我有一个使用 OpenCV 的 python 脚本，代码在 python 2.7 中有效，但在 python 3.7 中无效，我不知道为什么[重复]

Question

所以我正在做一个项目，有人给了我一些他们在 python 2.7 中创建的代码来实现它。然而，该项目在 python 3.7 上运行，当我尝试执行它时，我不断收到与标记函数相关的错误。有人可以查看它并告诉我缺少什么来执行该版本吗？我附上了用于测试功能的图像以及代码。

以下是我得到的错误：

Traceback (most recent call last):
  File "/home/pi/Downloads/distance_to_camera_2 (1).py", line 94, in <module>
    width_array=process_component(labels_im)
  File "/home/pi/Downloads/distance_to_camera_2 (1).py", line 71, in process_component
    x,y,w,h = cv2.boundingRect(cnts[0])
TypeError: Expected cv::UMat for argument 'array'

这是代码：

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



# Find distance from camera to object using Python and OpenCV
def distance_to_camera(knownWidth, focalLength, perWidth):
    # compute and return the distance from the maker to the camera
    return (knownWidth * focalLength) / perWidth



KNOWN_WIDTH = 8
focalLength = 545
# put your image here
img = cv2.imread("/home/pi/Downloads/many_blob.png")
cv2.imshow("img", img)
cv2.waitKey(1000)
image = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  # ensure binary 127
cv2.imshow("image", image)
cv2.waitKey(1000)
kernel = np.ones((5,5),np.uint8)
erosion = cv2.erode(image,kernel,iterations = 5)
dilate=cv2.dilate(erosion,kernel,iterations = 5)
edged = cv2.Canny(dilate, 0, 128)
cv2.imshow("edged", edged)
cv2.waitKey(1000)

connectivity=8
num_labels,labels_im = cv2.connectedComponents(edged,connectivity)

# Function only for labels display  (debuging only)
def imshow_components(labels):
    # Map component labels to hue val
    label_hue = np.uint8(179*labels/np.max(labels))
    blank_ch = 255*np.ones_like(label_hue)
    labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])

    # cvt to BGR for display
    labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)

    # set bg label to black
    labeled_img[label_hue==0] = 0
    #labeled_img[labels==0] = 0

    cv2.imshow('labeled.png', labeled_img)
    cv2.waitKey(1000)
    cv2.imwrite('labeled_img.png',labeled_img)
    #cv2.imwrite('label_hue.png',label_hue)

def process_component(labels):
    width = np.zeros(np.max(labels))
    for i in range(1,np.max(labels)+1):
        tmp_im= labels.copy()
        tmp_im[:] = 0
        tmp_im[labels==i] = 255
        file="imlabel_%d.png"%(i, )
        cv2.imwrite(file,tmp_im)
        tmp_im = tmp_im.astype(np.uint8)
        cnts = cv2.findContours(tmp_im, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

        # bounding box of the countour
        x,y,w,h = cv2.boundingRect(cnts[0])
        width[i-1] = w
        tmp_im=cv2.rectangle(tmp_im,(x,y),(x+w,y+h),(255,0,0),2)
        # center = center of the bounding box
        center=(x+w/2,y+h/2)
        cv2.circle(tmp_im, center, 3, (255,0,0), 2, 8, 0)

        cv2.imshow(file, tmp_im)
        cv2.waitKey(1000)
    return width

width_array=process_component(labels_im)
imshow_components(labels_im)
cv2.imwrite('labels_img.png',labels_im)

for i in range(1,np.max(labels_im)+1):
    w=width_array[i-1]
    #marker = find_marker(image)
    dist_cm = distance_to_camera(KNOWN_WIDTH, focalLength, w)
    print("distance en cm = %d",dist_cm)

这是我第一次在堆栈溢出上发帖，所以如果我应该发布其他任何内容来帮助我，请告诉我。

这是我一直在尝试使用的图像： https://i.stack.imgur.com/ONhUA.png

Answer 1

这是避免在 Python/OpenCV 中 findContours() 的返回值中正确访问轮廓项问题的一种方法。

findContours 有两种可能的返回值数量，具体取决于您使用的 OpenCV 版本。所以要访问在返回值列表中可以找到轮廓的位置：

替换

contours,hierarchy = cv2.findContours(tmp_im, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

和

contours,hierarchy = cv2.findContours(tmp_im, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

第二行表示如果返回值列表中元素个数为2，则使用返回值列表中的第一个返回值contours[0]，否则使用第二个返回值contours[1]得到到等高线

Answer 2

当您调用cv2.boundingRect(cnts) 时，cnts 的值是错误的。我四处阅读，这是因为您使用的版本已过时。现在似乎从cv2.findContours(...) 返回了更多信息，因此您只需提取它即可。

def process_component()：变化

contours,hierarchy = cv2.findContours(tmp_im, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = contours[0]

