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How can I find contours inside ROI using opencv and Python?

Tags:

python

opencv

roi

Im trying to find contours in a specific area of the image. Is it possible to just show the contours inside the ROI and not the contours in the rest of the image? I read in another similar post that I should use a mask, but I dont think I used it correctly. Im new to openCV and Python, so any help is much appriciated.

import numpy as np
import cv2

cap = cv2.VideoCapture('size4.avi')
x, y, w, h= 150, 50, 400 ,350
roi = (x, y, w, h)

while(True): 
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, 127, 255, 0)
    im2, contours, hierarchy = cv2.findContours(thresh,    cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

    roi = cv2.rectangle(frame, (x,y), (x+w, y+h), (0,0,255), 2)
    mask = np.zeros(roi.shape,np.uint8)
    cv2.drawContours(mask, contours, -1, (0,255,0), 3)

    cv2.imshow('img', frame)
like image 826
agrom Avatar asked Feb 02 '17 14:02

agrom


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How do I get contours using OpenCV?

So before finding contours, apply threshold or canny edge detection. Since OpenCV 3.2, findContours() no longer modifies the source image but returns a modified image as the first of three return parameters. In OpenCV, finding contours is like finding white object from black background.

How does OpenCV detect contours?

Contour Detection using OpenCV (Python/C++) Using contour detection, we can detect the borders of objects, and localize them easily in an image. It is often the first step for many interesting applications, such as image-foreground extraction, simple-image segmentation, detection and recognition.


Video Answer


2 Answers

Since you claim to be a novice, I have worked out a solution along with an illustration.

Consider the following to be your original image:

enter image description here

Assume that the following region in red is your region of interest (ROI), where you would like to find your contours:

enter image description here

First, construct an image of black pixels of the same size. It MUST BE OF SAME size:

black = np.zeros((img.shape[0], img.shape[1], 3), np.uint8) #---black in RGB

enter image description here

Now to form the mask and highlight the ROI:

black1 = cv2.rectangle(black,(185,13),(407,224),(255, 255, 255), -1)   #---the dimension of the ROI
gray = cv2.cvtColor(black,cv2.COLOR_BGR2GRAY)               #---converting to gray
ret,b_mask = cv2.threshold(gray,127,255, 0)                 #---converting to binary image

enter image description here

Now mask the image above with your original image:

fin = cv2.bitwise_and(th,th,mask = mask)

enter image description here

Now use cv2.findContours() to find contours in the image above.

Then use cv2.drawContours() to draw contours on the original image. You will finally obtain the following:

enter image description here

There might be better methods as well, but this was done so as to get you aware of the bitwise AND operation availabe in OpenCV which is exclusively used for masking

like image 60
Jeru Luke Avatar answered Oct 12 '22 23:10

Jeru Luke


For setting a ROI in Python, one uses standard NumPy indexing such as in this example.

So, to select the right ROI, you don't use the cv2.rectangle function (that is for drawing a rectangle), but you do this instead:

 _, thresh = cv2.threshold(gray, 127, 255, 0)
 roi = thresh[x:(x+w), y:(y+h)]
 im2, contours, hierarchy = cv2.findContours(roi, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
like image 39
Tomáš M. Avatar answered Oct 12 '22 23:10

Tomáš M.