In [1]:

    

import numpy as np
import cv2
import glob
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
from PIL import Image

from skimage.feature import hog
from scipy.ndimage.measurements import label

import time
from sklearn.svm import LinearSVC, SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split


    

In [2]:

    

# Read in car and non-car images
cars = []
images = glob.glob('../../P5_Data/vehicles/**/*.png', recursive=True)
for image in images:
    cars.append(image)
    
notcars = []
images = glob.glob('../../P5_Data/non-vehicles/**/*.png', recursive=True)
for image in images:
    notcars.append(image)

# Define a function to return some characteristics of the dataset 
def data_look(car_list, notcar_list):
    data_dict = {}
    # Define a key in data_dict "n_cars" and store the number of car images
    data_dict["n_cars"] = len(car_list)
    # Define a key "n_notcars" and store the number of notcar images
    data_dict["n_notcars"] = len(notcar_list)
    # Read in a test image, either car or notcar
    example_img = mpimg.imread(car_list[0])
    # Define a key "image_shape" and store the test image shape 3-tuple
    data_dict["image_shape"] = example_img.shape
    # Define a key "data_type" and store the data type of the test image.
    data_dict["data_type"] = example_img.dtype
    # Return data_dict
    return data_dict
    
data_info = data_look(cars, notcars)

print('Your function returned a count of', 
      data_info["n_cars"], ' cars and', 
      data_info["n_notcars"], ' non-cars')
print('of size: ',data_info["image_shape"], ' and data type:', 
      data_info["data_type"])
# choose random car / not-car indices and plot example images 
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
    
# Read in car / not-car images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])

# Plot the examples
fig = plt.figure(figsize=(15,15))
plt.subplot(121)
plt.imshow(car_image)
plt.title('Example Car Image')
plt.subplot(122)
plt.imshow(notcar_image)
plt.title('Example Not-car Image')
fig.tight_layout()


    

    




Your function returned a count of 8792  cars and 8968  non-cars
of size:  (64, 64, 3)  and data type: float32






    

In [3]:

    

# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=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=False, 
                                  visualise=True, feature_vector=False)
        return features, hog_image
    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=False, 
                       visualise=False, feature_vector=feature_vec, block_norm='L2-Hys')
        return features

# Generate a random index to look at a car image
ind = np.random.randint(0, len(cars))
# Read in the image
image = mpimg.imread(cars[ind])
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Define HOG parameters
orient = 9
pix_per_cell = 8
cell_per_block = 2
# Call our function with vis=True to see an image output
features, hog_image = get_hog_features(gray, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=True)


# Plot the examples
fig = plt.figure(figsize=(15,15))
plt.subplot(121)
plt.imshow(image, cmap='gray')
plt.title('Example Car Image')
plt.subplot(122)
plt.imshow(hog_image, cmap='gray')
plt.title('HOG Visualization')
fig.tight_layout()


    

    




/Users/phuongpham/anaconda3/anaconda/envs/carnd-term1/lib/python3.5/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15
  'be changed to `L2-Hys` in v0.15', skimage_deprecation)






    

In [4]:

    

# Generate a random index to look at a car image
ind = np.random.randint(0, len(cars))
# Read in the image
image = mpimg.imread(cars[ind])
YCrCb = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
Y_channel = YCrCb[:,:,0]
Cr_channel = YCrCb[:,:,1]
Cb_channel = YCrCb[:,:,2]

# Define HOG parameters
orient = 9
pix_per_cell = 8
cell_per_block = 2
# Call our function with vis=True to see an image output
features_1, hog_image_1 = get_hog_features(Y_channel, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=True)
features_2, hog_image_2 = get_hog_features(Y_channel, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=True)
features_3, hog_image_3 = get_hog_features(Y_channel, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=True)

