在图像阵列上应用边缘检测

Question

我试着用下面的代码做边缘检测：

lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)

# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape

# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
X = lfw_people.data
n_features = X.shape[1]

# the label to predict is the id of the person
y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]

print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)



###############################################################################
# Split into a training set and a test set using a stratified k fold

# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25)

我正在尝试使用sobel进行边缘检测，但据我所知，sobel用于1张图像。如何将其应用于多个图像或图像数组？

Answer 1

我相信在http://dsp.stackexchange.com或http://datascience.stackexchange.com上询问(对我来说，你听起来对你试图提取的特征相当不清楚)会是解决这个问题的更好的选择。