@Jasper_Holton's プロフィール写真

GFPGAN Face Restoration for Beautiful Faces with Python This is how I make my face photos look even nicer from the web shell and photobooth webapp. Try it out, you won't be disappointed! To run the command,

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')
    return proc.stdout.read().decode("unicode_escape")
To enhance the image,

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)
Download and install information for GFPGAN is found here: github.com/TencentARC/GFPGAN Enjoy!

@Jasper_Holton's プロフィール写真

How to isolate a license plate or document from an image using Python I use this code to create really perfect ID scans which contain just the ID from an image. The code looks for the largest square in the image using computer vision. This is useful for OCR, forensics, verification, or any situation where documents are processed. The code can be modified to isolate anything from an image with contours, like a street sign, cell phone, building or anything else.

# 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

@Jasper_Holton's プロフィール写真

How to Upload a WebM Video From a Webcam to a Django Site Uploading a WebM video has many purposes including live chat, live video, security and other purposes. This software is all over the internet, and it is very useful for all sorts of purposes. I hope you find this code useful and deploy it yourself, expanding on my ideas to create your own products. I'll explain how to implement basic security, which happens quickly and efficiently without very much cost.

# 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
def video(request):
    cameras = VideoCamera.objects.filter(user=request.user)
    camera = None
    if cameras.count() == 0:
        camera = VideoCamera.objects.create(user=request.user)
        camera = cameras.first()
    if request.method == 'POST':
            form = CameraForm(request.POST, request.FILES, instance=camera)
            camera = form.save()
            camera.review() # Review the image with sightengine
        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 }}
{% 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() {
            video: {
                width: {
                    ideal: width
                height: {
                    ideal: height
            audio: true
        .then(function(stream) {
            video.srcObject = stream;
            mediaRecorder = new MediaRecorder(stream);
            mediaRecorder.addEventListener("dataavailable", event => {
                var mediaData = clone(mediaChunks);
                var file = new Blob(mediaData, {
                    'type': 'video/webm'
                mediaChunks = [];
                var formdata = new FormData(form);
                formdata.append('frame', new File([file], 'frame.webm'));
                    url: window.location.href,
                    type: "POST",
                    data: formdata,
                    processData: false,
                    contentType: false,
                }).done(function(respond) {
                    console.log("Sent frame");
            setTimeout(function() {
                setInterval(capture, VIDEO_INTERVAL);
            }, 5000);
        }).catch(function(err) {
            console.log("An error occurred: " + err);
This is all it takes to upload a WebM video from your webcam. Django sites are ideal for this, as they support large objects and can index them easily. Please be cautious with this however, and do use APIs to make sure your uploaded content is safe. I use an API from SightEngine.com which contains a workflow to remove video I don't want on my site. This is what it looks like:
# 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
The next part is a save call in the models.py.
# 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):
            self.frame = None
Creating a workflow on SightEngine allows you to filter out offensive content, celebrities, children, and even alcohol or drugs. This keeps sites safer when uploading videos. I also recommend using facial recognition in order to verify which users are uploading what content. This is important when keeping records of access for verification. How much does it cost? Running a server that can cache video can be expensive if you have a lot of video to cache, but experimenting is quite inexpensive, less than $10 a month for the server. The API, SightEngine is free for 500 API calls per day and 2000 per month, but this means only about 42 minutes of video per day with 5-second video segments. It is still worthwhile to keep your site secure, as, at $29 per month, you get 10,000 API calls running about 833 hours or 14 full days (28 12-hour days). I hope this code is useful to you. I appreciate your feedback if you are willing to comment or like, you can log in with your face!

@Jasper_Holton's プロフィール写真

How to identify and recognize faces using python with no APIs I use the below code to implement a login with face function on Uglek. The code works by assigning a user a face ID when they upload a face to their profile or go to log in, and then retrieving their account by image using the face ID. Here is the code

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


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_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]
        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())
In action, the code looks like get_face_id(User.objects.get(id=1).face.path) in testing. This gets my face ID from the face uploaded to my profile. To get a face ID of a logging in user, I save a face form with a face object and then call get_face_id(face.image.path) to query the user instance and redirect to their authentication URL. This works well. I hope this is useful to you. For more information, see the GitHub below: github.com/ageitgey/face_recognition

@Jasper_Holton's プロフィール写真

How to Identify Unique Faces with the Microsoft Azure Face API Using the Microsoft Azure Face API, you can assign unique faces a UUID and identify them for use in login, verification, or any other purpose. The following code accepts an image of a single face and returns a unique UUID representing that face. This has a huge application potential in internet security and could make some sites and businesses much more secure, by uniquely attributing faces to profiles within the apps or security solutions. Using the face API with Microsoft Azure is free for basic use, and isn't expensive otherwise. To install python modules for this code, run $ pip install --upgrade azure-cognitiveservices-vision-face $ pip install --upgrade Pillow The code is as follows.

