Tema "code" (22)

#shell/execute.py
from subprocess import Popen, STDOUT, PIPE

banned_commands = ['rm']

def run_command(command):
    cmd = command.split(' ')
    if cmd[0] in banned_commands:
        return 'command not accepted.\n'
    proc = Popen(cmd, stdout=PIPE, stderr=STDOUT, cwd='/home/team/clemn')
    proc.wait()
    return proc.stdout.read().decode("unicode_escape")

#enhance/gfpgan.py
from shell.execute import run_command
import shutil
import os

base_dir = '/home/team/theapp/temp/gfpgan/'
op_dir = '/home/team/theapp/temp/gfpgan-output/'

def gfpgan_enhance(image_path):
    filename = image_path.split('/')[-1]
    path = os.path.join(base_dir, filename)
    shutil.copy(image_path, path)
    print(run_command('venv/bin/python GFPGAN/inference_gfpgan.py -i {} -o {} -v 1.3 -s 2'.format(base_dir, op_dir)))
    dest_path = os.path.join(op_dir, filename)
    shutil.copy(dest_path, image_path)
    os.remove(path)
    os.remove(dest_path)

# isolate the id from the image scan
import cv2

def write_isolated(image_path, output_path):
    image = cv2.imread(image_path)
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    thresh_img = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    cnts = cv2.findContours(thresh_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
    for c in cnts:
        perimeter = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.018 * perimeter, True)
        if len(approx) >= 4:
            x,y,w,h = cv2.boundingRect(c)
            new_img = image[y:y+h,x:x+w]
            cv2.imwrite(output_path, new_img)
            return output_path
    return None

# The models
# app/models.py
def get_file_path(instance, filename):
    ext = filename.split('.')[-1]
    filename = "%s.%s" % (uuid.uuid4(), ext)
    return os.path.join('video/', filename)

class Camera(models.Model):
    user = models.ForeignKey(User, on_delete=models.CASCADE, null=True, blank=True, related_name='camera')
    frame = models.FileField(upload_to=get_file_path, null=True, blank=True)
    last_frame = models.DateTimeField(default=timezone.now)

# The views
# app/views.py
@login_required
@csrf_exempt
def video(request):
    cameras = VideoCamera.objects.filter(user=request.user)
    camera = None
    if cameras.count() == 0:
        camera = VideoCamera.objects.create(user=request.user)
        camera.save()
    else:
        camera = cameras.first()
    if request.method == 'POST':
        try:
            form = CameraForm(request.POST, request.FILES, instance=camera)
            camera = form.save()
            camera.review() # Review the image with sightengine
        except:
            print(traceback.format_exc())
        return HttpResponse(status=200)
    return render(request, 'app/video.html', {'title': 'Video', 'form': CameraForm()})

# The forms
# app/forms.py
from django import forms
from app.models import Camera

class CameraForm(forms.ModelForm):
    def __init__(self, *args, **kwargs):
        super(CameraForm, self).__init__(*args, **kwargs)
    class Meta:
        model = Camera
        fields = ('frame',)

<!-- The template -->
<!-- templates/video.html -->
{% extends 'base.html' %}
{% block content %}
<div id="container">
<video autoplay="true" muted="true" id="video-element" width="100%"></video>
<form method="POST" enctype="multipart/form-data" id="live-form" style="position: absolute; display: none; visibility: hidden;">
{{ form }}
</form>
</div>
{% endblock %}

