In [1]:
%%HTML
<style> code {background-color : lightblue !important;} </style>
In [2]:
#Run the code in the cell below to extract object points
#and image points for camera calibration.
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
In [3]:
nx = 9 #inside x corners
ny = 6 #inside y corners
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx,ny), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
else:
print('findChessboardCorners failed:', fname)
In [4]:
def get_undistorted_img(img, mtx, dist):
undistorted_img = cv2.undistort(img, mtx, dist, None, mtx)
return undistorted_img
In [5]:
#If the above cell ran sucessfully, you should now have objpoints and imgpoints needed for camera calibration.
#Run this cell to calibrate, calculate distortion coefficients, and test undistortion on an image!
# Test undistortion on an image
img = cv2.imread('camera_cal/calibration1.jpg')
img_size = (img.shape[1], img.shape[0])
#Caliberate the camera and compute the camera matrix and distortion coefficients
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
undistorted_img = get_undistorted_img(img, mtx, dist)
cv2.imwrite('camera_cal/pattern.jpg',undistorted_img)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(undistorted_img)
ax2.set_title('Undistorted Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
#plt.savefig("combined_signs_vehicles_xygrad.png")
plt.show()
In [6]:
# 1) Convert to grayscale
def get_grayscale_img(image):
grayscale_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) #since image was read using mpimg.imread()
return grayscale_img
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if image was read using cv2.imread()
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def abs_sobel_thresh(gray_img, orient='x', sobel_kernel=3, thresh=(0, 255)):
# 2) Take the derivative in x or y given orient = 'x' or 'y'
if orient == 'x':
sobel_value = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
elif orient == 'y':
sobel_value = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
else:
print("You must specify an orientation - x or y")
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel_value)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
max_sobel = np.max(abs_sobel)
scaled_sobel = np.uint8(255*abs_sobel/max_sobel)
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
grad_binary = np.zeros_like(scaled_sobel)
# 6) Return this mask as your binary_output image
grad_binary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
#grad_binary = np.copy(img) # Remove this line
return grad_binary
In [8]:
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# 2) Take the gradient in x and y separately
sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Calculate the gradient magnitude
magnitude = np.sqrt(sobel_x**2 + sobel_y**2)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_8bit_magnitude = np.uint8(255*magnitude/np.max(magnitude))
# 5) Create a binary mask where mag thresholds are met
mag_binary = np.zeros_like(scaled_8bit_magnitude)
mag_binary[(scaled_8bit_magnitude >= mag_thresh[0]) & (scaled_8bit_magnitude <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
#mag_binary = np.copy(img) # Remove this line
return mag_binary
In [9]:
def dir_threshold(gray_img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 2) Take the gradient in x and y separately
sobel_x = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobel_y = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobel_x = np.absolute(sobel_x)
abs_sobel_y = np.absolute(sobel_y)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
gradient_dir = np.arctan2(abs_sobel_y, abs_sobel_x)
gradient_dir = np.absolute(gradient_dir)
# 5) Create a binary mask where direction thresholds are met
dir_binary = np.zeros_like(gradient_dir)
dir_binary[(gradient_dir >= thresh[0]) & (gradient_dir <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
#dir_binary = np.copy(img) # Remove this line
return dir_binary
In [10]:
K_SIZE = 17 # Choose a larger odd number to smooth gradient measurements
In [11]:
def get_hls_image(img):
#img = np.copy(img)
#hls_img = cv2.cvtColor(img, cv2.COLOR_BGR2HLS).astype(np.float)
hls_img = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
return hls_img
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def get_binary_img(img, s_thresh=(170, 200), sx_thresh=(10, 200)): #s_thresh = 170, 200 and sx_thresh = 20,100
#hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS).astype(np.float)
hls = get_hls_image(img)
h_channel = hls[:,:,0]
