[FIXED] How to obtain (x,y) coordinates of a shape found using mask in OpenCV, Python?

Issue

I am trying to locate certain items on an image. The image in simplified form looks like this:

I would like to obtain the (x,y) coordinates of the bold black text on top of the second rectangle, as well as of the three colored rectangles.

I have the masks ready, except for the mask of the black text that I wasn't able to figure out. However, the text is always on top of the rectangle so if I'd be able to figure out the position of the bottom large rectangle, I would have the position of the text too.

These are the outputs I got:

I tried using the ConnectedComponents function based on this comment but apart from coloring and grouping the various objects, I didn't manage to move forward, so I didn't include that snippet below to make things as clear as possible.

Here is my code so far:

import cv2
import numpy as np
import imutils

PATH = "stackoverflow.png"
img = cv2.imread(PATH)
imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

mask_border = cv2.inRange(imgHSV,np.array([0,0,170]),np.array([0,0,175]))
mask_green  = cv2.inRange(imgHSV,np.array([76,221,167]),np.array([76,221,167]))
mask_pink   = cv2.inRange(imgHSV,np.array([168,41,245]),np.array([172,41,252]))
mask_red    = cv2.inRange(imgHSV,np.array([4,207,251]),np.array([4,207,251]))
#mask_black  = ???

all_masks = cv2.bitwise_or(mask_border, mask_green)
all_masks = cv2.bitwise_or(all_masks, mask_pink)
all_masks = cv2.bitwise_or(all_masks, mask_red)

cv2.imshow("Masks", all_masks)

imgResult = cv2.bitwise_and(img,img,mask=all_masks)

cv2.imshow("Output", imgResult)

cv2.waitKey(0)

Solution

You can binarise image and then apply some morphological operations to get the proper connected components. Here is an approach. You can fine-tune this to get proper output.

import numpy as np
import cv2
import os
image=cv2.imread('path/to/image.jpg')
###binarising
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
ret2,th2 = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

###applying morphological operations to dilate the image
kernel=np.ones((3,3),np.uint8)
dilated=cv2.dilate(th2,kernel,iterations=3)

### finding contours, can use connectedcomponents aswell
_,contours,_ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

### converting to bounding boxes from polygon
contours=[cv2.boundingRect(cnt) for cnt in contours]
### drawing rectangle for each contour for visualising
for cnt in contours:
    x,y,w,h=cnt
    cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)

Normal binary image

Dilated image

output with detected bounding boxes

Answered By - Sreekiran A R