In [1]:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
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
from sklearn.model_selection import train_test_split
from skimage.feature import hog
%matplotlib inline
In [2]:
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
if vis == True: # Call with two outputs if vis==True to visualize the HOG
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, block_norm='L1')
return features, hog_image
else: # Otherwise call with one output
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, block_norm='L1')
return features
# Define a function to compute binned color features
def bin_spatial(img, size=(16, 16)):
return cv2.resize(img, size).ravel()
# Define a function to compute color histogram features
def color_hist(img, nbins=32):
ch1 = np.histogram(img[:,:,0], bins=nbins, range=(0, 256))[0]#We need only the histogram, no bins edges
ch2 = np.histogram(img[:,:,1], bins=nbins, range=(0, 256))[0]
ch3 = np.histogram(img[:,:,2], bins=nbins, range=(0, 256))[0]
hist = np.hstack((ch1, ch2, ch3))
return hist
The extract_features function extracl all nessesary features from images. It also augment the train dataset by horizontal image flipping.
In [3]:
# Define a function to extract features from a list of images
def img_features(feature_image, spatial_feat, hist_feat, hog_feat, hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel):
file_features = []
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#print 'spat', spatial_features.shape
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
#print 'hist', hist_features.shape
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:
feature_image = cv2.cvtColor(feature_image, cv2.COLOR_LUV2RGB)
feature_image = cv2.cvtColor(feature_image, cv2.COLOR_RGB2GRAY)
hog_features = get_hog_features(feature_image[:,:], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#print 'hog', hog_features.shape
# Append the new feature vector to the features list
file_features.append(hog_features)
return file_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_p in imgs:
file_features = []
image = cv2.imread(file_p) # Read in each imageone by one
# 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)
file_features = img_features(feature_image, spatial_feat, hist_feat, hog_feat, hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel)
features.append(np.concatenate(file_features))
feature_image=cv2.flip(feature_image,1) # Augment the dataset with flipped images
file_features = img_features(feature_image, spatial_feat, hist_feat, hog_feat, hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel)
features.append(np.concatenate(file_features))
return features # Return list of feature vectors
Here we create lists of vehicles and not-vehicles images provided by Udacity. Corrisponding folders contain unzilled archives.
In [4]:
# Read in cars and notcars
images = glob.glob('*vehicles/*/*')
cars = []
notcars = []
for image in images:
if 'non' in image:
notcars.append(image)
else:
cars.append(image)
## Uncomment if you need to reduce the sample size
#sample_size = 500
#cars = cars[0:sample_size]
#notcars = notcars[0:sample_size]
print(len(cars))
print(len(notcars))
As we can see, there are about the same number of objects of both classes, so, we do not need to balance number of images.
In [5]:
# Define parameters for feature extraction
color_space = 'LUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 8 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 0 # 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 = True # Histogram features on or off
hog_feat = True # HOG features on or off
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)
print('Car samples: ', len(car_features))
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)
print('Notcar samples: ', len(notcar_features))
X = np.vstack((car_features, notcar_features)).astype(np.float64)
X_scaler = StandardScaler().fit(X) # Fit a per-column scaler
scaled_X = X_scaler.transform(X) # Apply the scaler to X
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features)))) # Define the labels vector
# Split up data into randomized training and test sets
X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.2, random_state=22)
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]))
svc = LinearSVC(loss='hinge') # Use a linear SVC
t=time.time() # Check the training time for the SVC
svc.fit(X_train, y_train) # Train the classifier
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4)) # Check the score of the SVC
In [6]:
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 to draw bounding boxes on an image
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
imcopy = np.copy(img) # Make a copy of the image
for bbox in bboxes: # Iterate through the bounding boxes
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
return imcopy
In [7]:
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)
In [8]:
# 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=8,
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
# A function to show an image
def show_img(img):
if len(img.shape)==3: #Color BGR image
plt.figure()
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
else: # Grayscale image
plt.figure()
plt.imshow(img, cmap='gray')
In [9]:
t=time.time() # Start time
for image_p in glob.glob('test_images/test*.jpg'):
image = cv2.imread(image_p)
draw_image = np.copy(image)
windows = slide_window(image, x_start_stop=[None, None], y_start_stop=[400, 640],
xy_window=(128, 128), xy_overlap=(0.85, 0.85))
hot_windows = []
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)
show_img(window_img)
print(round(time.time()-t, 2), 'Seconds to process test images')
As we can see on examples above, the classifier successfully finds cars on the test images. However, there is a false positive example, so, we will need to apply a kind of filter (such as heat map) and the classifier failed to find a car on th 3rd image because it is too small for it. That is why, we will need to use multi scale windows.
To increase performance we need to analize the smallest possible number of windows. That is why, we will scan with a search window not across the whole image, but only areas where a new car can appear and also we are going to scan areas where a car was detected (track cars).
On every frame we look for new passing cars (red areas on sides) cars and new far cars (blue area).
In [10]:
image = cv2.imread('test_images/test2.jpg')
windows = slide_window(image, x_start_stop=[930, None], y_start_stop=[420, 650],
xy_window=(128, 128), xy_overlap=(0.75, 0.75))
windows += slide_window(image, x_start_stop=[0, 350], y_start_stop=[420, 650],
xy_window=(128, 128), xy_overlap=(0.75, 0.75))
window_img = draw_boxes(image, windows, color=(0, 0, 255), thick=6)
windows = slide_window(image, x_start_stop=[400, 880], y_start_stop=[400, 470],
xy_window=(48, 48), xy_overlap=(0.75, 0.75))
window_img = draw_boxes(window_img, windows, color=(255, 0, 0), thick=6)
show_img(window_img)
In [11]:
image = cv2.imread('test_images/test5.jpg')
track = (880, 450)
w_size = 80
windows = slide_window(image, x_start_stop=[track[0]-w_size,track[0]+w_size],
y_start_stop=[track[1]-w_size,track[1]+w_size],
xy_window=(128, 128), xy_overlap=(0.75, 0.75))
window_img = draw_boxes(image, windows, color=(0, 0, 255), thick=6)
windows = slide_window(image, x_start_stop=[track[0]-w_size,track[0]+w_size],
y_start_stop=[track[1]-int(w_size),track[1]+int(w_size)],
xy_window=(48, 48), xy_overlap=(0.75, 0.75))
window_img = draw_boxes(window_img, windows, color=(255, 0, 0), thick=6)
show_img(window_img)
The following code chunk find windows with a car in a given range with windows of a given scale.
In [12]:
def convert_color(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
def find_cars(img, ystart, ystop, xstart, xstop, scale, step):
boxes = []
draw_img = np.zeros_like(img)
img_tosearch = img[ystart:ystop,xstart:xstop,:]
ctrans_tosearch = convert_color(img_tosearch)
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)-1
nyblocks = (ch1.shape[0] // pix_per_cell)-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) -1
cells_per_step = step # 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)
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_features = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = ctrans_tosearch[ytop:ytop+window, xleft:xleft+window]
# 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_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)+xstart
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
boxes.append(((int(xbox_left), int(ytop_draw+ystart)),(int(xbox_left+win_draw),int(ytop_draw+win_draw+ystart))))
return boxes
Here we process individual images or videos. To increase performance we skip every 2nd frame because we do not expect very fast moving of the detected cars. We filter all found windows by a heatmap approach (with THRES threshold), suggested in lectures.
In order to reduce jitter a function filt applies a simple low-pass filter on the new and the previous cars boxes coordinates and sizes.
In [13]:
from scipy.ndimage.measurements import label
THRES = 3 # Minimal overlapping boxes
ALPHA = 0.75 # Filter parameter, weight of the previous measurements
image = cv2.imread('test_images/test1.jpg')
track_list = []#[np.array([880, 440, 76, 76])]
#track_list += [np.array([1200, 480, 124, 124])]
THRES_LEN = 32
Y_MIN = 440
heat_p = np.zeros((720, 1280)) # Store prev heat image
boxes_p = [] # Store prev car boxes
n_count = 0 # Frame counter
In [14]:
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 heatmap # Return updated heatmap
def apply_threshold(heatmap, threshold): # Zero out pixels below the threshold in the heatmap
heatmap[heatmap < threshold] = 0
return heatmap
def filt(a,b,alpha): # Smooth the car boxes
return a*alpha+(1.0-alpha)*b
def len_points(p1, p2): # Distance beetween two points
return np.sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2)
def track_to_box(p): # Create box coordinates out of its center and span
return ((int(p[0]-p[2]),int(p[1]-p[3])),(int(p[0]+p[2]), int(p[1]+p[3])))
def draw_labeled_bboxes(labels):
