python - 检测具有特定颜色的圆形物体

Question

我的目标是检测下图中所有的紫色花粉，并在其中放入字母“P”。

但结果显示，它总是误认一个黑色区域。

更改圆检测中的半径没有帮助，因为我还有很多相似的图像要处理。那么我应该怎么做才能改善它呢？

这是我的代码:

# coding: utf-8


import cv2
import numpy as np


path = "./sample.JPG"
font = cv2.FONT_HERSHEY_COMPLEX

def image_resize(image, width = None, height = None, 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]

    # if both the width and height are None, then return the
    # original image
    if width is None and height is None:
        return image

    # check to see if the width is None
    if width is None:
        # calculate the ratio of the height and construct the
        # dimensions
        r = height / float(h)
        dim = (int(w * r), height)

    # otherwise, the height is None
    else:
        # calculate the ratio of the width and construct the
        # dimensions
        r = width / float(w)
        dim = (width, int(h * r))

    # resize the image
    resized = cv2.resize(image, dim, interpolation = inter)

    # return the resized image
    return resized

iml = cv2.imread(path,cv2.IMREAD_COLOR)
img = image_resize(iml,width=960)

cimg = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
#cv2.GaussianBlur(cimg, (9,9),3)
cimg = cv2.medianBlur(cimg,5)

circles = cv2.HoughCircles(cimg[:,:,0],cv2.HOUGH_GRADIENT,1,cimg.shape[0]/16,param1=15,param2=20,minRadius=18,maxRadius=38)
circles = np.uint16(np.around(circles))[0,:]

for i in circles:
     cv2.putText(img,'P',(i[0],i[1]), font, 0.5,(0,255,0),1,cv2.LINE_AA)

cv2.imwrite("./output.jpg",img)

此外，我还尝试使用颜色检测，因为我要检测的所有颜色都相同(紫色)。我关注instructions here但还是不行。

Answer 1

我认为如果您仔细选择正确的 hsv 范围，您可以直接在 HSV 颜色空间中检测到紫色。此颜色图取 self 的其他答案。

我为此任务选择Hue(120,160)、Saturation(180, 255)、Value(50, 255)以获取 mask 。

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, (120, 180, 50), (160, 255, 255))

然后就可以对mask进行处理了。

链接可能有帮助:

1. How to define a threshold value to detect only green colour objects in an image :Opencv

2. Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)

3. RGB range for color red