python - 如何在视频处理中保留多个前一帧的记录

Question

我目前正在使用 opencv 和 python 研究自动驾驶汽车的车道检测。我已经使用 houghline 变换来获得道路上的线条。从这些线中，我分别计算了具有负斜率和正斜率的线的平均斜率和截距值。为了减少错误，我想跟踪计算出的前 3 帧的平均斜率和截距。这样我就可以检查当前帧的平均斜率和截距值的偏差并相应地进行更正。有没有办法跟踪前 3 帧？

这是我到目前为止所做的。这是针对当前帧的。我想保留最多 3 个前一帧的计算平均值，并从当前帧访问这些值。

import numpy as np
import cv2
import math

cap = cv2.VideoCapture('video3.mov')

while (cap.isOpened()):

  ret, frame = cap.read() 
  hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HLS)

  # HSL color mask
  lower_white = np.uint8([0, 140, 0])
  upper_white = np.uint8([255, 255, 255])
  mask = cv2.inRange(hsv, lower_white, upper_white)
  res = cv2.bitwise_and(frame, frame, mask=mask)

  height = np.size(hsv, 0)
  width = np.size(hsv, 1)

  rows, cols = hsv.shape[:2]

  bottom_left = [cols * 0, rows * 0.9]
  top_left = [cols * 0.4, rows * 0.7]
  bottom_right = [cols * 1, rows * 0.9]
  top_right = [cols * 0.6, rows * 0.7]

  vertices = np.array([[bottom_left, top_left, top_right, bottom_right]], dtype=np.int32)

  maskzero = np.zeros_like(res)
  cv2.fillPoly(maskzero, vertices, (255,) * maskzero.shape[2])
  maskedimg = cv2.bitwise_and(res, maskzero)

  # smoothed = cv2.medianBlur(maskedimg,3)
  gaussian = cv2.GaussianBlur(maskedimg, (3, 3), 0)

  # apply canny on masked image
  cannymask = cv2.Canny(gaussian, 150, 250)

  # apply hough transform houghlinesP
  lines = cv2.HoughLinesP(cannymask, rho=1, theta=np.pi / 180, threshold=20, minLineLength=10, maxLineGap=300)

  left_slope = []
  right_slope = []
  left_intercept = []
  right_intercept = []
  total_right_length = []
  total_left_length = []

  if lines is not None:
      for line in lines:
         for x1, y1, x2, y2 in line:

            if (x2 - x1) != 0:
                slope = (y2 - y1) / (x2 - x1)
                intercept = y1 - slope * x1
                length = np.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2)
                angle = math.atan2(y2-y1,x2-x1)
                degree = angle * 180 / np.pi

           if x2 == x1 or y2 == y1:
               continue

           elif slope > 0:
              if int(degree) in range (27,41):
                 right_slope.append(slope)
                 right_intercept.append(intercept)
                 total_right_length.append(length)
              angle = math.atan2(y2 - y1, x2 - x1)
              degree = angle * 180 / np.pi
              print("positive",degree)

        elif slope < 0:
            if int(degree) in range (-62,-31):
                left_slope.append(slope)
                left_intercept.append(intercept)
                total_left_length.append(length)
            angle = math.atan2(y2 - y1, x2 - x1)
            degree = angle * 180 / np.pi
            print("negative",degree)
            degreeint= int(degree)
            #cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

  sum_right_length = np.sum(total_right_length) if len(total_right_length) > 0 else None
  right_mean_slope=np.mean(right_slope) if len(right_slope)>0 else None
  right_mean_intercept=np.mean(right_intercept) if len(right_intercept) > 0 else None

  sum_left_length = np.sum(total_left_length) if len(total_left_length) > 0 else None
  left_mean_slope = np.mean(left_slope) if len(left_slope)>0 else None
  left_mean_intercept = np.mean(left_intercept) if len(left_intercept) > 0 else None

  right_x1=0
  right_y1=0
  right_x2=0
  right_y2=0

  left_x1=0
  left_x2=0
  left_y2=0
  left_y1=0

  y1 = frame.shape[0] # bottom of the image
  y2 = y1*0.7
  if right_mean_intercept is not None and right_mean_slope is not None:
     right_x1 = int((y1 - right_mean_intercept)/right_mean_slope)
     right_x2 = int((y2 - right_mean_intercept)/right_mean_slope)
     right_y1 = int(y1)
     right_y2 = int(y2)
 if left_mean_intercept is not None and left_mean_slope is not None:
     left_x1 = int((y1 - left_mean_intercept)/left_mean_slope)
     left_x2 = int((y2 - left_mean_intercept)/left_mean_slope)
     left_y1 = int(y1)
     left_y2 = int(y2)

  cv2.line(frame, (right_x1, right_y1), (right_x2, right_y2), (0, 0, 255), 10)
  cv2.line(frame, (left_x1, left_y1), (left_x2, left_y2), (255, 0,0), 10)
  cv2.imshow("New_lines", frame)

if cv2.waitKey(100) & 0xFF == ord('q'):
    break

cap.release()
cv2.destroyAllWindows()

我想保留前 3 帧的左右平均斜率和截距值，并从当前帧访问它们。

Answer 1

import numpy as np
import cv2
import math

cap = cv2.VideoCapture('video3.mov')

previous = [ ]   # We will keep track of previous 3 frames in this list
while (cap.isOpened()):

  ret, frame = cap.read() 
  hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HLS)
  if len(previous) == 3 :
       # do your thing like calculating the mean
       for x in range(3):
           print(previous[x])   # This is how you can access the previous frames

       # ================================================================
       # After you are done add the below two lines of code 
       # Below two lines are necessary to keep track of recent 3 frames
       # ================================================================
       previous = previous[1:]  # Removing the first frame so now length of previous is 2
       previous.append(frame)   # Adding the current frame so length of previous is 3 again
  else : 
       # else condition is for if frames are less then 3
       # else condition is for the very beginning when you previous will start with empty list 
       previous.append(frame)
       continue # If you want to do computation even when length of previous is less than 3 than comment the continue statement 
if cv2.waitKey(100) & 0xFF == ord('q'):
    break

cap.release()
cv2.destroyAllWindows()