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while True:
        # Grab a single frame of video
        ret, frame = video_capture.read()

        # Resize frame of video to 1/4 size for faster face recognition processing
        small_frame = cv2.resize(frame, (0, 0), fx=QUALITY, fy=QUALITY)

        # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
        rgb_small_frame = small_frame[:, :, ::-1]

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def video_parser(video_name = "videos/{}".format("vid1.mp4")):
    video_capture = cv2.VideoCapture(video_name)
    OFFSET = 50
    OFFSET_TOP = 100
    QUALITY = 0.25
    CLASSES = {
        "lannister": ['tyrion', 'cersei', 'jaime', 'joffery', 'myrcella', 'tommen', 'tywin'],

    #-----------------------

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import numpy as np
import cv2
import face_recognition

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export async function onResultFetching(img){
    return async (dispatch) => {
        dispatch(onResultPending())
        URL = HOSTS[0]
        result_obj = {
            result: {
                class: 'Loading...'
            },
            resultLoading: false,
            isError: false

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{
    "axios": "^0.18.0",
    "expo": "^32.0.0",
    "form-data": "^2.3.3",
    "react": "16.5.0",
    "react-native": "https://github.com/expo/react-native/archive/sdk-32.0.0.tar.gz",
    "react-native-elements": "^1.1.0",
    "react-navigation": "^3.6.0",
    "react-redux": "^6.0.0",
    "redux": "^4.0.1",

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from django.urls import path
from . import views

urlpatterns = [
    path('classify/', views.classify),
]

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from rest_framework.response import Response
from rest_framework.decorators import api_view, permission_classes
from rest_framework.permissions import AllowAny
from rest_framework.status import (
        HTTP_200_OK,
        HTTP_400_BAD_REQUEST,
    )

from .classification_research.CNN import CNN

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import cv2
import numpy as np
import os


import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression

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# Here we set model configuration It all defined at the beginig
model.fit(
    {'input': X}, 
    {'targets': Y}, 
    n_epoch=EPOCHE, 
    validation_set=({'input': test_x}, {'targets': test_y}), 
    snapshot_step=500, show_metric=True, run_id=MODEL_NAME
)

model.save(MODEL_NAME)

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import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression


convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')

#layer
convnet = conv_2d(convnet, 32, 2, activation='relu')