# Plot the examples
fig = plt.figure(figsize=(15,10))        
plt.subplot(3,4,1)
plt.imshow(Y_channel, cmap='gray')
plt.title('Car CH-1')
plt.subplot(3,4,2)
plt.imshow(hog_image_1, cmap='gray')
plt.title('Car CH-1 Hog')
plt.subplot(3,4,5)
plt.imshow(Cr_channel, cmap='gray')
plt.title('Car CH-2')
plt.subplot(3,4,6)
plt.imshow(hog_image_2, cmap='gray')
plt.title('Car CH-2 Hog')
plt.subplot(3,4,9)
plt.imshow(Cb_channel, cmap='gray')
plt.title('Car CH-3')
plt.subplot(3,4,10)
plt.imshow(hog_image_3, cmap='gray')
plt.title('Car CH-3 Hog')

image = mpimg.imread(notcars[ind])
YCrCb = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
Y_channel = YCrCb[:,:,0]
Cr_channel = YCrCb[:,:,1]
Cb_channel = YCrCb[:,:,2]

# Call our function with vis=True to see an image output
features_1, hog_image_1 = get_hog_features(Y_channel, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=True)
features_2, hog_image_2 = get_hog_features(Y_channel, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=True)
features_3, hog_image_3 = get_hog_features(Y_channel, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=True)
      
plt.subplot(3,4,3)
plt.imshow(Y_channel, cmap='gray')
plt.title('Not-Car CH-1')
plt.subplot(3,4,4)
plt.imshow(hog_image_1, cmap='gray')
plt.title('Not-Car CH-1 Hog')
plt.subplot(3,4,7)
plt.imshow(Cr_channel, cmap='gray')
plt.title('Not-Car CH-2')
plt.subplot(3,4,8)
plt.imshow(hog_image_2, cmap='gray')
plt.title('Not-Car CH-2 Hog')
plt.subplot(3,4,11)
plt.imshow(Cb_channel, cmap='gray')
plt.title('Not-Car CH-3')
plt.subplot(3,4,12)
plt.imshow(hog_image_3, cmap='gray')
plt.title('Not-Car CH-3 Hog')
fig.tight_layout()


    

    




/Users/phuongpham/anaconda3/anaconda/envs/carnd-term1/lib/python3.5/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15
  'be changed to `L2-Hys` in v0.15', skimage_deprecation)






    

In [12]:

    

def convert_color(img, conv='RGB2YCrCb'):
    if conv == 'RGB2YCrCb':
        return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    if conv == 'BGR2YCrCb':
        return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
    if conv == 'RGB2LUV':
        return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)

# Define a function to compute binned color features  
def bin_spatial(img, size=(32, 32)):
    # Use cv2.resize().ravel() to create the feature vector
    features = cv2.resize(img, size).ravel() 
    # Return the feature vector
    return features

# Define a 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

# Define a function to extract features from a list of images
# Have this function call bin_spatial(), color_hist() and get_hog_features()
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)
        # apply color conversion if other than 'RGB'
        if color_space != 'RGB':
            if color_space == 'HSV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
            elif color_space == 'LUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
            elif color_space == 'HLS':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
            elif color_space == 'YUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
            elif color_space == 'YCrCb':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
        else: feature_image = np.copy(image)      

        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


    

In [6]:

    

### TODO: Tweak these parameters and see how the results change.
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9  # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = "ALL" # 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
y_start_stop = [None, None] # Min and max in y to search in slide_window()

# Extract features
t=time.time()
car_features = extract_features(cars, 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)
notcar_features = extract_features(notcars, 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)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract features...')


    

    




230.18 Seconds to extract features...

In [7]:

    

if len(car_features) > 0:
    # Create an array stack of feature vectors
    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))))
     
    # Plot an example of raw and scaled features
    car_ind = np.random.randint(0, len(cars))
    fig = plt.figure(figsize=(12,4))
    plt.subplot(131)
    plt.imshow(mpimg.imread(cars[car_ind]))
    plt.title('Original Image')
    plt.subplot(132)
    plt.plot(X[car_ind])
    plt.title('Raw Features')
    plt.subplot(133)
    plt.plot(scaled_X[car_ind])
    plt.title('Normalized Features')
    fig.tight_layout()
else: 
    print('Your function only returns empty feature vectors...')


