OpenCV“迷雾”车道识别的反思

标题有些拗口，就这样吧。

这个例子就是为了说明，算力离智能还有很远很远的距离。

机器人迷雾之算力与智能_zhangrelay的博客-CSDN博客

测试图片如下：

车道图

其中第一幅图是仿真案例：

ROS2+Gazebo+OpenCV之mobot仿真视觉传感器_zhangrelay的博客-CSDN博客 

在如上文章中提及。

同时，可以参考：

 机器人方向的刚性需求→个人思考←_zhangrelay的博客-CSDN博客

只有一个图可用，如下：

一看效果太好了，可以上路测试啦。

其实，换个图基本就不行了，但是人眼分辨所有测试图都是不会出错的。

看个离谱的：

妙不妙？

更离谱的：

慌不慌？

疑问一：参数不合适。

反问，人在不同城市开车或者走路的时候，会先调参数吗？

疑问二：算法不够智能。

反问，算法是计算方法，和智能有联系，但关联性参考：

“机器人迷雾之算力与智能“

疑问三，这种方式合适吗。

思考：先抛开算法，仅仅考虑算力，类比一个人类指标智力。

开车开的好的人，一定学历高吗？

赛车手中，高学历一定成绩有优势吗……

还是先回归常识，再搞算法，否则，事倍功半。

目前，有会独立思考的机器人程序吗？或者真正意义上的智能体？

附录

测试算法如下（引用）：

import cv2 # Import the OpenCV library to enable computer vision
import numpy as np # Import the NumPy scientific computing library
import edge_detection as edge # Handles the detection of lane lines
import matplotlib.pyplot as plt # Used for plotting and error checking

# Author: Addison Sears-Collins
# Description: Implementation of the Lane class 

# Make sure the video file is in the same directory as your code
filename = 'orig_lane_detection_1.mp4'
file_size = (1920,1080) # Assumes 1920x1080 mp4
scale_ratio = 1 # Option to scale to fraction of original size. 

# We want to save the output to a video file
output_filename = 'orig_lane_detection_1_lanes.mp4'
output_frames_per_second = 20.0 

# Global variables
prev_leftx = None
prev_lefty = None
prev_rightx = None
prev_righty = None
prev_left_fit = []
prev_right_fit = []

prev_leftx2 = None
prev_lefty2 = None
prev_rightx2 = None
prev_righty2 = None
prev_left_fit2 = []
prev_right_fit2 = []

class Lane:
  """
  Represents a lane on a road.
  """
  def __init__(self, orig_frame):
    """
	  Default constructor
		
    :param orig_frame: Original camera image (i.e. frame)
    """
    self.orig_frame = orig_frame

    # This will hold an image with the lane lines		
    self.lane_line_markings = None

    # This will hold the image after perspective transformation
    self.warped_frame = None
    self.transformation_matrix = None
    self.inv_transformation_matrix = None

    # (Width, Height) of the original video frame (or image)
    self.orig_image_size = self.orig_frame.shape[::-1][1:]

    width = self.orig_image_size[0]
    height = self.orig_image_size[1]
    self.width = width
    self.height = height
	
    # Four corners of the trapezoid-shaped region of interest
    # You need to find these corners manually.
    self.roi_points = np.float32([
      (int(0.456*width),int(0.544*height)), # Top-left corner
      (0, height-1), # Bottom-left corner			
      (int(0.958*width),height-1), # Bottom-right corner
      (int(0.6183*width),int(0.544*height)) # Top-right corner
    ])
		
    # The desired corner locations  of the region of interest
    # after we perform perspective transformation.
    # Assume image width of 600, padding == 150.
    self.padding = int(0.25 * width) # padding from side of the image in pixels
    self.desired_roi_points = np.float32([
      [self.padding, 0], # Top-left corner
      [self.padding, self.orig_image_size[1]], # Bottom-left corner			
      [self.orig_image_size[
        0]-self.padding, self.orig_image_size[1]], # Bottom-right corner
      [self.orig_image_size[0]-self.padding, 0] # Top-right corner
    ]) 
		