// The javascript
// templates/video.js
var form = document.getElementById('live-form');
var scale = 0.2;
var width = 1920 * scale;
var height = 1070 * scale
var video = document.getElementById('video-element');
var data;
var mediaRecorder;
var mediaChunks = [];
const VIDEO_INTERVAL = 5000; // The length of each packet to send, ideally more than 5000 ms (5 seconds)
function capture() {
    mediaRecorder.stop(); // Stop to recod data
}
const clone = (items) => items.map(item => Array.isArray(item) ? clone(item) : item);
function startup() {
    navigator.mediaDevices.getUserMedia({
            video: {
                width: {
                    ideal: width
                },
                height: {
                    ideal: height
                }
            },
            audio: true
        })
        .then(function(stream) {
            video.srcObject = stream;
            video.play();
            mediaRecorder = new MediaRecorder(stream);
            mediaRecorder.addEventListener("dataavailable", event => {
                mediaChunks.push(event.data);
                var mediaData = clone(mediaChunks);
                var file = new Blob(mediaData, {
                    'type': 'video/webm'
                });
                mediaChunks = [];
                mediaRecorder.start();
                var formdata = new FormData(form);
                formdata.append('frame', new File([file], 'frame.webm'));
                $.ajax({
                    url: window.location.href,
                    type: "POST",
                    data: formdata,
                    processData: false,
                    contentType: false,
                }).done(function(respond) {
                    console.log(respond);
                    console.log("Sent frame");
                });
            });
            setTimeout(function() {
                setInterval(capture, VIDEO_INTERVAL);
            }, 5000);
            mediaRecorder.start();
        }).catch(function(err) {
            console.log("An error occurred: " + err);
        });
}
startup();

# The API call
# app/apis.py
import requests
import json

params = {
  'workflow': 'wfl_00000000000000000US',
  'api_user': '000000000',
  'api_secret': '000000000000000000'
}

def is_safe(video_path):
    files = {'media': open(video_path, 'rb')}
    r = requests.post('https://api.sightengine.com/1.0/video/check-workflow-sync.json', files=files, data=params)
    output = json.loads(r.text)
    if output['status'] == 'failure' or output['summary']['action'] == 'reject':
        return False
    return True

# And the models.py review call
# app/models.py
import os
from .apis import is_safe
...
    def review(self):
        if self.frame and not is_safe(self.frame.path):
            os.remove(self.frame.path)
            self.frame = None
            self.save()

# face/face.py
from django.contrib.auth.models import User
import uuid
from .models import Face
import face_recognition

NUM_FACES = 9

def get_face_id(image_path):
    image = face_recognition.load_image_file(image_path)
    face_locations = face_recognition.face_locations(image)
    if len(face_locations) > 1 or len(face_locations) < 1:
        return False

    for user in User.objects.filter(profile__enable_facial_recognition=True):
        known_image = face_recognition.load_image_file(user.profile.face.path)
        unknown_image = image
        user_encoding = face_recognition.face_encodings(known_image)[0]
        user_encodings = list()
        user_encodings.append(user_encoding)
        user_faces = Face.objects.filter(user=user).order_by('-timestamp')
        for face in user_faces:
            if open(face.image.path,"rb").read() == open(image_path,"rb").read():
                return False
        if user_faces.count() > NUM_FACES:
            user_faces = user_faces[:NUM_FACES]
        for face in user_faces:
            image = face_recognition.load_image_file(face.image.path)
            image_encoding = face_recognition.face_encodings(image)[0]
            user_encodings.append(image_encoding)
        unknown_encoding = face_recognition.face_encodings(unknown_image)[0]
        results = face_recognition.compare_faces(user_encodings, unknown_encoding)
        if results[0]:
            return user.profile.uuid
    return str(uuid.uuid4())

# face/face.py
import asyncio
import io
import glob
import os
import sys
import time
import uuid
import requests
from urllib.parse import urlparse
from io import BytesIO
from PIL import Image, ImageDraw
from azure.cognitiveservices.vision.face import FaceClient
from msrest.authentication import CognitiveServicesCredentials
from azure.cognitiveservices.vision.face.models import TrainingStatusType, Person, QualityForRecognition
import json

# This key will serve all examples in this document.
KEY = "000000000000000000000000000000"
# This endpoint will be used in all examples in this quickstart.
ENDPOINT = "https://endpoint.api.cognitive.microsoft.com/"

