How to use `cv2.findContours` in different OpenCV versions?

Question

I am trying to use OpenCV with Python in order to detect squares in a live video feed from a Raspberry Pi camera. However, the cv2.GaussianBlur and cv2.Canny functions in the code below are causing the following error: "TypeError: numpy.ndarray' object is not callable".

I cannot seem to resolve the error. Any help is appreciated.

Code taken from https://www.pyimagesearch.com/2015/05/04/target-acquired-finding-targets-in-drone-and-quadcopter-video-streams-using-python-and-opencv/#comment-446639

import cv2

# load the video
camera = cv2.VideoCapture(0)

# keep looping
while True:
  # grab the current frame and initialize the status text
  (grabbed, frame) = camera.read()
  status = "No Targets"

  # check to see if we have reached the end of the
  # video
  if not grabbed:
     break

  # convert the frame to grayscale, blur it, and detect edges
  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  blurred = cv2.GaussianBlur(gray, (7, 7), 0)
  edged = cv2.Canny(blurred, 50, 150)

  # find contours in the edge map
  (cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
     cv2.CHAIN_APPROX_SIMPLE)

  # loop over the contours
  for c in cnts:
    # approximate the contour
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.01 * peri, True)

    # ensure that the approximated contour is "roughly" rectangular
    if len(approx) >= 4 and len(approx) <= 6:
        # compute the bounding box of the approximated contour and
        # use the bounding box to compute the aspect ratio
        (x, y, w, h) = cv2.boundingRect(approx)
        aspectRatio = w / float(h)

        # compute the solidity of the original contour
        area = cv2.contourArea(c)
        hullArea = cv2.contourArea(cv2.convexHull(c))
        solidity = area / float(hullArea)

        # compute whether or not the width and height, solidity, and
        # aspect ratio of the contour falls within appropriate bounds
        keepDims = w > 25 and h > 25
        keepSolidity = solidity > 0.9
        keepAspectRatio = aspectRatio >= 0.8 and aspectRatio <= 1.2

        # ensure that the contour passes all our tests
        if keepDims and keepSolidity and keepAspectRatio:
            # draw an outline around the target and update the status
            # text
            cv2.drawContours(frame, [approx], -1, (0, 0, 255), 4)
            status = "Target(s) Acquired"

        # draw the status text on the frame
    cv2.putText(frame, status, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
    (0, 0, 255), 2)

  # show the frame and record if a key is pressed
  cv2.imshow("Frame", frame)
  key = cv2.waitKey(1) & 0xFF

  # if the 'q' key is pressed, stop the loop
  if key == ord("q"):
     break

# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()

Mycetozoan

Answer 1

An alternative to work with 2.x 、3.x、4.x is:

cnts, hiers = cv2.findContours(...)[-2:]

---

Notice:

cv2.findContours has changed since OpenCV 3.x, but in OpenCV 4.0 it changes back!!!

In OpenCV 3.4:

findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> image, contours, hierarchy

In OpenCV 4.0:

findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> contours, hierarchy

Hypnotism

Answer 2

Here is another way to store all the tuples returned from cv2.findContours() irrespective of the OpenCV version installed in your system/environment:

First, get the version of OpenCV installed (we don't want the entire version just the main number either 3 or 4) :

import cv2
major_number = cv2.__version__[0]

Based on the version either of the following two statements will be executed and the corresponding variables will be populated:

if major_number == '4':
    contours, hierarchy = cv2.findContours(img_binary, cv2.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)

elif major_number == '3':
    img, contours, hierarchy = cv2.findContours(img_binary, cv2.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)

The contours returned from the function in either scenarios will be stored in contours.

3.X.X version: documentation

4.X.X version: documentation

Fringe

Answer 3

Here is an automatic method, that I learned from @nathancy. The if ternary if test, determines how many return values there are and selected the appropriate one for the contours.

contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

Wormy