为什么在使用 python 对视频运行图像处理算法时出现 ( Can't parse 'pt1'. ) ？

Question

我正在尝试在 python 上使用霍夫变换运行车道检测 和

我不断收到以下错误：

无法解析“pt1”。索引为 0 的序列项类型错误 47号线，

对应这一行：

 cv2.line(line_image, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 10)

我也收到此错误：

TypeError: cannot unpack non-iterable numpy.float64 object
line 6, in make_points
    slope, intercept = line
    ^^^^^^^^^^^^^^^^

视频格式为720x1280（720p）

这是全部代码

import cv2
import numpy as np
import matplotlib.pyplot as plt

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]]

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

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, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 10)

    return line_image

def region_of_interest(canny):
    height = canny.shape[0]
    width = canny.shape[1]
    mask = np.zeros_like(canny)
    triangle = np.array([[
    (200, height),
    (800, 350),
    (1200, height),]], np.int32)
    cv2.fillPoly(mask, triangle, 255)
    masked_image = cv2.bitwise_and(canny, mask)
    return masked_image

cap = cv2.VideoCapture("Video_City.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) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

知道如何解决这个问题吗？我在图像上尝试了该算法，效果很好。我没有得到任何视频输出。

Answer 1

您尚未共享视频文件，因此我无法重现您的确切问题。相反，我尝试在用于车道检测的不同视频上运行您的代码，并且遇到了类似的异常。

立即出现的一个问题是在函数

average_slope_intercept', it's possible that 'left_fit' or 'right_fit' lists to be empty, which causes 

make_points` 中抛出异常。我已经修改了您的代码来解决这个问题，至少在我的测试视频上它有效。

这是我更改后的代码：

import cv2
import numpy as np
import matplotlib.pyplot as plt

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]]

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
    if len(left_fit) > 0:
        left_fit_average = np.average(left_fit, axis=0)
        left_line = make_points(image, left_fit_average)
    else:
        left_line = None
    if len(right_fit) > 0:
        right_fit_average = np.average(right_fit, axis=0) if len(right_fit) > 0 else 0
        right_line = make_points(image, right_fit_average)
    else:
        right_line = None

    averaged_lines = [left_line, right_line]
    return averaged_lines

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

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

    return line_image

def region_of_interest(canny):
    height = canny.shape[0]
    width = canny.shape[1]
    mask = np.zeros_like(canny)
    triangle = np.array([[
    (200, height),
    (800, 350),
    (1200, height),]], np.int32)
    cv2.fillPoly(mask, triangle, 255)
    masked_image = cv2.bitwise_and(canny, mask)
    return masked_image

cap = cv2.VideoCapture("Video_City.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=5, 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) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()