    

In [8]:

    

# 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)

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]))


    

    




Using: 9 orientations 8 pixels per cell and 2 cells per block
Feature vector length: 6108

In [9]:

    

# TODO: Import 'r2_score'
from sklearn.metrics import r2_score

def performance_metric(y_true, y_predict):
    """ Calculates and returns the performance score between 
        true and predicted values based on the metric chosen. """
    
    # TODO: Calculate the performance score between 'y_true' and 'y_predict'
    score = r2_score(y_true, y_predict)
    
    # Return the score
    return score


    

In [11]:

    

from sklearn.metrics import make_scorer 
from sklearn.model_selection import GridSearchCV 
from sklearn.cross_validation import ShuffleSplit

def fit_model(X, y):
    """ Performs grid search over the 'C' parameter for a 
        SVM trained on the input data [X, y]. """
    
    # Create cross-validation sets from the training data
    # sklearn version 0.18: ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)
    # sklearn versiin 0.17: ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None)
    random_state = np.random.randint(0, 100)
    cv_sets = ShuffleSplit(X.shape[0], n_iter=5, test_size=0.20, random_state=random_state)

    # TODO: Create a decision tree regressor object
    svc = SVC(random_state=random_state)

    # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10
    params = {'C': [1, 10], 'kernel': ['rbf', 'linear']}

    # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' 
    scoring_fnc = make_scorer(performance_metric)

    # TODO: Create the grid search object
    grid = GridSearchCV(svc, params, scoring=scoring_fnc, cv=cv_sets) 

    # Fit the grid search object to the data to compute the optimal model
    grid = grid.fit(X, y)

    # Return the optimal model after fitting the data
    return grid.best_estimator_

# Fit the training data to the model using grid search
result = fit_model(X_train, y_train)
print(result)


    

    




SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=25, shrinking=True,
  tol=0.001, verbose=False)

In [14]:

    

# Use a SVC 
random_state = np.random.randint(0, 100)
svc = SVC(C=10,kernel='rbf', random_state=random_state)
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 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()

n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')


    

    




161.2 Seconds to train SVC...
Test Accuracy of SVC =  0.9938
My SVC predicts:  [ 0.  0.  0.  0.  0.  0.  1.  1.  0.  1.]
For these 10 labels:  [ 0.  0.  0.  0.  0.  0.  1.  1.  0.  1.]
0.15954 Seconds to predict 10 labels with SVC

In [17]:

    

# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, 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):    
    #1) Define an empty list to receive features
    img_features = []
    #2) Apply color conversion if other than 'RGB'
    if color_space != 'RGB':
        if color_space == 'HSV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
        elif color_space == 'LUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
        elif color_space == 'HLS':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
        elif color_space == 'YUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
        elif color_space == 'YCrCb':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    else: feature_image = np.copy(img)      
    #3) Compute spatial features if flag is set
    if spatial_feat == True:
        spatial_features = bin_spatial(feature_image, size=spatial_size)
        #4) Append features to list
        img_features.append(spatial_features)
    #5) Compute histogram features if flag is set
    if hist_feat == True:
        hist_features = color_hist(feature_image, nbins=hist_bins)
        #6) Append features to list
        img_features.append(hist_features)
    #7) Compute HOG features if flag is set
    if hog_feat == True:
        if hog_channel == 'ALL':
            hog_features = []
            for channel in range(feature_image.shape[2]):
                hog_features.extend(get_hog_features(feature_image[:,:,channel], 
                                    orient, pix_per_cell, cell_per_block, 
                                    vis=False, feature_vec=True))      
        else:
            hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, 
                        pix_per_cell, cell_per_block, vis=False, feature_vec=True)
        #8) Append features to list
        img_features.append(hog_features)

    #9) Return concatenated array of features
    return np.concatenate(img_features)

