diff --git a/bob/pad/face/test/test.py b/bob/pad/face/test/test.py
index c4d1a9bd5073019e44b2d97d9f2e67fbd9de19e2..d6afe44fe18c17b3aaf306e19bec9fd9766fd8de 100644
--- a/bob/pad/face/test/test.py
+++ b/bob/pad/face/test/test.py
@@ -28,10 +28,10 @@ from ..extractor import LBPHistogram
from ..extractor import ImageQualityMeasure
-from ..utils import face_detection_utils
-
import random
+from ..preprocessor.FaceCropAlign import detect_face_landmarks_in_image
+
from bob.bio.video.preprocessor import Wrapper
@@ -39,7 +39,7 @@ def test_detect_face_landmarks_in_image_mtcnn():
img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
assert len(img) == 3
- annotations = face_detection_utils.detect_face_landmarks_in_image(
+ annotations = detect_face_landmarks_in_image(
img, method='mtcnn')
assert len(annotations['landmarks']) == 68
assert len(annotations['left_eye']) == 2
@@ -50,25 +50,11 @@ def test_detect_face_landmarks_in_image_mtcnn():
#assert len(annotations['left_eye']) == (176, 220)
-def test_detect_face_landmarks_in_video_mtcnn():
-
- img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
- assert len(img) == 3
- frame_container = bob.bio.video.FrameContainer()
- frame_container.add(1, img)
- frame_container.add(2, img)
-
- annotations = face_detection_utils.detect_face_landmarks_in_video(
- frame_container, method='mtcnn')
- assert len(annotations) == 2
- assert len(annotations['1']['landmarks']) == 68
-
-
def test_detect_face_landmarks_in_image_dlib():
img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
assert len(img) == 3
- annotations = face_detection_utils.detect_face_landmarks_in_image(
+ annotations = detect_face_landmarks_in_image(
img, method='dlib')
assert len(annotations['landmarks']) == 68
assert len(annotations['left_eye']) == 2
@@ -79,20 +65,6 @@ def test_detect_face_landmarks_in_image_dlib():
#assert len(annotations['left_eye']) == (176, 220)
-def test_detect_face_landmarks_in_video_dlib():
-
- img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
- assert len(img) == 3
- frame_container = bob.bio.video.FrameContainer()
- frame_container.add(1, img)
- frame_container.add(2, img)
-
- annotations = face_detection_utils.detect_face_landmarks_in_video(
- frame_container, method='dlib')
- assert len(annotations) == 2
- assert len(annotations['1']['landmarks']) == 68
-
-
#==============================================================================
def test_lbp_histogram():
lbp = LBPHistogram()
diff --git a/bob/pad/face/utils/__init__.py b/bob/pad/face/utils/__init__.py
index 4444a68057622b8ebef09f5f12fd586aeca7fb82..fe90985c44f031beee8771dcc8ec134836f5907b 100644
--- a/bob/pad/face/utils/__init__.py
+++ b/bob/pad/face/utils/__init__.py
@@ -1,6 +1,3 @@
-from .face_detection_utils import (detect_face_in_image, detect_faces_in_video,
- detect_face_landmarks_in_image,
- detect_face_landmarks_in_video, get_eye_pos)
from .load_utils import (frames, number_of_frames, yield_frames,
normalize_detections, yield_faces, scale_face, blocks)
diff --git a/bob/pad/face/utils/face_detection_utils.py b/bob/pad/face/utils/face_detection_utils.py
deleted file mode 100644
index dd4b0946b7b760521bc4d194c4be4eac8723e95e..0000000000000000000000000000000000000000
--- a/bob/pad/face/utils/face_detection_utils.py
+++ /dev/null
@@ -1,274 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-"""
-This file contains face detection utils.
-"""
-# ==============================================================================
-# Import here:
-
-import importlib
-import numpy as np
-
-
-# ==============================================================================
-def get_eye_pos(lm):
- """
- This function returns the locations of left and right eyes
-
- **Parameters:**
-
- ``lm`` : :py:class:`numpy.ndarray`
- A numpy array containing the coordinates of facial landmarks, (68X2)
-
- **Returns:**
-
- ``right_eye``
- A tuple containing the location of right eye,
-
- ``left_eye``
- A tuple containing the location of left eye
-
- """
-
- # Mean position of eye corners as eye centers , casted to int()
-
- left_eye_t = (lm[36, :] + lm[39, :]) / 2.0
- right_eye_t = (lm[42, :] + lm[45, :]) / 2.0
-
- right_eye = (int(left_eye_t[1]), int(left_eye_t[0]))
- left_eye = (int(right_eye_t[1]), int(right_eye_t[0]))
-
- return right_eye, left_eye
-
-
-# ==============================================================================
-def detect_face_in_image(image, method="dlib"):
- """
- This function detects a face in the input image.
-
- **Parameters:**
-
- ``image`` : 3D :py:class:`numpy.ndarray`
- A color image to detect the face in.
-
- ``method`` : :py:class:`str`
- A package to be used for face detection. Options supported by this
- package: "dlib" (dlib is a dependency of this package). If bob.ip.mtcnn
- is installed in your system you can use it as-well (bob.ip.mtcnn is NOT
- a dependency of this package).
-
- **Returns:**
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing annotations of the face bounding box.
- Dictionary must be as follows ``{'topleft': (row, col), 'bottomright': (row, col)}``.
- If no annotations found an empty dictionary is returned.