其次，当您使用cv2.circle(...) 绘制圆圈时，您正在传递一个浮点元组。 cv2.circle(...) 显然不能使用浮点数，因此首先将元组中的每个值转换为 int。您可能希望在转换为 int 之前对这些浮点数进行四舍五入。

def process_component()：变化

center = ( int(x+w/2), int(y+h/2) )
# Or use the rounded version below.
# center = ( int(round(x+w/2)), int(round(y+h/2)) )
cv2.circle(tmp_im, center, 3, (255,0,0), 2, 8, 0)

这是包含这些实现的脚本。

import os
import cv2
import numpy as np
from matplotlib import pyplot as plt


def distance_to_camera(knownWidth, focalLength, perWidth):
    """ 
    Find distance between camera and object with OpenCV.
    Return the distance from the maker (object?) to the camera.
    """ 
    return (knownWidth * focalLength) / perWidth



def imshow_components(labels):
    """ Display labels: For debugging purposes"""
    # Map component labels to hue val
    label_hue = np.uint8(179*labels/np.max(labels))
    blank_ch = 255*np.ones_like(label_hue)
    labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])

    # cvt to BGR for display
    labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)

    # set bg label to black
    labeled_img[label_hue==0] = 0
    #labeled_img[labels==0] = 0

    cv2.imshow('labeled.png', labeled_img)
    cv2.waitKey(1000)
    cv2.imwrite('labeled_img.png',labeled_img)
    #cv2.imwrite('label_hue.png',label_hue)



def process_component(labels):
    """ 
    Describe what happens here.
    width of ____ is ___.
    Find contours of ___ for ___.
    etc. Write what happens in this __docstr__
    return the ______.
    """
    
    width = np.zeros(np.max(labels)) 
    
    for i in range(1,np.max(labels)+1):
        
        tmp_im= labels.copy()
        tmp_im[:] = 0
        tmp_im[labels==i] = 255
        
        file="imlabel_%d.png"%(i, )
        cv2.imwrite(file,tmp_im)
        tmp_im = tmp_im.astype(np.uint8)

        ##########################
        # Here was the first issue. https://docs.opencv.org/3.1.0/dd/d49/tutorial_py_contour_features.html
        contours,hierarchy = cv2.findContours(tmp_im, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        cnts = contours[0]

        x,y,w,h = cv2.boundingRect(cnts)
        ##########################

        width[i-1] = w
        tmp_im=cv2.rectangle(tmp_im,(x,y),(x+w,y+h),(255,0,0),2)
        

        ##########################
        # HERE IS THE NEXT ISSUE. 
        # Fixed it. The center tuple (x, y) cannot be of type floats apparently. Convert each value to an int after division.
        # center = center of the bounding box
        center=(int(x+w/2),int(y+h/2))

        cv2.circle(tmp_im, center, 3, (255,0,0), 2, 8, 0)
        ##########################

        cv2.imshow(file, tmp_im)
        cv2.waitKey(1000)
        
        
    return width




def main():
    """ Call this function to run the program. Put some detail in here. """
    
    # Get the path to the filename. (Im on windows right now)
    filename =  "many_blob.png"
    path = os.path.join(os.getcwd(), filename)
    
    
    # What are these?
    KNOWN_WIDTH = 8
    focalLength = 545
    
    # Read the path as an cv2 image and show it. Wait 1 sec.  
    img = cv2.imread(path)
    cv2.imshow("img", img)
    cv2.waitKey(1000)
    
    # Do something else here. I dont know what, sorry idk about cv2
    image = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  # ensure binary 127
    cv2.imshow("image", image)
    cv2.waitKey(1000)
    
    # Do something with parameters that will affect the image.
    kernel = np.ones((5,5),np.uint8)
    erosion = cv2.erode(image,kernel,iterations = 5)
    dilate=cv2.dilate(erosion,kernel,iterations = 5)
    edged = cv2.Canny(dilate, 0, 128)
    
    # Show the affected image.
    cv2.imshow("edged", edged)
    cv2.waitKey(1000)
    
    # idk.
    connectivity=8
    num_labels,labels_im = cv2.connectedComponents(edged,connectivity)
    
    # Do something here.
    width_array=process_component(labels_im)
    imshow_components(labels_im)
    cv2.imwrite('labels_img.png',labels_im)
    
    
    
    # do some more things here.
    for i in range(1,np.max(labels_im)+1):
        w=width_array[i-1]
        #marker = find_marker(image)
        dist_cm = distance_to_camera(KNOWN_WIDTH, focalLength, w)
        print("distance en cm = %d",dist_cm)




        
        
        
    
        
if __name__ == '__main__':
    """ If this file is executed, run the main function. """
    
    main()