我正在使用 D-lib 库来使用视觉识别。所以我打算使用文档中给出的选项来训练我自己的分类器。与 C++ 相比，我使用 Python 作为语言平台。
因此，我使用 imglab 工具创建了两个 .xml 文件进行训练和测试。我必须在 imglab 工具中标记所有主题名称吗？我有近 20000 张图像。会不会很困难？我们有一个简单的方法吗？请找到与所附场景匹配的代码。
import os
import sys
import glob
import dlib
from skimage import io
# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory. This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.
faces_folder = "/media/praveen/SSD/NIVL_Ocular/NIR_Ocular_Training"
# Now let's do the training. The train_simple_object_detector() function has a
# bunch of options, all of which come with reasonable default values. The next
# few lines goes over some of these options.
options = dlib.simple_object_detector_training_options()
# Since faces are left/right symmetric we can tell the trainer to train a
# symmetric detector. This helps it get the most value out of the training
# data.
options.add_left_right_image_flips = False
# The trainer is a kind of support vector machine and therefore has the usual
# SVM C parameter. In general, a bigger C encourages it to fit the training
# data better but might lead to overfitting. You must find the best C value
# empirically by checking how well the trained detector works on a test set of
# images you haven't trained on. Don't just leave the value set at 5. Try a
# few different C values and see what works best for your data.
options.C = 5
# Tell the code how many CPU cores your computer has for the fastest training.
options.num_threads = 4
options.be_verbose = True
training_xml_path = os.path.join(faces_folder, "/media/praveen/SSD/NIVL_Ocular/praveen_ocular_dataset.xml")
testing_xml_path = os.path.join(faces_folder, "/media/praveen/SSD/NIVL_Ocular/praveen_ocular_test_dataset.xml")
# This function does the actual training. It will save the final detector to
# detector.svm. The input is an XML file that lists the images in the training
# dataset and also contains the positions of the face boxes. To create your
# own XML files you can use the imglab tool which can be found in the
# tools/imglab folder. It is a simple graphical tool for labeling objects in
# images with boxes. To see how to use it read the tools/imglab/README.txt
# file. But for this example, we just use the training.xml file included with
# dlib.
dlib.train_simple_object_detector(training_xml_path, "detector.svm", options)
# Now that we have a face detector we can test it. The first statement tests
# it on the training data. It will print(the precision, recall, and then)
# average precision.
print("") # Print blank line to create gap from previous output
print("Training accuracy: {}".format(
dlib.test_simple_object_detector(training_xml_path, "detector.svm")))
# However, to get an idea if it really worked without overfitting we need to
# run it on images it wasn't trained on. The next line does this. Happily, we
# see that the object detector works perfectly on the testing images.
print("Testing accuracy: {}".format(
dlib.test_simple_object_detector(testing_xml_path, "detector.svm")))
#
# # Now let's use the detector as you would in a normal application. First we
# # will load it from disk.
# detector = dlib.simple_object_detector("detector.svm")
#
# # We can look at the HOG filter we learned. It should look like a face. Neat!
# win_det = dlib.image_window()
# win_det.set_image(detector)
#
# # Now let's run the detector over the images in the faces folder and display the
# # results.
# print("Showing detections on the images in the faces folder...")
# win = dlib.image_window()
# for f in glob.glob(os.path.join(faces_folder, "*.png")):
# print("Processing file: {}".format(f))
# img = io.imread(f)
# dets = detector(img)
# print("Number of faces detected: {}".format(len(dets)))
# for k, d in enumerate(dets):
# print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
# k, d.left(), d.top(), d.right(), d.bottom()))
#
# win.clear_overlay()
# win.set_image(img)
# win.add_overlay(dets)
# dlib.hit_enter_to_continue()