This is a tip I found when I tried to detect a red ball.
One of the popular methods is using Hough Circle Transform.
This method is difficult to explain, so I list some references.↓↓
However, the Hough transform is heavy, slow, and inaccurate. Besides, the size of the detected circle is not stable.
I was wondering how to deal with it, then an experienced friend told me that "it is better to use a minimum circumscribed circle".
I actually used it, and I found it was much more accurate (it doesn't mean truly accurate, though) and smooth.
it would be more accurate to say that it seems to recognize circles with good accuracy because it's actually not recognizing circles. It only detects certain colors, so the detection is fast and accurate.
I recognized a red ball displayed on my smartphone as a demo.
It seems good, isn't it?
Of course, there are cases of the Hough Circle Transform is a suitable choice, but this time I want to recognize only one red ball, so using the minimum circumscribed circle was comfortable enough.
An example code is shown below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
# -*- coding: utf-8 -*- import numpy as np import cv2 def getCircle(frame, lower_color, upper_color): MIN_RADIUS = 25 # Convert color space of an image to HSV hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # Apply Gaussian blur to improve recognition accuracy blur = cv2.GaussianBlur(hsv, (9, 9), 0) # Extract color in specified range color = cv2.inRange(blur, lower_color, upper_color) # Noise reduction with opening and closing element8 = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], np.uint8) oc = cv2.morphologyEx(color, cv2.MORPH_OPEN, element8) oc = cv2.morphologyEx(oc, cv2.MORPH_CLOSE, element8) # Extract contours img, contours, hierarchy = cv2.findContours(oc, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) print("{} contours.".format(len(contours))) if len(contours) > 0: # Get the most biggest red area contours.sort(key=cv2.contourArea, reverse=True) cnt = contours[0] # Detect a circle using a minimum circumscribed circle (x, y), radius = cv2.minEnclosingCircle(cnt) center = (int(x), int(y)) radius = int(radius) # Omit a too small circle if radius < MIN_RADIUS: return None else: return center, radius else: return None if __name__ == '__main__': # Start an internal camera (set 0 as an argument if only one camera is connected) cap = cv2.VideoCapture(0) while True: # Extract an red circle frame = cap.read()[1] getframe = getCircle(frame, np.array([130, 80, 80]), np.array([200, 255, 255])) if getframe is not None: # Draw a blue circle on a circle we found # getframe[0]:Center coordinates、getframe[1]:radius cv2.circle(frame, getframe[0], getframe[1], (255, 0, 0), 2) print(getframe[1]) # Show movie with the result of detection cv2.imshow('Circle Detect', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # Release the camera cap.release() cv2.destroyAllWindows() |
Everyone, let's use the minimum circumscribed circle!
輪郭に関するチュートリアル OpenCVにおける輪郭(領域) — OpenCV-Python Tutorials 1 documentation 輪郭抽出 OpenCVの輪郭検出は,黒い背景から白い物体の輪郭を検出すると仮定しています.物体は白(明るい色),背景は黒(暗い色)と覚えておいてください. とある。 cv2.findContours 輪郭抽出と、その輪郭に対する処理を人多りやてみる。 void findContours(const Mat& image, vector& contours, int mode, int method, Point off… opencv チュートリアルチャレンジ9 OpenCVにおける輪郭(領域) - 機械学習備忘録 - 機械学習備忘録 |