iamatulsingh

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, ZeroPadding2D, MaxPooling2D, \
                                    Dropout, Flatten, Activation


def get_model():
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))

iamatulsingh

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

from PIL import Image
import numpy as np
from matplotlib import pyplot as plt

import tensorflow as tf
from tensorflow.keras.models import Model

iamatulsingh

def crop_face(filename, required_size=(224, 224)):
    img = cv2.imread(filename)
    detector = MTCNN()
    results = detector.detect_faces(img)
    x, y, width, height = results[0]['box']
    face = img[y:y+height, x:x+width]
    image = Image.fromarray(face)
    image = image.resize(required_size)
    face_array = np.asarray(image)
    return face_array, face

iamatulsingh

import os
from keras.applications import ResNet50
from keras.applications import InceptionV3
from keras.applications import Xception
from keras.applications import VGG16
from keras.applications import VGG19
from keras.applications import imagenet_utils
from keras.applications.inception_v3 import preprocess_input
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img

iamatulsingh

import os
import glob

import streamlit as st
from main import predict
from PIL import Image


# all models list
models_list = [

iamatulsingh

from tensorflow.keras import models
from tensorflow.keras.layers import Activation, ZeroPadding2D, MaxPooling2D, Conv2D, Flatten, Dense, Dropout
from tensorflow.keras import regularizers

def get_model():
    model = models.Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(64, (3, 3), activation='relu'))

iamatulsingh

import os
import numpy as np
import matplotlib.pyplot as plt
from model import get_model
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import optimizers

from face_detection_operation import get_detected_face

iamatulsingh

from os.path import join, exists
from os import mkdir, listdir
import glob

from mtcnn import MTCNN
from PIL import Image
import numpy as np
import cv2

iamatulsingh

import os
from face_recognition import FaceRecognition


if __name__ == '__main__':
    model_name = "face_recognition.h5"
    image_path = 'test.jpg'
    face_recognition = FaceRecognition()
    face_recognition.training()
    face_recognition.save_model(model_name)

iamatulsingh

class HW25P(Peripheral):

    def __init__(self, mac_address, timeout=0.5, isSecure=False, debug=False):
        FORMAT = '%(asctime)-15s %(name)s (%(levelname)s) > %(message)s'
        logging.basicConfig(format=FORMAT)
        log_level = logging.WARNING if not debug else logging.DEBUG
        self._log = logging.getLogger(self.__class__.__name__)
        self._log.setLevel(log_level)

        self._log.info('Connecting to ' + mac_address)