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# -*- coding: utf-8 -*-
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"""
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Created on Wed Jul 29 19:47:08 2020
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@author: hp
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"""
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import cv2
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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def get_landmark_model(saved_model='models/pose_model'):
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    """
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    Get the facial landmark model. 
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    Original repository: https://github.com/yinguobing/cnn-facial-landmark
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    Parameters
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    ----------
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    saved_model : string, optional
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        Path to facial landmarks model. The default is 'models/pose_model'.
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    Returns
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    -------
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    model : Tensorflow model
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        Facial landmarks model
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    """
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    #model = keras.models.load_model(saved_model)
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    model = tf.saved_model.load(saved_model)
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    return model
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def get_square_box(box):
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    """Get a square box out of the given box, by expanding it."""
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    left_x = box[0]
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    top_y = box[1]
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    right_x = box[2]
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    bottom_y = box[3]
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    box_width = right_x - left_x
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    box_height = bottom_y - top_y
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    # Check if box is already a square. If not, make it a square.
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    diff = box_height - box_width
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    delta = int(abs(diff) / 2)
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    if diff == 0:                   # Already a square.
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        return box
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    elif diff > 0:                  # Height > width, a slim box.
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        left_x -= delta
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        right_x += delta
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        if diff % 2 == 1:
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            right_x += 1
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    else:                           # Width > height, a short box.
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        top_y -= delta
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        bottom_y += delta
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        if diff % 2 == 1:
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            bottom_y += 1
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    # Make sure box is always square.
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    assert ((right_x - left_x) == (bottom_y - top_y)), 'Box is not square.'
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    return [left_x, top_y, right_x, bottom_y]
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def move_box(box, offset):
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        """Move the box to direction specified by vector offset"""
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        left_x = box[0] + offset[0]
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        top_y = box[1] + offset[1]
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        right_x = box[2] + offset[0]
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        bottom_y = box[3] + offset[1]
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        return [left_x, top_y, right_x, bottom_y]
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def detect_marks(img, model, face):
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    """
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    Find the facial landmarks in an image from the faces
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    Parameters
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    ----------
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    img : np.uint8
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        The image in which landmarks are to be found
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    model : Tensorflow model
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        Loaded facial landmark model
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    face : list
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        Face coordinates (x, y, x1, y1) in which the landmarks are to be found
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    Returns
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    -------
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    marks : numpy array
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        facial landmark points
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    """
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    offset_y = int(abs((face[3] - face[1]) * 0.1))
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    box_moved = move_box(face, [0, offset_y])
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    facebox = get_square_box(box_moved)
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    h, w = img.shape[:2]
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    if facebox[0] < 0:
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        facebox[0] = 0
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    if facebox[1] < 0:
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        facebox[1] = 0
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    if facebox[2] > w:
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        facebox[2] = w
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    if facebox[3] > h:
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        facebox[3] = h
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    face_img = img[facebox[1]: facebox[3],
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                     facebox[0]: facebox[2]]
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    face_img = cv2.resize(face_img, (128, 128))
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    face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
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    # # Actual detection.
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    predictions = model.signatures["predict"](
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        tf.constant([face_img], dtype=tf.uint8))
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    # Convert predictions to landmarks.
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    marks = np.array(predictions['output']).flatten()[:136]
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    marks = np.reshape(marks, (-1, 2))
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    marks *= (facebox[2] - facebox[0])
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    marks[:, 0] += facebox[0]
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    marks[:, 1] += facebox[1]
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    marks = marks.astype(np.uint)
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    return marks
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def draw_marks(image, marks, color=(0, 255, 0)):
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    """
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    Draw the facial landmarks on an image
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    Parameters
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    ----------
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    image : np.uint8
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        Image on which landmarks are to be drawn.
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    marks : list or numpy array
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        Facial landmark points
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    color : tuple, optional
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        Color to which landmarks are to be drawn with. The default is (0, 255, 0).
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    Returns
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    -------
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    None.
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    """
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    for mark in marks:
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        cv2.circle(image, (mark[0], mark[1]), 2, color, -1, cv2.LINE_AA)
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