collision_avoidance_behaviour

#!/usr/bin/env python
import numpy as np
import argparse
import cv2
import random


def find_red_blobs(image):
    '''finds a big red blob and returns its centre coordinates'''
 
    # create NumPy arrays from the boundaries
    lower = np.array([0,0,130], dtype = "uint8")
    upper = np.array([200,130,255], dtype = "uint8")
 
    # find the colors within the specified boundaries
    # boundaries are blue, green, red
    mask = cv2.inRange(image, lower, upper)

    # mask out around the identified parts of the image
    output = cv2.bitwise_and(image, image, mask = mask)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4,4))
    eroded = cv2.erode(output, kernel, iterations = 1)

    #show the eroded image     
    #cv2.imshow("Eroded",eroded)
    #cv2.waitKey(0)


    #threshold the image
    gray = cv2.cvtColor(eroded, cv2.COLOR_BGR2GRAY)
    ret,thresh1 = cv2.threshold(gray,70,255,cv2.THRESH_BINARY)

    #show the thresholded image    
    #cv2.imshow("Thresholded",thresh1)
    #cv2.waitKey(0)


    #get the contours of what ever is left in the image
    #opencv3 version
    #contour_im, contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    #opencv2 version
    contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    #find the largest contour
    largest_area = 0
    largest_area_index = 0
    for i in range(len(contours)):
        area = cv2.contourArea(contours[i])
        if area > largest_area:
            largest_area = area
            largest_area_index = i

    print("largest contour is ",largest_area_index,largest_area)

    #draw a circle around it
    (x,y),radius = cv2.minEnclosingCircle(contours[largest_area_index])
    center = (int(x),int(y))
    radius = int(radius)

    #show the circle
    cv2.circle(image,center,radius,(0,255,0),2)
    cv2.imshow('Circles', image)
    cv2.waitKey(0)
    
    return int(x),int(y)


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", help = "path to the image")
args = vars(ap.parse_args())
 
# load the image
image = cv2.imread(args["image"])
print(find_red_blobs(image))