BEAT - tpereira/periocular

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

This algorithm is splittable

Endpoint Groups 1

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: main

Endpoint Name	Data Format	Nature
image	system/array_3d_uint8/1	Input
eye_centers	system/eye_positions/1	Input
cropped_image	system/array_2d_uint8/1	Output

Parameters 7

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name	Description	Type	Default	Range/Choices
left-eye-x	Final position of the left eye (x-coord)	uint32	44
left-eye-y	Final position of the left eye (y-coord)	uint32	12
color	Final color channel	string	gray	gray, red, green, blue
crop-height	Final image height	uint32	25
crop-width	Final image width	uint32	58
right-eye-x	Final position of the right eye (x-coord)	uint32	11
right-eye-y	Final position of the right eye (y-coord)	uint32	12

import bob
import numpy

class Algorithm:

def __init__(self):
        self.crop_height = 25
        self.crop_width  = 58
        self.right_eye_y = 12
        self.right_eye_x = 11
        self.left_eye_y  = 12
        self.left_eye_x  = 44
        self.color       = "gray"

def setup(self, parameters):
        self.crop_height = parameters.get('crop-height', self.crop_height)
        self.crop_width  = parameters.get('crop-width',  self.crop_width)
        self.right_eye_y = parameters.get('right-eye-y', self.right_eye_y)
        self.right_eye_x = parameters.get('right-eye-x', self.right_eye_x)
        self.left_eye_y  = parameters.get('left-eye-y',  self.left_eye_y)
        self.left_eye_x  = parameters.get('left-eye-x',  self.left_eye_x)
        self.color       = parameters.get('color',  self.color)

self.cropper = bob.ip.FaceEyesNorm(self.crop_height, self.crop_width,
                self.right_eye_y, self.right_eye_x, self.left_eye_y, self.left_eye_x)

return True

def process(self, inputs, outputs):

image = inputs["image"].data.value

if (self.color == "gray"):
          image = bob.ip.rgb_to_gray(image)
        elif (self.color == "red"):
          image = image[0,:,:]
        elif (self.color == "green"):
          image = image[1,:,:]
        elif (self.color == "blue"):
          image = image[2,:,:]

eye_positions = inputs["eye_centers"].data

outputs["cropped_image"].write({
            'value': self.cropper(image.astype("uint8"), float(eye_positions.right.y), float(eye_positions.right.x),
                                   float(eye_positions.left.y), float(eye_positions.left.x))
        })

return True

The code for this algorithm in Python
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This algorithm performs a RGB to grayscale conversion of an image followed by a periocular cropping of the face, given the eye center coordinates.

This implementation relies on the Bob library.

The inputs are:

image: an RGB image as a three-dimensional array of uint8, the first dimension being the number of color planes (3), and the second and third dimensions corresponding to the height and width of the original image, respectively.
eye_centers: the coordinates of the eye centers in the original image space

The output cropped_image is a grayscale cropped image as a two-dimensional array of floats (64 bits).

No experiments are using this algorithm.

This algorithm was never executed.

algorithms tpereira periocular_crop 4

Endpoint Groups 1

Group: main

Parameters 7

algorithms

tpereira

periocular_crop

4