mirror of https://github.com/davisking/dlib.git
83 lines
2.7 KiB
Python
Executable File
83 lines
2.7 KiB
Python
Executable File
#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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# This example shows how faces were jittered and augmented to create training
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# data for dlib's face recognition model. It takes an input image and
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# disturbs the colors as well as applies random translations, rotations, and
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# scaling.
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#
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# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
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# You can install dlib using the command:
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# pip install dlib
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#
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# Alternatively, if you want to compile dlib yourself then go into the dlib
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# root folder and run:
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# python setup.py install
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#
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# Compiling dlib should work on any operating system so long as you have
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# CMake installed. On Ubuntu, this can be done easily by running the
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# command:
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# sudo apt-get install cmake
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#
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# Also note that this example requires Numpy which can be installed
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# via the command:
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# pip install numpy
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#
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# The image file used in this example is in the public domain:
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# https://commons.wikimedia.org/wiki/File:Tom_Cruise_avp_2014_4.jpg
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import sys
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import dlib
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def show_jittered_images(window, jittered_images):
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'''
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Shows the specified jittered images one by one
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'''
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for img in jittered_images:
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window.set_image(img)
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dlib.hit_enter_to_continue()
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if len(sys.argv) != 2:
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print(
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"Call this program like this:\n"
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" ./face_jitter.py shape_predictor_5_face_landmarks.dat\n"
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"You can download a trained facial shape predictor from:\n"
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" http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n")
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exit()
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predictor_path = sys.argv[1]
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face_file_path = "../examples/faces/Tom_Cruise_avp_2014_4.jpg"
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# Load all the models we need: a detector to find the faces, a shape predictor
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# to find face landmarks so we can precisely localize the face
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detector = dlib.get_frontal_face_detector()
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sp = dlib.shape_predictor(predictor_path)
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# Load the image using dlib
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img = dlib.load_rgb_image(face_file_path)
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# Ask the detector to find the bounding boxes of each face.
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dets = detector(img)
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num_faces = len(dets)
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# Find the 5 face landmarks we need to do the alignment.
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faces = dlib.full_object_detections()
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for detection in dets:
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faces.append(sp(img, detection))
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# Get the aligned face image and show it
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image = dlib.get_face_chip(img, faces[0], size=320)
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window = dlib.image_window()
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window.set_image(image)
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dlib.hit_enter_to_continue()
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# Show 5 jittered images without data augmentation
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jittered_images = dlib.jitter_image(image, num_jitters=5)
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show_jittered_images(window, jittered_images)
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# Show 5 jittered images with data augmentation
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jittered_images = dlib.jitter_image(image, num_jitters=5, disturb_colors=True)
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show_jittered_images(window, jittered_images)
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