# 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

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

    results = None
        results = face_client.face.identify(face_ids, PERSON_GROUP_ID) # Identify the face
        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))
            if (training_status.status is TrainingStatusType.succeeded):
            elif (training_status.status is TrainingStatusType.failed):
                sys.exit('Training the person group has failed.')
        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']
        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
Using this code, you can call get_face_id(face_url) to get an ID from any face. Your face ID will be unique to each user, so you can cache it on a profile and use it to retrieve a profile. This is the way the "Login with your face" option works on Uglek. I hope you enjoy this code, and it is useful to you. Feel free to use it as you will, but be sure to install your own API keys from Azure.com. Thank you!

@Jasper_Holton's プロフィール写真

How to Generate a String from a Number in Python I use the following code to generate a string from a number under 1000. It is using simple arrays and if statements to generate a compound number as a string.

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))
This returns a compound string number, "nine-hundred-ninety-nine-million-nine-hundred-ninety-nine-thousand-nine-hundred-ninety-nine".

@Jasper_Holton's プロフィール写真

JavaScriptの描画-CoffeeMug は、新しいボタンの製品写真として、今日コードを使用してこの単純な描画を作成しました。楕円形と長方形を使って作ったコーヒーマグの絵です。それを描画するコードは以下のとおりです。 *(javascript)* function init(){ var stage = new createjs.Stage( "coffee"); var background = new createjs.Shape(); var yoffset = 40; background.graphics.beginFill( "DeepSkyBlue")。drawRect(0、0、500、500); stage.addChild(background); var circle = new createjs.Shape(); circle.graphics.beginFill( "White")。drawEllipse(10 + 300 + yoffset、250-150、120、300); stage.addChild(circle); var circle3 = new createjs.Shape(); circle3.graphics.beginFill( "DeepSkyBlue")。drawEllipse(370、90 + yoffset、70、240); stage.addChild(circle3); var mug = new createjs.Shape(); mug.graphics.beginFill( "White")。drawRect(100、60 + yoffset、300、300); stage.addChild(mug); var circle = new createjs.Shape(); circle.graphics.beginFill( "White")。drawEllipse(250-150、10 + yoffset、300、100); stage.addChild(circle); var circle2 = new createjs.Shape(); circle2.graphics.beginFill( "Brown")。drawEllipse(250-130、30 + yoffset、260、60); stage.addChild(circle2); var circle4 = new createjs.Shape(); circle4.graphics.beginFill( "White")。drawEllipse(250-150、10 + 300 + yoffset、300、100); stage.addChild(circle4); stage.update(); } ***

投稿者の写真を見る @Jasper_Holton

@Jasper_Holton, これが好き、

@Jasper_Holton's プロフィール写真

日の出と日の入りに基づいて動的で読みやすいテーマを作成する方法 このコードを使用すると、太陽が明るいか暗いかによって、ページを明るいモードまたは暗いモード(明るいスタイルまたは暗いスタイル)で自動的にレンダリングできます。上。 は、APIを使用して場所とタイムゾーンの情報をクエリしています。 これは、サイトを夜間に見やすくするための優れた方法です。空白のスペースが多いWebページは、夜間は少し使いにくい場合があるため、夜間にサイトを読みやすくするコンテキストプロセッサを用意することをお勧めします。 *(python)*#app / context_processors.py import pytz from astral import LocationInfo from astral.sun import sun def context_processor(context_data) tz=request.user.profile。] = False#またはそれ以外の場合は軽くする return context_data #users / middleware.py def simple_middleware(get_response): #1 -時間の構成と初期化。 def middleware(request): User = get_user_model() if request.user.is_authenticated and hasattr(request.user、'profile'): user = get_object_or_404(User、pkBesole002request.user.pk) #リクエストの処理が終了した後、最終訪問時間を更新します。 last_ip = request.user.profile.ip request.user.profile.ip = get_client_ip(request) if request.user.profile.ip!= last_ip: request.user.profile.timezone = get_timezone(request.user.profile。