    # Histogram that shows the white pixel peaks for lane line detection
    self.histogram = None
		
    # Sliding window parameters
    self.no_of_windows = 10
    self.margin = int((1/12) * width)  # Window width is +/- margin
    self.minpix = int((1/24) * width)  # Min no. of pixels to recenter window
		
    # Best fit polynomial lines for left line and right line of the lane
    self.left_fit = None
    self.right_fit = None
    self.left_lane_inds = None
    self.right_lane_inds = None
    self.ploty = None
    self.left_fitx = None
    self.right_fitx = None
    self.leftx = None
    self.rightx = None
    self.lefty = None
    self.righty = None
		
    # Pixel parameters for x and y dimensions
    self.YM_PER_PIX = 7.0 / 400 # meters per pixel in y dimension
    self.XM_PER_PIX = 3.7 / 255 # meters per pixel in x dimension
		
    # Radii of curvature and offset
    self.left_curvem = None
    self.right_curvem = None
    self.center_offset = None

  def calculate_car_position(self, print_to_terminal=False):
    """
    Calculate the position of the car relative to the center
		
    :param: print_to_terminal Display data to console if True		
    :return: Offset from the center of the lane
    """
    # Assume the camera is centered in the image.
    # Get position of car in centimeters
    car_location = self.orig_frame.shape[1] / 2

    # Fine the x coordinate of the lane line bottom
    height = self.orig_frame.shape[0]
    bottom_left = self.left_fit[0]*height**2 + self.left_fit[
      1]*height + self.left_fit[2]
    bottom_right = self.right_fit[0]*height**2 + self.right_fit[
      1]*height + self.right_fit[2]

    center_lane = (bottom_right - bottom_left)/2 + bottom_left 
    center_offset = (np.abs(car_location) - np.abs(
      center_lane)) * self.XM_PER_PIX * 100

    if print_to_terminal == True:
      print(str(center_offset) + 'cm')
			
    self.center_offset = center_offset
      
    return center_offset

  def calculate_curvature(self, print_to_terminal=False):
    """
    Calculate the road curvature in meters.

    :param: print_to_terminal Display data to console if True
    :return: Radii of curvature
    """
    # Set the y-value where we want to calculate the road curvature.
    # Select the maximum y-value, which is the bottom of the frame.
    y_eval = np.max(self.ploty)    

    # Fit polynomial curves to the real world environment
    left_fit_cr = np.polyfit(self.lefty * self.YM_PER_PIX, self.leftx * (
      self.XM_PER_PIX), 2)
    right_fit_cr = np.polyfit(self.righty * self.YM_PER_PIX, self.rightx * (
      self.XM_PER_PIX), 2)
			
    # Calculate the radii of curvature
    left_curvem = ((1 + (2*left_fit_cr[0]*y_eval*self.YM_PER_PIX + left_fit_cr[
                    1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curvem = ((1 + (2*right_fit_cr[
                    0]*y_eval*self.YM_PER_PIX + right_fit_cr[
                    1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
	
    # Display on terminal window
    if print_to_terminal == True:
      print(left_curvem, 'm', right_curvem, 'm')
			
    self.left_curvem = left_curvem
    self.right_curvem = right_curvem

    return left_curvem, right_curvem		
		
  def calculate_histogram(self,frame=None,plot=True):
    """
    Calculate the image histogram to find peaks in white pixel count
		
    :param frame: The warped image
    :param plot: Create a plot if True
    """
    if frame is None:
      frame = self.warped_frame
			
    # Generate the histogram
    self.histogram = np.sum(frame[int(
		      frame.shape[0]/2):,:], axis=0)

    if plot == True:
		
      # Draw both the image and the histogram
      figure, (ax1, ax2) = plt.subplots(2,1) # 2 row, 1 columns
      figure.set_size_inches(10, 5)
      ax1.imshow(frame, cmap='gray')
      ax1.set_title("Warped Binary Frame")
      ax2.plot(self.histogram)
      ax2.set_title("Histogram Peaks")
      plt.show()
			
    return self.histogram

  def display_curvature_offset(self, frame=None, plot=False):
    """
    Display curvature and offset statistics on the image
		
