import os
import numpy as np
from Camera import Camera
from StereoCamera import StereoCamera
import cv2
import matplotlib.pyplot as plt
if __name__ == '__main__':
camera = Camera(image_root_dir='../images/right')
camera.calibrate()
image = cv2.imread('../images/right/right03.jpg')
print(
'error, the total sum of squared distances between the observed feature points imagePoints and the projected object points objectPoints :'
)
print(camera.error, '\n')
print('camera matrix :')
print(camera.camera_matrix, '\n')
print('distortion coefficients')
print(camera.distortion_coefficients, '\n')
class LaneFinder():
def __init__(self, cal_files=[]):
self.camera = Camera()
self.camera.calibrate(cal_files)
temp = cv2.imread(cal_files[0])
_, _ = self.camera.calibrate_perspective(temp)
self.img_shape = temp.shape
self.left_lane = Line(self.img_shape)
self.right_lane = Line(self.img_shape)
def start_lane_find(self, binary_warped):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :],
axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# 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
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
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 later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# 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
### TO-DO: Find the four below boundaries of the window ###
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)
### TO-DO: Identify the nonzero pixels in x and y within the window ###
good_left_inds = ((nonzerox < win_xleft_high) &
(nonzerox > win_xleft_low)
& (nonzeroy < win_y_high) &
(nonzeroy > win_y_low)).nonzero()[0]
good_right_inds = ((nonzerox < win_xright_high) &
(nonzerox > win_xright_low)
& (nonzeroy < win_y_high) &
(nonzeroy > win_y_low)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
### TO-DO: If you found > minpix pixels, recenter next window ###
### (`right` or `leftx_current`) on their mean position ###
if good_left_inds.shape[0] > minpix:
histogram = np.sum(
binary_warped[(nwindows - (window + 1)) *
window_height:(nwindows - window) *
window_height, win_xleft_low:win_xleft_high],
axis=0)
try:
leftx_current = np.argmax(histogram) + win_xleft_low
except ValueError as e:
print(e)
self.left_lane.detected = False
return binary_warped
if good_right_inds.shape[0] > minpix:
histogram = np.sum(
binary_warped[(nwindows - (window + 1)) *
window_height:(nwindows - window) *
window_height,
win_xright_low:win_xright_high],
axis=0)
try:
rightx_current = np.argmax(histogram) + win_xright_low
except ValueError as e:
print(e)
self.right_lane.detected = False
return
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError as e:
# Avoids an error if the above is not implemented fully
print(e)
# 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]
left_fitx, right_fitx, ploty = self.fit_poly(binary_warped.shape,
leftx, lefty, rightx,
righty)
# Color detected pixels
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
margin = 5
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(out_img, np.int_([left_line_pts]), (255, 255, 0))
cv2.fillPoly(out_img, np.int_([right_line_pts]), (255, 255, 0))
return out_img
def search_around_poly(self, binary_warped):
margin = 100
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# USING PREV fit not AVG fit for search
left_fit = self.left_lane.current_fit
right_fit = self.right_lane.current_fit
left_fit_old = left_fit[0] * nonzeroy**2 + left_fit[
1] * nonzeroy + left_fit[2]
right_fit_old = right_fit[0] * nonzeroy**2 + right_fit[
1] * nonzeroy + right_fit[2]
left_lane_inds = ((nonzerox < left_fit_old + margin) &
(nonzerox > left_fit_old - margin)).nonzero()[0]
right_lane_inds = ((nonzerox < right_fit_old + margin) &
(nonzerox > right_fit_old - margin)).nonzero()[0]
# 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 new polynomials
left_fitx, right_fitx, ploty = self.fit_poly(binary_warped.shape,
leftx, lefty, rightx,
righty)
## Visualization ##
# Create an image to draw on and an image to show the selection window
out_img = np.dstack(
(binary_warped, binary_warped, binary_warped)) * 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]), (255, 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (255, 255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Plot the polynomial lines onto the image
#plt.plot(left_fitx, ploty, color='yellow')
#plt.plot(right_fitx, ploty, color='yellow')
## End visualization steps ##
return result
def fit_poly(self, img_shape, leftx, lefty, rightx, righty):
### Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, deg=2)
right_fit = np.polyfit(righty, rightx, deg=2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0] - 1, img_shape[0])
### Calc both polynomials using ploty, left_fit and right_fit ###
try:
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]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1 * ploty**2 + 1 * ploty
right_fitx = 1 * ploty**2 + 1 * ploty
self.left_lane.detected = False
self.right_lane.detected = False
else:
self.left_lane.detected = True
self.right_lane.detected = True
self.left_lane.update_fit(left_fitx, left_fit)
self.right_lane.update_fit(right_fitx, right_fit)
return left_fitx, right_fitx, ploty
def get_lines(self, img):
binary_warped = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Find our lane pixels first
if not self.left_lane.detected or not self.right_lane.detected:
print('starting lane find')
out_img = self.start_lane_find(binary_warped)
else:
out_img = self.search_around_poly(binary_warped)
return out_img
def get_vehicle_lane_position(self, px_to_m=3.7 / 700):
center = self.left_lane.bestx[-1] + (
(self.right_lane.bestx[-1] - self.left_lane.bestx[-1]) / 2)
veh_pos = self.img_shape[1] / 2
return px_to_m * (veh_pos - center)
def reset_lines(self):
self.left_lane = Line(self.img_shape)
self.right_lane = Line(self.img_shape)
def process_image(self, image):
self.top_down = self.camera.get_top_down(image)
self.binary, self.s, self.sx = self.camera.pipeline(self.top_down)
self.binary_lines = self.get_lines(self.binary)
left_curv = self.left_lane.update_radius()
right_curv = self.right_lane.update_radius()
position = self.get_vehicle_lane_position()
self.annotated = self.camera.show_lane_data(
self.camera.display_lane(image, self.left_lane, self.right_lane),
left_curv, right_curv, position)
return self.annotated
