zoecarver

template<class T>
struct Container
{
	union wrapper {
		constexpr wrapper(): b() {}
		~wrapper() = default;
		
		bool b;
		T t;
	} w;

zoecarver

template<class _Tp>
struct __shared_if
{
    typedef shared_ptr<_Tp> __shared_single;
};
                            
template<class _Tp>
struct __shared_if<_Tp[]>
{
    typedef shared_ptr<typename remove_extent<_Tp>::type> __shared_array_unknown_bound;

zoecarver

# theme
POWERLEVEL9K_MODE='nerdfont-complete'
POWERLEVEL9K_PROMPT_ON_NEWLINE=true
POWERLEVEL9K_LEFT_SEGMENT_SEPARATOR=''
POWERLEVEL9K_RIGHT_SEGMENT_SEPARATOR=''
POWERLEVEL9K_LEFT_SUBSEGMENT_SEPARATOR=''
POWERLEVEL9K_RIGHT_SUBSEGMENT_SEPARATOR=''
POWERLEVEL9K_MULTILINE_FIRST_PROMPT_PREFIX="%F{blue}\u256D\u2500%F{white}"
POWERLEVEL9K_MULTILINE_LAST_PROMPT_PREFIX="%F{blue}\u2570\uf460%F{white} "
POWERLEVEL9K_LEFT_PROMPT_ELEMENTS=(root_indicator dir dir_writable_joined)

zoecarver

input_layer = blocks[0]
input_shape = (int(input_layer['shape']), 
    int(input_layer['shape']), 
    int(input_layer['channels']))

true_boxes = Input(shape=(1, 1, 1, TRUE_BOX_BUFFER , 4))
model_input = Input(input_shape)
x = model_input

skip_connection = None

zoecarver

import cv2
from cv2 import COLOR_RGB2GRAY

from skimage.feature import hog
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC

import matplotlib.pyplot as plt

from glob import glob

zoecarver

test_image = cv2.imread('test.jpg', COLOR_RGB2GRAY) # load test image
test_image = cv2.resize(test_image, (200, 400)) # resize

# get all sliding windows we want
search_windows = \
    sliding_window(test_image, y_stop=200, window=(64, 64), overlap=(.7, .7)) + \
    sliding_window(test_image, y_stop=250, window=(80, 80), overlap=(.6, .6)) + \
    sliding_window(test_image, y_stop=300, window=(96, 96), overlap=(.5, .5)) + \
    sliding_window(test_image, y_stop=350, window=(128, 128), overlap=(.4, .4))

zoecarver

def randcolorvalue():
    return float(randint(0, 255)) / 255


def randcolor():
    return randcolorvalue(), randcolorvalue(), randcolorvalue()


def draw_boxes(image, boxes):
    image = np.copy(image)

zoecarver

X = np.vstack([people_features, not_people_features])
y = np.concatenate([np.ones(people_len), np.zeros(not_people_len)])

train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.2, shuffle=True)

classifier = LinearSVC(verbose=1)
classifier.fit(train_x, train_y)
print('Accuracy: %s' % classifier.score(test_x, test_y))

zoecarver

def get_hog_features(image, visualize=False):
    features = hog(
        image,
        orientations=9,
        pixels_per_cell=(8, 8),
        cells_per_block=(2, 2),
        visualize=visualize,
        feature_vector=True,
        block_norm='L1'
    )

zoecarver

people_glob = glob('dataset/people/*.png')
background_glob = glob('dataset/not-people/*.png')

people = []
not_people = []

for filename in people_glob:
    image = cv2.imread(filename, COLOR_RGB2GRAY)
    image = cv2.resize(image, (64, 64))
    people.append(image)