#!/usr/bin/python
# 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
#   takes place during the training of a face recognition model using metric learning.
#
# 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.  This code will also use CUDA if you have CUDA and cuDNN
#   installed.
#
#   Compiling dlib 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
#
#   Also note that this example requires OpenCV and Numpy which can be installed
#   via the command:
#       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:
#   https://commons.wikimedia.org/wiki/File:Tom_Cruise_avp_2014_4.jpg
import sys

import dlib
import cv2
import numpy as np

def show_jittered_images(jittered_images):
    '''
        Shows the specified jittered images one by one
    '''
    for img in jittered_images:
        cv_bgr_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        cv2.imshow('image',cv_bgr_img)
        cv2.waitKey(0)

if len(sys.argv) != 2:
    print(
        "Call this program like this:\n"
        "   ./face_jitter.py shape_predictor_5_face_landmarks.dat\n"
        "You can download a trained facial shape predictor from:\n"
        "    http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n")
    exit()

predictor_path = sys.argv[1]
face_file_path = "../examples/faces/Tom_Cruise_avp_2014_4.jpg"

# Load all the models we need: a detector to find the faces, a shape predictor
# to find face landmarks so we can precisely localize the face
detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)

# Load the image using OpenCV
bgr_img = cv2.imread(face_file_path)
if bgr_img is None:
    print("Sorry, we could not load '{}' as an image".format(face_file_path))
    exit()

# Convert to RGB since dlib uses RGB images
img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)

# Ask the detector to find the bounding boxes of each face.
dets = detector(img)

num_faces = len(dets)

# Find the 5 face landmarks we need to do the alignment.
faces = dlib.full_object_detections()
for detection in dets:
    faces.append(sp(img, detection))

# Get the aligned face image and show it
image = dlib.get_face_chip(img, faces[0], size=320)
cv_rgb_image = np.array(image).astype(np.uint8)
cv_bgr_img = cv2.cvtColor(cv_rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('image',cv_bgr_img)
cv2.waitKey(0)

# Show 5 jittered images without data augmentation
jittered_images = dlib.jitter_image(cv_rgb_image, num_jitters=5)
show_jittered_images(jittered_images)

# Show 5 jittered images with data augmentation
jittered_images = dlib.jitter_image(cv_rgb_image, num_jitters=5, disturb_colors=True)
show_jittered_images(jittered_images)
cv2.destroyAllWindows()