Vehicle Detection

Overview

Vehicle detection using these machine learning and computer vision techniques.

Linear SVM
HOG(Histogram of Oriented Gradients) feature extraction
Color space conversion
Space binning
Histogram of color extraction
Sliding Window

Note

First, you need to get training data(cars and not-cars). You can get car images from GTI vehicle image database, KITTI vision benchmark). And over 1500 images per each is good for this project.

Set up environment

pip install -r requirements.txt

Run jupyter notebook

jupyter notebook

Defining utility functions

import glob
import time
import cv2
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
%matplotlib inline

# a 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)
        # 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

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=False,
                                  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=False,
                       visualise=vis, feature_vector=feature_vec)
        return features

def bin_spatial(img, size=(32, 32)):
    color1 = cv2.resize(img[:,:,0], size).ravel()
    color2 = cv2.resize(img[:,:,1], size).ravel()
    color3 = cv2.resize(img[:,:,2], size).ravel()
    return np.hstack((color1, color2, color3))

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)
    channel2_hist = np.histogram(img[:,:,1], bins=nbins)
    channel3_hist = np.histogram(img[:,:,2], bins=nbins)
    # 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

Collecting data

# Get image file names
images = glob.glob('./training-data/*/*/*.png')
cars = []
notcars = []
all_cars = []
all_notcars = []

for image in images:
    if 'nonvehicle' in image:
        all_notcars.append(image)
    else:
        all_cars.append(image)

# Get only 1/5 of the training data to avoid overfitting
for ix, notcar in enumerate(all_notcars):
    if ix % 5 == 0:
        notcars.append(notcar)

for ix, car in enumerate(all_cars):
    if ix % 5 == 0:
        cars.append(car)

car_image = mpimg.imread(cars[5])
notcar_image = mpimg.imread(notcars[0])

def compare_images(image1, image2, image1_exp="Image 1", image2_exp="Image 2"):
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
    f.tight_layout()
    ax1.imshow(image1)
    ax1.set_title(image1_exp, fontsize=20)
    ax2.imshow(image2)
    ax2.set_title(image2_exp, fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

compare_images(car_image, notcar_image, "Car", "Not Car")

Extracting features

color_space = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 15  # 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 = (32, 32) # Spatial binning dimensions
hist_bins = 32    # 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

converted_car_image = cv2.cvtColor(car_image, cv2.COLOR_RGB2YUV)
car_ch1 = converted_car_image[:,:,0]
car_ch2 = converted_car_image[:,:,1]
car_ch3 = converted_car_image[:,:,2]

converted_notcar_image = cv2.cvtColor(notcar_image, cv2.COLOR_RGB2YUV)
notcar_ch1 = converted_notcar_image[:,:,0]
notcar_ch2 = converted_notcar_image[:,:,1]
notcar_ch3 = converted_notcar_image[:,:,2]

car_hog_feature, car_hog_image = get_hog_features(car_ch1,
                                        orient, pix_per_cell, cell_per_block,
                                        vis=True, feature_vec=True)

notcar_hog_feature, notcar_hog_image = get_hog_features(notcar_ch1,
                                        orient, pix_per_cell, cell_per_block,
                                        vis=True, feature_vec=True)

car_ch1_features = cv2.resize(car_ch1, spatial_size)
car_ch2_features = cv2.resize(car_ch2, spatial_size)
car_ch3_features = cv2.resize(car_ch3, spatial_size)
notcar_ch1_features = cv2.resize(notcar_ch1, spatial_size)
notcar_ch2_features = cv2.resize(notcar_ch2, spatial_size)
notcar_ch3_features = cv2.resize(notcar_ch3, spatial_size)

def show_images(image1, image2, image3, image4,  image1_exp="Image 1", image2_exp="Image 2", image3_exp="Image 3", image4_exp="Image 4"):
    f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(image1)
    ax1.set_title(image1_exp, fontsize=20)
    ax2.imshow(image2)
    ax2.set_title(image2_exp, fontsize=20)
    ax3.imshow(image3)
    ax3.set_title(image3_exp, fontsize=20)
    ax4.imshow(image4)
    ax4.set_title(image4_exp, fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

show_images(car_ch1, car_hog_image, notcar_ch1, notcar_hog_image, "Car ch 1", "Car ch 1 HOG", "Not Car ch 1", "Not Car ch 1 HOG")    
show_images(car_ch1, car_ch1_features, notcar_ch1, notcar_ch1_features, "Car ch 1", "Car ch 1 features", "Not Car ch 1", "Not Car ch 1 features")    
show_images(car_ch2, car_ch2_features, notcar_ch2, notcar_ch2_features, "Car ch 2", "Car ch 2 features", "Not Car ch 2", "Not Car ch 2 features")    
show_images(car_ch3, car_ch3_features, notcar_ch3, notcar_ch3_features, "Car ch 3", "Car ch 3 features", "Not Car ch 3", "Not Car ch 3 features")

