In [1]:
# Import Libraries
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import glob
import time
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
# NOTE: the next import is only valid for scikit-learn version <= 0.17
# for scikit-learn >= 0.18 use:
from sklearn.model_selection import train_test_split
#from sklearn.cross_validation import train_test_split
from scipy.ndimage.measurements import label
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn import svm
from sklearn.svm import SVC
In [2]:
# Helper Functions - Color, Histogram, HOG
# Function to get the correct Color Space
def get_color_space(img, color_space='RGB'):
# Convert image to new color space (if specified)
if color_space != 'RGB':
if color_space == 'HSV':
feature_img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'HLS':
feature_img = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'LUV':
feature_img = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'YUV':
feature_img = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_img = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
elif color_space == 'BGR2YCrCb':
feature_img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
elif color_space == 'YCrCb':
feature_img = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else:
feature_img = np.copy(img)
return feature_img
# Function to compute binned color features
def bin_spatial(img, size=(32, 32)):
# Use cv2.resize().ravel() to create the feature vector
color1 = cv2.resize(img[:,:,0], size).ravel()
color2 = cv2.resize(img[:,:,1], size).ravel()
color3 = cv2.resize(img[:,:,2], size).ravel()
features = np.hstack((color1, color2, color3))
# Return the feature vector
return features
# Function to compute color histogram features
def color_hist(img, nbins=32, bins_range=(0, 256)):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
# Function to get HOG features
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
# Function to extract features from a list of images
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
file_features = []
# Read in each one by one
image = mpimg.imread(file)
feature_image = get_color_space(image, color_space)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
file_features.append(hog_features)
features.append(np.concatenate(file_features))
# Return list of feature vectors
return features
# Function to add heat to get image ready to reject false positives
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap # Iterate through list of bboxes
# If the classifier is working well, then the "hot" parts of the map
# are where the cars are, and by imposing a threshold, you can reject
# areas affected by false positives.
# Function to reject false positives
def reject_false_positives(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
In [3]:
# Function that can extract features using hog sub-sampling and make predictions
def find_vehicles(img, ystart, ystop, scale, svc, X_scaler, c_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
rect_windows = []
img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = get_color_space(img_tosearch, color_space=c_space)
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
'''
print('nblocks_per_window', nblocks_per_window)
print('nxsteps', nxsteps)
print('nysteps', nysteps)
'''
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (32,32))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
if spatial_feat == True and hist_feat == True and hog_feat == True:
#print("spat + hist + hog")
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
elif spatial_feat == True and hist_feat == True:
#print("spat + hist")
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features)).reshape(1, -1))
elif spatial_feat == True and hog_feat == True:
#print("spat + hog")
test_features = X_scaler.transform(np.hstack((spatial_features, hog_features)).reshape(1, -1))
elif hist_feat == True and hog_feat == True:
#print("hist + hog")
test_features = X_scaler.transform(np.hstack((hist_features, hog_features)).reshape(1, -1))
else:
#print("hog")
test_features = X_scaler.transform(np.hstack((hog_features)).reshape(1, -1))
'''
dec = svc.decision_function(test_features)
test_prediction = int(dec > 0.75)
'''
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
'''
print('xbox_left', xbox_left)
print('ytop_draw', ytop_draw)
print('win_draw', win_draw)
'''
rect_windows.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))
#return draw_img
return rect_windows
In [4]:
# A function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thickness=6):
# Make a copy of the image
imcopy = np.copy(img)
# Random color
random_color = False
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
if color == 'random' or random_color:
color = (np.random.randint(0,255), np.random.randint(0,255), np.random.randint(0,255))
random_color = True
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thickness)
# Return the image copy with boxes drawn
return imcopy
# Function to draw bounding boxes on detected vehicles
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
In [5]:
# Function to train the classifier
def train_classifier(vehicle_images, non_vehicle_images, C_val=1000):
print('Starting to Train Classifier')
vehicle_features = extract_features(vehicle_images, 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)
not_vehicle_features = extract_features(non_vehicle_images, 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)
X = np.vstack((vehicle_features, not_vehicle_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(vehicle_features)), np.zeros(len(not_vehicle_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)
#find_best_hyper_parameters(X_train, X_test, y_train, y_test)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC(C=C_val)
# Check the training time for the SVC
start_time=time.time()
svc.fit(X_train, y_train)
end_time = time.time()
print(round(end_time-start_time, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
print('Finished Training Classifier')
return (svc, X_scaler)
In [6]:
# This function should take in two lists (vehicle_list and not_vehicle_list) and returns
# a dictionary with the keys "n_vehicles", "n_not_vehicles", "image_shape", and "data_type"
def explore_dataset(vehicle_list, not_vehicle_list):
data_dict = {}
# Define a key in data_dict "n_cars" and store the number of car images
data_dict["n_vehicles"] = len(vehicle_list)
# Define a key "n_notcars" and store the number of notcar images
data_dict["n_not_vehicles"] = len(not_vehicle_list)
# Read in a test image, either car or notcar
test_image = mpimg.imread(vehicle_list[0])
