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polynomial_fit.py
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polynomial_fit.py
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'''Writen by Jianguo Zhang, June 14, 2017.
'''
import numpy as np
import cv2
import pickle
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
from combined_thresh import combined_thresh
from Perspective_transform import perspective_transorm
#% matplotlib inline
def line_fit(binary_warped):
'''Find and fit lines'''
# binary_warped=cv2.cvtColor(binary_warped, cv2.COLOR_RGB2GRAY)
#plt.imshow(binary_warped)
# Assuming you have created a warped binary image called "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))*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(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 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 = 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)
# 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]
# Check whether we find enough points
# If not enough points, return None with error
min_inds = 10
if leftx.size < min_inds or rightx.size < min_inds:
return None
# 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(binary_warped)
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
# plt.xlim(0, 1280)
# plt.ylim(720, 0)
# Save parameters for calculate curvatures
curv_pickle = {}
curv_pickle["leftx"] = leftx
curv_pickle["rightx"] = rightx
curv_pickle["lefty"] = lefty
curv_pickle["righty"] = righty
curv_pickle["left_fit"] = left_fit
curv_pickle["right_fit"] = right_fit
# curv_pickle["left_lane_inds"] = left_lane_inds
# curv_pickle["right_lane_inds"] = right_lane_inds
return curv_pickle
def line_fit_visualize(binary_warped, curv_pickle):
'''visualize for line fit images'''
# Load parameters
left_fit = curv_pickle["left_fit"]
right_fit = curv_pickle["right_fit"]
leftx = curv_pickle["leftx"]
lefty = curv_pickle["lefty"]
rightx = curv_pickle["rightx"]
righty = curv_pickle["righty"]
# 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 = (np.dstack((binary_warped, binary_warped, binary_warped))*255).astype(np.uint8)
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [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)
def advanced_fit(binary_warped, left_fit, right_fit):
'''Assume you know where the lines are you have a fit! In the next frame of video you don't
need to do a blind search again, but instead you can just search in a margin around the
previous line position'''
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.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]
#print(leftx.size,rightx.size)
# Check whether we find enough points
# If not enough points, return None with error
min_inds = 10
if leftx.size < min_inds or rightx.size < min_inds:
return None
# 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]
# # 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]), (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)
# Save parameters for later use
curv_pickle ={}
curv_pickle["left_fit"] = left_fit
curv_pickle["right_fit"] = right_fit
curv_pickle["leftx"] = leftx
curv_pickle["rightx"] = rightx
curv_pickle["lefty"] = lefty
curv_pickle["righty"] = righty
return curv_pickle
def advanced_fit_visualize(binary_warped, curv_pickle):
'''visualize for advanced fit images'''
# Load parameters
left_fit = curv_pickle["left_fit"]
right_fit = curv_pickle["right_fit"]
leftx = curv_pickle["leftx"]
lefty = curv_pickle["lefty"]
rightx = curv_pickle["rightx"]
righty = curv_pickle["righty"]
# 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]
# 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).astype(np.uint8)
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[lefty,leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
margin = 100
# 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)
if __name__ == '__main__':
# Read in the saved camera matrix and distortion coefficients
dist_pickle = pickle.load(open("./camera_cal/camera_dist_pickle.p", "rb"))
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
# Take test2.jpg as an example
img = mpimg.imread('./test_images/test2.jpg')
# Undistorting image
undist = cv2.undistort(img, mtx, dist, None, mtx)
# Combined thresh
combined = combined_thresh(undist)
# Warped image
binary_warped, _, Minv = perspective_transorm(combined)
# # Line fit
curv_pickle=line_fit(binary_warped)
advanced_fit_visualize(binary_warped, curv_pickle)
plt.savefig('./output_img/test2_polynomial_fit.jpg' )
# Visualize images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
#ax2.imshow(polynomial_fit)
advanced_fit_visualize(binary_warped, curv_pickle)
ax2.set_title('Polynomial Fit Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.savefig('./output_img/test2_polynomial_fit_example.jpg')