/Users/hatanaka/anaconda3/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)

Training classifier

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)

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)

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()
# 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()

/Users/hatanaka/anaconda3/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)


Using: 15 orientations 8 pixels per cell and 2 cells per block
Feature vector length: 11988
2.56 Seconds to train SVC...
Test Accuracy of SVC =  0.9789

Sliding window

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)
    if conv == 'RGB2YUV':
        return cv2.cvtColor(img, cv2.COLOR_RGB2YUV)

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,:,:]  # sub-sampling
    ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YUV')
    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
    #nblocks_per_window = (window // pix_per_cell)-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, vis=False, feature_vec=False)
    hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=False)
    hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, vis=False, 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_stacked = np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1)
            test_features = X_scaler.transform(test_stacked)    
            #test_features = scaler.transform(np.array(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)
                bboxes.append(((int(xbox_left), int(ytop_draw+ystart)),(int(xbox_left+win_draw),int(ytop_draw+win_draw+ystart))))

    return draw_img, bboxes

def apply_sliding_window(image, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins):
    bboxes = []
    ystart = 400
    ystop = 500
    out_img, bboxes1 = find_cars(image, ystart, ystop, 1.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 400
    ystop = 500
    out_img, bboxes2 = find_cars(out_img, ystart, ystop, 1.3, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 410
    ystop = 500
    out_img, bboxes3 = find_cars(out_img, ystart, ystop, 1.4, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 420
    ystop = 556
    out_img, bboxes4 = find_cars(out_img, ystart, ystop, 1.6, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 430
    ystop = 556
    out_img, bboxes5 = find_cars (out_img, ystart, ystop, 1.8, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 430
    ystop = 556
    out_img, bboxes6 = find_cars (out_img, ystart, ystop, 2.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 440
    ystop = 556
    out_img, bboxes7 = find_cars (out_img, ystart, ystop, 1.9, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 400
    ystop = 556
    out_img, bboxes8 = find_cars (out_img, ystart, ystop, 1.3, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 400
    ystop = 556
    out_img, bboxes9 = find_cars (out_img, ystart, ystop, 2.2, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 500
    ystop = 656
    out_img, bboxes10 = find_cars (out_img, ystart, ystop, 3.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    bboxes.extend(bboxes1)
    bboxes.extend(bboxes2)
    bboxes.extend(bboxes3)
    bboxes.extend(bboxes4)
    bboxes.extend(bboxes5)
    bboxes.extend(bboxes6)
    bboxes.extend(bboxes7)
    bboxes.extend(bboxes8)
    bboxes.extend(bboxes9)
    bboxes.extend(bboxes10)

    return out_img, bboxes

image1 = mpimg.imread('./test_series/series1.jpg')
image2 = mpimg.imread('./test_series/series2.jpg')
image3 = mpimg.imread('./test_series/series3.jpg')
image4 = mpimg.imread('./test_series/series4.jpg')
image5 = mpimg.imread('./test_series/series5.jpg')
image6 = mpimg.imread('./test_series/series6.jpg')

output_image1, bboxes1 = apply_sliding_window(image1, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image2, bboxes2 = apply_sliding_window(image2, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image3, bboxes3 = apply_sliding_window(image3, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image4, bboxes4 = apply_sliding_window(image4, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image5, bboxes5 = apply_sliding_window(image5, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image6, bboxes6 = apply_sliding_window(image6, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)

image = mpimg.imread('./test_images/test4.jpg')
draw_image = np.copy(image)
output_image, bboxes = apply_sliding_window(image, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)

def show_images(image1, image2, image3,  image1_exp="Image 1", image2_exp="Image 2", image3_exp="Image 3"):
    f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(image1)
    ax1.set_title(image1_exp, fontsize=20)
    ax2.imshow(image2)
    ax2.set_title(image2_exp, fontsize=20)
    ax3.imshow(image3)
    ax3.set_title(image3_exp, fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

show_images(output_image1, output_image2, output_image3)
show_images(output_image4, output_image5, output_image6)

/Users/hatanaka/anaconda3/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)

Creating heatmap

from scipy.ndimage.measurements import label


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

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

heat = np.zeros_like(output_image[:,:,0]).astype(np.float)
# Add heat to each box in box list
heat = add_heat(heat, bboxes)

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

# 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)

def show_images(image1, image2,  image1_exp="Image 1", image2_exp="Image 2"):
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(image1)
    ax1.set_title(image1_exp, fontsize=20)
    ax2.imshow(image2, cmap='hot')
    ax2.set_title(image2_exp, fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

show_images(output_image, heatmap, "Car Positions", "Result")

