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A little bit of cleanup
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@ -1,8 +1,11 @@
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#!/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 are jittered and data augmentation using dlib's disturb_colors
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# takes place during the training of a face recognition model using metric learning.
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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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@ -25,7 +28,6 @@
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# Also note that this example requires OpenCV and Numpy which can be installed
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# via the command:
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# pip install opencv-python numpy
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# Or downloaded from http://opencv.org/releases.html
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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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@ -81,16 +83,15 @@ for detection in dets:
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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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cv_rgb_image = np.array(image).astype(np.uint8)
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cv_bgr_img = cv2.cvtColor(cv_rgb_image, cv2.COLOR_RGB2BGR)
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cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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cv2.imshow('image',cv_bgr_img)
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cv2.waitKey(0)
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# Show 5 jittered images without data augmentation
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jittered_images = dlib.jitter_image(cv_rgb_image, num_jitters=5)
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jittered_images = dlib.jitter_image(image, num_jitters=5)
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show_jittered_images(jittered_images)
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# Show 5 jittered images with data augmentation
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jittered_images = dlib.jitter_image(cv_rgb_image, num_jitters=5, disturb_colors=True)
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jittered_images = dlib.jitter_image(image, num_jitters=5, disturb_colors=True)
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show_jittered_images(jittered_images)
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cv2.destroyAllWindows()
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