Add facial landmark prediction examples for Python

python_examples/face_landmark_detection.py

+#!/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 find frontal human faces in an image and
+#   estimate their pose.  The pose takes the form of 68 landmarks.  These are
+#   points on the face such as the corners of the mouth, along the eyebrows, on
+#   the eyes, and so forth.
+#
+#   This face detector is made using the classic Histogram of Oriented
+#   Gradients (HOG) feature combined with a linear classifier, an image pyramid,
+#   and sliding window detection scheme.  The pose estimator was created by
+#   using dlib's implementation of the paper:
+#      One Millisecond Face Alignment with an Ensemble of Regression Trees by
+#      Vahid Kazemi and Josephine Sullivan, CVPR 2014
+#   and was trained on the iBUG 300-W face landmark dataset.
+#
+#   Also, note that you can train your own models using dlib's machine learning
+#   tools. See train_shape_predictor.py to see an example.
+#
+#   You can get the shape_predictor_68_face_landmarks.dat file from:
+#   http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
+#
+# COMPILING THE DLIB PYTHON INTERFACE
+#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
+#   you are using another python version or operating system then you need to
+#   compile the dlib python interface before you can use this file.  To do this,
+#   run compile_dlib_python_module.bat.  This should work on any operating
+#   system so long as you have CMake and boost-python installed.
+#   On Ubuntu, this can be done easily by running the command:
+#       sudo apt-get install libboost-python-dev cmake
+import sys
+import os
+import dlib
+import glob
+from skimage import io
+
+if len(sys.argv) != 3:
+    print(
+        "Give the path to the trained shape predictor model as the first "
+        "argument and then the directory containing the facial images.\n"
+        "For example, if you are in the python_examples folder then "
+        "execute this program by running:\n"
+        "    ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
+        "You can download a trained facial shape predictor from:\n"
+        "    http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2")
+    exit()
+
+predictor_path = sys.argv[1]
+faces_folder_path = sys.argv[2]
+
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor(predictor_path)
+win = dlib.image_window()
+
+for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
+    print("Processing file: {}".format(f))
+    img = io.imread(f)
+
+    win.clear_overlay()
+    win.set_image(img)
+
+    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()))
+        shapes = predictor(img, d)
+        print("Part 0: {}, Part 1: {} ...".format(shapes.part(0),
+                                                  shapes.part(1)))
+        # Add all facial landmarks one at a time
+        win.add_overlay(shapes)
+
+    win.add_overlay(dets)
+    raw_input("Hit enter to continue")

python_examples/train_object_detector.py

@@ -73,12 +73,14 @@ dlib.train_simple_object_detector(training_xml_path, "detector.svm", options)
 # 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")))
+    dlib.test_simple_object_detector(training_xml_path, "detector.svm",
+                                     upsample_amount=1)))
 # 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")))
+    dlib.test_simple_object_detector(testing_xml_path, "detector.svm",
+                                     upsample_amount=1)))
 
 # Now let's use the detector as you would in a normal application.  First we
 # will load it from disk.
@@ -92,7 +94,7 @@ win_det.set_image(detector)
 # results.
 print("Showing detections on the images in the faces folder...")
 win = dlib.image_window()
-for f in glob.glob(faces_folder + "/*.jpg"):
+for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
     print("Processing file: {}".format(f))
     img = io.imread(f)
     dets = detector(img)
@@ -125,10 +127,11 @@ boxes_img2 = ([dlib.rectangle(left=154, top=46, right=228, bottom=121),
 # train_simple_object_detector().
 boxes = [boxes_img1, boxes_img2]
 
-dlib.train_simple_object_detector(images, boxes, "detector2.svm", options)
+detector2 = dlib.train_simple_object_detector(images, boxes, options)
+# We could save this detector by uncommenting the following
+#detector2.save('detector2.svm')
 
 # Now let's load the trained detector and look at its HOG filter!
-detector2 = dlib.simple_object_detector("detector2.svm")
 win_det.set_image(detector2)
 raw_input("Hit enter to continue")
 
@@ -136,5 +139,5 @@ raw_input("Hit enter to continue")
 # test_simple_object_detector().  If you have already loaded your training
 # images and bounding boxes for the objects then you can call it as shown
 # below.
-print("Training accuracy: {}".format(
-    dlib.test_simple_object_detector(images, boxes, "detector.svm")))
+print("\nTraining accuracy: {}".format(
+    dlib.test_simple_object_detector(images, boxes, detector2)))

python_examples/train_shape_predictor.py

+#!/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 sp.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 THE DLIB PYTHON INTERFACE
+#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
+#   you are using another python version or operating system then you need to
+#   compile the dlib python interface before you can use this file.  To do this,
+#   run compile_dlib_python_module.bat.  This should work on any operating
+#   system so long as you have CMake and boost-python installed.
+#   On Ubuntu, this can be done easily by running the command:
+#       sudo apt-get install libboost-python-dev cmake
+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 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
+
+# This function 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")
+testing_xml_path = os.path.join(faces_folder, "testing_with_face_landmarks.xml")
+
+dlib.train_shape_predictor(training_xml_path, "predictor.dat", options)
+
+# Now that we have a facial landmark predictor we can test it.  The first
+# statement tests it on the training data.  It will print the mean average error
+print("")  # Print blank line to create gap from previous output
+print("Training accuracy: {}".format(
+    dlib.test_shape_predictor(training_xml_path, "predictor.dat")))
+# 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_shape_predictor(testing_xml_path, "predictor.dat")))
+
+# Now let's use the detector 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
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("predictor.dat")
+
+# Now let's run the detector and 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)
+
+    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()))
+        shapes = predictor(img, d)
+        print("Part 0: {}, Part 1: {} ...".format(shapes.part(0),
+                                                  shapes.part(1)))
+        # Add all facial landmarks one at a time
+        win.add_overlay(shapes)
+
+    win.add_overlay(dets)
+    raw_input("Hit enter to continue")
+
+# Finally, note that you don't have to use the XML based input to
+# train_shape_predictor().  If you have already loaded your training
+# images and fll_object_detections for the objects then you can call it with
+# the existing objects.