    :param: plot Display the plot if True
    :return: Image with lane lines and curvature
    """	
    image_copy = None
    if frame is None:
      image_copy = self.orig_frame.copy()
    else:
      image_copy = frame

    cv2.putText(image_copy,'Curve Radius: '+str((
      self.left_curvem+self.right_curvem)/2)[:7]+' m', (int((
      5/600)*self.width), int((
      20/338)*self.height)), cv2.FONT_HERSHEY_SIMPLEX, (float((
      0.5/600)*self.width)),(
      255,255,255),2,cv2.LINE_AA)
    cv2.putText(image_copy,'Center Offset: '+str(
      self.center_offset)[:7]+' cm', (int((
      5/600)*self.width), int((
      40/338)*self.height)), cv2.FONT_HERSHEY_SIMPLEX, (float((
      0.5/600)*self.width)),(
      255,255,255),2,cv2.LINE_AA)
			
    if plot==True:       
      cv2.imshow("Image with Curvature and Offset", image_copy)
			
    return image_copy
    
  def get_lane_line_previous_window(self, left_fit, right_fit, plot=False):
    """
    Use the lane line from the previous sliding window to get the parameters
    for the polynomial line for filling in the lane line
    :param: left_fit Polynomial function of the left lane line
    :param: right_fit Polynomial function of the right lane line
    :param: plot To display an image or not
    """
    # margin is a sliding window parameter
    margin = self.margin

    # Find the x and y coordinates of all the nonzero 
    # (i.e. white) pixels in the frame.			
    nonzero = self.warped_frame.nonzero()  
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
		
    # Store left and right lane pixel indices
    left_lane_inds = ((nonzerox > (left_fit[0]*(
      nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (
      nonzerox < (left_fit[0]*(
      nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin))) 
    right_lane_inds = ((nonzerox > (right_fit[0]*(
      nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (
      nonzerox < (right_fit[0]*(
      nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin))) 			
    self.left_lane_inds = left_lane_inds
    self.right_lane_inds = right_lane_inds

    # Get the left and right lane line pixel locations	
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]	

    global prev_leftx2
    global prev_lefty2 
    global prev_rightx2
    global prev_righty2
    global prev_left_fit2
    global prev_right_fit2
		
    # Make sure we have nonzero pixels		
    if len(leftx)==0 or len(lefty)==0 or len(rightx)==0 or len(righty)==0:
      leftx = prev_leftx2
      lefty = prev_lefty2
      rightx = prev_rightx2
      righty = prev_righty2

    self.leftx = leftx
    self.rightx = rightx
    self.lefty = lefty
    self.righty = righty
		
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2) 

    # Add the latest polynomial coefficients		
    prev_left_fit2.append(left_fit)
    prev_right_fit2.append(right_fit)

    # Calculate the moving average	
    if len(prev_left_fit2) > 10:
      prev_left_fit2.pop(0)
      prev_right_fit2.pop(0)
      left_fit = sum(prev_left_fit2) / len(prev_left_fit2)
      right_fit = sum(prev_right_fit2) / len(prev_right_fit2)

    self.left_fit = left_fit
    self.right_fit = right_fit
		
    prev_leftx2 = leftx
    prev_lefty2 = lefty 
    prev_rightx2 = rightx
    prev_righty2 = righty
		
    # Create the x and y values to plot on the image
    ploty = np.linspace(
      0, self.warped_frame.shape[0]-1, self.warped_frame.shape[0]) 
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    self.ploty = ploty
    self.left_fitx = left_fitx
    self.right_fitx = right_fitx
		
    if plot==True:
		
      # Generate images to draw on
      out_img = np.dstack((self.warped_frame, self.warped_frame, (
                           self.warped_frame)))*255
      window_img = np.zeros_like(out_img)
			
