Note that I learned that there is a way to recognize and cut out a circular object.

Preparation

OpenCV preparation

First, make OpenCV available from Python.

In the recent Anaconda environment, Python 3.6 is installed, but the OpenCV3 conda package for the osx-64 environment that can be installed on Python 3.6 has not been released, so prepare a Python 3.5 environment.

For the time being, I created a virtual environment with conda.

conda create -n py35 python=3.5 anaconda

Then, after source ~ / .pyenv / versions / anaconda3-4.4.0 / bin / activate py35, do conda install --channel https://conda.anaconda.org/menpo opencv3.

Preparation of sample image

I didn't have the right image, so I downloaded and used the ImageJ sample.

https://imagej.nih.gov/ij/images/embryos.jpg

Implement

Try to implement it while referring to http://docs.opencv.org/3.1.0/da/d53/tutorial_py_houghcircles.html.

`houghcircles_sample.py`


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

img = cv2.imread('img/embryos.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray, 5)

circles = cv2.HoughCircles(blur, cv2.HOUGH_GRADIENT,
								dp=1, minDist=20, param1=50, param2=30,
								minRadius=10, maxRadius=100)
circles = np.uint16(np.around(circles))
for (x, y, r) in circles[0]:
	cv2.circle(img, (x, y), r, (0, 255, 0), 2)
	cv2.circle(img, (x, y), 2, (0, 0, 255), 3)

plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

Execution result

Not bad. Depending on the adjustment of param1 and param2, it seems that cells can be counted properly.

Today's code

https://github.com/fukuit/Python_SelfLearning/blob/master/hough/houghcircles_sample.py

Circular object recognition using Hough transform