global track_list
track_list_l = []
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)))
#img = draw_boxes(np.copy(img), [bbox], color=(255,0,255), thick=3)
size_x = (bbox[1][0]-bbox[0][0])/2.0 #Size of the found box
size_y = (bbox[1][1]-bbox[0][1])/2.0
asp_d = size_x / size_y
size_m = (size_x + size_y)/2
x = size_x+bbox[0][0]
y = size_y+bbox[0][1]
asp = (y-Y_MIN)/130.0+1.2 # Best rectangle aspect ratio for the box (coefficients from perspectieve measurements and experiments)
if x>1050 or x<230:
asp*=1.4
asp = max(asp, asp_d) # for several cars chunk
size_ya = np.sqrt(size_x*size_y/asp)
size_xa = int(size_ya*asp)
size_ya = int(size_ya)
if x > (-3.049*y+1809): #If the rectangle on the road, coordinates estimated from a test image
track_list_l.append(np.array([x, y, size_xa, size_ya]))
if len(track_list) > 0:
track_l = track_list_l[-1]
dist = []
for track in track_list:
dist.append(len_points(track, track_l))
min_d = min(dist)
if min_d < THRES_LEN:
ind = dist.index(min_d)
track_list_l[-1] = filt(track_list[ind], track_list_l[-1], ALPHA)
track_list = track_list_l
boxes = []
for track in track_list_l:
#print(track_to_box(track))
boxes.append(track_to_box(track))
return boxes
def frame_proc(img, lane = False, video = False, vis = False):
global heat_p, boxes_p, n_count
if (video and n_count%2==0) or not video: # Skip every second video frame
heat = np.zeros_like(img[:,:,0]).astype(np.float)
boxes = []
boxes = find_cars(img, 400, 650, 950, 1280, 2.0, 2)
boxes += find_cars(img, 400, 500, 950, 1280, 1.5, 2)
boxes += find_cars(img, 400, 650, 0, 330, 2.0, 2)
boxes += find_cars(img, 400, 500, 0, 330, 1.5, 2)
boxes += find_cars(img, 400, 460, 330, 950, 0.75, 3)
for track in track_list:
y_loc = track[1]+track[3]
lane_w = (y_loc*2.841-1170.0)/3.0
if lane_w < 96:
lane_w = 96
lane_h = lane_w/1.2
lane_w = max(lane_w, track[2])
xs = track[0]-lane_w
xf = track[0]+lane_w
if track[1] < Y_MIN:
track[1] = Y_MIN
ys = track[1]-lane_h
yf = track[1]+lane_h
if xs < 0: xs=0
if xf > 1280: xf=1280
if ys < Y_MIN - 40: ys=Y_MIN - 40
if yf > 720: yf=720
size_sq = lane_w / (0.015*lane_w+0.3)
scale = size_sq / 64.0
# Apply multi scale image windows
boxes+=find_cars(img, ys, yf, xs, xf, scale, 2)
boxes+=find_cars(img, ys, yf, xs, xf, scale*1.25, 2)
boxes+=find_cars(img, ys, yf, xs, xf, scale*1.5, 2)
boxes+=find_cars(img, ys, yf, xs, xf, scale*1.75, 2)
if vis:
cv2.rectangle(img, (int(xs), int(ys)), (int(xf), int(yf)), color=(0,255,0), thickness=3)
heat = add_heat(heat, boxes)
heat_l = heat_p + heat
heat_p = heat
heat_l = apply_threshold(heat_l,THRES) # Apply threshold to help remove false positives
# Visualize the heatmap when displaying
heatmap = np.clip(heat_l, 0, 255)
# Find final boxes from heatmap using label function
labels = label(heatmap)
#print((labels[0]))
cars_boxes = draw_labeled_bboxes(labels)
boxes_p = cars_boxes
else:
cars_boxes = boxes_p
# if lane: #If we was asked to draw the lane line, do it
# if video:
# img = laneline.draw_lane(img, True)
# else:
# img = laneline.draw_lane(img, False)
imp = draw_boxes(np.copy(img), cars_boxes, color=(0, 0, 255), thick=6)
if vis:
imp = draw_boxes(imp, boxes, color=(0, 255, 255), thick=2)
for track in track_list:
cv2.circle(imp, (int(track[0]), int(track[1])), 5, color=(255, 0, 255), thickness=4)
n_count += 1
return imp
show_img(frame_proc(image, lane=True, vis=False))
Here we process all three task videos of the project.
Video processing approach with moviepy from the first project of the Udacity Self-Driving Car Nanodegreee.
In [35]:
from moviepy.editor import VideoFileClip
n_count = 0
# laneline.init_params(0.0)
def process_image(image):
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
return cv2.cvtColor(frame_proc(image, video=True, vis=False), cv2.COLOR_BGR2RGB)
output_v = 'project_video_proc.mp4'
clip1 = VideoFileClip("project_video.mp4")
clip = clip1.fl_image(process_image)
%time clip.write_videofile(output_v, audio=False)
In [36]:
from IPython.display import HTML
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output_v))
Out[36]:
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