      # Add color to the left and right line pixels
      out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
      out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [
                                                                     0, 0, 255]
      # Create a polygon to show the search window area, and recast 
      # the x and y points into a usable format for cv2.fillPoly()
      margin = self.margin
      left_line_window1 = np.array([np.transpose(np.vstack([
                                    left_fitx-margin, ploty]))])
      left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
                                    left_fitx+margin, ploty])))])
      left_line_pts = np.hstack((left_line_window1, left_line_window2))
      right_line_window1 = np.array([np.transpose(np.vstack([
                                     right_fitx-margin, ploty]))])
      right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([
                                     right_fitx+margin, ploty])))])
      right_line_pts = np.hstack((right_line_window1, right_line_window2))
			
      # Draw the lane onto the warped blank image
      cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
      cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
      result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
      
      # Plot the figures 
      figure, (ax1, ax2, ax3) = plt.subplots(3,1) # 3 rows, 1 column
      figure.set_size_inches(10, 10)
      figure.tight_layout(pad=3.0)
      ax1.imshow(cv2.cvtColor(self.orig_frame, cv2.COLOR_BGR2RGB))
      ax2.imshow(self.warped_frame, cmap='gray')
      ax3.imshow(result)
      ax3.plot(left_fitx, ploty, color='yellow')
      ax3.plot(right_fitx, ploty, color='yellow')
      ax1.set_title("Original Frame")  
      ax2.set_title("Warped Frame")
      ax3.set_title("Warped Frame With Search Window")
      plt.show()
			
  def get_lane_line_indices_sliding_windows(self, plot=False):
    """
    Get the indices of the lane line pixels using the 
    sliding windows technique.
		
    :param: plot Show plot or not
    :return: Best fit lines for the left and right lines of the current lane 
    """
    # Sliding window width is +/- margin
    margin = self.margin

    frame_sliding_window = self.warped_frame.copy()

    # Set the height of the sliding windows
    window_height = np.int(self.warped_frame.shape[0]/self.no_of_windows)		

    # Find the x and y coordinates of all the nonzero 
    # (i.e. white) pixels in the frame.	
    nonzero = self.warped_frame.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])	
		
    # Store the pixel indices for the left and right lane lines
    left_lane_inds = []
    right_lane_inds = []
		
    # Current positions for pixel indices for each window,
    # which we will continue to update
    leftx_base, rightx_base = self.histogram_peak()
    leftx_current = leftx_base
    rightx_current = rightx_base

    # Go through one window at a time
    no_of_windows = self.no_of_windows
		
    for window in range(no_of_windows):
      
      # Identify window boundaries in x and y (and right and left)
      win_y_low = self.warped_frame.shape[0] - (window + 1) * window_height
      win_y_high = self.warped_frame.shape[0] - window * window_height
      win_xleft_low = leftx_current - margin
      win_xleft_high = leftx_current + margin
      win_xright_low = rightx_current - margin
      win_xright_high = rightx_current + margin
      cv2.rectangle(frame_sliding_window,(win_xleft_low,win_y_low),(
        win_xleft_high,win_y_high), (255,255,255), 2)
      cv2.rectangle(frame_sliding_window,(win_xright_low,win_y_low),(
        win_xright_high,win_y_high), (255,255,255), 2)

      # Identify the nonzero pixels in x and y within the window
      good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
                          (nonzerox >= win_xleft_low) & (
                           nonzerox < win_xleft_high)).nonzero()[0]
      good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
                           (nonzerox >= win_xright_low) & (
                            nonzerox < win_xright_high)).nonzero()[0]
														
      # Append these indices to the lists
      left_lane_inds.append(good_left_inds)
      right_lane_inds.append(good_right_inds)
        
      # If you found > minpix pixels, recenter next window on mean position
      minpix = self.minpix
      if len(good_left_inds) > minpix:
        leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
      if len(good_right_inds) > minpix:        
        rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
					
    # Concatenate the arrays of indices
    left_lane_inds = np.concatenate(left_lane_inds)
    right_lane_inds = np.concatenate(right_lane_inds)

    # Extract the pixel coordinates for the left and right lane lines
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds] 
    righty = nonzeroy[right_lane_inds]

