请问这些错误是什么意思啊该怎么改

import cv2
import random
import numpy as np
from enum import Enum
from detectColor import detectColor
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def detectColor(image): # receives an ROI containing a single light
# convert BGR image to HSV

#截取roi区域
hsv_img = cv2.cvtColor(image,cv2.COLOR_BGR2HSV)




# min and max HSV values
red_min = np.array([0,5,150])
red_max = np.array([8,255,255])
red_min2 = np.array([175,5,150])
red_max2 = np.array([180,255,255])

yellow_min = np.array([20,5,150])
yellow_max = np.array([30,255,255])

green_min = np.array([35,5,150])
green_max = np.array([90,255,255])

# apply red, yellow, green thresh to image
# 利用cv2.inRange函数设阈值，去除背景部分?????
red_thresh = cv2.inRange(hsv_img,red_min,red_max)+cv2.inRange(hsv_img,red_min2,red_max2)
yellow_thresh = cv2.inRange(hsv_img,yellow_min,yellow_max)
green_thresh = cv2.inRange(hsv_img,green_min,green_max)

# apply blur to fix noise in thresh
# 进行中值滤波
red_blur = cv2.medianBlur(red_thresh,5)
yellow_blur = cv2.medianBlur(yellow_thresh,5)
green_blur = cv2.medianBlur(green_thresh,5)

# checks which colour thresh has the most white pixels
red = cv2.countNonZero(red_blur)#返回灰度值不为0的像素数
yellow = cv2.countNonZero(yellow_blur)
green = cv2.countNonZero(green_blur)

# the state of the light is the one with the greatest number of white pixels
lightColor = max(red,yellow,green)

# pixel count must be greater than 60 to be a valid colour state (solid light or arrow)
# since the ROI is a rectangle that includes a small area around the circle
# which can be detected as yellow
if lightColor > 60:
    if lightColor == red:
        return 1
    elif lightColor == yellow:
        return 2
    elif lightColor == green:
        return 3
else:
    return 0

class TLState(Enum):
red = 1
yellow = 2
green = 3
red_yellowArrow = 4
red_greenArrow = 5
green_yellowArrow = 6
green_greenArrow = 7
redArrow = 8
yellowArrow = 9
greenArrow = 10
flashingYellowArrow = 11

class TLType(Enum):
regular = 0
five_lights = 1
four_lights = 2

def imgResize(image, height, inter = cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and grab the image size
dim = None
(h, w) = image.shape[:2]
# calculate the ratio of the height and construct the dimensions
r = height / float(h)
dim = (int(w * r), height)
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized

def detectState(image, TLType):
image = imgResize(image,200)
(height, width) = image.shape[:2]
output = image.copy()
gray = cv2.cvtColor (image,cv2.COLOR_BGR2GRAY)
#霍夫圆环检测
circles = cv2. HoughCircles(gray ,cv2.HOUGH_GRADIENT,1 ,20,param1=50 ,param2=30 ,minRadius=15 , maxRadius=30)
overallState = 0
stateArrow = 0
stateSolid = 0
if circles is not None :
circles = np.uint16(np.around(circles))

    for i in circles[0,:] :
        if i[1] < i[2]:
            i[1] = i[2]
            roi = image[(i[1] -i[2]):(i[1]+i[2]),(i[0] -i[2]):(i[0]+i[2])]
            color = detectColor(roi)
            if color > 0:
                if TLType == 1 and i[0] < width/2 and i[1] > height/3:
                    stateArrow = color
                elif TLType == 2:
                        stateArrow = color
                        if i[1] > height/2 and i[1] < height/4*3:
                            stateArrow = color + 2
                else:
                        stateSolid = color
if TLType == 1:
    overallState = stateArrow + stateSolid + 1
elif TLType == 2:
    overallState = stateArrow + 7
else:
    overallState = stateSolid

return overallState

def plot_light_result(images):

for i, image in enumerate(images):
    plt.subplot(1, len(images), i+1)
    lena = mpimg.imread(image)
    label = TLState(detectState(cv2.imread(image),TLType.regular.value)).name
    plt.title(label)
    plt.imshow(lena)
plt.show()

light_path = ["images/redlight.jpg","images/greenlight.jpg", "images/yellow.jpg"]
random.shuffle(light_path)
plot_light_result(light_path)

def plot_arrow_result(images):

for i, image in enumerate(images):
    plt.subplot(1, len(images), i+1)
    lena = mpimg.imread(image)
    label = TLState(detectState(cv2.imread(image),TLType.five_lights.value)).name
    plt.title(label)
    plt.imshow(imgResize(lena, 200))
plt.show()

arrow_path = ["images/bh12.jpg", "images/bh1.jpg"]
random.shuffle(arrow_path)
plot_arrow_result(arrow_path)

img

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