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

Question

我在徘徊，因为代码被阻止了。我请求你的帮助。

它发生在……

• 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

错误反馈是...

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

完整的代码是....

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()

随着openCV版本的变化，我认为可能需要更改选项设置。我找到了几个文档并尝试了它们，但它们都无法正常工作。

我在做什么？？？我在做什么？？？我在做什么？？？我在做什么？？？我在做什么？？？

Answer 1

谢谢。我解决了这个问题。我认为问题的原因是起源或M。感谢您的帮助，我们安全地解决了它。谢谢！

原因是随着 openCV 的版本升级，它不再支持“int 类型”。 因此，我必须在代码中将输入变量更改为“无符号整数”。