- """
-
- try:
- face_detection_module = importlib.import_module("bob.ip." + method)
- except ImportError:
- raise ImportError("No module named bob.ip." + method)
-
- if not hasattr(face_detection_module, 'FaceDetector'):
- raise AttributeError(
- "bob.ip." + method + " module has no attribute FaceDetector")
-
- data = face_detection_module.FaceDetector().detect_single_face(image)
-
- annotations = {}
-
- if (data is not None) and (not all([x is None for x in data])):
-
- bounding_box = data[0]
-
- annotations['topleft'] = bounding_box.topleft
-
- annotations['bottomright'] = bounding_box.bottomright
-
- return annotations
-
-
-# ==============================================================================
-def detect_faces_in_video(frame_container, method="dlib"):
- """
- This function detects a face in each farme of the input video.
-
- **Parameters:**
-
- ``frame_container`` : FrameContainer
- FrameContainer containing the frames data.
-
- ``method`` : :py:class:`str`
- A package to be used for face detection. Options supported by this
- package: "dlib" (dlib is a dependency of this package). If bob.ip.mtcnn
- is installed in your system you can use it as-well (bob.ip.mtcnn is NOT
- a dependency of this package).
-
- **Returns:**
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
- If no annotations found an empty dictionary is returned.
- """
-
- annotations = {}
-
- for idx, frame in enumerate(frame_container):
-
- image = frame[1]
-
- frame_annotations = detect_face_in_image(image, method)
-
- if frame_annotations:
-
- annotations[str(idx)] = frame_annotations
-
- return annotations
-
-
-# ==============================================================================
-def detect_face_landmarks_in_image(image, method="dlib"):
- """
- This function detects a face in the input image. Two oprions for face detector , but landmark detector is always the same
-
- **Parameters:**
-
- ``image`` : 3D :py:class:`numpy.ndarray`
- A color image to detect the face in.
-
- ``method`` : :py:class:`str`
- A package to be used for face detection. Options supported by this
- package: "dlib" (dlib is a dependency of this package). If bob.ip.mtcnn
- is installed in your system you can use it as-well (bob.ip.mtcnn is NOT
- a dependency of this package).
-
- **Returns:**
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing annotations of the face bounding box, eye locations and facial landmarks.
- Dictionary must be as follows ``{'topleft': (row, col), 'bottomright': (row, col), 'left_eye': (row, col), 'right_eye': (row, col), 'landmarks': [(col1,row1), (col2,row2), ...]}``.
- If no annotations found an empty dictionary is returned.
- Where (rowK,colK) is the location of Kth facial landmark (K=0,...,67).
- """
-
- ### Face detector
-
- try:
- face_detection_module = importlib.import_module("bob.ip." + method)
-
- except ImportError:
-
- print("No module named bob.ip." + method +
- " trying to use default method!")
-
- try:
- face_detection_module = importlib.import_module("bob.ip.dlib")
- method = "dlib"
- except ImportError:
- raise ImportError("No module named bob.ip.dlib")
-
- if not hasattr(face_detection_module, 'FaceDetector'):
- raise AttributeError(
- "bob.ip." + method + " module has no attribute FaceDetector!")
-
- #### Landmark detector
-
- try:
- landmark_detection_module = importlib.import_module(
- "bob.ip.facelandmarks")
- except ImportError:
- raise ImportError("No module named bob.ip.facelandmarks!!")
-
- if not hasattr(landmark_detection_module,
- 'detect_landmarks_on_boundingbox'):
- raise AttributeError(
- "bob.ip.facelandmarksmodule has no attribute detect_landmarks_on_boundingbox!"
- )
-
- face_detector = face_detection_module.FaceDetector()
-
- data = face_detector.detect_single_face(image)
-
- annotations = {}
-
- if (data is not None) and (not all([x is None for x in data])):
-
- bounding_box = data[0]
-
- bounding_box_scaled = bounding_box.scale(0.95, True) # is ok for dlib
-
- lm = landmark_detection_module.detect_landmarks_on_boundingbox(
- image, bounding_box_scaled)
-
- if lm is not None:
-
- lm = np.array(lm)
-
- lm = np.vstack((lm[:, 1], lm[:, 0])).T
-
- #print("LM",lm)
-
- right_eye, left_eye = get_eye_pos(lm)
-
- points = []
-
- for i in range(lm.shape[0]):
-
- points.append((int(lm[i, 0]), int(lm[i, 1])))
-
- annotations['topleft'] = bounding_box.topleft
-
- annotations['bottomright'] = bounding_box.bottomright
-
- annotations['landmarks'] = points
-
- annotations['left_eye'] = left_eye
-
- annotations['right_eye'] = right_eye
-
- return annotations
-
-
-# ==============================================================================
-def detect_face_landmarks_in_video(frame_container, method="dlib"):
- """
- This function detects a face and face landmarks in each farme of the input video.
-
- **Parameters:**
-
- ``frame_container`` : FrameContainer
- FrameContainer containing the frames data.
-
- ``method`` : :py:class:`str`
- A package to be used for face detection. Options supported by this
- package: "dlib" (dlib is a dependency of this package) and "mtcnn".
- If its found it will be used else defaults to "dlib"
-
- **Returns:**
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col), 'left_eye': (row, col), 'right_eye': (row, col), 'landmarks': [(col1,row1), (col2,row2), ...]}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
- Where (colK,rowK) is the location of Kth facial landmark (K=0,...,67).
- If no annotations found an empty dictionary is returned.
- """
-
- annotations = {}
-
- for idx, frame in enumerate(frame_container):
-
- image = frame[1]
-
- frame_annotations = detect_face_landmarks_in_image(image, method)
-
- if frame_annotations:
-
- annotations[str(idx)] = frame_annotations
-
- return annotations