实战 | 车道线检测项目，霍夫变换 & Spatial CNN

import cv2 as cv
# The video feed is read in as a VideoCapture objectcap = cv.VideoCapture("input.mp4")while (cap.isOpened()):    # ret = a boolean return value from getting the frame, frame = the current frame being projected in the video    ret, frame = cap.read()    # Frames are read by intervals of 10 milliseconds. The programs breaks out of the while loop when the user presses the 'q' key    if cv.waitKey(10) & 0xFF == ord('q'):        break
# The following frees up resources and closes all windowscap.release()cv.destroyAllWindows()

# import cv2 as cv
def do_canny(frame):    # Converts frame to grayscale because we only need the luminance channel for detecting edges - less computationally expensive    gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)    # Applies a 5x5 gaussian blur with deviation of 0 to frame - not mandatory since Canny will do this for us    blur = cv.GaussianBlur(gray, (5, 5), 0)    # Applies Canny edge detector with minVal of 50 and maxVal of 150    canny = cv.Canny(blur, 50, 150)    return canny
# cap = cv.VideoCapture("input.mp4")# while (cap.isOpened()):#     ret, frame = cap.read()
    canny = do_canny(frame)
#     if cv.waitKey(10) & 0xFF == ord('q'):#         break
# cap.release()# cv.destroyAllWindows()

# import cv2 as cvimport numpy as npimport matplotlib.pyplot as plt
# def do_canny(frame):#     gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)#     blur = cv.GaussianBlur(gray, (5, 5), 0)#     canny = cv.Canny(blur, 50, 150)#     return canny
def do_segment(frame):    # Since an image is a multi-directional array containing the relative intensities of each pixel in the image, we can use frame.shape to return a tuple: [number of rows, number of columns, number of channels] of the dimensions of the frame    # frame.shape[0] give us the number of rows of pixels the frame has. Since height begins from 0 at the top, the y-coordinate of the bottom of the frame is its height    height = frame.shape[0]    # Creates a triangular polygon for the mask defined by three (x, y) coordinates    polygons = np.array([                            [(0, height), (800, height), (380, 290)]                        ])    # Creates an image filled with zero intensities with the same dimensions as the frame    mask = np.zeros_like(frame)    # Allows the mask to be filled with values of 1 and the other areas to be filled with values of 0    cv.fillPoly(mask, polygons, 255)    # A bitwise and operation between the mask and frame keeps only the triangular area of the frame    segment = cv.bitwise_and(frame, mask)    return segment
# cap = cv.VideoCapture("input.mp4")# while (cap.isOpened()):#     ret, frame = cap.read()    # canny = do_canny(frame)
    # First, visualize the frame to figure out the three coordinates defining the triangular mask    plt.imshow(frame)    plt.show()    segment = do_segment(canny)
#     if cv.waitKey(10) & 0xFF == ord('q'):#         break
# cap.release()# cv.destroyAllWindows()

import cv2 as cv# import numpy as np# # import matplotlib.pyplot as plt
# def do_canny(frame):#     gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)#     blur = cv.GaussianBlur(gray, (5, 5), 0)#     canny = cv.Canny(blur, 50, 150)#     return canny
# def do_segment(frame):#     height = frame.shape[0]#     polygons = np.array([#                             [(0, height), (800, height), (380, 290)]#                         ])#     mask = np.zeros_like(frame)#     cv.fillPoly(mask, polygons, 255)#     segment = cv.bitwise_and(frame, mask)#     return segment
# cap = cv.VideoCapture("input.mp4")# while (cap.isOpened()):#     ret, frame = cap.read()#     canny = do_canny(frame)#     # plt.imshow(frame)#     # plt.show()#     segment = do_segment(canny)
    # cv.HoughLinesP(frame, distance resolution of accumulator in pixels (larger = less precision), angle resolution of accumulator in radians (larger = less precision), threshold of minimum number of intersections, empty placeholder array, minimum length of line in pixels, maximum distance in pixels between disconnected lines)    hough = cv.HoughLinesP(segment, 2, np.pi / 180, 100, np.array([]), minLineLength = 100, maxLineGap = 50)
#     if cv.waitKey(10) & 0xFF == ord('q'):#         break
# cap.release()# cv.destroyAllWindows()

