From f3924df70b7888752884812d046bf850fa8f03fb Mon Sep 17 00:00:00 2001
From: Olegs NIKISINS <onikisins@italix03.idiap.ch>
Date: Fri, 18 Jan 2019 18:48:53 +0100
Subject: [PATCH] Added the configuration file to assess teh quality for
128x128 images
---
.../celeb_a/quality_assessment_config_128.py | 162 ++++++++++++++++++
1 file changed, 162 insertions(+)
create mode 100644 bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py
diff --git a/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py b/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py
new file mode 100644
index 00000000..d7b80a7f
--- /dev/null
+++ b/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Quality assessment configuration file for the CelebA database to be used
+with quality assessment script.
+
+Note: this config checks the quality of the preprocessed(!) data. Here the
+preprocessed data is sored in ``.hdf5`` files, as a frame container with
+one frame. Frame contains a BW image of the facial regions of the size
+128x128 pixels.
+
+The config file MUST contain at least the following functions:
+
+``load_datafile(file_name)`` - returns the ``data`` given ``file_name``, and
+
+``assess_quality(data, **assess_quality_kwargs)`` - returns ``True`` for good
+quality ``data``, and ``False`` for low quality data, and
+
+``assess_quality_kwargs`` - a dictionary with kwargs for ``assess_quality()``
+function.
+
+@author: Olegs Nikisins
+"""
+
+# =============================================================================
+# Import here:
+
+import pkg_resources
+
+import cv2
+
+from bob.bio.video.preprocessor import Wrapper
+
+import numpy as np
+
+import bob.ip.color
+
+# =============================================================================
+def detect_eyes_in_bw_image(image):
+ """
+ Detect eyes in the image using OpenCV.
+
+ **Parameters:**
+
+ ``image`` : 2D :py:class:`numpy.ndarray`
+ A BW image to detect the eyes in.
+
+ **Returns:**
+
+ ``eyes`` : 2D :py:class:`numpy.ndarray`
+ An array containing coordinates of the bounding boxes of detected eyes.
+ The dimensionality of the array:
+ ``num_of_detected_eyes x coordinates_of_bbx``
+ """
+
+ eye_model = pkg_resources.resource_filename('bob.pad.face.config',
+ 'quality_assessment/models/eye_detector.xml')
+
+ eye_cascade = cv2.CascadeClassifier(eye_model)
+
+ if len(image.shape) == 3:
+
+ image = bob.ip.color.rgb_to_gray(image)
+
+ eyes = eye_cascade.detectMultiScale(image)
+
+ return eyes
+
+
+# =============================================================================
+def load_datafile(file_name):
+ """
+ Load data from file given filename. Here the data file is an hdf5 file
+ containing a framecontainer with one frame. The data in the frame is
+ a BW image of the facial region.
+
+ **Parameters:**
+
+ ``file_name`` : str
+ Absolute name of the file.
+
+ **Returns:**
+
+ ``data`` : 2D :py:class:`numpy.ndarray`
+ Data array containing the image of the facial region.
+ """
+
+ frame_container = Wrapper().read_data(file_name)
+
+ data = frame_container[0][1]
+
+ return data
+
+
+# =============================================================================
+face_size = 128
+eyes_distance=((face_size + 1) / 2.)
+eyes_center=(face_size / 4., (face_size - 0.5) / 2.)
+
+eyes_expected = [[eyes_center[0], eyes_center[1]-eyes_distance/2.],
+ [eyes_center[0], eyes_center[1]+eyes_distance/2.]]
+
+assess_quality_kwargs = {}
+assess_quality_kwargs["eyes_expected"] = eyes_expected
+assess_quality_kwargs["threshold"] = 10
+
+
+# =============================================================================
+def assess_quality(data, eyes_expected, threshold):
+ """
+ Assess the quality of the data sample, which in this case is an image of
+ the face of the size 128x128 pixels. The quality assessment is based on the
+ eye detection. If two eyes are detected, and they are located in the
+ pre-defined positions, then quality is good, otherwise the quality is low.
+
+ **Parameters:**
+
+ ``data`` : 2D :py:class:`numpy.ndarray`
+ Data array containing the image of the facial region. The size of the
+ image is 128x128.
+
+ ``eyes_expected`` : list
+ A list containing expected coordinates of the eyes. The format is
+ as follows:
+ [ [left_y, left_x], [right_y, right_x] ]
+
+ ``threshold`` : int
+ A maximum allowed distance between expected and detected centers of
+ the eyes.
+
+ **Returns:**
+
+ ``quality_flag`` : bool
+ ``True`` for good quality data, ``False`` otherwise.
+ """
+
+ quality_flag = False
+
+ eyes = detect_eyes_in_bw_image(data)
+
+ if isinstance(eyes, np.ndarray):
+
+ if eyes.shape[0] == 2: # only consider the images with two eyes detected
+
+ # coordinates of detected centers of the eyes: [ [left_y, left_x], [right_y, right_x] ]:
+ eyes_detected = []
+ for (ex,ey,ew,eh) in eyes:
+ eyes_detected.append( [ey + eh/2., ex + ew/2.] )
+
+ dists = [] # dits between detected and expected:
+ for a, b in zip(eyes_detected, eyes_expected):
+ dists.append( np.linalg.norm(np.array(a)-np.array(b)) )
+
+ max_dist = np.max(dists)
+
+ if max_dist < threshold:
+
+ quality_flag = True
+
+ return quality_flag
+
+
--
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