Test_lines_Average_M5.py

#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import math
# %matplotlib inline

# reading in an image
imageorig = mpimg.imread('test_images/frames_021.jpg')
image = np.copy(imageorig)

# printing out some stats and plotting
print('This image is:', type(image), 'with dimensions:', image.shape)
plt.imshow(image)  # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')


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
    (assuming your grayscaled image is called 'gray')
    you should call plt.imshow(gray, cmap='gray')"""
    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Or use BGR2GRAY if you read an image with cv2.imread()
    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)


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 get_slopes_houghlines(houghlines):
    """
    NOTE: This function returns the slopes of the hough lines
    :param houghlines is hough lines
    :return: return the array of each segment's slopes
    """
    slopes = []
    for line in houghlines:
        for x1, y1, x2, y2 in line:
            slopes.append((y2 - y1)/(x2 - x1))
    return slopes


def getLeftLanesIndex(slopes_all):
    """
    NOTE: This Function is to get the left lanes index according to the negtive slopes of hough lines
    :param slopes_all: hoough slopes array which has elements of negtive and positive slopes
    :return: Return the index array of left lanes in the hough lines
    """
    n = len(slopes_all)
    slopes_left_index = []
    for i in range(n):
        if slopes_all[i] < 0:
            slopes_left_index.append(i)
    return slopes_left_index


def getRightLanesIndex(slopes_all):
    """
    NOTE: This function is to get the right lanes index according to the positive hough lines
    :param slopes_all: hoough slopes array which has elements of negtive and positive slopes
    :return: Return the index array of right lanes in the hough lines
    """
    slope_right_index = []
    n = len(slopes_all)
    for i in range(n):
        if slopes_all[i] > 0:
            slope_right_index.append(i)
    return slope_right_index


def getAverageInterceptofLines(houghlines, index):
    """

    :param houghlines:
    :param index:
    :return:
    """
    b = 0
    number = 0
    n = len(index)

    for i in range(n):
        # define x1, y1, x2, y2
        x1 = houghlines[index[i], 0, 0]
        y1 = houghlines[index[i], 0, 1]
        x2 = houghlines[index[i], 0, 2]
        y2 = houghlines[index[i], 0, 3]
        if x1 == x2:
            break
            print("I'm out!")
            # calculate intercept of each line
        else:
            # b.append((x1 * y1 + x2 * y2 - 2 * x1 * y2)/(x2 - x1))
            b += (x2 * y1 - x1 * y2)/(x2 - x1)
            number = number + 1

    return b/number


def draw_lines(img, lines, color=[255, 0, 0], thickness=8):
    """
    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 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

# Python 3 has support for cool math symbols.


# 函数测试
# points = np.array([[[1, 3, 3, 4]], [[5, 8, 7, 9]]])
# a = single_line_slope(points)
# print("This ia a test，a = ", a)

def GetGeneralEquation(x1, y1, x2, y2):
    # A*x + B*y + c = 0
    A = y2 - y1
    B = x1 - x2
    C = y1 * x2 - x1 * y2
    return A, B, C


def GetIntersectionPoint(x1, y1, x2, y2, x3, y3, x4, y4):
    # A1*x + B1*y + C1 =0;
    # A2*x + B2*y + C2 = 0
    A1, B1, C1 = GetGeneralEquation(x1, y1, x2, y2)
    A2, B2, C2 = GetGeneralEquation(x3, y3, x4, y4)
    m = A1 * B2 - A2 * B1
    if m == 0:
        print("无交点")
    else:
        x0 = (C2 * B1 - C1 * B2)/m
        y0 = (C1 * A2 - C2 * A1)/m
    return x0, y0


def getSlopesAverage(houghlines, index):
    """
    NOTE: To get
    :param houghlines:
    :param index:
    :return:
    """
    slopes = get_slopes_houghlines(houghlines)
    n = len(index)
    sum = 0
    for i in range(n):
        sum += math.atan(slopes[index[i]])
    return math.tan(sum/n)


def lines_average(img, rho, theta, threshold, min_line_len, max_line_gap):
    """
    Note:
    :param img:
    :param rho:
    :param theta:
    :param threshold:
    :param min_line_len:
    :param max_line_gap:
    :return:
    """
    sizeImage = np.shape(img)

    # Get Hough Lines, namley segment end points
    houghlinesall = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)

    slopesHoughLines = get_slopes_houghlines(houghlinesall)
    leftIntercept = getAverageInterceptofLines(houghlinesall, getLeftLanesIndex(slopesHoughLines))
    rightIntercept = getAverageInterceptofLines(houghlinesall, getRightLanesIndex(slopesHoughLines))

    # Get the average slope of left and right lane
    slopesall = get_slopes_houghlines(houghlinesall)
    slopeLeftLaneAve = getSlopesAverage(houghlinesall, getLeftLanesIndex(slopesall))
    slopeRightLaneAve = getSlopesAverage(houghlinesall, getRightLanesIndex(slopesall))

    # get the average intercept of left and right lane
    bLeft = getAverageInterceptofLines(houghlinesall, getLeftLanesIndex(slopesall))
    bRight = getAverageInterceptofLines(houghlinesall, getRightLanesIndex(slopesall))

    # calculate the end points of lanes
    # calculate left lane bottom point
    alfa = 0.60
    beta = 0.60
    if slopeLeftLaneAve == 0:
        x_start_left = int(bLeft)
    else:
        x_start_left = int((sizeImage[0] - bLeft)/slopeLeftLaneAve)
    y_start_left = sizeImage[0]

    # calculate left lane left up point
    if slopeLeftLaneAve == 0:
        x_end_left = int(bLeft)
    else:
        x_end_left = int((sizeImage[0]*alfa - bLeft)/slopeLeftLaneAve)
    y_end_left = int(sizeImage[0]*alfa)

