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Diffstat (limited to 'ml/dlib/python_examples/train_shape_predictor.py')
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diff --git a/ml/dlib/python_examples/train_shape_predictor.py b/ml/dlib/python_examples/train_shape_predictor.py deleted file mode 100755 index 23758b2ce..000000000 --- a/ml/dlib/python_examples/train_shape_predictor.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/python -# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt -# -# This example program shows how to use dlib's implementation of the paper: -# One Millisecond Face Alignment with an Ensemble of Regression Trees by -# Vahid Kazemi and Josephine Sullivan, CVPR 2014 -# -# In particular, we will train a face landmarking model based on a small -# dataset and then evaluate it. If you want to visualize the output of the -# trained model on some images then you can run the -# face_landmark_detection.py example program with predictor.dat as the input -# model. -# -# It should also be noted that this kind of model, while often used for face -# landmarking, is quite general and can be used for a variety of shape -# prediction tasks. But here we demonstrate it only on a simple face -# landmarking task. -# -# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE -# You can install dlib using the command: -# pip install dlib -# -# Alternatively, if you want to compile dlib yourself then go into the dlib -# root folder and run: -# python setup.py install -# or -# python setup.py install --yes USE_AVX_INSTRUCTIONS -# if you have a CPU that supports AVX instructions, since this makes some -# things run faster. -# -# Compiling dlib should work on any operating system so long as you have -# CMake installed. On Ubuntu, this can be done easily by running the -# command: -# sudo apt-get install cmake -# -# Also note that this example requires scikit-image which can be installed -# via the command: -# pip install scikit-image -# Or downloaded from http://scikit-image.org/download.html. - -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. -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() -# Now make the object responsible for training the model. -# This algorithm has a bunch of parameters you can mess with. The -# documentation for the shape_predictor_trainer explains all of them. -# You should also read Kazemi's paper which explains all the parameters -# in great detail. However, here I'm just setting three of them -# differently than their default values. I'm doing this because we -# have a very small dataset. In particular, setting the oversampling -# to a high amount (300) effectively boosts the training set size, so -# that helps this example. -options.oversampling_amount = 300 -# I'm also reducing the capacity of the model by explicitly increasing -# the regularization (making nu smaller) and by using trees with -# smaller depths. -options.nu = 0.05 -options.tree_depth = 2 -options.be_verbose = True - -# dlib.train_shape_predictor() does the actual training. It will save the -# final predictor to predictor.dat. The input is an XML file that lists the -# images in the training dataset and also contains the positions of the face -# parts. -training_xml_path = os.path.join(faces_folder, "training_with_face_landmarks.xml") -dlib.train_shape_predictor(training_xml_path, "predictor.dat", options) - -# Now that we have a model we can test it. dlib.test_shape_predictor() -# measures the average distance between a face landmark output by the -# shape_predictor and where it should be according to the truth data. -print("\nTraining accuracy: {}".format( - dlib.test_shape_predictor(training_xml_path, "predictor.dat"))) -# The real test is to see how well it does on data it wasn't trained on. We -# trained it on a very small dataset so the accuracy is not extremely high, but -# it's still doing quite good. Moreover, if you train it on one of the large -# face landmarking datasets you will obtain state-of-the-art results, as shown -# in the Kazemi paper. -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"))) - -# Now let's use it as you would in a normal application. First we will load it -# from disk. We also need to load a face detector to provide the initial -# estimate of the facial location. -predictor = dlib.shape_predictor("predictor.dat") -detector = dlib.get_frontal_face_detector() - -# Now let's run the detector and shape_predictor over the images in the faces -# folder and display the results. -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() - |