    # Fit a second order polynomial curve to the pixel coordinates for
    # the left and right lane lines
    left_fit = None
    right_fit = None
		
    global prev_leftx
    global prev_lefty 
    global prev_rightx
    global prev_righty
    global prev_left_fit
    global prev_right_fit

    # Make sure we have nonzero pixels		
    if len(leftx)==0 or len(lefty)==0 or len(rightx)==0 or len(righty)==0:
      leftx = prev_leftx
      lefty = prev_lefty
      rightx = prev_rightx
      righty = prev_righty
		
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2) 

    # Add the latest polynomial coefficients		
    prev_left_fit.append(left_fit)
    prev_right_fit.append(right_fit)

    # Calculate the moving average	
    if len(prev_left_fit) > 10:
      prev_left_fit.pop(0)
      prev_right_fit.pop(0)
      left_fit = sum(prev_left_fit) / len(prev_left_fit)
      right_fit = sum(prev_right_fit) / len(prev_right_fit)

    self.left_fit = left_fit
    self.right_fit = right_fit
		
    prev_leftx = leftx
    prev_lefty = lefty 
    prev_rightx = rightx
    prev_righty = righty

    if plot==True:
		
      # Create the x and y values to plot on the image  
      ploty = np.linspace(
        0, frame_sliding_window.shape[0]-1, frame_sliding_window.shape[0])
      left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
      right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]

      # Generate an image to visualize the result
      out_img = np.dstack((
        frame_sliding_window, frame_sliding_window, (
        frame_sliding_window))) * 255
			
      # Add color to the left line pixels and right line pixels
      out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
      out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [
        0, 0, 255]
				
      # Plot the figure with the sliding windows
      figure, (ax1, ax2, ax3) = plt.subplots(3,1) # 3 rows, 1 column
      figure.set_size_inches(10, 10)
      figure.tight_layout(pad=3.0)
      ax1.imshow(cv2.cvtColor(self.orig_frame, cv2.COLOR_BGR2RGB))
      ax2.imshow(frame_sliding_window, cmap='gray')
      ax3.imshow(out_img)
      ax3.plot(left_fitx, ploty, color='yellow')
      ax3.plot(right_fitx, ploty, color='yellow')
      ax1.set_title("Original Frame")  
      ax2.set_title("Warped Frame with Sliding Windows")
      ax3.set_title("Detected Lane Lines with Sliding Windows")
      plt.show()  		
			
    return self.left_fit, self.right_fit

  def get_line_markings(self, frame=None):
    """
    Isolates lane lines.
  
	  :param frame: The camera frame that contains the lanes we want to detect
    :return: Binary (i.e. black and white) image containing the lane lines.
    """
    if frame is None:
      frame = self.orig_frame
			
    # Convert the video frame from BGR (blue, green, red) 
    # color space to HLS (hue, saturation, lightness).
    hls = cv2.cvtColor(frame, cv2.COLOR_BGR2HLS)

    ################### Isolate possible lane line edges ######################
		
    # Perform Sobel edge detection on the L (lightness) channel of 
    # the image to detect sharp discontinuities in the pixel intensities 
    # along the x and y axis of the video frame.		     
    # sxbinary is a matrix full of 0s (black) and 255 (white) intensity values
    # Relatively light pixels get made white. Dark pixels get made black.
    _, sxbinary = edge.threshold(hls[:, :, 1], thresh=(120, 255))
    sxbinary = edge.blur_gaussian(sxbinary, ksize=3) # Reduce noise
		
    # 1s will be in the cells with the highest Sobel derivative values
    # (i.e. strongest lane line edges)
    sxbinary = edge.mag_thresh(sxbinary, sobel_kernel=3, thresh=(110, 255))

    ######################## Isolate possible lane lines ######################
  
    # Perform binary thresholding on the S (saturation) channel 
    # of the video frame. A high saturation value means the hue color is pure.
    # We expect lane lines to be nice, pure colors (i.e. solid white, yellow)
    # and have high saturation channel values.
    # s_binary is matrix full of 0s (black) and 255 (white) intensity values
    # White in the regions with the purest hue colors (e.g. >130...play with
    # this value for best results).
    s_channel = hls[:, :, 2] # use only the saturation channel data
    _, s_binary = edge.threshold(s_channel, (130, 255))
	
