Train train_shape_predictor.py 文件报错

Question

我正在尝试使用 http://dlib.net/train_shape_predictor.py.html

训练 Dlib 的形状预测器

#!/usr/bin/python

import os
import sys
import glob

import dlib
from skimage import io

if len(sys.argv) != 2:
    print(
        "Give the path to the examples/faces directory as the argument to this "
        "program. For example, if you are in the python_examples folder then "
        "execute this program by running:\n"
        "    ./train_shape_predictor.py ../examples/faces")
    exit()
faces_folder = sys.argv[1]

options = dlib.shape_predictor_training_options()

options.oversampling_amount = 300

options.nu = 0.05
options.tree_depth = 2
options.be_verbose = True

training_xml_path = os.path.join(faces_folder, "training_with_face_landmarks.xml")
print(training_xml_path)
dlib.train_shape_predictor(training_xml_path, "predictor.dat", options)

print("\nTraining accuracy: {}".format(
    dlib.test_shape_predictor(training_xml_path, "predictor.dat")))

testing_xml_path = os.path.join(faces_folder, "testing_with_face_landmarks.xml")
print("Testing accuracy: {}".format(
    dlib.test_shape_predictor(testing_xml_path, "predictor.dat")))


predictor = dlib.shape_predictor("predictor.dat")
detector = dlib.get_frontal_face_detector()

print("Showing detections and predictions on the images in the faces folder...")
win = dlib.image_window()
for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
    print("Processing file: {}".format(f))
    img = io.imread(f)

    win.clear_overlay()
    win.set_image(img)

    # Ask the detector to find the bounding boxes of each face. The 1 in the
    # second argument indicates that we should upsample the image 1 time. This
    # will make everything bigger and allow us to detect more faces.
    dets = detector(img, 1)
    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()))
        # Get the landmarks/parts for the face in box d.
        shape = predictor(img, d)
        print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
                                                  shape.part(1)))
        # Draw the face landmarks on the screen.
        win.add_overlay(shape)

    win.add_overlay(dets)
    dlib.hit_enter_to_continue()

输出：

/home/msc/Face_Rec/abc/training_with_face_landmarks.xml
Training with cascade depth: 10
Training with tree depth: 2
Training with 500 trees per cascade level.
Training with nu: 0.05
Training with random seed: 
Training with oversampling amount: 300
Training with feature pool size: 400
Training with feature pool region padding: 0
Training with lambda_param: 0.1
Training with 20 split tests.
Traceback (most recent call last):
  File "train_shape_predictor.py", line 29, in <module>
    dlib.train_shape_predictor(training_xml_path, "predictor.dat", options)
RuntimeError: 

Error detected at line 248.
Error detected in file /tmp/pip_build_root/dlib/dlib/../dlib/image_processing/shape_predictor_trainer.h.
Error detected in function dlib::shape_predictor dlib::shape_predictor_trainer::train(const image_array&, const std::vector<std::vector<dlib::full_object_detection> >&) const [with image_array = dlib::array<dlib::array2d<unsigned char> >].

Failing expression was objects[i][j].num_parts() != 0.
     shape_predictor shape_predictor_trainer::train()
     You can't give objects that don't have any parts to the trainer.

请有人帮我解决这个错误。提前致谢。

Answer 1

首先，让我们弄清楚 object 和 parts 在这里的意思。
假设您正在训练一个模型来检测人脸的特征点。所以，

object是脸and
部分是它的地标位置。

所以，这个错误意味着你犯了以下两个错误之一：
1. 只用对象标记（或注释）数据集而不用它的部分，或者，
2. 你给零件贴上标签，但作为盒子。

如果是前者。别这样。
如果是后者，您的 .xml 文件必须如下所示： feature points annotated as boxes

  <image file='/path/to/image/directory/<filename>.jpg'>
    <box top='145' left='114' width='239' height='257'>

    <box top='178' left='157' width='1' height='1'/>
    <box top='179' left='158' width='1' height='1'/>
    <box top='155' left='211' width='1' height='1'/>
    <box top='152' left='245' width='1' height='1'/>
    <box top='187' left='292' width='1' height='1'/>
    <box top='340' left='343' width='1' height='1'/>
  </image>

虽然它应该看起来像这样：特征点被注释为对象的一部分

  <image file='path/to/image/directory/<filename>.jpg'>
    <box top='185' left='114' width='238' height='261'>
      <label>Hand</label>
      <part name='13' x='192' y='200'/>
      <part name='20' x='219' y='195'/>
      <part name='27' x='277' y='225'/>
      <part name='34' x='345' y='382'/>
      <part name='38' x='192' y='430'/>
      <part name='6' x='123' y='235'/>
    </box>
  </image>

我们如何到达那里？
1.在命令行输入

$ imglab ./<dataset_file>.xml --parts "<label1> <label2> <label3> <label4>"

例如：

$ imglab ./data-set.xml --parts "head ears right_eye left_eye"

2。当 imglab window 打开时：

a) 注释感兴趣对象周围的框

b) 双击框并右键单击您希望表示特征点的位置。弹出菜单会提示您所有可能的标签。

有关imglab的更多信息，您可以前往：imglab面板上的关于->帮助。要么， imglab -h

希望这对您有所帮助。祝你好运！

Train train_shape_predictor.py 文件报错

Train train_shape_predictor.py file gives an error

python

face-detection

dlib