diff --git a/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py b/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py
index 70420d5ef8f40d634459af86e79af3c1b9489b75..cdf772f2f63e5fb331e40b1222373ad2259ded5a 100644
--- a/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py
+++ b/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py
@@ -33,57 +33,7 @@ from bob.bio.video.preprocessor import Wrapper
import numpy as np
-# =============================================================================
-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)
-
- 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
+from bob.pad.face.config.quality_assessment.celeb_a.quality_assessment_config_128 import detect_eyes_in_bw_image, load_datafile, assess_quality
# =============================================================================
@@ -98,59 +48,3 @@ assess_quality_kwargs = {}
assess_quality_kwargs["eyes_expected"] = eyes_expected
assess_quality_kwargs["threshold"] = 7
-
-# =============================================================================
-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 64x64 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 64x64.
-
- ``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
-
-
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
index d7b80a7fa23b211b0f53cf8b1dc6337b2d7c96f1..3eb4e0879d14ff97905c451e560fbd2103c37271 100644
--- 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
@@ -109,7 +109,7 @@ 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
+ the face of the size (face_size x face_size) 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.
@@ -117,7 +117,7 @@ def assess_quality(data, eyes_expected, threshold):
``data`` : 2D :py:class:`numpy.ndarray`
Data array containing the image of the facial region. The size of the
- image is 128x128.
+ image is (face_size x face_size).
``eyes_expected`` : list
A list containing expected coordinates of the eyes. The format is