# Define a key "image_shape" and store the test image shape 3-tuple
data_dict["image_shape"] = test_image.shape
# Define a key "data_type" and store the data type of the test image.
data_dict["data_type"] = test_image.dtype
# Return data_dict
return data_dict
In [7]:
# Tune-able hyperparameters
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 8 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 1 # HOG cells per block
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = False # Histogram features on or off
hog_feat = True # HOG features on or off
C_value=1000 #1,10,100,1000
In [8]:
vehicle_images = glob.glob('datasets/classifying_images/vehicles/*/*.png')
non_vehicle_images = glob.glob('datasets/classifying_images/non_vehicles/*/*.png')
data_info = explore_dataset(vehicle_images, non_vehicle_images)
print('The explore dataset function returned a count of')
print(data_info["n_vehicles"], 'vehicles and')
print(data_info["n_not_vehicles"], 'non-vehicles')
print('of size: ',data_info["image_shape"])
print('and data type:', data_info["data_type"])
# Getting equal data for different classification
sample_size = len(vehicle_images)
if (len(vehicle_images) > len(non_vehicle_images)):
sample_size = len(non_vehicle_images)
vehicle_images = vehicle_images[0:sample_size]
non_vehicle_images = non_vehicle_images[0:sample_size]
print("Number of vehicle images used for training classifier: ", len(vehicle_images))
print("Number of non-vehicle images used for training classifier: ", len(non_vehicle_images))
In [9]:
# Training the classifier
svc, X_scaler = train_classifier(vehicle_images, non_vehicle_images, C_val=C_value)
In [10]:
#For Testing purposes
image = mpimg.imread('test_images/test1.jpg')
ystart = round(image.shape[0]/2)
ystop = round(image.shape[0] - 20)
scale = 1.0
print (ystart, ystop)
draw_image = np.copy(image)
'''
cropped_image_to_search = image[ystart:ystop,:,:]
print(image.shape)
print(cropped_image_to_search.shape)
draw_image = np.copy(cropped_image_to_search)
ystart = 0
ystop = cropped_image_to_search.shape[0]
print(ystart, ystop)
'''
rect_windows = find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
print(len(rect_windows), 'rect windows found in image')
print(rect_windows)
# Draw detected vehicles on image
detected_vehicles = draw_boxes(draw_image, rect_windows, color=(0,0,255), thickness=6)
plt.figure(figsize=(10,10))
plt.imshow(detected_vehicles)
plt.show()
In [11]:
rect_windows = []
image = mpimg.imread('test_images/test1.jpg')
ystart = round(image.shape[0]/2)
ystop = round(image.shape[0] - 20)
cropped_image_to_search = image[ystart:ystop,:,:]
#draw_image = np.copy(cropped_image_to_search)
draw_image = np.copy(image)
#ystart = 0
#ystop = cropped_image_to_search.shape[0]
scale = 1.0
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 1.5
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 2.0
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 2.5
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 3.0
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
'''
scale = 3.5
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 4.0
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 4.5
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
ystart = 400
ystop = 680
scale = 1.0#, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0
rect_windows.append(find_vehicles(draw_image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
'''
bounding_box_list = [item for sublist in rect_windows for item in sublist]
possible_vehicles = draw_boxes(draw_image, bounding_box_list, color='random', thickness=6)
plt.figure(figsize=(10,10))
plt.imshow(possible_vehicles)
plt.show()
print('Number of bounding boxes: ', len(bounding_box_list))
In [12]:
