How can I find contours inside ROI using opencv and Python?
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)
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:
Assume that the following region in red is your region of interest (ROI), where you would like to find your contours:
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
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
Now mask the image above with your original image:
fin = cv2.bitwise_and(th,th,mask = mask)
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:
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
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)