# import cv2 as cv# import numpy as np# # import matplotlib.pyplot as plt
# def do_canny(frame):#     gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)#     blur = cv.GaussianBlur(gray, (5, 5), 0)#     canny = cv.Canny(blur, 50, 150)#     return canny
# def do_segment(frame):#     height = frame.shape[0]#     polygons = np.array([#                             [(0, height), (800, height), (380, 290)]#                         ])#     mask = np.zeros_like(frame)#     cv.fillPoly(mask, polygons, 255)#     segment = cv.bitwise_and(frame, mask)#     return segment
def calculate_lines(frame, lines):    # Empty arrays to store the coordinates of the left and right lines    left = []    right = []    # Loops through every detected line    for line in lines:        # Reshapes line from 2D array to 1D array        x1, y1, x2, y2 = line.reshape(4)        # Fits a linear polynomial to the x and y coordinates and returns a vector of coefficients which describe the slope and y-intercept        parameters = np.polyfit((x1, x2), (y1, y2), 1)        slope = parameters[0]        y_intercept = parameters[1]        # If slope is negative, the line is to the left of the lane, and otherwise, the line is to the right of the lane        if slope < 0:            left.append((slope, y_intercept))        else:            right.append((slope, y_intercept))    # Averages out all the values for left and right into a single slope and y-intercept value for each line    left_avg = np.average(left, axis = 0)    right_avg = np.average(right, axis = 0)    # Calculates the x1, y1, x2, y2 coordinates for the left and right lines    left_line = calculate_coordinates(frame, left_avg)    right_line = calculate_coordinates(frame, right_avg)    return np.array([left_line, right_line])
def calculate_coordinates(frame, parameters):    slope, intercept = parameters    # Sets initial y-coordinate as height from top down (bottom of the frame)    y1 = frame.shape[0]    # Sets final y-coordinate as 150 above the bottom of the frame    y2 = int(y1 - 150)    # Sets initial x-coordinate as (y1 - b) / m since y1 = mx1 + b    x1 = int((y1 - intercept) / slope)    # Sets final x-coordinate as (y2 - b) / m since y2 = mx2 + b    x2 = int((y2 - intercept) / slope)    return np.array([x1, y1, x2, y2])
def visualize_lines(frame, lines):    # Creates an image filled with zero intensities with the same dimensions as the frame    lines_visualize = np.zeros_like(frame)    # Checks if any lines are detected    if lines is not None:        for x1, y1, x2, y2 in lines:            # Draws lines between two coordinates with green color and 5 thickness            cv.line(lines_visualize, (x1, y1), (x2, y2), (0, 255, 0), 5)    return lines_visualize
# cap = cv.VideoCapture("input.mp4")# while (cap.isOpened()):#     ret, frame = cap.read()#     canny = do_canny(frame)#     # plt.imshow(frame)#     # plt.show()#     segment = do_segment(canny)#     hough = cv.HoughLinesP(segment, 2, np.pi / 180, 100, np.array([]), minLineLength = 100, maxLineGap = 50)
    # Averages multiple detected lines from hough into one line for left border of lane and one line for right border of lane    lines = calculate_lines(frame, hough)    # Visualizes the lines    lines_visualize = visualize_lines(frame, lines)    # Overlays lines on frame by taking their weighted sums and adding an arbitrary scalar value of 1 as the gamma argument    output = cv.addWeighted(frame, 0.9, lines_visualize, 1, 1)    # Opens a new window and displays the output frame    cv.imshow("output", output)
#     if cv.waitKey(10) & 0xFF == ord('q'):#         break
# cap.release()# cv.destroyAllWindows()

