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detection.py
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detection.py
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#!/usr/local/bin/python3
# -*- coding: utf-8 -*-
from moviepy.editor import VideoFileClip
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import argparse
import math
import cv2
##
# @Author David Awad
# Detection.py, traces and identifies lane
# markings in an image or .mp4 video
# usage: detection.py [-h] [-f FILE] [-v VIDEO]
def region_of_interest(img, vertices):
#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=8):
# reshape lines to a 2d matrix
lines = lines.reshape(lines.shape[0], lines.shape[2])
# create array of slopes
slopes = (lines[:,3] - lines[:,1]) /(lines[:,2] - lines[:,0])
# remove junk from lists
lines = lines[~np.isnan(lines) & ~np.isinf(lines)]
slopes = slopes[~np.isnan(slopes) & ~np.isinf(slopes)]
# convert lines into list of points
lines.shape = (lines.shape[0]//2,2)
# Right lane
# move all points with negative slopes into right "lane"
right_slopes = slopes[slopes < 0]
right_lines = np.array(list(filter(lambda x: x[0] > (img.shape[1]/2), lines)))
max_right_x, max_right_y = right_lines.max(axis=0)
min_right_x, min_right_y = right_lines.min(axis=0)
# Left lane
# all positive slopes go into left "lane"
left_slopes = slopes[slopes > 0]
left_lines = np.array(list(filter(lambda x: x[0] < (img.shape[1]/2), lines)))
max_left_x, max_left_y = left_lines.max(axis=0)
min_left_x, min_left_y = left_lines.min(axis=0)
# Curve fitting approach
# calculate polynomial fit for the points in right lane
right_curve = np.poly1d(np.polyfit(right_lines[:,1], right_lines[:,0], 2))
left_curve = np.poly1d(np.polyfit(left_lines[:,1], left_lines[:,0], 2))
# shared ceiling on the horizon for both lines
min_y = min(min_left_y, min_right_y)
# use new curve function f(y) to calculate x values
max_right_x = int(right_curve(img.shape[0]))
min_right_x = int(right_curve(min_right_y))
min_left_x = int(left_curve(img.shape[0]))
r1 = (min_right_x, min_y)
r2 = (max_right_x, img.shape[0])
print('Right points r1 and r2,', r1, r2)
cv2.line(img, r1, r2, color, thickness)
l1 = (max_left_x, min_y)
l2 = (min_left_x, img.shape[0])
print('Left points l1 and l2,', l1, l2)
cv2.line(img, l1, l2, color, thickness)
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_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
draw_lines(line_img, lines)
return line_img
# Takes in a single frame or an image and returns a marked image
def mark_lanes(image):
if image is None: raise ValueError("no image given to mark_lanes")
# grayscale the image to make finding gradients clearer
gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
# Define a kernel size and apply Gaussian smoothing
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size), 0)
# Define our parameters for Canny and apply
low_threshold = 50
high_threshold = 150
edges_img = cv2.Canny(np.uint8(blur_gray), low_threshold, high_threshold)
imshape = image.shape
vertices = np.array([[(0, imshape[0]),
(450, 320),
(490, 320),
(imshape[1], imshape[0]) ]],
dtype=np.int32)
masked_edges = region_of_interest(edges_img, vertices )
# Define the Hough transform parameters
rho = 2 # distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
threshold = 15 # minimum number of votes (intersections in Hough grid cell)
min_line_length = 20 # minimum number of pixels making up a line
max_line_gap = 20 # maximum gap in pixels between connectable line segments
line_image = hough_lines(masked_edges, rho, theta, threshold, min_line_length, max_line_gap)
# Draw the lines on the edge image
# initial_img * α + img * β + λ
lines_edges = cv2.addWeighted(image, 0.8, line_image, 1, 0)
return lines_edges
def read_image_for_marking(img_filepath):
# read in the image
image = mpimg.imread(img_filepath)
print('Reading image :', img_filepath, '\nDimensions:', image.shape)
marked_lanes = mark_lanes(image)
# show the image to plotter and then save it to a file
plt.imshow(marked_lanes)
plt.savefig(img_filepath[:-4] + '_output.png')
if __name__ == "__main__":
# set up parser
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--file", help="filepath for image to mark", default='test_images/solidWhiteRight.jpg')
parser.add_argument("-v", "--video", help="filepath for video to mark")
args = parser.parse_args()
if args.video:
clip = VideoFileClip(args.video)
clip = clip.fl_image(mark_lanes)
clip.write_videofile('output_' + args.video, audio=False)
else:
# if nothing passed running algorithm on image
read_image_for_marking(args.file)