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A little bit of cleanup

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Davis King 2017-12-08 10:16:08 -05:00
parent 07febbc9de
commit 3a0a63da39
1 changed files with 9 additions and 8 deletions
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17
python_examples/face_jitter.py
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@ -1,8 +1,11 @@
#!/usr/bin/python #!/usr/bin/python
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
# #
# This example shows how faces are jittered and data augmentation using dlib's disturb_colors # This example shows how faces were jittered and augmented to create training
# takes place during the training of a face recognition model using metric learning. # data for dlib's face recognition model. It takes an input image and
# disturbs the colors as well as applies random translations, rotations, and
# scaling.
# #
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE # COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
# You can install dlib using the command: # You can install dlib using the command:
@ -25,7 +28,6 @@
# Also note that this example requires OpenCV and Numpy which can be installed # Also note that this example requires OpenCV and Numpy which can be installed
# via the command: # via the command:
# pip install opencv-python numpy # pip install opencv-python numpy
# Or downloaded from http://opencv.org/releases.html
# #
# The image file used in this example is in the public domain: # The image file used in this example is in the public domain:
# https://commons.wikimedia.org/wiki/File:Tom_Cruise_avp_2014_4.jpg # https://commons.wikimedia.org/wiki/File:Tom_Cruise_avp_2014_4.jpg
@ -81,16 +83,15 @@ for detection in dets:
# Get the aligned face image and show it # Get the aligned face image and show it
image = dlib.get_face_chip(img, faces[0], size=320) image = dlib.get_face_chip(img, faces[0], size=320)
cv_rgb_image = np.array(image).astype(np.uint8) cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv_bgr_img = cv2.cvtColor(cv_rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('image',cv_bgr_img) cv2.imshow('image',cv_bgr_img)
cv2.waitKey(0) cv2.waitKey(0)
# Show 5 jittered images without data augmentation # Show 5 jittered images without data augmentation
jittered_images = dlib.jitter_image(cv_rgb_image, num_jitters=5) jittered_images = dlib.jitter_image(image, num_jitters=5)
show_jittered_images(jittered_images) show_jittered_images(jittered_images)
# Show 5 jittered images with data augmentation # Show 5 jittered images with data augmentation
jittered_images = dlib.jitter_image(cv_rgb_image, num_jitters=5, disturb_colors=True) jittered_images = dlib.jitter_image(image, num_jitters=5, disturb_colors=True)
show_jittered_images(jittered_images) show_jittered_images(jittered_images)
cv2.destroyAllWindows() cv2.destroyAllWindows()