Thimira

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

model = ResNet50(weights='imagenet')

img_path = 'Data/Jellyfish.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)

Thimira

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input, decode_predictions
import numpy as np

model = VGG16(weights='imagenet')

img_path = 'Data/Jellyfish.jpg'
img = image.load_img(img_path, target_size=(224, 224))
img_data = image.img_to_array(img)

Thimira

'''
Using Correlation Trackers in Dlib, you can track any object in a video stream without needing to train a custom object detector.
Check out the tutorial at: http://www.codesofinterest.com/2018/02/track-any-object-in-video-with-dlib.html
'''
import numpy as np
import cv2
import dlib

# this variable will hold the coordinates of the mouse click events.
mousePoints = []

Thimira

import numpy as np
import cv2
import dlib
from scipy.spatial import distance as dist
from scipy.spatial import ConvexHull

PREDICTOR_PATH = "../data/dlib_models/shape_predictor_68_face_landmarks.dat"

FULL_POINTS = list(range(0, 68))
FACE_POINTS = list(range(17, 68))

Thimira

'''
Using Bottleneck Features for Multi-Class Classification in Keras

We use this technique to build powerful (high accuracy without overfitting) Image Classification systems with small
amount of training data.

The full tutorial to get this code working can be found at the "Codes of Interest" Blog at the following link,
https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html

Please go through the tutorial before attempting to run this code, as it explains how to setup your training data.

Thimira

# Blur Your Face Automatically with OpenCV and Dlib
# See tutorial at https://www.youtube.com/watch?v=QKggnWdCTNY

import numpy as np
import cv2
import dlib

video_capture = cv2.VideoCapture(1)
detector = dlib.get_frontal_face_detector()

Thimira

import tensorflow as tf

import glob
import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
from tensorflow.keras import layers
import time

Thimira

# Using a circular mask as the blur area to automatically blur the face in a video, giving it a more cleaner look. 
# We're using OpenCV, and Dlib with Python.
# See tutorial here: https://youtu.be/1p1lUyLGB2E

import numpy as np
import cv2
import dlib

video_capture = cv2.VideoCapture(1)
detector = dlib.get_frontal_face_detector()

Thimira

import numpy as np
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D, Input
from keras.applications.inception_v3 import InceptionV3
from keras.utils.np_utils import to_categorical
from keras import optimizers
from keras.callbacks import EarlyStopping, ModelCheckpoint
from datetime import datetime, timedelta
import matplotlib.pyplot as plt

Thimira

from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
import numpy as np

def VGG_16(weights_path=None):
    input_shape=(224,224,3)

    model = Sequential()