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    ==> Installing dependencies for opencv: cmake, pkg-config, libpng
    ==> Installing opencv dependency: cmake
    ==> Downloading http://www.cmake.org/files/v2.8/cmake-2.8.12.tar.gz
    ######################################################################## 100.0%
    ==> ./bootstrap --prefix=/usr/local/Cellar/cmake/2.8.12 --system-libs --no-system-libarchive --datadir=/shar
    ==> make
    ==> make install
    Warning: Could not link cmake. Unlinking...
    Error: The `brew link` step did not complete successfully

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# k-Means clustering for Normal Distributions - Almost from scratch!

import numpy as np
import scipy as sp
import random
from math import radians, cos, sin, asin, sqrt


def haversine(lon1, lat1, lon2, lat2):
	"""

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import cv2
 
scaling = 10
 
webcam = cv2.VideoCapture(0)
haar = cv2.CascadeClassifier("/usr/local/Cellar/opencv/2.4.8.2/share/OpenCV/lbpcascades/lbpcascade_frontalface.xml")
 
 
if webcam.isOpened(): # try to get the first frame
    rval, frame = webcam.read()

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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from PIL import Image
from torch.utils.data import Dataset


class CollectionsDataset(Dataset):
    def __init__(self, 
                 csv_file, 
                 root_dir, 
                 num_classes, 
                 transform=None):

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class CollectionsDatasetTest(Dataset):

    def __init__(self, 
                 csv_file, 
                 root_dir, 
                 transform=None):
        self.data = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

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import torch.nn as nn
import pretrainedmodels as pm

model = pm.__dict__["resnet50"](pretrained='imagenet')

model.avg_pool = nn.AdaptiveAvgPool2d(1)
model.last_linear = nn.Sequential(
    nn.BatchNorm1d(2048),
    nn.Dropout(p=0.25),
    nn.Linear(in_features=2048, out_features=2048),

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def train_model(model, 
                data_loader, 
                dataset_size, 
                optimizer, 
                scheduler, 
                num_epochs):
    criterion = nn.BCEWithLogitsLoss()
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

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import torch
from torchvision import transforms

# define some re-usable stuff
IMAGE_SIZE = 224
NUM_CLASSES = 1103
BATCH_SIZE = 32
device = torch.device("cuda:0")
IMG_MEAN = model_ft.mean
IMG_STD = model_ft.std

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import torch.optim as optim
from torch.optim import lr_scheduler

plist = [
        {'params': model_ft.layer4.parameters(), 'lr': 1e-5},
        {'params': model_ft.last_linear.parameters(), 'lr': 5e-3}
        ]
optimizer_ft = optim.Adam(plist, lr=0.001)
lr_sch = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)