    # Perform binary thresholding on the R (red) channel of the 
		# original BGR video frame. 
    # r_thresh is a matrix full of 0s (black) and 255 (white) intensity values
    # White in the regions with the richest red channel values (e.g. >120).
    # Remember, pure white is bgr(255, 255, 255).
    # Pure yellow is bgr(0, 255, 255). Both have high red channel values.
    _, r_thresh = edge.threshold(frame[:, :, 2], thresh=(120, 255))

    # Lane lines should be pure in color and have high red channel values 
    # Bitwise AND operation to reduce noise and black-out any pixels that
    # don't appear to be nice, pure, solid colors (like white or yellow lane 
    # lines.)		
    rs_binary = cv2.bitwise_and(s_binary, r_thresh)

    ### Combine the possible lane lines with the possible lane line edges ##### 
    # If you show rs_binary visually, you'll see that it is not that different 
    # from this return value. The edges of lane lines are thin lines of pixels.
    self.lane_line_markings = cv2.bitwise_or(rs_binary, sxbinary.astype(
                              np.uint8))	
    return self.lane_line_markings
		
  def histogram_peak(self):
    """
    Get the left and right peak of the histogram

    Return the x coordinate of the left histogram peak and the right histogram
    peak.
    """
    midpoint = np.int(self.histogram.shape[0]/2)
    leftx_base = np.argmax(self.histogram[:midpoint])
    rightx_base = np.argmax(self.histogram[midpoint:]) + midpoint

    # (x coordinate of left peak, x coordinate of right peak)
    return leftx_base, rightx_base
		
  def overlay_lane_lines(self, plot=False):
    """
    Overlay lane lines on the original frame
    :param: Plot the lane lines if True
    :return: Lane with overlay
    """
    # Generate an image to draw the lane lines on 
    warp_zero = np.zeros_like(self.warped_frame).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))		
		
    # Recast the x and y points into usable format for cv2.fillPoly()
    pts_left = np.array([np.transpose(np.vstack([
                         self.left_fitx, self.ploty]))])
    pts_right = np.array([np.flipud(np.transpose(np.vstack([
                          self.right_fitx, self.ploty])))])
    pts = np.hstack((pts_left, pts_right))
		
    # Draw lane on the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))

    # Warp the blank back to original image space using inverse perspective 
    # matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, self.inv_transformation_matrix, (
                                  self.orig_frame.shape[
                                  1], self.orig_frame.shape[0]))
    
    # Combine the result with the original image
    result = cv2.addWeighted(self.orig_frame, 1, newwarp, 0.3, 0)
		
    if plot==True:
     
      # Plot the figures 
      figure, (ax1, ax2) = plt.subplots(2,1) # 2 rows, 1 column
      figure.set_size_inches(10, 10)
      figure.tight_layout(pad=3.0)
      ax1.imshow(cv2.cvtColor(self.orig_frame, cv2.COLOR_BGR2RGB))
      ax2.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
      ax1.set_title("Original Frame")  
      ax2.set_title("Original Frame With Lane Overlay")
      plt.show()   

    return result			
	
  def perspective_transform(self, frame=None, plot=False):
    """
    Perform the perspective transform.
    :param: frame Current frame
    :param: plot Plot the warped image if True
    :return: Bird's eye view of the current lane
    """
    if frame is None:
      frame = self.lane_line_markings
			
    # Calculate the transformation matrix
    self.transformation_matrix = cv2.getPerspectiveTransform(
      self.roi_points, self.desired_roi_points)

    # Calculate the inverse transformation matrix			
    self.inv_transformation_matrix = cv2.getPerspectiveTransform(
      self.desired_roi_points, self.roi_points)

    # Perform the transform using the transformation matrix
    self.warped_frame = cv2.warpPerspective(
      frame, self.transformation_matrix, self.orig_image_size, flags=(
     cv2.INTER_LINEAR))	

    # Convert image to binary
    (thresh, binary_warped) = cv2.threshold(
      self.warped_frame, 127, 255, cv2.THRESH_BINARY)			
    self.warped_frame = binary_warped

