Question

我希望能够计算检测到的对象中的像素数。我正在使用cv2.threshold函数。这是一些sudo代码。

import cv2
import numpy as np
import time

while True:
    cam= cv2.VideoCapture(0)
    while(cam.isOpened())
        ret, image = cam.read()
        image = cv2.GaussianBlur(image, (5,5), 0)
        Image1 = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
        lower= np.array([30,40,40], dtype='uint8')
        upper= np.array([95,240,240], dtype='uint8')
        Thresh= cv2.inRange(Image1, lower, upper)

从现在开始，我不知道如何计算我的物体的像素。你如何找到二进制图像的轮廓？我想可以在Thresh / mask上使用cv2.bitwise_和一个完整的黑色图像，但这似乎可能很慢，而且我也不知道如何创建像这样的完全黑白图像。 / p>

所以TD：LR，你如何计算二进制图像中对象的像素数？

注意：我实际上只是在最大的物体之后，只需要像素数，而不是图像。

编辑：我没有尝试计算检测到的像素总数，我已经做过了。想要从具有最大数量的对象中检测到的像素数。

Answer 1

我就这样做了

import cv2
import numpy as np
import time
from scipy.ndimage import (labeled_comprehension, label, measurements, generate_binary_structure) # new import

while True:
    cam= cv2.VideoCapture(0)
    while(cam.isOpened())
        ret, image = cam.read() # record image
        image = cv2.GaussianBlur(image, (5,5), 0) # blur to remove noise
        Image1 = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # convert to better color scheme
        lower= np.array([30,40,40], dtype='uint8') # low green
        upper= np.array([95,240,240], dtype='uint8') # high green
        Thresh= cv2.inRange(Image1, lower, upper) # returns array with 255 as pixel if in threshold
        struct = generate_binary_structure(2,2) # seems necessary for some reason
        Label, features = label(Thresh, struct) # label is object, features is number of objects
        Arange = np.arange(1, features+1) # seems necessary for some reason
        Biggest = sorted(labeled_comprehension(Thresh, Label, Arange, np.sum, float, -1))[features-1]//255 # counts and organises the objects based on size. [features-1] means last object, ie: biggest. //255 because that's each pixel work (from thresh)

Python OpenCv2，计算彩色对象的轮廓

1 个答案: