我的目的是使用HOG描述符识别汽车徽标。我正在跟踪链接的教程https://gurus.pyimagesearch.com/lesson-sample-histogram-of-iented-gradients-and-car-logo-recognition/#。我在单独的文件夹中测试和训练图像。
使用以下代码提取HOG功能时：
# 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)
我遇到此错误：
/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'
如何清除此错误？
在找到边缘图像之前，请将其转换为uint8类型：
edged = np.uint8(edged)
cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
我已经编辑了您的代码。我在系统上检查了它，它工作正常。尝试这个：
# 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)
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