Сбой утверждения OpenCV (4.0.0) в функции 'contourArea'

Question

Моя цель - идентифицировать автомобильные логотипы, используя дескрипторы HOG.Я следую учебник связан https://gurus.pyimagesearch.com/lesson-sample-histogram-of-oriented-gradients-and-car-logo-recognition/#.У меня есть изображения для тестирования и обучения в отдельных папках.

При извлечении функций HOG с использованием следующего кода:

# import the necessary packages
from sklearn.neighbors import KNeighborsClassifier
from skimage import exposure
from skimage import feature
from imutils import paths
import argparse
import imutils
import cv2

# construct the argument parse and parse command line arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--training", required=True, help="Path to the logos training dataset")
ap.add_argument("-t", "--test", required=True, help="Path to the test dataset")
args = vars(ap.parse_args())

# initialize the data matrix and labels
print('[INFO] extracting features...')
data = []
labels = []


# loop over the image paths in the training set
for imagePath in paths.list_images(args["training"]):
    # extract the make of the car
    make = imagePath.split("/")[-2]

    # load the image, convert it to grayscale, and detect edges
    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    edged = imutils.auto_canny(gray)

    # find contours in the edge map, keeping only the largest one which
    # is presmumed to be the car logo
    cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if imutils.is_cv2() else cnts[1]
    c = max(cnts, key=cv2.contourArea)

    # extract the logo of the car and resize it to a canonical width
    # and height
    (x, y, w, h) = cv2.boundingRect(c)
    logo = gray[y:y + h, x:x + w]
    logo = cv2.resize(logo, (200, 100))

    # extract Histogram of Oriented Gradients from the logo
    H = feature.hog(logo, orientations=9, pixels_per_cell=(10, 10),
        cells_per_block=(2, 2), transform_sqrt=True, block_norm="L1")

    # update the data and labels
    data.append(H)
    labels.append(make)

Я столкнулся с этой ошибкой:

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
  import imp
[INFO] extracting features...
Traceback (most recent call last):
  File "hog.py", line 36, in <module>
    c = max(cnts, key=cv2.contourArea)
cv2.error: OpenCV(4.0.0) /Users/travis/build/skvark/opencv-python/opencv/modules/imgproc/src/shapedescr.cpp:272: error: (-215:Assertion failed) npoints >= 0 && (depth == CV_32F || depth == CV_32S) in function 'contourArea'

Как сделатьЛи я удалить эту ошибку?.

Answer 1

Перед тем, как найти число изображений обрамленный , преобразуйте его в тип uint8:

edged = np.uint8(edged)
cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

Я отредактировал твой код. Я проверил это в моей системе, она работает нормально. Попробуйте это:

# import the necessary packages
from sklearn.neighbors import KNeighborsClassifier
from skimage import exposure
from skimage import feature
from imutils import paths
import argparse
import imutils
import cv2
# construct the argument parse and parse command line arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--training", required=True, help="Path to the logos training dataset")
ap.add_argument("-t", "--test", required=True, help="Path to the test dataset")
args = vars(ap.parse_args())

# initialize the data matrix and labels
print('[INFO] extracting features...')
data = []
labels = []


# loop over the image paths in the training set
for imagePath in paths.list_images(args["training"]):
    # extract the make of the car
    make = imagePath.split("/")[-2]

    # load the image, convert it to grayscale, and detect edges
    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    edged = imutils.auto_canny(gray)

    # find contours in the edge map, keeping only the largest one which
    # is presmumed to be the car logo
    edged = np.uint8(edged)
    cnts, _ =  = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if cnts is not None:
        cnts = cnts[0] if imutils.is_cv2() else cnts[1]
        c = max(cnts, key=cv2.contourArea)

        # extract the logo of the car and resize it to a canonical width
        # and height
        (x, y, w, h) = cv2.boundingRect(c)
        logo = gray[y:y + h, x:x + w]
        logo = cv2.resize(logo, (200, 100))

        # extract Histogram of Oriented Gradients from the logo
        H = feature.hog(logo, orientations=9, pixels_per_cell=(10, 10),
            cells_per_block=(2, 2), transform_sqrt=True, block_norm="L1")

        # update the data and labels
        data.append(H)
        labels.append(make)