import cv2 as cvimport numpy as np# import matplotlib.pyplot as plt
def do_canny(frame):    # Converts frame to grayscale because we only need the luminance channel for detecting edges - less computationally expensive    gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)    # Applies a 5x5 gaussian blur with deviation of 0 to frame - not mandatory since Canny will do this for us    blur = cv.GaussianBlur(gray, (5, 5), 0)    # Applies Canny edge detector with minVal of 50 and maxVal of 150    canny = cv.Canny(blur, 50, 150)    return canny
def do_segment(frame):    # Since an image is a multi-directional array containing the relative intensities of each pixel in the image, we can use frame.shape to return a tuple: [number of rows, number of columns, number of channels] of the dimensions of the frame    # frame.shape[0] give us the number of rows of pixels the frame has. Since height begins from 0 at the top, the y-coordinate of the bottom of the frame is its height    height = frame.shape[0]    # Creates a triangular polygon for the mask defined by three (x, y) coordinates    polygons = np.array([                            [(0, height), (800, height), (380, 290)]                        ])    # Creates an image filled with zero intensities with the same dimensions as the frame    mask = np.zeros_like(frame)    # Allows the mask to be filled with values of 1 and the other areas to be filled with values of 0    cv.fillPoly(mask, polygons, 255)    # A bitwise and operation between the mask and frame keeps only the triangular area of the frame    segment = cv.bitwise_and(frame, mask)    return segment
def calculate_lines(frame, lines):    # Empty arrays to store the coordinates of the left and right lines    left = []    right = []    # Loops through every detected line    for line in lines:        # Reshapes line from 2D array to 1D array        x1, y1, x2, y2 = line.reshape(4)        # Fits a linear polynomial to the x and y coordinates and returns a vector of coefficients which describe the slope and y-intercept        parameters = np.polyfit((x1, x2), (y1, y2), 1)        slope = parameters[0]        y_intercept = parameters[1]        # If slope is negative, the line is to the left of the lane, and otherwise, the line is to the right of the lane        if slope < 0:            left.append((slope, y_intercept))        else:            right.append((slope, y_intercept))    # Averages out all the values for left and right into a single slope and y-intercept value for each line    left_avg = np.average(left, axis = 0)    right_avg = np.average(right, axis = 0)    # Calculates the x1, y1, x2, y2 coordinates for the left and right lines    left_line = calculate_coordinates(frame, left_avg)    right_line = calculate_coordinates(frame, right_avg)    return np.array([left_line, right_line])
def calculate_coordinates(frame, parameters):    slope, intercept = parameters    # Sets initial y-coordinate as height from top down (bottom of the frame)    y1 = frame.shape[0]    # Sets final y-coordinate as 150 above the bottom of the frame    y2 = int(y1 - 150)    # Sets initial x-coordinate as (y1 - b) / m since y1 = mx1 + b    x1 = int((y1 - intercept) / slope)    # Sets final x-coordinate as (y2 - b) / m since y2 = mx2 + b    x2 = int((y2 - intercept) / slope)    return np.array([x1, y1, x2, y2])
def visualize_lines(frame, lines):    # Creates an image filled with zero intensities with the same dimensions as the frame    lines_visualize = np.zeros_like(frame)    # Checks if any lines are detected    if lines is not None:        for x1, y1, x2, y2 in lines:            # Draws lines between two coordinates with green color and 5 thickness            cv.line(lines_visualize, (x1, y1), (x2, y2), (0, 255, 0), 5)    return lines_visualize
# The video feed is read in as a VideoCapture objectcap = cv.VideoCapture("input.mp4")while (cap.isOpened()):    # ret = a boolean return value from getting the frame, frame = the current frame being projected in the video    ret, frame = cap.read()    canny = do_canny(frame)    cv.imshow("canny", canny)    # plt.imshow(frame)    # plt.show()    segment = do_segment(canny)    hough = cv.HoughLinesP(segment, 2, np.pi / 180, 100, np.array([]), minLineLength = 100, maxLineGap = 50)    # Averages multiple detected lines from hough into one line for left border of lane and one line for right border of lane    lines = calculate_lines(frame, hough)    # Visualizes the lines    lines_visualize = visualize_lines(frame, lines)    cv.imshow("hough", lines_visualize)    # Overlays lines on frame by taking their weighted sums and adding an arbitrary scalar value of 1 as the gamma argument    output = cv.addWeighted(frame, 0.9, lines_visualize, 1, 1)    # Opens a new window and displays the output frame    cv.imshow("output", output)    # Frames are read by intervals of 10 milliseconds. The programs breaks out of the while loop when the user presses the 'q' key    if cv.waitKey(10) & 0xFF == ord('q'):        break# The following frees up resources and closes all windowscap.release()cv.destroyAllWindows()