      No CMAKE_CXX_COMPILER could be found.
    
      Tell CMake where to find the compiler by setting either the environment
      variable "CXX" or the CMake cache entry CMAKE_CXX_COMPILER to the full path
      to the compiler, or to the compiler name if it is in the PATH.
    
    
    -- Configuring incomplete, errors occurred!
    See also "/tmp/pip-install-2m1peq73/dlib_d6f82528b68745578021b2f234f89d7c/build/temp.linux-x86_64-3.9/CMakeFiles/CMakeOutput.log".
    See also "/tmp/pip-install-2m1peq73/dlib_d6f82528b68745578021b2f234f89d7c/build/temp.linux-x86_64-3.9/CMakeFiles/CMakeError.log".
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-install-2m1peq73/dlib_d6f82528b68745578021b2f234f89d7c/setup.py", line 222, in <module>
        setup(
      File "/usr/local/lib/python3.9/site-packages/setuptools/__init__.py", line 153, in setup
        return distutils.core.setup(**attrs)
      File "/usr/local/lib/python3.9/distutils/core.py", line 148, in setup
        dist.run_commands()
      File "/usr/local/lib/python3.9/distutils/dist.py", line 966, in run_commands
        self.run_command(cmd)
      File "/usr/local/lib/python3.9/distutils/dist.py", line 985, in run_command
        cmd_obj.run()
      File "/usr/local/lib/python3.9/site-packages/setuptools/command/install.py", line 61, in run
        return orig.install.run(self)
      File "/usr/local/lib/python3.9/distutils/command/install.py", line 546, in run
        self.run_command('build')
      File "/usr/local/lib/python3.9/distutils/cmd.py", line 313, in run_command
        self.distribution.run_command(command)
      File "/usr/local/lib/python3.9/distutils/dist.py", line 985, in run_command
        cmd_obj.run()
      File "/usr/local/lib/python3.9/distutils/command/build.py", line 135, in run
        self.run_command(cmd_name)
      File "/usr/local/lib/python3.9/distutils/cmd.py", line 313, in run_command
        self.distribution.run_command(command)
      File "/usr/local/lib/python3.9/distutils/dist.py", line 985, in run_command
        cmd_obj.run()
      File "/tmp/pip-install-2m1peq73/dlib_d6f82528b68745578021b2f234f89d7c/setup.py", line 134, in run
        self.build_extension(ext)
      File "/tmp/pip-install-2m1peq73/dlib_d6f82528b68745578021b2f234f89d7c/setup.py", line 171, in build_extension
        subprocess.check_call(cmake_setup, cwd=build_folder)
      File "/usr/local/lib/python3.9/subprocess.py", line 373, in check_call
        raise CalledProcessError(retcode, cmd)

ANSWER

Answered 2021-Nov-06 at 18:15

The container base python:3.9-slim is very stripped down. If your application requires CMake which often implies the gcc compiler as well, you have at least two options:

Use a more feature rich base container such as debian:buster
Choose a container with those tools already configured.

Example Dockerfile to build a base container:

FROM debian:buster
RUN apt update && apt install -y gcc clang clang-tools cmake python3

You can then use that container as the base for future containers or modify the Dockerfile to include your application.

Docker debian:buster

Source https://stackoverflow.com/questions/69865503

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

Vulnerabilities

No vulnerabilities reported

Install face_recognition

First, make sure you have dlib already installed with Python bindings:.
How to install dlib from source on macOS or Ubuntu
Jetson Nano installation instructions Please follow the instructions in the article carefully. There is current a bug in the CUDA libraries on the Jetson Nano that will cause this library to fail silently if you don't follow the instructions in the article to comment out a line in dlib and recompile it.
Raspberry Pi 2+ installation instructions
@masoudr's Windows 10 installation guide (dlib + face_recognition)
Download the pre-configured VM image (for VMware Player or VirtualBox).

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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