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finding_lane_1.py
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finding_lane_1.py
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# This Python file uses the following encoding: utf-8
# -*- coding: cp949 -*-
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import random
import os, sys
import copy
input_type = 'video' #'video' # 'image'
# cap = cv2.VideoCapture('challenge.mp4')
# cap = cv2.VideoCapture('solidYellowLeft.mp4')
# cap = cv2.VideoCapture('solidWhiteRight.mp4')
cap = cv2.VideoCapture('lane2.mp4')
fit_result, l_fit_result, r_fit_result, L_lane, R_lane = [], [], [], [], []
# Define the codec and create VideoWriter object
# fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use the lower case
# out = cv2.VideoWriter('output.mp4', fourcc, 20.0, ( 960, 540 ))
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
you should call plt.imshow(gray, cmap='gray')"""
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def canny(img, low_threshold, high_threshold):
"""Applies the Canny transform"""
return cv2.Canny(img, low_threshold, high_threshold)
def gaussian_blur(img, kernel_size):
"""Applies a Gaussian Noise kernel"""
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
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
def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
"""
NOTE: this is the function you might want to use as a starting point once you want to
average/extrapolate the line segments you detect to map out the full
extent of the lane (going from the result shown in raw-lines-example.mp4
to that shown in P1_example.mp4).
Think about things like separating line segments by their
slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
line vs. the right line. Then, you can average the position of each of
the lines and extrapolate to the top and bottom of the lane.
This function draws `lines` with `color` and `thickness`.
Lines are drawn on the image inplace (mutates the image).
If you want to make the lines semi-transparent, think about combining
this function with the weighted_img() function below
"""
for line in lines:
for x1, y1, x2, y2 in line:
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def draw_circle(img, lines, color=[0, 0, 255]):
for line in lines:
cv2.circle(img, (line[0], line[1]), 2, color, -1)
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
"""
`img` should be the output of a Canny transform.
Returns an image with hough lines drawn.
"""
lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len,
maxLineGap=max_line_gap)
line_arr = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
# draw_lines(line_arr, lines)
return lines
def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):
"""
`img` is the output of the hough_lines(), An image with lines drawn on it.
Should be a blank image (all black) with lines drawn on it.
`initial_img` should be the image before any processing.
The result image is computed as follows:
initial_img * α + img * β + λ
NOTE: initial_img and img must be the same shape!
"""
return cv2.addWeighted(initial_img, α, img, β, λ)
def Collect_points(lines):
# reshape [:4] to [:2]
interp = lines.reshape(lines.shape[0] * 2, 2)
# interpolation & collecting points for RANSAC
for line in lines:
if np.abs(line[3] - line[1]) > 5:
tmp = np.abs(line[3] - line[1])
a = line[0];
b = line[1];
c = line[2];
d = line[3]
slope = (line[2] - line[0]) / (line[3] - line[1])
for m in range(0, tmp, 5):
if slope > 0:
new_point = np.array([[int(a + m * slope), int(b + m)]])
interp = np.concatenate((interp, new_point), axis=0)