# If the classifier is working well, then the "hot" parts of the map
# are where the vehicle are. add_heat to determine vehicle locations
heatmap_img = np.zeros_like(draw_image[:,:,0]).astype(np.float)
heatmap_img = add_heat(heatmap_img, bounding_box_list)
plt.figure(figsize=(10,10))
plt.imshow(heatmap_img, cmap='hot')
plt.show()
In [13]:
# By applying a threshold limit, you can reject areas
# affected by false positives and remove areas detected as
# vehicle locations incorrectly
threshold_limit = 1 #1,2 or 3
heatmap_img = reject_false_positives(heatmap_img, threshold_limit)
plt.figure(figsize=(10,10))
plt.imshow(heatmap_img, cmap='hot')
plt.show()
In [14]:
# Once you have a thresholded heat-map, there are many ways you could go
# about trying to figure out how many cars you have in each frame and
# which pixels belong to which cars, but one of the most straightforward
# solutions is to use the label() function from scipy.ndimage.measurements.
labels = label(heatmap_img)
plt.figure(figsize=(10,10))
plt.imshow(labels[0], cmap='gray')
print(labels[1], 'Vehicles found')
# Draw bounding boxes on a copy of the image
detected_vehicles = draw_labeled_bboxes(np.copy(draw_image), labels)
# Display the image
plt.figure(figsize=(10,10))
plt.imshow(detected_vehicles)
plt.show()
In [15]:
# Function to detect vehicles in any image
# Returns bounding bounds list of detected vehicles in an image
def detect_vehicle(image):
rect_windows = []
ystart = round(image.shape[0]/2)
ystop = round(image.shape[0] - 20)
'''
cropped_image_to_search = image[ystart:ystop,:,:]
draw_image = np.copy(cropped_image_to_search)
ystart = 0
ystop = image.shape[0]
'''
scale = 1.0
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 1.5
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 2.0
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 2.5
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 3.0
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 3.5
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 4.0
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
scale = 4.5
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
'''
ystart = 400
ystop = 680
scale = 1.0#, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0
rect_windows.append(find_vehicles(image, ystart, ystop, scale, svc, X_scaler, color_space, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins))
#print(len(rect_windows), 'rect windows found in image')
'''
bounding_box_list = [item for sublist in rect_windows for item in sublist]
return bounding_box_list
In [16]:
# This is the function that will be called for
# each frame from a video
def process_frame(image, threshold_limit=4):
#cropped_image = crop_image(image)
#windows = detect_vehicle(cropped_image)
windows = detect_vehicle(image)
heat = np.zeros_like(image[:,:,0]).astype(np.float)
heat = add_heat(heat, windows)
heat = reject_false_positives(heat, threshold_limit)
heatmap = np.clip(heat, 0, 255)
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(image), labels)
# Uncomment the plt lines to test while rendering
#plt.imshow(draw_img)
#plt.show()
return draw_img
In [17]:
# For testing purposes
test_images = glob.glob('test_images/*.jpg')
for image_name in test_images:
image = mpimg.imread(image_name)
draw_img = process_frame(image, 4)
plt.figure(figsize=(10,10))
plt.imshow(draw_img)
plt.show()
In [18]:
from moviepy.editor import VideoFileClip
'''
vehicle_images = glob.glob('datasets/classifying_images/vehicles/*/*.png')
non_vehicle_images = glob.glob('datasets/classifying_images/non_vehicles/*/*.png')
svc, X_scaler = train_classifier(vehicle_images, non_vehicle_images)
'''
video_output = 'detect_vehicles_&_track.mp4'
clip = VideoFileClip("project_video.mp4")
white_clip = clip.fl_image(process_frame)
white_clip.write_videofile(video_output, audio=False)
In [ ]: