# Getting x,y coordinates from an object in a video

Software: python, opencv,ubunu 16.04

Hello, I am working on a project where I am trying to get the real world x,y coordinates for some cubes using the region of interest. I was able to get the region of interest and find the center coordinates for my objects. The part I am stuck on is getting the center coordinates relative to the region of interest in my case it is a white sheet of paper that is about 8 1/4 x 11 3/4 inches. How can I find the real world center coordinates of the objects(shown in picture) relative to the size of the region of interest? This is only a prototype and I later be using a region of interest that is 25cmx25cm. I have included a picture of my work and my code for reference.

Thank you

https://imgur.com/a/r5MtAxO

import cv2
import numpy as np

#find the center of an object
def center(contours):
# calculate moments for each contour
for c in contours:
M = cv2.moments(c)

if M["m00"] != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
cX, cY = 0, 0
tempStr = str(cX) + ", " + str(cY)
cv2.circle(frame, (cX, cY), 1, (0, 0, 0), -1) #make a dot at the center of the object
cv2.putText(frame, tempStr, (cX - 25, cY - 25),cv2.FONT_HERSHEY_TRIPLEX, 0.4, (0, 0, 0), 1) #print the coordinates on the image

#get the region of interest
def find_ROI(frame):
image = frame.copy()

#change to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#Get binary image
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

#create structural element
struc_ele = np.ones((5, 5), np.uint8)

#Use Open Morphology
img_open = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, struc_ele, iterations = 1)

#find contours
_, ctrs, _ = cv2.findContours(img_open.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr))

for i, ctr in enumerate(sorted_ctrs):
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)

# Getting ROI
roi = image[y:y + h, x:x + w]

# show ROI
cv2.imshow('Region of Interest',roi)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('marked areas', image)

cap = cv2.VideoCapture(0)

if __name__ == "__main__":
while True:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

find_ROI(frame)
# l_h = cv2.getTrackbarPos("L - H", "Trackbars")
# l_s = cv2.getTrackbarPos("L - S", "Trackbars")
# l_v = cv2.getTrackbarPos("L - V", "Trackbars")
# u_h = cv2.getTrackbarPos("U - H", "Trackbars")
# u_s = cv2.getTrackbarPos("U - S", "Trackbars")
# u_v = cv2.getTrackbarPos("U - V", "Trackbars")

#Create kernel to use in filter
kernel = np.ones((5, 5), np.uint8)

#Create filter for yellow
lower_yellow = np.array([15, 100, 100]) #Lower boundary values for HSV
upper_yellow = np.array([30, 255, 255]) #Upper boundary values for HSV

yellow_mask = cv2.inRange(hsv, lower_yellow, upper_yellow) # Threshold the HSV image to get only yellow colors
opening_y = cv2.morphologyEx(yellow_mask, cv2.MORPH_OPEN, kernel, iterations = 2) #Use morphology open to rid ...
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