l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
# Stack each channel
# Note color_binary[:, :, 0] is all 0s, effectively an all black image. It might
# be beneficial to replace this channel with something else.
color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))
# Combine the two binary thresholds
combined_binary_img = np.zeros_like(sxbinary)
combined_binary_img[(s_binary == 1) | (sxbinary == 1)] = 1
##height, width = gray.shape
img_height, img_width, channel_num = img.shape
'''
print(img_height)
print(img_width)
print(channel_num)
'''
# region_of_interest (ROI) mask
roi_mask = np.zeros_like(combined_binary_img)
roi_vertices = np.array([[0,img_height-1], [img_width/2, int(0.5*img_height)], [img_width-1, img_height-1]], dtype=np.int32)
#print("region_of_interest_vertices", roi_vertices)
cv2.fillPoly(roi_mask, [roi_vertices], 1)
masked_binary_img = cv2.bitwise_and(combined_binary_img, roi_mask)
return masked_binary_img
'''
return color_binary_img, combined_binary_img
'''
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img = mpimg.imread('test_images/straight_lines1.jpg')
img_size = img.shape
print(img_size)
sx_thresh_min = 20
sx_thresh_max = 100
sx_thresh = (sx_thresh_min, sx_thresh_max)
s_thresh_min = 170
s_thresh_max = 255
s_thresh = (s_thresh_min, s_thresh_max)
l_thresh_min = 10
l_thresh_max = 125
l_thresh = (l_thresh_min, l_thresh_max)
#color_binary_img, combined_binary_img = get_binary_img(img, s_thresh, sx_thresh)
#color_binary_img, combined_binary_img = get_binary_img2(img, s_thresh, sx_thresh, l_thresh)
masked_binary_img = get_binary_img(img, s_thresh, sx_thresh)
In [14]:
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(masked_binary_img, cmap='gray')
ax2.set_title('Combined Binary Masked Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
In [15]:
src_bottom_left = [220,720]
src_bottom_right = [1110, 720]
src_top_left = [570, 470]
src_top_right = [720, 470]
# Destination points are chosen such that straight lanes
# appear more or less parallel in the transformed image.
dest_bottom_left = [320,720]
dest_bottom_right = [920, 720]
dest_top_left = [320, 1]
dest_top_right = [920, 1]
'''
src = np.float32(
[[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
[((img_size[0] / 6) - 10), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])
print(src)
print(dst)
'''
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In [16]:
def get_warped_img(masked_binary_img):
# Vertices extracted manually for performing a perspective transform
src = np.float32([src_bottom_left, src_bottom_right, src_top_right, src_top_left])
dst = np.float32([dest_bottom_left, dest_bottom_right, dest_top_right, dest_top_left])
M = cv2.getPerspectiveTransform(src, dst)
M_inv = cv2.getPerspectiveTransform(dst, src)
img_size = (masked_binary_img.shape[1], masked_binary_img.shape[0])
warped_img = cv2.warpPerspective(masked_binary_img, M, img_size , flags=cv2.INTER_LINEAR)
return warped_img, M, M_inv
In [17]:
def plot_warped_img(warped_img):
plot_pts = np.array([src_bottom_left, src_bottom_right, src_top_right, src_top_left], np.int32)
plot_pts = plot_pts.reshape((-1,1,2))
img_copy = img.copy()
cv2.polylines(img_copy,[plot_pts],True,(255,0,0), thickness=3)
plot_pts = np.array([dest_bottom_left, dest_bottom_right, dest_top_right, dest_top_left], np.int32)
plot_pts = plot_pts.reshape((-1,1,2))
warped_img_copy = warped_img.copy()
cv2.polylines(warped_img_copy,[plot_pts],False,(255,0,0), thickness=3)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img_copy)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(warped_img, cmap='gray')
ax2.set_title('Warped Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [18]:
warped_img, M, M_inv = get_warped_img(masked_binary_img)
#warped_img = get_warped_img(img)
plot_warped_img(warped_img)
In [19]:
def get_histogram_peaks(binary_warped_img):
histogram = np.sum(binary_warped_img[binary_warped_img.shape[0]//2:,:], axis=0)
return histogram
In [20]:
peakPixels = get_histogram_peaks(warped_img) #find peaks
# Peak in the first half indicates the likely position of the left lane
midpoint = np.int(peakPixels.shape[0]//2)
leftx_peak = np.argmax(peakPixels[:midpoint])
# Peak in the second half indicates the likely position of the right lane
rightx_peak = np.argmax(peakPixels[midpoint:]) + midpoint
print(leftx_peak, rightx_peak)
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plt.plot(peakPixels)
Out[21]:
In [22]:
global left_fit
global right_fit
left_fit = []
right_fit = []
In [23]:
def findAndPlotLanePixels(warped_img):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(warped_img[warped_img.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((warped_img, warped_img, warped_img))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(warped_img.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = warped_img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = warped_img.shape[0] - (window+1)*window_height
win_y_high = warped_img.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
'''
print("left_fit", left_fit)
print("right_fit", right_fit)
'''
# Generate x and y values for plotting
ploty = np.linspace(0, warped_img.shape[0]-1, warped_img.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
return left_fit, right_fit
In [24]:
left_fit, right_fit = findAndPlotLanePixels(warped_img)
In [25]:
print(left_fit)
print(right_fit)
In [26]:
#Using lanes that have already been detected to find lane pixels
#This way you do not have to search for sliding window again once the lane is detected
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped" or "warped_image")
