checkpoint5.py

# 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()