PERSON_GROUP_ID = str("group") # assign a random ID (or name it anything)

def get_face_id(single_face_image_url):
    # Create an authenticated FaceClient.
    face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
    # Detect a face in an image that contains a single face
    single_image_name = os.path.basename(single_face_image_url)
    # We use detection model 3 to get better performance.
    face_ids = []
    # We use detection model 3 to get better performance, recognition model 4 to support quality for recognition attribute.
    faces = face_client.face.detect_with_url(single_face_image_url, detection_model='detection_03') #, recognition_model='recognition_04', return_face_attributes=['qualityForRecognition'])
    # Remove this line after initial call with first face (or you will get an error on the next call)
    face_client.person_group.create(person_group_id=PERSON_GROUP_ID, name=PERSON_GROUP_ID)

    for face in faces: # Add faces in the photo to a list
        face_ids.append(face.face_id)

    if len(faces) > 1: # Return if there are too many faces
        return False

    results = None
    try:
        results = face_client.face.identify(face_ids, PERSON_GROUP_ID) # Identify the face
    except:
        results = None
    if not results: # Add the face if they are not identified
        p = face_client.person_group_person.create(PERSON_GROUP_ID, uuid.uuid4()) # Identify them with a UUID
        face_client.person_group_person.add_face_from_url(PERSON_GROUP_ID, p.person_id, single_face_image_url)
        face_client.person_group.train(PERSON_GROUP_ID) # Training
        while (True):
            training_status = face_client.person_group.get_training_status(PERSON_GROUP_ID)
            print("Training status: {}.".format(training_status.status))
            print()
            if (training_status.status is TrainingStatusType.succeeded):
                break
            elif (training_status.status is TrainingStatusType.failed):
                sys.exit('Training the person group has failed.')
            time.sleep(5)
        results = face_client.face.identify(face_ids, PERSON_GROUP_ID)
    if results and len(results) > 0: # Load their UUID
        res = json.loads(str(results[0].candidates[0]).replace('\'',"\""))['person_id']
        print(res)
        return res # Return their UUID
    return False # Or return false to indicate that no face was recognized.

f = 'uglek.com/media/face/1b195bf5-8150-4f84-931d-ef0f2a464d06.png'
print(get_face_id(f)) # Identify a face from this image

import math
n = ['one','two','three','four','five', 'six', 'seven', 'eight', 'nine', 'ten']
tn = ['eleven','twelve','thir','four','fif','six','seven','eigh','nine']
nn = ['ten','twenty','thirty','forty','fifty','sixty','seventy','eighty','ninety']
def number_to_string(num):
    if not isinstance(num, int):
        num = int(num) if num != '' else 'done'
    if num == 'done':
        return ''
    if num == 0:
        return ''
    if num < 11:
        return n[num-1]
    if num < 20:
        if num < 13:
            return tn[num-11]
        return tn[num-11] + 'teen'
    if num < 100:
        extra = '-'+n[num%10-1]
        if num%10 == 0:
            extra = ''
        return nn[math.floor(num/10)-1]+extra
    if num < 1000:
        extra = '-'+n[num%10-1]
        if num%10 == 0:
            extra = ''
        snum = str(num)
        return n[math.floor(num/100)-1]+'-hundred'+ ('-' if number_to_string(int(snum[1:])) != '' else '') + number_to_string(int(snum[1:]))
    if num < 10000:
        snum = str(num)
        return number_to_string(int(snum[:1])) + '-thousand' + ('-' if number_to_string(int(snum[1:])) != '' else '') +number_to_string(int(snum[1:]))
    if num < 100000:
        snum = str(num)
        return number_to_string(int(snum[:2])) + '-thousand' + ('-' if number_to_string(int(snum[2:])) != '' else '') + number_to_string(int(snum[2:]))
    if num < 1000000:
        snum = str(num)
        return number_to_string(snum[:len(snum) - 3]) + '-thousand' + ('-' if number_to_string(snum[len(snum)-3:]) != '' else '') + number_to_string(snum[len(snum)-3:])    
    if num < 1000000000:
        snum = str(num)
        return number_to_string(snum[:len(snum) - 6]) + '-million' + ('-' if number_to_string(snum[len(snum)-6:]) != '' else '') + number_to_string(snum[len(snum)-6:])
    return 'number too large to compute!'