elif slope < 0:
new_point = np.array([[int(a - m * slope), int(b - m)]])
interp = np.concatenate((interp, new_point), axis=0)
return interp
def get_random_samples(lines):
one = random.choice(lines)
two = random.choice(lines)
if two[0] == one[0]: # extract again if values are overlapped
while two[0] == one[0]:
two = random.choice(lines)
one, two = one.reshape(1, 2), two.reshape(1, 2)
three = np.concatenate((one, two), axis=1)
three = three.squeeze()
return three
def compute_model_parameter(line):
# y = mx+n
m = (line[3] - line[1]) / (line[2] - line[0])
n = line[1] - m * line[0]
# ax+by+c = 0
a, b, c = m, -1, n
par = np.array([a, b, c])
return par
def compute_distance(par, point):
# distance between line & point
return np.abs(par[0] * point[:, 0] + par[1] * point[:, 1] + par[2]) / np.sqrt(par[0] ** 2 + par[1] ** 2)
def model_verification(par, lines):
# calculate distance
distance = compute_distance(par, lines)
# total sum of distance between random line and sample points
sum_dist = distance.sum(axis=0)
# average
avg_dist = sum_dist / len(lines)
return avg_dist
def draw_extrapolate_line(img, par, color=(0, 0, 255), thickness=2):
x1, y1 = int(-par[1] / par[0] * img.shape[0] - par[2] / par[0]), int(img.shape[0])
x2, y2 = int(-par[1] / par[0] * (img.shape[0] / 2 + 100) - par[2] / par[0]), int(img.shape[0] / 2 + 100)
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
return img
def get_fitline(img, f_lines):
rows, cols = img.shape[:2]
output = cv2.fitLine(f_lines, cv2.DIST_L2, 0, 0.01, 0.01)
vx, vy, x, y = output[0], output[1], output[2], output[3]
x1, y1 = int(((img.shape[0] - 1) - y) / vy * vx + x), img.shape[0] - 1
x2, y2 = int(((img.shape[0] / 2 + 100) - y) / vy * vx + x), int(img.shape[0] / 2 + 100)
result = [x1, y1, x2, y2]
return result
def draw_fitline(img, result_l, result_r, color=(255, 0, 255), thickness=10):
# draw fitting line
lane = np.zeros_like(img)
cv2.line(lane, (int(result_l[0]), int(result_l[1])), (int(result_l[2]), int(result_l[3])), color, thickness)
cv2.line(lane, (int(result_r[0]), int(result_r[1])), (int(result_r[2]), int(result_r[3])), color, thickness)
pts = np.array([[result_l[0],result_l[1]],[result_l[2],result_l[3]],[result_r[2],result_r[3]],[result_r[0],result_r[1]]], np.int32)
pts = pts.reshape((-1,1,2))
cv2.fillPoly(lane,[pts], (0,255,0))
# add original image & extracted lane lines
final = weighted_img(lane, img, 1, 0.5)
return final
def erase_outliers(par, lines):
# distance between best line and sample points
distance = compute_distance(par, lines)
# filtered_dist = distance[distance<15]
filtered_lines = lines[distance < 13, :]
return filtered_lines
def smoothing(lines, pre_frame):
# collect frames & print average line
lines = np.squeeze(lines)
avg_line = np.array([0, 0, 0, 0])
for ii, line in enumerate(reversed(lines)):
if ii == pre_frame:
break
avg_line += line
avg_line = avg_line / pre_frame
return avg_line
def ransac_line_fitting(img, lines, min=100):
global fit_result, l_fit_result, r_fit_result
best_line = np.array([0, 0, 0])
#print('len:{}'.format(len(lines)))
if len(lines) != 0:
for i in range(30):
sample = get_random_samples(lines)
parameter = compute_model_parameter(sample)
cost = model_verification(parameter, lines)
if cost < min: # update best_line
min = cost
best_line = parameter
if min < 3: break
# erase outliers based on best line
filtered_lines = erase_outliers(best_line, lines)
fit_result = get_fitline(img, filtered_lines)
#print('fit')
#print(fit_result)
else:
#print('fit')
#print(fit_result)
if (fit_result[3] - fit_result[1]) / (fit_result[2] - fit_result[0]) < 0:
l_fit_result = fit_result
return l_fit_result
else:
r_fit_result = fit_result
return r_fit_result
if (fit_result[3] - fit_result[1]) / (fit_result[2] - fit_result[0]) < 0:
l_fit_result = fit_result
return l_fit_result
else:
r_fit_result = fit_result
return r_fit_result
def detect_lanes_img(img):
height, width = img.shape[:2]
# Set ROI
vertices = np.array(
[[(50, height), (width / 2 - 45, height / 2 + 60), (width / 2 + 45, height / 2 + 60), (width - 50, height)]],
dtype=np.int32)
ROI_img = region_of_interest(img, vertices)
# Convert to grayimage
# g_img = grayscale(img)
# Apply gaussian filter
blur_img = gaussian_blur(ROI_img, 3)
# Apply Canny edge transform
canny_img = canny(blur_img, 70, 210)
# to except contours of ROI image
vertices2 = np.array(
[[(52, height), (width / 2 - 43, height / 2 + 62), (width / 2 + 43, height / 2 + 62), (width - 52, height)]],
dtype=np.int32)
canny_img = region_of_interest(canny_img, vertices2)
# Perform hough transform
# Get first candidates for real lane lines
line_arr = hough_lines(canny_img, 1, 1 * np.pi / 180, 30, 10, 20)
# if can't find any lines
if line_arr is None:
final = copy.copy(img)
# draw_lines(img, line_arr, thickness=2)
elif (line_arr.shape[0] == 0 or line_arr.shape[1] == 0 or line_arr.shape[2] == 0 ):
final = copy.copy(frame)
else :
#print(line_arr)
#a = line_arr
#print('{},{},{}\n'.format(a.shape[0],a.shape[1],a.shape[2]))
#print('ab\n')
line_arr = np.squeeze(line_arr)
#print (line_arr)
#print(line_arr.shape)
if (len(line_arr.shape) != 2 ):
final = copy.copy(img)
else :
# Get slope degree to separate 2 group (+ slope , - slope)
slope_degree = (np.arctan2(line_arr[:, 1] - line_arr[:, 3], line_arr[:, 0] - line_arr[:, 2]) * 180) / np.pi
# ignore horizontal slope lines
line_arr = line_arr[np.abs(slope_degree) < 160]
slope_degree = slope_degree[np.abs(slope_degree) < 160]
# ignore vertical slope lines
line_arr = line_arr[np.abs(slope_degree) > 95]
slope_degree = slope_degree[np.abs(slope_degree) > 95]
L_lines, R_lines = line_arr[(slope_degree > 0), :], line_arr[(slope_degree < 0), :]
# print(line_arr.shape,' ',L_lines.shape,' ',R_lines.shape)
# if can't find any lines
if L_lines is None and R_lines is None:
final = copy.copy(img)
else :
# interpolation & collecting points for RANSAC
L_interp = Collect_points(L_lines)
R_interp = Collect_points(R_lines)
if (len(L_interp) == 0 or len(R_interp)== 0):
final = copy.copy(img)
else :
# draw_circle(img,L_interp,(255,255,0))
# draw_circle(img,R_interp,(0,255,255))
# erase outliers based on best line
left_fit_line = ransac_line_fitting(img, L_interp)
right_fit_line = ransac_line_fitting(img, R_interp)
# smoothing by using previous frames
L_lane.append(left_fit_line), R_lane.append(right_fit_line)
if len(L_lane) > 10:
left_fit_line = smoothing(L_lane, 10)
if len(R_lane) > 10:
right_fit_line = smoothing(R_lane, 10)
final = draw_fitline(img, left_fit_line, right_fit_line)
return final
if __name__ == '__main__':
if input_type == 'image':
frame = cv2.imread('./test_images/solidYellowCurve.jpg')
if frame.shape[0] != 540: # resizing for challenge video
frame = cv2.resize(frame, None, fx=3 / 4, fy=3 / 4, interpolation=cv2.INTER_AREA)
result = detect_lanes_img(frame)
cv2.imshow('result', result)
cv2.waitKey(0)
elif input_type == 'video':
while (cap.isOpened()):
ret, frame = cap.read()
if frame.shape[0] != 540: # resizing for challenge video
frame = cv2.resize(frame, None, fx=3 / 4, fy=3 / 4, interpolation=cv2.INTER_AREA)
result = detect_lanes_img(frame)
cv2.imshow('result', result)
# out.write(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()