def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None], 
                    xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
    # If x and/or y start/stop positions not defined, set to image size
    if x_start_stop[0] == None:
        x_start_stop[0] = 0
    if x_start_stop[1] == None:
        x_start_stop[1] = img.shape[1]
    if y_start_stop[0] == None:
        y_start_stop[0] = 0
    if y_start_stop[1] == None:
        y_start_stop[1] = img.shape[0]
    # Compute the span of the region to be searched    
    xspan = x_start_stop[1] - x_start_stop[0]
    yspan = y_start_stop[1] - y_start_stop[0]
    # Compute the number of pixels per step in x/y
    nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
    ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
    # Compute the number of windows in x/y
    nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
    ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
    nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step) 
    ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step) 
    # Initialize a list to append window positions to
    window_list = []
    # Loop through finding x and y window positions
    # Note: you could vectorize this step, but in practice
    # you'll be considering windows one by one with your
    # classifier, so looping makes sense
    for ys in range(ny_windows):
        for xs in range(nx_windows):
            # Calculate window position
            startx = xs*nx_pix_per_step + x_start_stop[0]
            endx = startx + xy_window[0]
            starty = ys*ny_pix_per_step + y_start_stop[0]
            endy = starty + xy_window[1]
            # Append window position to list
            window_list.append(((startx, starty), (endx, endy)))
    # Return the list of windows
    return window_list

# Define a function you will pass an image 
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB', 
                    spatial_size=(32, 32), hist_bins=32, 
                    hist_range=(0, 256), orient=9, 
                    pix_per_cell=8, cell_per_block=2, 
                    hog_channel=0, spatial_feat=True, 
                    hist_feat=True, hog_feat=True):

    #1) Create an empty list to receive positive detection windows
    on_windows = []
    #2) Iterate over all windows in the list
    for window in windows:
        #3) Extract the test window from original image
        test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))      
        #4) Extract features for that window using single_img_features()
        features = single_img_features(test_img, 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)
        #5) Scale extracted features to be fed to classifier
        test_features = scaler.transform(np.array(features).reshape(1, -1))
        #6) Predict using your classifier
        prediction = clf.predict(test_features)
        #7) If positive (prediction == 1) then save the window
        if prediction == 1:
            on_windows.append(window)
    #8) Return windows for positive detections
    return on_windows

# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # Make a copy of the image
    imcopy = np.copy(img)
    # Iterate through the bounding boxes
    for bbox in bboxes:
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
    # Return the image copy with boxes drawn
    return imcopy


    

In [18]:

    

images = glob.glob('./test_images/*.jpg')

for fname in images:
    image = mpimg.imread(fname)

    draw_image = np.copy(image)
    # Uncomment the following line if you extracted training
    # data from .png images (scaled 0 to 1 by mpimg) and the
    # image you are searching is a .jpg (scaled 0 to 255)
    image = image.astype(np.float32)/255

    windows = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                        xy_window=(96, 96), xy_overlap=(0.5, 0.5))

    hot_windows = search_windows(image, windows, svc, X_scaler, 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)                       

    window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 255), thick=6)                    

    fig = plt.figure()
    plt.imshow(window_img)
    fig.tight_layout()


    

In [20]:

    

# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
    
    draw_img = np.copy(img)
    img = img.astype(np.float32)/255
    
    img_tosearch = img[ystart:ystop,:,:]
    ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
    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
    
    # 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)
    
    bboxes = []
    
    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], (64,64))
          
            # 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
            test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))    
            #test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))    
            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)
                cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6) 
                bbox = ((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart))
                bboxes.append(bbox)
    return draw_img, bboxes
    
y_start = 400
y_stop = 656
scales = [1, 1.5, 2, 2.5, 3]

images = glob.glob('./test_images/*.jpg')

for fname in images:
    image = mpimg.imread(fname)

    box_list = []
    for scale in scales: 
        out_img, boxes = find_cars(image, y_start, y_stop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
        box_list = box_list + boxes
    draw_image = np.copy(image)
    out_img = draw_boxes(draw_image, box_list, color=(0, 0, 255), thick=6)
    
    fig = plt.figure()
    plt.imshow(out_img)
    fig.tight_layout()


    

In [22]:

    

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
    
def apply_threshold(heatmap, threshold):
    # Zero out pixels below the threshold
    heatmap[heatmap <= threshold] = 0
    # Return thresholded map
    return heatmap


    

In [23]:

    

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 [24]:

    

y_start = 400
y_stop = 656
scales = [1, 1.5, 2, 2.5, 3]

images = glob.glob('./test_images/*.jpg')

for fname in images:
    image = mpimg.imread(fname)
    heat = np.zeros_like(image[:,:,0]).astype(np.float)
    
    box_list = []
    for scale in scales: 
        out_img, boxes = find_cars(image, y_start, y_stop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
        box_list = box_list + boxes
        
    positions_img = draw_boxes(np.copy(image), box_list, color=(0, 0, 255), thick=6)
    
