Question

我正在使用D-lib库来使用眼睛识别。所以我打算使用文档中给出的选项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文件的培训和测试。我是否必须在imglab工具中标记所有主题名称？我有接近20000张图片。难道不难吗？我们有一个简单的方法吗？请找到与所附方案相匹配的代码。

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()

Answer 1

简单地说，是的，因为你想使用train dlib的物体探测器，它需要一个标记的（最多一个边界框）数据集，否则你将使用一个可用的标记数据集。

imglab的主要功能是创建边界框，并在评论中写出：

要创建自己的XML文件，可以使用imglab工具在tools / imglab文件夹中找到。它是一个简单的图形工具用方框标记图像中的对象。

原始论文请参考： https://arxiv.org/pdf/1502.00046v1.pdf

实际上，正如你所说，这真的很难。对象检测或识别的主要挑战之一是创建数据集。这就是为什么，研究人员使用Mechanical Turk类似的网站来利用人群的力量。

训练D-lib图像

1 个答案: