OpenCV 断言失败：（-215：断言失败）npoints >= 0 && (depth == CV_32F \|\| depth == CV_32S)

Question

我在 这个网站 上找到了以下代码：

 import os
import os.path
import cv2
import glob
import imutils
CAPTCHA_IMAGE_FOLDER = "generated_captcha_images"
OUTPUT_FOLDER = "extracted_letter_images"

# Get a list of all the captcha images we need to process
captcha_image_files = glob.glob(os.path.join(CAPTCHA_IMAGE_FOLDER, "*"))
counts = {}

# loop over the image paths
for (i, captcha_image_file) in enumerate(captcha_image_files):
    print("[INFO] processing image {}/{}".format(i + 1, len(captcha_image_files)))

    # Since the filename contains the captcha text (i.e. "2A2X.png" has the text "2A2X"),
    # grab the base filename as the text
    filename = os.path.basename(captcha_image_file)
    captcha_correct_text = os.path.splitext(filename)[0]

    # Load the image and convert it to grayscale
    image = cv2.imread(captcha_image_file)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Add some extra padding around the image
    gray = cv2.copyMakeBorder(gray, 8, 8, 8, 8, cv2.BORDER_REPLICATE)

    # threshold the image (convert it to pure black and white)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

    # find the contours (continuous blobs of pixels) the image
    contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Hack for compatibility with different OpenCV versions
    contours = contours[0] if imutils.is_cv2() else contours[1]

    letter_image_regions = []

    # Now we can loop through each of the four contours and extract the letter
    # inside of each one
    for contour in contours:
        # Get the rectangle that contains the contour
        (x, y, w, h) = cv2.boundingRect(contour)

        # Compare the width and height of the contour to detect letters that
        # are conjoined into one chunk
        if w / h > 1.25:
            # This contour is too wide to be a single letter!
            # Split it in half into two letter regions!
            half_width = int(w / 2)
            letter_image_regions.append((x, y, half_width, h))
            letter_image_regions.append((x + half_width, y, half_width, h))
        else:
            # This is a normal letter by itself
            letter_image_regions.append((x, y, w, h))

    # If we found more or less than 4 letters in the captcha, our letter extraction
    # didn't work correcly. Skip the image instead of saving bad training data!
    if len(letter_image_regions) != 4:
        continue

    # Sort the detected letter images based on the x coordinate to make sure
    # we are processing them from left-to-right so we match the right image
    # with the right letter
    letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])

    # Save out each letter as a single image
    for letter_bounding_box, letter_text in zip(letter_image_regions, captcha_correct_text):
        # Grab the coordinates of the letter in the image
        x, y, w, h = letter_bounding_box

        # Extract the letter from the original image with a 2-pixel margin around the edge
        letter_image = gray[y - 2:y + h + 2, x - 2:x + w + 2]

        # Get the folder to save the image in
        save_path = os.path.join(OUTPUT_FOLDER, letter_text)

        # if the output directory does not exist, create it
        if not os.path.exists(save_path):
            os.makedirs(save_path)

        # write the letter image to a file
        count = counts.get(letter_text, 1)
        p = os.path.join(save_path, "{}.png".format(str(count).zfill(6)))
        cv2.imwrite(p, letter_image)

        # increment the count for the current key
        counts[letter_text] = count + 1

当我尝试运行代码时，出现以下错误：

 [INFO] processing image 1/9955
Traceback (most recent call last):
  File "extract_single_letters_from_captchas.py", line 47, in <module>
    (x, y, w, h) = cv2.boundingRect(contour)
cv2.error: OpenCV(4.0.0) /Users/travis/build/skvark/opencv-python/opencv/modules/imgproc/src/shapedescr.cpp:741: error: (-215:Assertion failed) npoints >= 0 && (depth == CV_32F || depth == CV_32S) in function 'pointSetBoundingRect'

我尝试在 StackOverflow 上搜索解决方案，但没有找到任何类似的解决方案。

---

编辑（见评论）：

• type(contour[0]) = <class 'numpy.ndarray'>

• len(contour) = 4

原文由 Nazim Kerimbekov 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

这是在做错事：

 contours = contours[0] if imutils.is_cv2() else contours[1]

imutils.is_cv2() 正在返回 False 即使它应该返回 True 。如果您不介意删除此依赖项，请更改为：

 contours = contours[0]

---

我找到了原因。您正在学习的教程可能是在 OpenCV 4 发布之前发布的。 OpenCV 3 changed cv2.findContours(...) to return image, contours, hierarchy , while OpenCV 2’s cv2.findContours(...) and OpenCV 4’s cv2.findContours(...) return contours, hierarchy .因此，在 OpenCV 4 之前，如果您使用 OpenCV 2，它应该是 contours[0] else contours[1] 是正确的。如果你还想有这种“兼容”，可以改成：

 contours = contours[1] if imutils.is_cv3() else contours[0]

原文由 Berriel 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 2

在 OpenCV4 中，cv2.findContours 只有 2 个返回值。 轮廓是第一个值

contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

请注意，我添加了下划线以放弃 hierarchy 的其他返回值

原文由 sam 发布，翻译遵循 CC BY-SA 4.0 许可协议