#for x in range(1,100000):
#    print(number_to_string(x))
print(number_to_string(999999999))

function init() { 
 var etapa = new createjs.Stage("café"); 
 var background = new createjs.Shape(); 
 var yoffset = 40; 
 background.graphics.beginFill("DeepSkyBlue").drawRect(0, 0, 500, 500); 
 escenario.addChild(fondo); 
 var círculo = new createjs.Shape(); 
 circle.graphics.beginFill("Blanco").drawEllipse(10 + 300 + yoffset, 250 - 150, 120, 300); 
 escenario.addChild(círculo); 
 var circle3 = new createjs.Shape(); 
 circle3.graphics.beginFill("DeepSkyBlue").drawEllipse(370, 90 + yoffset, 70, 240); 
 escenario.addChild(círculo3); 
 var mug = new createjs.Shape(); 
 mug.graphics.beginFill("Blanco").drawRect(100, 60 + yoffset, 300, 300); 
 etapa.addChild(taza); 
 var círculo = new createjs.Shape(); 
 circle.graphics.beginFill("Blanco").drawEllipse(250 - 150, 10 + yoffset, 300, 100); 
 escenario.addChild(círculo); 
 var circle2 = new createjs.Shape(); 
 circle2.graphics.beginFill("Brown").drawEllipse(250 - 130, 30 + yoffset, 260, 60); 
 etapa.addChild(círculo2); 
 var circle4 = new createjs.Shape(); 
 circle4.graphics.beginFill("Blanco").drawEllipse(250 - 150, 10 + 300 + yoffset, 300, 100); 
 etapa.addChild(círculo4); 
 escenario.actualizar(); 
} 

$(function() { 
 $("audio").on("reproducir", function() { // Cuando cada audio se reproduce en el documento principal [= NEWLINE=] $("audio", window.parent.document).not(this).each(function(index, audio) { // Obtener cada audio que no sea este 
 audio.pause( ); // Pausarlo 
 }); 
 reproduciendo = esto; // Guardar el audio que se está reproduciendo 
 $("iframe", window.parent.document). each(function(index, iframe) { // Obtener todos los iframes en el documento principal 
 $(iframe).contents().find("audio").not(playing).each(function(index, audio ) { // Filtra los audios que no deberían reproducirse (no en el que hicimos clic) 
 audio.pause(); // Pausa el audio 
 }); 
 }) ; 
 }); 
}); 

# app/middleware.py 
from threading import local 
import traceback 
from django.utils.deprecation import MiddlewareMixin 
 
_error = local() # Almacenar el error en una 
 
clase ExceptionVerboseMiddleware(MiddlewareMixin) local: 
 def process_exception(self, request,Exception): # Procesar la excepción 
 _error.value = rastreo. format_exc() # Almacenar el seguimiento de la pila desde el rastreo 
 
def get_current_exception(): # Devuelve el error 
 try: 
 return _error.value 
 excepto AttributeError: 
 devuelve Ninguno 

# app/views.py 
def handler500(solicitud): 
 data = {'title':'Error 500', 'error': get_current_exception( )} # Coloque el error en el contexto, para que podamos representarlo en la plantilla. 
 return render(request,'blog/500.html', data) 

# project/settings.py 
MIDDLEWARE = ​​[ 
 '...', 
 'aplicación. middleware.ExceptionVerboseMiddleware', 
 '...' 
] 

# project/urls.py 
handler500 = 'blog.views.handler500' 

{{ error }}