# It's now much easier to find line pixels!
def findAndPlotNextLanePixels(warped_img, left_fit, right_fit):
nonzero = warped_img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, warped_img.shape[0]-1, warped_img.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
#Create an image to draw on and an image to show the selection window
out_img = np.dstack((warped_img, warped_img, warped_img))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
'''
print("ploty",ploty)
print("left_fitx", left_fitx)
print("right_fitx", right_fitx)
'''
return ploty, left_fitx, right_fitx
In [27]:
ploty, left_fitx, right_fitx = findAndPlotNextLanePixels(warped_img, left_fit, right_fit)
'''
print("ploty",ploty)
print("left_fitx", left_fitx)
print("right_fitx", right_fitx)
'''
Out[27]:
In [ ]:
def get_radius_of_curvature(ploty, left_fitx, right_fitx):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Define y-value where we want radius of curvature
# I'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty*ym_per_pix, left_fitx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, right_fitx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curve_rad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curve_rad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
#print(left_curverad, 'm', right_curverad, 'm')
# Example values: 632.1 m 626.2 m
return left_curve_rad, right_curve_rad
In [ ]:
left_curve_rad, right_curve_rad = get_radius_of_curvature(ploty, left_fitx, right_fitx)
print(left_curve_rad, 'm', right_curve_rad, 'm')
print("average radius of curvature (in meters): ", ((left_curve_rad + right_curve_rad)/2) )
In [ ]:
def draw_driving_area(img, warped_img, M_inv, ploty, left_fitx, right_fitx):
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0)) #R=0;G=255;B=0
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, M_inv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(img, 1, newwarp, 0.3, 0)
#result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
return result
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print("img", img)
print("warped_img", warped_img)
print("M_inv", M_inv)
print("ploty", ploty)
print("left_fitx", left_fitx)
print("right_fitx", right_fitx)
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result = draw_driving_area(img, warped_img, M_inv, ploty, left_fitx, right_fitx)
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plt.imshow(result)
plt.show()
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def define_thresholds():
sx_thresh_min = 20
sx_thresh_max = 100
sx_thresh = (sx_thresh_min, sx_thresh_max)
s_thresh_min = 170
s_thresh_max = 255
s_thresh = (s_thresh_min, s_thresh_max)
l_thresh_min = 10
l_thresh_max = 125
l_thresh = (l_thresh_min, l_thresh_max)
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def define_src_and_dst_for_warping():
src_bottom_left = [220,720]
src_bottom_right = [1110, 720]
src_top_left = [570, 470]
src_top_right = [720, 470]
# Destination points are chosen such that straight lanes appear more or less parallel in the transformed image.
dest_bottom_left = [320,720]
dest_bottom_right = [920, 720]
dest_top_left = [320, 1]
dest_top_right = [920, 1]
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def adv_lane_detection_pipeline(img):
#define_thresholds
define_thresholds()
#get_masked_img
masked_binary_img = get_binary_img(img, s_thresh, sx_thresh)
#define_src_and_dst_for_warping
define_src_and_dst_for_warping()
#get_warped_img
warped_img, M, M_inv = get_warped_img(masked_binary_img)
#get_histogram_peaks
peakPixels = get_histogram_peaks(warped_img) #find peaks
# Peak in the first half indicates the likely position of the left lane
midpoint = np.int(peakPixels.shape[0]//2)
leftx_peak = np.argmax(peakPixels[:midpoint])
# Peak in the second half indicates the likely position of the right lane
rightx_peak = np.argmax(peakPixels[midpoint:]) + midpoint
#global left_fit
#global right_fit
left_fit = []
right_fit = []
#findAndPlotLanePixels
left_fit, right_fit = findAndPlotLanePixels(warped_img)
#findAndPlotNextLanePixels
ploty, left_fitx, right_fitx = findAndPlotNextLanePixels(warped_img, left_fit, right_fit)
#get_radius_of_curvature
left_curve_rad, right_curve_rad = get_radius_of_curvature(ploty, left_fitx, right_fitx)
#draw_driving_area
result = draw_driving_area(img, warped_img, M_inv, ploty, left_fitx, right_fitx)
return result
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img = mpimg.imread('test_images/test2.jpg')
result = adv_lane_detection_pipeline(img)
plt.imshow(result)
plt.show()
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# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
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video_output = 'adv_lane_detection_output.mp4'
clip1 = VideoFileClip("project_video.mp4")
lane_clip = clip1.fl_image(adv_lane_detection_pipeline) #NOTE: this function expects color images!!
%time lane_clip.write_videofile(video_output, audio=False)