Question: Image

![](https://i.stack.imgur.com/v02J6.jpg)

I am trying to find the position of the rectangle in the image. Considering the top part of the image as reference, how can I find the position of the rectangle? I want the coordinates of the four vertices.

I have tried findContours and bounded rectangle method but have not been able to get the coordinates. Help would be appreciated.

Answer: 
Just get contour and then find bounding rectangle. Check [this tutorial](https://docs.opencv.org/3.1.0/dd/d49/tutorial_py_contour_features.html) from official documentation. 

```
import cv2

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY)

_, contours, _ = cv2.findContours(thresh,
                                  cv2.RETR_TREE,
                                  cv2.CHAIN_APPROX_NONE)

x1, y1, w, h = cv2.boundingRect(contours[0])
x2, y2 = x1 + w, y1 + h
print((x1, y1), (x2, y2)) 
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)

cv2.imwrite('res.jpg', img)

```

Coordinates of upper-left and lower-right points of rectangle (*(x1, y1), (x2, y2)*):

```
(369, 0) (901, 433)

```

Result:

[![res.jpg](https://i.stack.imgur.com/D6hbu.jpg)](https://i.stack.imgur.com/D6hbu.jpg)
just adapting the [cv2 tutorial](https://docs.opencv.org/3.1.0/dd/d49/tutorial_py_contour_features.html), you can find the largest contour and then obtain its bounding rectangle.

```
img = cv2.imread('/home/kvnp/Desktop/v02J6.jpg',0) #read in the image with correct dtype 

_,thresh = cv2.threshold(img,200,255,0) # threshold the image 

_,contours,_ = cv2.findContours(thresh, 1, 2) # find the contours

cnt = sorted(contours, key = lambda c: -len(c))[0] # order from largest to smallest and select the largest 

x,y,w,h = cv2.boundingRect(cnt) # make bounding rectangle around the largest contour 

corners = np.array([[x,y],[x+w,y],[x+w,y+h],[x,y+h]]) # make array of corners sorted clockwise 

```

This image I get 

```
>>> corners 

array([[371,   1],
   [900,   1],
   [900, 430],
   [371, 430]])

```