    # calculate right lane bottom point
    if slopeRightLaneAve == 0:
        x_start_right = int(bRight)
    else:
        x_start_right = int((sizeImage[0] - bRight)/slopeRightLaneAve)
    y_start_right = sizeImage[0]

    # calculate right lane up point
    if slopeRightLaneAve == 0:
        x_end_right = int(bRight)
    else:
        x_end_right = int((sizeImage[0]*beta - bRight)/slopeRightLaneAve)
    y_end_right = int(sizeImage[0]*beta)

    # Define the polygon vertices
    leftlane = np.array([[x_start_left, y_start_left, x_end_left, y_end_left]])
    rightlane = np.array([[x_start_right, y_start_right, x_end_right, y_end_right]])
    endpoints = np.array([leftlane, rightlane])

    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    draw_lines(line_img, endpoints)
    return line_img


def weighted_img(img, initial_img, α=0.8, β=1., γ=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 + γ
    NOTE: initial_img and img must be the same shape!
    """
    return cv2.addWeighted(initial_img, α, img, β, γ)


import os
os.listdir("test_images/")

# TODO: Build your pipeline that will draw lane lines on the test_images
# then save them to the test_images_output directory.

# Step 1: Gray scaling the image
grayscalimg = grayscale(image)
plt.imshow(grayscalimg, cmap='gray')
plt.show()

# Step 2: Gaussian smoothing
grayblure = gaussian_blur(grayscalimg, 5)
plt.imshow(grayblure,cmap='gray')
plt.show()

# Step 3: Canny detection
low_threshold = 80
high_threshold = 180
edgescanny = canny(grayblure, low_threshold, high_threshold)
plt.imshow(edgescanny, cmap='gray')
plt.show()

# Step 4: RoI
# Define vertices of ROI Polygon Region and Get the ROI
# to decrease calculation amount of Canny edge detection, I chose to select ROI first, but this caused new edges.
vertices = np.array([[(120, 540), (420, 330), (550, 330), (900, 540)]], dtype=np.int32)
region_selection = region_of_interest(edgescanny, vertices)
plt.imshow(region_selection, cmap='gray')
plt.show()

# Step 5: Get and Draw hough lines
rho = 2
theta = np.pi/180
threshold = 10
min_line_len = 30
max_line_gap = 15
Lines_edges = hough_lines(region_selection, rho, theta, threshold, min_line_len, max_line_gap)
mix = weighted_img(Lines_edges,imageorig, α=0.8, β=1.0, γ=0)
plt.imshow(mix, cmap='gray')
plt.show()

# Step 6: Average and extrapolate hough lines
rho = 2
theta = np.pi/180
threshold = 10
min_line_len = 20
max_line_gap = 20
Lines_average = lines_average(region_selection, rho, theta, threshold, min_line_len, max_line_gap)
plt.imshow(Lines_average)
plt.show()

# Step 7: mix orginal image awith average lines
image_final = weighted_img(Lines_average, imageorig, α=0.8, β=1.0, γ=0)
plt.imshow(image_final)
plt.show()



# # *********************************************************************************************************************#
# # Import everything needed to edit/save/watch video clips
# from moviepy.editor import VideoFileClip
# from IPython.display import HTML
#
#
# def process_image(image):
#     # NOTE: The output you return should be a color image (3 channel) for processing video below
#     # TODO: put your pipeline here,
#     # you should return the final output (image where lines are drawn on lanes)
#
#     def process_image(image):
#     # NOTE: The output you return should be a color image (3 channel) for processing video below
#     # TODO: put your pipeline here,
#     # you should return the final output (image where lines are drawn on lanes)
#
#     imageinitial = np.copy(image)
#     grayscalimg = grayscale(image)
#     grayblure = gaussian_blur(grayscalimg, 5)
#     low_threshold = 80
#     high_threshold = 180
#     edgescanny = canny(grayblure, low_threshold, high_threshold)
#     vertices = np.array([[(120, 540), (420, 330), (550, 330), (900, 540)]], dtype=np.int32)
#     region_selection = region_of_interest(edgescanny, vertices)
#     rho = 2
#     theta = np.pi/180
#     threshold = 15
#     min_line_len = 20
#     max_line_gap = 7
#     Lines_average = lines_average(region_selection, rho, theta, threshold, min_line_len, max_line_gap)
#     image_final = weighted_img(Lines_average, imageinitial, α=0.8, β=1.0, γ=0)
#     return image_final
#
#
# white_output = 'test_videos_output/solidWhiteRight.mp4'
# ## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
# ## To do so add .subclip(start_second,end_second) to the end of the line below
# ## Where start_second and end_second are integer values representing the start and end of the subclip
# ## You may also uncomment the following line for a subclip of the first 5 seconds
# ##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
# clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4")
# white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
# # %time white_clip.write_videofile(white_output, audio=False)
#
# HTML("""
# <video width="960" height="540" controls>
#   <source src="{0}">
# </video>
# """.format(white_output))
#
# yellow_output = 'test_videos_output/solidYellowLeft.mp4'
# ## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
# ## To do so add .subclip(start_second,end_second) to the end of the line below
# ## Where start_second and end_second are integer values representing the start and end of the subclip
# ## You may also uncomment the following line for a subclip of the first 5 seconds
# ##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)
# clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')
# yellow_clip = clip2.fl_image(process_image)
# # %time yellow_clip.write_videofile(yellow_output, audio=False)
#
# HTML("""
# <video width="960" height="540" controls>
#   <source src="{0}">
# </video>
# """.format(yellow_output))