    # Display the perspective transformed (i.e. warped) frame
    if plot == True:
      warped_copy = self.warped_frame.copy()
      warped_plot = cv2.polylines(warped_copy, np.int32([
                    self.desired_roi_points]), True, (147,20,255), 3)

      # Display the image
      while(1):
        cv2.imshow('Warped Image', warped_plot)
			
        # Press any key to stop
        if cv2.waitKey(0):
          break

      cv2.destroyAllWindows()	
			
    return self.warped_frame		
	
  def plot_roi(self, frame=None, plot=False):
    """
    Plot the region of interest on an image.
    :param: frame The current image frame
    :param: plot Plot the roi image if True
    """
    if plot == False:
      return
			
    if frame is None:
      frame = self.orig_frame.copy()

    # Overlay trapezoid on the frame
    this_image = cv2.polylines(frame, np.int32([
      self.roi_points]), True, (147,20,255), 3)

    # Display the image
    while(1):
      cv2.imshow('ROI Image', this_image)
			
      # Press any key to stop
      if cv2.waitKey(0):
        break

    cv2.destroyAllWindows()
	
def main():

  # Load a video
  cap = cv2.VideoCapture(filename)

  # Create a VideoWriter object so we can save the video output
  fourcc = cv2.VideoWriter_fourcc(*'mp4v')
  result = cv2.VideoWriter(output_filename,  
                           fourcc, 
                           output_frames_per_second, 
                           file_size) 
	
  # Process the video
  while cap.isOpened():
		
    # Capture one frame at a time
    success, frame = cap.read() 
		
    # Do we have a video frame? If true, proceed.
    if success:
		
      # Resize the frame
      width = int(frame.shape[1] * scale_ratio)
      height = int(frame.shape[0] * scale_ratio)
      frame = cv2.resize(frame, (width, height))
			
      # Store the original frame
      original_frame = frame.copy()

      # Create a Lane object
      lane_obj = Lane(orig_frame=original_frame)

      # Perform thresholding to isolate lane lines
      lane_line_markings = lane_obj.get_line_markings()

      # Plot the region of interest on the image
      lane_obj.plot_roi(plot=False)

      # Perform the perspective transform to generate a bird's eye view
      # If Plot == True, show image with new region of interest
      warped_frame = lane_obj.perspective_transform(plot=False)

      # Generate the image histogram to serve as a starting point
      # for finding lane line pixels
      histogram = lane_obj.calculate_histogram(plot=False)	
	
      # Find lane line pixels using the sliding window method 
      left_fit, right_fit = lane_obj.get_lane_line_indices_sliding_windows(
        plot=False)

      # Fill in the lane line
      lane_obj.get_lane_line_previous_window(left_fit, right_fit, plot=False)
	
      # Overlay lines on the original frame
      frame_with_lane_lines = lane_obj.overlay_lane_lines(plot=False)

      # Calculate lane line curvature (left and right lane lines)
      lane_obj.calculate_curvature(print_to_terminal=False)

      # Calculate center offset  																
      lane_obj.calculate_car_position(print_to_terminal=False)
	
      # Display curvature and center offset on image
      frame_with_lane_lines2 = lane_obj.display_curvature_offset(
        frame=frame_with_lane_lines, plot=False)
				
      # Write the frame to the output video file
      result.write(frame_with_lane_lines2)
			
      # Display the frame 
      cv2.imshow("Frame", frame_with_lane_lines2) 	

      # Display frame for X milliseconds and check if q key is pressed
      # q == quit
      if cv2.waitKey(25) & 0xFF == ord('q'):
        break
		
    # No more video frames left
    else:
      break
			
  # Stop when the video is finished
  cap.release()
	
  # Release the video recording
  result.release()
	
  # Close all windows
  cv2.destroyAllWindows() 
	
main()

文章来源: zhangrelay.blog.csdn.net，作者：zhangrelay，版权归原作者所有，如需转载，请联系作者。

原文链接：zhangrelay.blog.csdn.net/article/details/125435368