Detecting largest red, blue, and green object and show centroid

Python
1

The following code detects red, blue, and green colored objects and put bounding box around them. I want it to only detect the largest red, blue, and green object if more than one object of the same color exists. So if there are two red objects, two green objects, and two blue objects in the image, it should only detect the largest red, largest blue, and largest green object, and show their centroid.

import cap
import numpy as np
import cv2

# Capturing video through webcam
webcam = cv2.VideoCapture(0)

# Start a while loop
while (1):

    # Reading the video from the
    # webcam in image frames
    _, imageFrame = webcam.read()

    # Convert the imageFrame in
    # BGR(RGB color space) to
    # HSV(hue-saturation-value)
    # color space
    hsvFrame = cv2.cvtColor(imageFrame, cv2.COLOR_BGR2HSV)

    # Set range for red color and
    # define mask
    red_lower = np.array([136, 87, 111], np.uint8)
    red_upper = np.array([180, 255, 255], np.uint8)
    red_mask = cv2.inRange(hsvFrame, red_lower, red_upper)

    # Set range for green color and
    # define mask
    green_lower = np.array([25, 52, 72], np.uint8)
    green_upper = np.array([102, 255, 255], np.uint8)
    green_mask = cv2.inRange(hsvFrame, green_lower, green_upper)

    # Set range for blue color and
    # define mask
    blue_lower = np.array([94, 80, 2], np.uint8)
    blue_upper = np.array([120, 255, 255], np.uint8)
    blue_mask = cv2.inRange(hsvFrame, blue_lower, blue_upper)

    # Morphological Transform, Dilation
    # for each color and bitwise_and operator
    # between imageFrame and mask determines
    # to detect only that particular color
    kernal = np.ones((5, 5), "uint8")

    # For red color
    red_mask = cv2.dilate(red_mask, kernal)
    res_red = cv2.bitwise_and(imageFrame, imageFrame,
                              mask=red_mask)

    # For green color
    green_mask = cv2.dilate(green_mask, kernal)
    res_green = cv2.bitwise_and(imageFrame, imageFrame,
                                mask=green_mask)

    # For blue color
    blue_mask = cv2.dilate(blue_mask, kernal)
    res_blue = cv2.bitwise_and(imageFrame, imageFrame,
                               mask=blue_mask)

    # Creating contour to track red color
    contours, hierarchy = cv2.findContours(red_mask,
                                           cv2.RETR_TREE,
                                           cv2.CHAIN_APPROX_SIMPLE)

    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if (area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            imageFrame = cv2.rectangle(imageFrame, (x, y),
                                       (x + w, y + h),
                                       (0, 0, 255), 2)

            cv2.putText(imageFrame, "Red Colour", (x, y),
                        cv2.FONT_HERSHEY_SIMPLEX, 1.0,
                        (0, 0, 255))

    # Creating contour to track green color
    contours, hierarchy = cv2.findContours(green_mask,
                                           cv2.RETR_TREE,
                                           cv2.CHAIN_APPROX_SIMPLE)

    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if (area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            imageFrame = cv2.rectangle(imageFrame, (x, y),
                                       (x + w, y + h),
                                       (0, 255, 0), 2)

            cv2.putText(imageFrame, "Green Colour", (x, y),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        1.0, (0, 255, 0))

    # Creating contour to track blue color
    contours, hierarchy = cv2.findContours(blue_mask,
                                           cv2.RETR_TREE,
                                           cv2.CHAIN_APPROX_SIMPLE)
    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if (area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            imageFrame = cv2.rectangle(imageFrame, (x, y),
                                       (x + w, y + h),
                                       (255, 0, 0), 2)

            cv2.putText(imageFrame, "Blue Colour", (x, y),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        1.0, (255, 0, 0))

    # Program Termination
    cv2.imshow("Multiple Color Detection in Real-TIme", imageFrame)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        cap.release()
        cv2.destroyAllWindows()
        break
2

The following code detects all the red, blue, and green objects. I want it to only detect the largest red, blue, and green object, and show their centroids.

# Python code for Multiple Color Detection

import numpy as np
import cv2

# Capturing video through webcam
webcam = cv2.VideoCapture(0)

# Start a while loop
while(1):
	
	# Reading the video from the
	# webcam in image frames
	_, imageFrame = webcam.read()

	# Convert the imageFrame in
	# BGR(RGB color space) to
	# HSV(hue-saturation-value)
	# color space
	hsvFrame = cv2.cvtColor(imageFrame, cv2.COLOR_BGR2HSV)

	# Set range for red color and
	# define mask
	red_lower = np.array([136, 87, 111], np.uint8)
	red_upper = np.array([180, 255, 255], np.uint8)
	red_mask = cv2.inRange(hsvFrame, red_lower, red_upper)

	# Set range for green color and
	# define mask
	green_lower = np.array([25, 52, 72], np.uint8)
	green_upper = np.array([102, 255, 255], np.uint8)
	green_mask = cv2.inRange(hsvFrame, green_lower, green_upper)

	# Set range for blue color and
	# define mask
	blue_lower = np.array([94, 80, 2], np.uint8)
	blue_upper = np.array([120, 255, 255], np.uint8)
	blue_mask = cv2.inRange(hsvFrame, blue_lower, blue_upper)
	
	# Morphological Transform, Dilation
	# for each color and bitwise_and operator
	# between imageFrame and mask determines
	# to detect only that particular color
	kernal = np.ones((5, 5), "uint8")
	
	# For red color
	red_mask = cv2.dilate(red_mask, kernal)
	res_red = cv2.bitwise_and(imageFrame, imageFrame,
							mask = red_mask)
	
	# For green color
	green_mask = cv2.dilate(green_mask, kernal)
	res_green = cv2.bitwise_and(imageFrame, imageFrame,
								mask = green_mask)
	
	# For blue color
	blue_mask = cv2.dilate(blue_mask, kernal)
	res_blue = cv2.bitwise_and(imageFrame, imageFrame,
							mask = blue_mask)

	# Creating contour to track red color
	contours, hierarchy = cv2.findContours(red_mask,
										cv2.RETR_TREE,
										cv2.CHAIN_APPROX_SIMPLE)
	
	for pic, contour in enumerate(contours):
		area = cv2.contourArea(contour)
		if(area > 300):
			x, y, w, h = cv2.boundingRect(contour)
			imageFrame = cv2.rectangle(imageFrame, (x, y),
									(x + w, y + h),
									(0, 0, 255), 2)
			
			cv2.putText(imageFrame, "Red Colour", (x, y),
						cv2.FONT_HERSHEY_SIMPLEX, 1.0,
						(0, 0, 255))	

	# Creating contour to track green color
	contours, hierarchy = cv2.findContours(green_mask,
										cv2.RETR_TREE,
										cv2.CHAIN_APPROX_SIMPLE)
	
	for pic, contour in enumerate(contours):
		area = cv2.contourArea(contour)
		if(area > 300):
			x, y, w, h = cv2.boundingRect(contour)
			imageFrame = cv2.rectangle(imageFrame, (x, y),
									(x + w, y + h),
									(0, 255, 0), 2)
			
			cv2.putText(imageFrame, "Green Colour", (x, y),
						cv2.FONT_HERSHEY_SIMPLEX,
						1.0, (0, 255, 0))

	# Creating contour to track blue color
	contours, hierarchy = cv2.findContours(blue_mask,
										cv2.RETR_TREE,
										cv2.CHAIN_APPROX_SIMPLE)
	for pic, contour in enumerate(contours):
		area = cv2.contourArea(contour)
		if(area > 300):
			x, y, w, h = cv2.boundingRect(contour)
			imageFrame = cv2.rectangle(imageFrame, (x, y),
									(x + w, y + h),
									(255, 0, 0), 2)
			
			cv2.putText(imageFrame, "Blue Colour", (x, y),
						cv2.FONT_HERSHEY_SIMPLEX,
						1.0, (255, 0, 0))
			
	# Program Termination
	cv2.imshow("Multiple Color Detection in Real-TIme", imageFrame)
	if cv2.waitKey(10) & 0xFF == ord('q'):
		cap.release()
		cv2.destroyAllWindows()
		break
3

you have only presented a task. you haven’t asked a question, or explained what prevents you from completing this task.

4

My question is how to detect only the largest red, green, and blue object and how to show their centroid.

5

When you already have a collection of contours and know how to calculate the area or other size metrics for each, finding the largest, and doing things only on that, is the simplest possible generic programming task…

6

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