    # Add heat to each box in box list
    heat = add_heat(heat,box_list)

    # Apply threshold to help remove false positives
    heat = apply_threshold(heat,1)

    # Visualize the heatmap when displaying    
    heatmap = np.clip(heat, 0, 255)

    # Find final boxes from heatmap using label function
    labels = label(heatmap)
    draw_img = draw_labeled_bboxes(np.copy(image), labels)

    fig = plt.figure(figsize=(15,15))
    plt.subplot(131)
    plt.imshow(positions_img)
    plt.title('Detected positions')
    plt.subplot(132)
    plt.imshow(heatmap, cmap='hot')
    plt.title('Heat Map')
    plt.subplot(133)
    plt.imshow(draw_img)
    plt.title('Car Positions')
    fig.tight_layout()


    

In [25]:

    

# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML


    

In [26]:

    

import imageio
imageio.plugins.ffmpeg.download()


    

In [66]:

    

def process_frame(img):
    heat = np.zeros_like(img[:,:,0]).astype(np.float)
    
    # Find bounding boxes of current frame
    box_list = []
    for scale in scales: 
        out_img, boxes = find_cars(img, y_start, y_stop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
        box_list = box_list + boxes

    # Add bounding boxes of current frame to buffer
    global box_buffer 
    box_buffer.append(box_list)

    # Get only last nb_frames frames to build heatmap
    tmp_box_list = box_buffer[-nb_frames:]
    new_box_list = []
    for tmp in tmp_box_list:
        new_box_list = new_box_list + tmp
    
    # Add heat to each box in box list
    heat = add_heat(heat,new_box_list)

    # Apply threshold to help remove false positives
    heat = apply_threshold(heat, heat_threshold)

    # Visualize the heatmap when displaying    
    heatmap = np.clip(heat, 0, 255)
    #avg_heatmap= new_frame_factor * heatmap + (1-new_frame_factor) * avg_heatmap

    # Find final boxes from heatmap using label function
    labels = label(heatmap)
    draw_img = draw_labeled_bboxes(np.copy(img), labels)
    
    return draw_img


    

In [70]:

    

ystart = 400
ystop = 656
scales = [1.5]
box_buffer = []
heat_threshold = 5
nb_frames = 5


    

In [71]:

    

## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
in_vid = './project_video.mp4'
out_vid = './project_video_out_3.mp4'

clip = VideoFileClip(in_vid)
video_clip = clip.fl_image(process_frame)
%time video_clip.write_videofile(out_vid, audio=False)


    

    




[MoviePy] >>>> Building video ./project_video_out_3.mp4
[MoviePy] Writing video ./project_video_out_3.mp4






    




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[MoviePy] Done.
[MoviePy] >>>> Video ready: ./project_video_out_3.mp4 

CPU times: user 1h 22min 21s, sys: 49 s, total: 1h 23min 10s
Wall time: 1h 23min 2s

In [72]:

    

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">4
</video>
""".format(out_vid))


    

    
Out[72]:

In [76]:

    

ystart = 400
ystop = 656
scales = [1, 1.5]
box_buffer = []
heat_threshold = 5
nb_frames = 5


    

In [77]:

    

## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
in_vid = './project_video.mp4'
out_vid = './project_video_out_2.mp4'

clip = VideoFileClip(in_vid)
video_clip = clip.fl_image(process_frame)
%time video_clip.write_videofile(out_vid, audio=False)


    

    




[MoviePy] >>>> Building video ./project_video_out_2.mp4
[MoviePy] Writing video ./project_video_out_2.mp4






    




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[MoviePy] Done.
[MoviePy] >>>> Video ready: ./project_video_out_2.mp4 

CPU times: user 5h 26min 53s, sys: 2min 11s, total: 5h 29min 5s
Wall time: 5h 29min 17s

In [78]:

    

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">4
</video>
""".format(out_vid))


    

    
Out[78]:

In [ ]: