detect_img.py

import argparse
import numpy as np 
import cv2
import matplotlib.pyplot as plt

# Code for parsing command line arguments

parser = argparse.ArgumentParser(description='Detects centres and arms in an image')
parser.add_argument('filename', metavar='F', type=str, help='Path to the image file for detection')

args = parser.parse_args()

img  = cv2.imread(args.filename)
hsv_frame = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

green_sensitivity = 7
pink_sensitivity = 10
lower_green = np.array([60 - green_sensitivity, 100, 50])
upper_green = np.array([60 + green_sensitivity, 255, 255])
lower_pink = np.array([175 - pink_sensitivity, 100, 50])
upper_pink = np.array([175 + pink_sensitivity, 255, 255])

centre_mask = cv2.inRange(hsv_frame, lower_green, upper_green) 
arms_mask = cv2.inRange(hsv_frame, lower_pink, upper_pink)
mask = cv2.add(centre_mask,arms_mask)
res = cv2.bitwise_and(img, img, mask=mask)


# First we find the centres. We look at all contours from filtering the green marker. 
# The 6 centres will have area ~350 so we select those contours specifically.
# We also detect all the arms. 

_, green_ctrs, _ = cv2.findContours(centre_mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
_, pink_ctrs, _ = cv2.findContours(arms_mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
centres = []
for c in green_ctrs:
    M = cv2.moments(c)
    if M["m00"] > 300:
        centres.append(c)

arms = []
for c in pink_ctrs:
	M = cv2.moments(c)
	if M["m00"] > 50:
		arms.append(c)


# Now we draw and mark the centres of each green marker
centre_coords = []
for c in centres:
	M = cv2.moments(c)
	# calculate x,y coordinate of center
	cX = int(M["m10"] / M["m00"])
	cY = int(M["m01"] / M["m00"])
	cv2.circle(res, (cX, cY), 2, (0, 0, 255), -1)
	cv2.circle(res, (cX, cY), 80, (0, 0, 255), 1)
	# print('x = {}y = {}'.format(cX,cY))
	centre_coords.append([cX,cY])

# Similarly for the arms


cv2.drawContours(res,centres,-1,color=(255,0,0))  
cv2.drawContours(res,arms,-1,color=(0,255,0))  

# Let's try to isolate each spinner
window_size = 180
for index, centre in enumerate(centre_coords):
	cen_x, cen_y = centre
	min_x = max(cen_x - window_size//2, 0)
	max_x = min(cen_x + window_size//2, 639)
	min_y = max(cen_y - window_size//2, 0)
	max_y = min(cen_y + window_size//2, 479)

	# cv2.rectangle(res,(min_x,min_y),(max_x,max_y),(0,0,255))

	# Apparently for ROI the indices work in the reverse order [y,x]
	roi = res[min_y:max_y,min_x:max_x]
	cv2.imshow("spinner"+str(index+1), roi)


# cv2.imshow('Mask', mask)
# cv2.imshow('Snapshot',img)
# cv2.imshow('Result', res)
cv2.waitKey(0)
cv2.destroyAllWindows()