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helper.py
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helper.py
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import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
def undistort(distorted_img, mtx, dist):
# Use the OpenCV undistort() function to remove distortion
return cv2.undistort(distorted_img, mtx, dist, None, mtx)
def get_persp_trans_matrices(src, dst):
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
return M, Minv
# Define a function that takes an image, number of x and y points,
# camera matrix and distortion coefficients
def corners_unwarp(img, mtx, dist, src, dst, nx=9, ny=6):
undist = undistort(img, mtx, dist)
# Grab the image shape
img_size = (img.shape[1], img.shape[0])
M, Minv = get_persp_trans_matrices(src, dst)
# Warp the image using OpenCV warpPerspective()
warped = cv2.warpPerspective(undist, M, img_size)
# Return the resulting image and matrix
return undist, warped, M, Minv
def cal_undistort(img, objpoints, imgpoints):
# Use cv2.calibrateCamera() and cv2.undistort()
gray = bgr_to_gray(img)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
def bgr_to_gray(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def rgb_to_gray(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def calibrate_camera(cal_path, nx=9, ny=6):
objpoints = []
imgpoints = []
#prepare object points
objp = np.zeros((ny*nx, 3), np.float32)
objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)
cal_images = glob.glob(cal_path)
for fname in cal_images:
#read image
img = mpimg.imread(fname)
gray = rgb_to_gray(img)
#find chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
#display corners
#print(fname)
#img = cv2.drawChessboardCorners(img, (nx, ny), corners, ret)
#plt.imshow(img)
#plt.show()
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
return ret, mtx, dist, rvecs, tvecs
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
"""
#defining a blank mask to start with
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
# Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_select(img, thresh=(0, 255)):
# 1) Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
# 2) Apply a threshold to the S channel
s = hls[:,:,2]
binary_output = np.zeros_like(s)
binary_output[(s>thresh[0]) & (s<=thresh[1])] = 1
# 3) Return a binary image of threshold result
return binary_output
def sobel(image, threshold=(0,255), direction_threshold=(np.pi/6, 5*np.pi/6)):
# Choose a Sobel kernel size
ksize = 3 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(40,255))
#grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh=threshold)
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=threshold)
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=direction_threshold)
combined = np.zeros_like(dir_binary)
combined[(((mag_binary == 1) & (dir_binary == 1)))] = 1
#plt.imshow(combined)
#plt.show()
combined = np.zeros_like(dir_binary)
#combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
combined[((gradx == 1) | ((mag_binary == 1) & (dir_binary == 1)))] = 1
return combined
def abs_sobel_thresh(gray, orient='x', sobel_kernel=3, thresh=(0, 255)):
# 1) Convert to grayscale
#gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the derivative in x or y given orient = 'x' or 'y'
if orient == 'x':
sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
elif orient == 'y':
sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
sbinary = np.zeros_like(scaled_sobel)
sbinary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return sbinary
def mag_thresh(gray, sobel_kernel=3, mag_thresh=(0, 255)):
# Convert to grayscale
#gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
return binary_output
def dir_threshold(gray, sobel_kernel=3, thresh=(0, np.pi/2)):
# Grayscale
#gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
return binary_output
def find_lanes(binary_warped, left_fit = None, right_fit = None):
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Set the width of the windows +/- margin
margin = 100
if (left_fit == None and right_fit == None):
histogram = np.sum(binary_warped[binary_warped.shape[0]/2:,:], axis=0)
# 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(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.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 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 = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.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)
else:
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
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)))
# 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, binary_warped.shape[0]-1, binary_warped.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)
#plt.show()
return left_fit, right_fit, leftx, lefty, rightx, righty, ploty
def find_curvature(leftx, lefty, rightx, righty, ploty):
# 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
y_eval = np.max(ploty)
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((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_curverad = ((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')
return left_curverad, right_curverad
def find_off_center(center, left, right):
xm_per_pix = 3.7/700
return ((right + left)/2 - center)*xm_per_pix