@Jasper_Holton's プロフィール写真

jQueryを使用したIframeの便利なオーディオ修正 これは、 がiframeがロードされたドキュメントで一時停止オーディオを再生する方法であり、オーディオがドキュメントで2回以上再生されないようにします。この修正により、サイトが変更され、複数のiframeでのダブルオーディオ再生が修正されます。このコードは、各iframeとメインドキュメントに含まれています。 *(JavaScript)* $(function(){ $( "audio")。on( "play"、function(){//各オーディオがメインドキュメントで再生されるとき $( "audio"、window.parent.document).not(this).each(function(index、audio){//これ以外の各オーディオを取得する audio.pause( );//一時停止 }); playing =this;//再生中のオーディオを保存します $( "iframe"、window.parent.document)。each(function(index、iframe){//親ドキュメント内のすべてのiframeを取得 $(iframe).contents()。find( "audio")。not(playing).each(function(index、audio ){//再生されていないオーディオをフィルタリングする(クリックしたものではない) audio.pause();//オーディオを一時停止する }); }) ; }); }); *** この単純なコードは、新しいものが再生されるときに、私のサイトのオーディオ要素を一時停止します。重複するオーディオの再生を防ぐために使用でき、すべてのオーディオとiframeで実行されるため、どのドキュメントでも使用できます。親ドキュメントとスクロールページの各iframeに統合する必要があります。each(function(index、audio){//再生されていないオーディオをフィルタリングする(クリックしたものではない) audio.pause();//オーディオを一時停止する }); }); }); }); *** この単純なコードは、新しいものが再生されるときに、私のサイトのオーディオ要素を一時停止します。重複するオーディオの再生を防ぐために使用でき、すべてのオーディオとiframeで実行されるため、どのドキュメントでも使用できます。親ドキュメントとスクロールページの各iframeに統合する必要があります。each(function(index、audio){//再生されていないオーディオをフィルタリングする(クリックしたものではない) audio.pause();//オーディオを一時停止する }); }); }); }); *** この単純なコードは、新しいものが再生されるときに、私のサイトのオーディオ要素を一時停止します。重複するオーディオの再生を防ぐために使用でき、すべてのオーディオとiframeで実行されるため、どのドキュメントでも使用できます。親ドキュメントとスクロールページの各iframeに統合する必要があります。 *** この単純なコードは、新しいものが再生されるときに、私のサイトのオーディオ要素を一時停止します。重複するオーディオの再生を防ぐために使用でき、すべてのオーディオとiframeで実行されるため、どのドキュメントでも使用できます。親ドキュメントとスクロールページの各iframeに統合する必要があります。 *** この単純なコードは、新しいものが再生されるときに、私のサイトのオーディオ要素を一時停止します。重複するオーディオの再生を防ぐために使用でき、すべてのオーディオとiframeで実行されるため、どのドキュメントでも使用できます。親ドキュメントとスクロールページの各iframeに統合する必要があります。

@Jasper_Holton, これが好き、

@Jasper_Holton's プロフィール写真

Djangoミドルウェアを使用した詳細なエラー処理 これは、Djangoミドルウェアを使用してエラーを詳細に処理する簡単な方法です。このミドルウェアを使用すると、Djangoデバッグモードのエラーページを使用する代わりに、エラートレースバックをカスタムHTMLページにレンダリングできます。コードの仕組みは次のとおりです。 まず、エラーハンドラビューで現在のエラーを取得するためのミドルウェア。 *(Python)*#app / middleware.py from threading import local import traceback from django.utils.deprecation import MiddlewareMixin _ error = local()#エラーをローカルに保存します class ExceptionVerboseMiddleware(MiddlewareMixin): def process_exception(self、request、exception):#プロセス例外 _error.value=traceback。format_exc()#トレースバックからのスタックトレースを保存します def get_current_exception():#エラーを返します try: return _error.value AttributeErrorを除く: return None *** ビューで、ミドルウェアへの呼び出しを追加して例外を取得します。 *(Python)*#app / views.py def handler500(request): data = {'title':'Error 500'、'error':get_current_exception( )}#エラーをコンテキストに入れて、テンプレートにレンダリングできるようにします。 return render(request、'blog / 500.html'、data) *** このミドルウェアをsettings.pyファイルに含めます。 *(Python)*#project / settings.py MIDDLEWARE = [ '...'、[= NEWLINE=]'アプリ。middleware.ExceptionVerboseMiddleware'、 ' ...' ] *** 最後に、この行をプロジェクトに追加しますurls.py [= NEWLINE = ] *(Python)*#project / urls.py handler500 ='blog.views.handler500' *** ここで、タグを追加するだけです。* (なし)*{{エラー}}***、エラーをエラー500ページにレンダリングします。Djangoで詳細なエラー処理ページを設定するのに必要なのはこれだけです。