TypeError：无法解压不可迭代的 numpy.float64 对象

Question

以下代码使用 openCV 模块来识别道路上的车道。我使用 python 3.6 进行编码（我使用 atom IDE 进行开发。提供此信息是因为 stackoverflow 不允许我在没有不必要的信息行的情况下发布信息。所以请忽略括号中的 cmets） 该代码在给定的示例视频中运行良好。但是当我为另一个视频运行它时，它会引发以下错误：

(base) D:\Self-Driving course\finding-lanes>RayanFindingLanes.py
C:\Users\Tarun\Anaconda3\lib\site-packages\numpy\lib\function_base.py:392: RuntimeWarning: Mean of empty slice.
  avg = a.mean(axis)
C:\Users\Tarun\Anaconda3\lib\site-packages\numpy\core\_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
Traceback (most recent call last):
  File "D:\Self-Driving course\finding-lanes\RayanFindinglanes.py", line 81, in <module>
    averaged_lines = average_slope_intercept(frame, lines)
  File "D:\Self-Driving course\finding-lanes\RayanFindinglanes.py", line 51, in average_slope_intercept
    right_line = make_points(image, right_fit_average)
  File "D:\Self-Driving course\finding-lanes\RayanFindinglanes.py", line 56, in make_points
    slope, intercept = line
TypeError: cannot unpack non-iterable numpy.float64 object

错误是什么意思以及如何解决？

代码：

import cv2
import numpy as np

def canny(img):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    kernel = 5
    blur = cv2.GaussianBlur(gray,(kernel, kernel),0)
    canny = cv2.Canny(blur, 50, 150)
    return canny

def region_of_interest(canny):
    height = canny.shape[0]
    width = canny.shape[1]
    mask = np.zeros_like(canny)

    triangle = np.array([[
    (200, height),
    (550, 250),
    (1100, height),]], np.int32)

    cv2.fillPoly(mask, triangle, 255)
    masked_image = cv2.bitwise_and(canny, mask)
    return masked_image

def display_lines(img,lines):
    line_image = np.zeros_like(img)
    if lines is not None:
        for line in lines:
            for x1, y1, x2, y2 in line:
                cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
    return line_image

def average_slope_intercept(image, lines):
    left_fit    = []
    right_fit   = []
    if lines is None:
        return None
    for line in lines:
        for x1, y1, x2, y2 in line:
            fit = np.polyfit((x1,x2), (y1,y2), 1)
            slope = fit[0]
            intercept = fit[1]
            if slope < 0: # y is reversed in image
                left_fit.append((slope, intercept))
            else:
                right_fit.append((slope, intercept))
    # add more weight to longer lines
    left_fit_average  = np.average(left_fit, axis=0)
    right_fit_average = np.average(right_fit, axis=0)
    left_line  = make_points(image, left_fit_average)
    right_line = make_points(image, right_fit_average)
    averaged_lines = [left_line, right_line]
    return averaged_lines

def make_points(image, line):
    slope, intercept = line
    y1 = int(image.shape[0])# bottom of the image
    y2 = int(y1*3/5)         # slightly lower than the middle
    x1 = int((y1 - intercept)/slope)
    x2 = int((y2 - intercept)/slope)
    return [[x1, y1, x2, y2]]

cap = cv2.VideoCapture("test3.mp4")
while(cap.isOpened()):
    _, frame = cap.read()
    canny_image = canny(frame)
    cropped_canny = region_of_interest(canny_image)
    lines = cv2.HoughLinesP(cropped_canny, 2, np.pi/180, 100, np.array([]), minLineLength=40,maxLineGap=5)
    averaged_lines = average_slope_intercept(frame, lines)
    line_image = display_lines(frame, averaged_lines)
    combo_image = cv2.addWeighted(frame, 0.8, line_image, 1, 1)
    cv2.imshow("result", combo_image)
    if cv2.waitKey(1) == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

Answer 1

在某些帧中，所有斜率均 > 0，因此 left_fit 列表为空。因此，在计算 left_fit 平均值时会出现错误。解决此问题的一种方法是使用前一帧的 left_fit 平均值。我已经使用相同的方法解决了它。请查看下面的代码，如果它解决了您的问题，请告诉我。

global_left_fit_average = []
global_right_fit_average = []
def average_slope_intercept(image, lines):
    left_fit = []
    right_fit = []
    global global_left_fit_average
    global global_right_fit_average

    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line.reshape(4)
            parameters = np.polyfit((x1, x2), (y1,y2), 1)
            slope = parameters[0]
            intercept = parameters[1]
            if (slope < 0):
                left_fit.append((slope, intercept))
            else:
                right_fit.append((slope, intercept))
    if (len(left_fit) == 0):
        left_fit_average = global_left_fit_average
    else:
        left_fit_average = np.average(left_fit, axis=0)
        global_left_fit_average = left_fit_average

    right_fit_average = np.average(right_fit, axis=0)
    global_right_fit_average = right_fit_average
    left_line = make_corordinates(image, left_fit_average)
    right_line = make_corordinates(image, right_fit_average)
    return np.array([left_line, right_line])

Answer 2

HoughLinesP 返回一个列表，可以是空列表，不一定是None

所以函数average_slope_intercept中的行

if lines is None:
        return None

用处不大。

您需要查看len(lines) == 0