Added the configuration file to assess teh quality for 128x128 images

bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py

+#!/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
+
+