More heatmaps

def get_heatmap(bboxes):
    threshold = 1
    heat = np.zeros_like(output_image[:,:,0]).astype(np.float)
    heat = add_heat(heat, bboxes)
    heat = apply_threshold(heat, threshold)
    heatmap = np.clip(heat, 0, 255)
    return heatmap

def show_images(image1, image2,  image1_exp="Image 1", image2_exp="Image 2"):
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(image1)
    ax1.set_title(image1_exp, fontsize=20)
    ax2.imshow(image2, cmap='hot')
    ax2.set_title(image2_exp, fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

heatmap1 = get_heatmap(bboxes1)
heatmap2 = get_heatmap(bboxes2)
heatmap3 = get_heatmap(bboxes3)
heatmap4 = get_heatmap(bboxes4)
heatmap5 = get_heatmap(bboxes5)
heatmap6 = get_heatmap(bboxes6)
show_images(output_image1, heatmap1)
show_images(output_image2, heatmap2)
show_images(output_image3, heatmap3)
show_images(output_image4, heatmap4)
show_images(output_image5, heatmap5)
show_images(output_image6, heatmap6)

Labeled image

plt.imshow(labels[0], cmap='gray')

<matplotlib.image.AxesImage at 0x11c9d32e8>

Resulting bonding boxes

plt.imshow(draw_img)

<matplotlib.image.AxesImage at 0x11ca0eb38>

Applying to video

from collections import deque
history = deque(maxlen = 8)

def detect_cars(image):
    bboxes = []
    ystart = 400
    ystop = 500
    out_img, bboxes1 = find_cars(image, ystart, ystop, 1.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 400
    ystop = 500
    out_img, bboxes2 = find_cars(image, ystart, ystop, 1.3, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 410
    ystop = 500
    out_img, bboxes3 = find_cars(out_img, ystart, ystop, 1.4, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 420
    ystop = 556
    out_img, bboxes4 = find_cars(out_img, ystart, ystop, 1.6, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 430
    ystop = 556
    out_img, bboxes5 = find_cars (out_img, ystart, ystop, 1.8, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 430
    ystop = 556
    out_img, bboxes6 = find_cars (out_img, ystart, ystop, 2.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 440
    ystop = 556
    out_img, bboxes7 = find_cars (out_img, ystart, ystop, 1.9, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 400
    ystop = 556
    out_img, bboxes8 = find_cars (out_img, ystart, ystop, 1.3, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 400
    ystop = 556
    out_img, bboxes9 = find_cars (out_img, ystart, ystop, 2.2, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ystart = 500
    ystop = 656
    out_img, bboxes10 = find_cars (out_img, ystart, ystop, 3.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    bboxes.extend(bboxes1)
    bboxes.extend(bboxes2)
    bboxes.extend(bboxes3)
    bboxes.extend(bboxes4)
    bboxes.extend(bboxes5)
    bboxes.extend(bboxes6)
    bboxes.extend(bboxes7)
    bboxes.extend(bboxes8)
    bboxes.extend(bboxes9)
    bboxes.extend(bboxes10)

    heat = np.zeros_like(out_img[:,:,0]).astype(np.float)
    # Add heat to each box in box list
    heat = add_heat(heat, bboxes)

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

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

    heatmap = np.zeros_like(current_heatmap).astype(np.float)
    for heat in history:
        heatmap = heatmap + heat

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

    return draw_img

img = detect_cars(image)
plt.imshow(img)

import imageio
imageio.plugins.ffmpeg.download()
from moviepy.editor import VideoFileClip
from IPython.display import HTML

history = deque(maxlen = 8)
output = 'test_result.mp4'
clip = VideoFileClip("test_video.mp4")
video_clip = clip.fl_image(detect_cars)
%time video_clip.write_videofile(output, audio=False)

[MoviePy] >>>> Building video test_result.mp4
[MoviePy] Writing video test_result.mp4


 97%|█████████▋| 38/39 [00:42<00:01,  1.14s/it]


[MoviePy] Done.
[MoviePy] >>>> Video ready: test_result.mp4

CPU times: user 38.7 s, sys: 3.22 s, total: 41.9 s
Wall time: 44.6 s

history = deque(maxlen = 8)
output = 'result.mp4'
clip = VideoFileClip("project_video.mp4")
video_clip = clip.fl_image(detect_cars)
%time video_clip.write_videofile(output, audio=False)

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Vehicle Detection

Overview

Note

Set up environment

Run jupyter notebook

Defining utility functions

Collecting data

Extracting features

Training classifier

Sliding window

Creating heatmap

More heatmaps

Labeled image

Resulting bonding boxes

Applying to video

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Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
output		output
test_images		test_images
test_series		test_series
.gitignore		.gitignore
README.md		README.md
project_video.mp4		project_video.mp4
requirements.txt		requirements.txt
result.mp4		result.mp4
test_video.mp4		test_video.mp4
vehicle-detection.ipynb		vehicle-detection.ipynb

wavelets/vehicle-detection

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Vehicle Detection

Overview

Note

Set up environment

Run jupyter notebook

Defining utility functions

Collecting data

Extracting features

Training classifier

Sliding window

Creating heatmap

More heatmaps

Labeled image

Resulting bonding boxes

Applying to video

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