使用opencv和python-进行HoughCircles循环检测

I am trying to use OpenCV's (Hough)Circle detection to.. detect circles. I created a solid circle on a black background, tried to play with the parameters, used blur and everything, but I am just not able to make it find anything.

我正在尝试使用OpenCV(霍夫)圆检测来检测圆圈。我在黑色背景上创建了一个实心圆圈,尝试使用参数,使用模糊和一切,但我只是无法让它找到任何东西。

Any ideas, suggestions etc. would be great, thank you!

任何想法,建议等都会很棒,谢谢!

my current code is something like this:

我目前的代码是这样的:

import cv2
import numpy as np

"""
params = dict(dp=1,
              minDist=1,
              circles=None,
              param1=300,
              param2=290,
              minRadius=1,
              maxRadius=100)
"""

img = np.ones((200,250,3), dtype=np.uint8)
for i in range(50, 80, 1):
    for j in range(40, 70, 1):
        img[i][j]*=200

cv2.circle(img, (120,120), 20, (100,200,80), -1)


gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 200, 300)

cv2.imshow('shjkgdh', canny)
gray = cv2.medianBlur(gray, 5)
circles = cv2.HoughCircles(gray, cv2.cv.CV_HOUGH_GRADIENT, 1, 20,
              param1=100,
              param2=30,
              minRadius=0,
              maxRadius=0)

print circles
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
    cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)

cv2.imshow('circles', img)
k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

2 个解决方案

#1

Your code is working just fine. The problem is in your HoughCircles threshold parameters.

你的代码运行得很好。问题出在你的HoughCircles阈值参数中。

Let's try to understand the parameters that you're using from OpenCV Docs:

让我们尝试了解您在OpenCV Docs中使用的参数:

param1 – First method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the higher threshold of the two passed to the Canny() edge detector (the lower one is twice smaller).

param1 - 第一个特定于方法的参数。在CV_HOUGH_GRADIENT的情况下,它是传递给Canny()边缘检测器的两个较高阈值(较低的一个是两倍小)。

param2 – Second method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first.

param2 - 第二个特定于方法的参数。在CV_HOUGH_GRADIENT的情况下,它是检测阶段圆心的累加器阈值。它越小,可以检测到更多的假圆圈。将首先返回与较大累加器值对应的圆圈。

So, as you can see, internally the HoughCircles function calls the Canny edge detector, this means that you can use a gray image in the function, instead of their contours.

因此,正如您所看到的,HoughCircles函数在内部调用Canny边缘检测器,这意味着您可以在函数中使用灰色图像,而不是它们的轮廓。

Now reduce the param1 to 30 and param2 to 15 and see the results in the code that follows:

现在将param1减少到30并将param2减少到15并在下面的代码中查看结果:

import cv2
import numpy as np

img = np.ones((200,250,3), dtype=np.uint8)
for i in range(50, 80, 1):
    for j in range(40, 70, 1):
        img[i][j]*=200

cv2.circle(img, (120,120), 20, (100,200,80), -1)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

circles = cv2.HoughCircles(gray, cv2.cv.CV_HOUGH_GRADIENT, 1, 20,
              param1=30,
              param2=15,
              minRadius=0,
              maxRadius=0)

print circles
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
    cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)

cv2.imshow('circles', img)

k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()

#2

If you're not getting HoughCircles to bring you pixel perfect solutions for obvious circles then you're not using it right

如果你没有让HoughCircles为明显的圈子带来像素完美的解决方案那么你就没有正确使用它

Your mistake is you're trying to hand-tune your hyperparameters by yourself. That's not going to work. Have the computer auto-tune the parameters for you:

你的错误就是你试图亲自调整你的超参数。那不行。让计算机为您自动调整参数:

import numpy as np
import argparse
import cv2
import signal

from functools import wraps
import errno
import os
import copy

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())

image = cv2.imread(args["image"])
orig_image = np.copy(image)
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

cv2.imshow("gray", gray)
cv2.waitKey(0)

circles = None

minimum_circle_size = 100      #this is the range of possible circle in pixels you want to find
maximum_circle_size = 150     #maximum possible circle size you're willing to find in pixels

guess_dp = 1.0

number_of_circles_expected = 1          #we expect to find just one circle
breakout = False

max_guess_accumulator_array_threshold = 100     #minimum of 1, no maximum, (max 300?) the quantity of votes 
                                                #needed to qualify for a circle to be found.
circleLog = []

guess_accumulator_array_threshold = max_guess_accumulator_array_threshold

while guess_accumulator_array_threshold > 1 and breakout == False:
    #start out with smallest resolution possible, to find the most precise circle, then creep bigger if none found
    guess_dp = 1.0
    print("resetting guess_dp:" + str(guess_dp))
    while guess_dp < 9 and breakout == False:
        guess_radius = maximum_circle_size
        print("setting guess_radius: " + str(guess_radius))
        print(circles is None)
        while True:

            #HoughCircles algorithm isn't strong enough to stand on its own if you don't
            #know EXACTLY what radius the circle in the image is, (accurate to within 3 pixels) 
            #If you don't know radius, you need lots of guess and check and lots of post-processing 
            #verification.  Luckily HoughCircles is pretty quick so we can brute force.

            print("guessing radius: " + str(guess_radius) + 
                    " and dp: " + str(guess_dp) + " vote threshold: " + 
                    str(guess_accumulator_array_threshold))

            circles = cv2.HoughCircles(gray, 
                cv2.cv.CV_HOUGH_GRADIENT, 
                dp=guess_dp,               #resolution of accumulator array.
                minDist=100,                #number of pixels center of circles should be from each other, hardcode
                param1=50,
                param2=guess_accumulator_array_threshold,
                minRadius=(guess_radius-3),    #HoughCircles will look for circles at minimum this size
                maxRadius=(guess_radius+3)     #HoughCircles will look for circles at maximum this size
                )

            if circles is not None:
                if len(circles[0]) == number_of_circles_expected:
                    print("len of circles: " + str(len(circles)))
                    circleLog.append(copy.copy(circles))
                    print("k1")
                break
                circles = None
            guess_radius -= 5 
            if guess_radius < 40:
                break;

        guess_dp += 1.5

    guess_accumulator_array_threshold -= 2

#Return the circleLog with the highest accumulator threshold

# ensure at least some circles were found
for cir in circleLog:
    # convert the (x, y) coordinates and radius of the circles to integers
    output = np.copy(orig_image)

    if (len(cir) > 1):
        print("FAIL before")
        exit()

    print(cir[0, :])

    cir = np.round(cir[0, :]).astype("int")

    for (x, y, r) in cir:
        cv2.circle(output, (x, y), r, (0, 0, 255), 2)
        cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)

    cv2.imshow("output", np.hstack([orig_image, output]))
    cv2.waitKey(0)

The above code converts this:

上面的代码转换为:

To this:

For more information about what this is doing, see: https://*.com/a/46500223/445131

有关此操作的详细信息,请参阅:https://*.com/a/46500223/445131