def step02_test(args):
# list_all_images
cars, notcars = list_all_images(args)
# choose random car/notcar indices
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
# read in car / notcar images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
# define feature parameters
color_space = 'RGB' # can be RGB HSV LUV HLS YUV YCrCb
orient = 6
pix_per_cell = 8
cell_per_block = 2
hog_channel = 0 # can be 0 1 2 or 'ALL'
spatial_size = (16, 16) # spatial binning dimensions
hist_bins = 16 # number of histogram bins
spatial_feat = True # spatial features on or off
hist_feat = True # histogram features on or off
hog_feat = True # HOG features on or off
car_features, car_hog_image = single_img_features(
car_image,
color_space=color_space,
spatial_size=spatial_size,
hist_bins=hist_bins,
orient=orient,
pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel,
spatial_feat=spatial_feat,
hist_feat=hist_feat,
hog_feat=hog_feat,
vis=True)
notcar_features, notcar_hog_image = single_img_features(
notcar_image,
color_space=color_space,
spatial_size=spatial_size,
hist_bins=hist_bins,
orient=orient,
pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel,
spatial_feat=spatial_feat,
hist_feat=hist_feat,
hog_feat=hog_feat,
vis=True)
images = [car_image, car_hog_image, notcar_image, notcar_hog_image]
titles = ['car image', 'car HOG image', 'notcar_image', 'notcar HOG image']
#figure = plt.figure(figsize=(12, 3)) # , dpi=80)
#visualize(figure, 1, 4, images, titles)
figsize = (12, 3)
visualize(figsize, 4, images, titles)
def step02_test(args, var, index=(0, 0), flag=True):
# list_all_images
cars, notcars = list_all_images(args)
# choose random car/notcar indices
if flag:
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
print('car_ind: {}, notcar_ind: {}'.format(car_ind, notcar_ind))
else:
(car_ind, notcar_ind) = index
# read in car / notcar images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
# # define feature parameters
# color_space = 'RGB' # can be RGB HSV LUV HLS YUV YCrCb
# orient = 6
# pix_per_cell = 8
# cell_per_block = 2
# hog_channel = 0 # can be 0 1 2 or 'ALL'
# spatial_size = (16, 16) # spatial binning dimensions
# hist_bins = 16 # number of histogram bins
# spatial_feat = True # spatial features on or off
# hist_feat = True # histogram features on or off
# hog_feat = True # HOG features on or off
# set feature parameters
cell_per_block = var['cell_per_block'] # 4
color_space = var['color_space'] # 1 can be RGB HSV LUV HLS YUV YCrCb
hist_bins = var['hist_bins'] # 7 number of histogram bins, 16
hist_feat = var['hist_feat'] # 9 histogram features on or off
hog_channel = var['hog_channel'] # 5 can be 0 1 2 or 'ALL'
hog_feat = var['hog_feat'] # 10 HOG features on or off
orient = var['orient'] # 2
pix_per_cell = var['pix_per_cell'] # 3
#scale = var['scale']
spatial_feat = var['spatial_feat'] # 8 spatial features on or off
spatial_size = var[
'spatial_size'] # 6 spatial binning dimensions, (16, 16)
#ystart = var['y_start_stop'][0]
#ystop = var['y_start_stop'][1]
car_features, car_hog_image = single_img_features(
car_image,
color_space=color_space,
spatial_size=spatial_size,
hist_bins=hist_bins,
orient=orient,
pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel,
spatial_feat=spatial_feat,
hist_feat=hist_feat,
hog_feat=hog_feat,
vis=True)
notcar_features, notcar_hog_image = single_img_features(
notcar_image,
color_space=color_space,
spatial_size=spatial_size,
hist_bins=hist_bins,
orient=orient,
pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel,
spatial_feat=spatial_feat,
hist_feat=hist_feat,
hog_feat=hog_feat,
vis=True)
images = [car_image, car_hog_image, notcar_image, notcar_hog_image]
titles = ['car image', 'car HOG image', 'notcar_image', 'notcar HOG image']
#figure = plt.figure(figsize=(12, 3)) # , dpi=80)
#visualize(figure, 1, 4, images, titles)
figsize = (12, 3)
visualize(figsize, 4, images, titles)
def classifier(args, var, to_print=True):
if os.path.exists(args.pickled + 'svc.p'):
# de-serialize/load X_scaler, scaled_X, svc:
X_scaler = pickle.load(open(args.pickled + "X_scaler.p", "rb"))
scaled_X = pickle.load(open(args.pickled + "scaled_X.p", "rb"))
svc = pickle.load(open(args.pickled + "svc.p", "rb"))
else:
# list_all_images
cars, notcars = list_all_images(args)
# choose random car/notcar indices
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
# read in car / notcar images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
t = time.time()
n_samples = 1000
random_idxs = np.random.randint(0, len(cars), n_samples)
test_cars = np.array(cars)[random_idxs]
test_noncars = np.array(notcars)[random_idxs]
car_features = extract_features(test_cars, color_space=var['color_space'], spatial_size=var['spatial_size'], hist_bins=var['hist_bins'],
orient=var['orient'], pix_per_cell=var['pix_per_cell'], cell_per_block=var['cell_per_block'],
hog_channel=var['hog_channel'], spatial_feat=var['spatial_feat'], hist_feat=var['hist_feat'], hog_feat=var['hog_feat'])
notcar_features = extract_features(test_noncars, color_space=var['color_space'], spatial_size=var['spatial_size'], hist_bins=var['hist_bins'],
orient=var['orient'], pix_per_cell=var['pix_per_cell'], cell_per_block=var['cell_per_block'],
hog_channel=var['hog_channel'], spatial_feat=var['spatial_feat'], hist_feat=var['hist_feat'], hog_feat=var['hog_feat'])
if to_print: print(time.time()-t, 'Seconds to compute features...')
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# fit a per_column scaler
X_scaler = StandardScaler().fit(X)
# apply the scaler to X
scaled_X = X_scaler.transform(X)
# define the labels vector
y = np.hstack(( np.ones(len(car_features)), np.zeros(len(notcar_features)) ))
# split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.2, random_state=rand_state) # test_size=0.1
if to_print: print('Using:', var['orient'], 'orientations,', var['pix_per_cell'], 'pixels per cell,', var['cell_per_block'],
'cells per block,', var['hist_bins'], 'histogram bins, and', var['spatial_size'], 'spatial sampling')
if to_print: print('Feature vector length:', len(X_train[0]))
# use a linear SVC
svc = LinearSVC()
# check the training time for the SVC
t = time.time()
svc.fit(X_train, y_train) # https://stackoverflow.com/questions/40524790/valueerror-this-solver-needs-samples-of-at-least-2-classes-in-the-data-but-the
if to_print: print(round(time.time()-t, 2), 'Seconds to train SVC...')
# check the score of the SVC
if to_print: print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
if to_print: print('[SHAPE] Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# serialize/store X_scaler, scaled_X, svc:
pickle.dump(X_scaler, open(args.pickled + "X_scaler.p", 'wb'))
pickle.dump(scaled_X, open(args.pickled + "scaled_X.p", 'wb'))
pickle.dump(svc, open(args.pickled + "svc.p", 'wb'))
return X_scaler, scaled_X, svc