cv2.error: OpenCV(4.5.4):-1: error: (-5:Bad argument) in function 'warpPerspective'

Question

I'm wandering because the code is blocked. I ask for your help.

It occurred at...

• python: 3.8.9 (tags/v3.8.9:a743f81, Apr 6 2021, 14:02:34) [MSC v.1928 64 bit (AMD64)]
• opencv: 4.5.4

error feedback is...

Traceback (most recent call last):
  File "D:/001_DataAnalysisTools/pythonProject3/ex_opencv/main.py", line 517, in <module>
    auto_scan_image()
  File "D:/001_DataAnalysisTools/pythonProject3/ex_opencv/main.py", line 490, in auto_scan_image
    warped = cv2.warpPerspective(orig, M, (maxWidth, maxHeight), flags=cv2.INTER_LINEAR)

cv2.error: OpenCV(4.5.4) :-1: error: (-5:Bad argument) in function 'warpPerspective'
> Overload resolution failed:
>  - Can't parse 'dsize'. Sequence item with index 0 has a wrong type
>  - Can't parse 'dsize'. Sequence item with index 0 has a wrong type

full code is....

import numpy as np
import cv2


def order_points(pts):
    # initialzie a list of coordinates that will be ordered
    # such that the first entry in the list is the top-left,
    # the second entry is the top-right, the third is the
    # bottom-right, and the fourth is the bottom-left
    rect = np.zeros((4, 2), dtype="float32")

    # the top-left point will have the smallest sum, whereas
    # the bottom-right point will have the largest sum
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]

    # now, compute the difference between the points, the
    # top-right point will have the smallest difference,
    # whereas the bottom-left will have the largest difference
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]

    # return the ordered coordinates
    return rect


def auto_scan_image():
    # load the image and compute the ratio of the old height
    # to the new height, clone it, and resize it
    # document.jpg ~ docuemnt7.jpg
    image = cv2.imread('images/document.jpg')
    orig = image.copy()
    r = 800.0 / image.shape[0]
    dim = (int(image.shape[1] * r), 800)
    image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)

    # convert the image to grayscale, blur it, and find edges
    # in the image
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (3, 3), 0)
    edged = cv2.Canny(gray, 75, 200)

    # show the original image and the edge detected image
    print("STEP 1: Edge Detection")
    cv2.imshow("Image", image)
    cv2.imshow("Edged", edged)

    cv2.waitKey(0)
    cv2.destroyAllWindows()
    # cv2.waitKey(1)

    # find the contours in the edged image, keeping only the
    # largest ones, and initialize the screen contour
    cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]

    # loop over the contours
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)

        # if our approximated contour has four points, then we
        # can assume that we have found our screen
        if len(approx) == 4:
            screenCnt = approx
            break

    # show the contour (outline) of the piece of paper
    print("STEP 2: Find contours of paper")
    cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
    cv2.imshow("Outline", image)

    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.waitKey(1)

    # apply the four point transform to obtain a top-down
    # view of the original image
    rect = order_points(screenCnt.reshape(4, 2) / r)
    (topLeft, topRight, bottomRight, bottomLeft) = rect

    w1 = abs(bottomRight[0] - bottomLeft[0])
    w2 = abs(topRight[0] - topLeft[0])
    h1 = abs(topRight[1] - bottomRight[1])
    h2 = abs(topLeft[1] - bottomLeft[1])
    maxWidth = max([w1, w2])
    maxHeight = max([h1, h2])

    dst = np.float32([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]])

    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(orig, M, (maxWidth, maxHeight), flags=cv2.INTER_LINEAR)

    # show the original and scanned images
    print("STEP 3: Apply perspective transform")
    cv2.imshow("Warped", warped)

    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.waitKey(1)


if __name__ == '__main__':
    auto_scan_image()

As the version of openCV changes, I think it may be necessary to change the option setting. I found several documents and tried them, but they didn't work out properly.

what am I do??? what am I do??? what am I do??? what am I do??? what am I do???

Answer 1

Thank you. I solved the problem. I thought the cause of the problem was origin or M. Thanks to your help, we solved it safely. Thank you!

The reason is that as openCV was versioned up, it no longer supports 'int type'. Therefore, I have to change the input variable to 'unsigned integer' in the code.