From 34209d05fef0e574906f24fdff37975f1fb40e80 Mon Sep 17 00:00:00 2001
From: Olegs NIKISINS <onikisins@italix03.idiap.ch>
Date: Fri, 13 Oct 2017 14:06:21 +0200
Subject: [PATCH] Added a local face detection option to the VideoFaceCrop
class
---
.../config/preprocessor/video_face_crop.py | 31 +++++++
bob/pad/face/preprocessor/VideoFaceCrop.py | 15 ++++
bob/pad/face/utils/__init__.py | 0
bob/pad/face/utils/face_detection_utils.py | 87 +++++++++++++++++++
setup.py | 1 +
5 files changed, 134 insertions(+)
create mode 100644 bob/pad/face/config/preprocessor/video_face_crop.py
create mode 100644 bob/pad/face/utils/__init__.py
create mode 100644 bob/pad/face/utils/face_detection_utils.py
diff --git a/bob/pad/face/config/preprocessor/video_face_crop.py b/bob/pad/face/config/preprocessor/video_face_crop.py
new file mode 100644
index 00000000..ad7f963b
--- /dev/null
+++ b/bob/pad/face/config/preprocessor/video_face_crop.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+
+from bob.pad.face.preprocessor import VideoFaceCrop
+
+#=======================================================================================
+# Define instances here:
+
+CROPPED_IMAGE_SIZE = (64, 64) # The size of the resulting face
+CROPPED_POSITIONS = {'topleft' : (0,0) , 'bottomright' : CROPPED_IMAGE_SIZE}
+FIXED_POSITIONS = None
+MASK_SIGMA = None # The sigma for random values areas outside image
+MASK_NEIGHBORS = 5 # The number of neighbors to consider while extrapolating
+MASK_SEED = None # The seed for generating random values during extrapolation
+CHECK_FACE_SIZE_FLAG = True # Check the size of the face
+MIN_FACE_SIZE = 50
+USE_LOCAL_CROPPER_FLAG = True # Use the local face cropping class (identical to Ivana's paper)
+RGB_OUTPUT_FLAG = True # Return RGB cropped face using local cropper
+DETECT_FACES_FLAG = True # find annotations locally replacing the database annotations
+
+preprocessor_rgb_face_detect = VideoFaceCrop(cropped_image_size = CROPPED_IMAGE_SIZE,
+ cropped_positions = CROPPED_POSITIONS,
+ fixed_positions = FIXED_POSITIONS,
+ mask_sigma = MASK_SIGMA,
+ mask_neighbors = MASK_NEIGHBORS,
+ mask_seed = None,
+ check_face_size_flag = CHECK_FACE_SIZE_FLAG,
+ min_face_size = MIN_FACE_SIZE,
+ use_local_cropper_flag = USE_LOCAL_CROPPER_FLAG,
+ rgb_output_flag = RGB_OUTPUT_FLAG,
+ detect_faces_flag = DETECT_FACES_FLAG)
diff --git a/bob/pad/face/preprocessor/VideoFaceCrop.py b/bob/pad/face/preprocessor/VideoFaceCrop.py
index fd975676..97024f8d 100644
--- a/bob/pad/face/preprocessor/VideoFaceCrop.py
+++ b/bob/pad/face/preprocessor/VideoFaceCrop.py
@@ -18,6 +18,8 @@ import numpy as np
from bob.pad.face.preprocessor.ImageFaceCrop import ImageFaceCrop
+from ..utils.face_detection_utils import detect_faces_in_video
+
#==============================================================================
# Main body:
@@ -76,6 +78,12 @@ class VideoFaceCrop(Preprocessor, object):
returned. This flag is only valid when ``use_local_cropper_flag = True``.
Default: ``False``.
+ ``detect_faces_flag`` : :py:class:`bool`
+ If set to ``True`` the facial annotations will be generated using
+ face detection. Otherwise, annotations of the database are used for
+ cropping.
+ Default: ``False``.
+
``kwargs``
Remaining keyword parameters passed to the Base constructor, such as ``color_channel`` or ``dtype``.
"""
@@ -92,6 +100,7 @@ class VideoFaceCrop(Preprocessor, object):
min_face_size = 50,
use_local_cropper_flag = False,
rgb_output_flag = False,
+ detect_faces_flag = False,
**kwargs):
super(VideoFaceCrop, self).__init__(cropped_image_size = cropped_image_size,
@@ -104,6 +113,7 @@ class VideoFaceCrop(Preprocessor, object):
min_face_size = min_face_size,
use_local_cropper_flag = use_local_cropper_flag,
rgb_output_flag = rgb_output_flag,
+ detect_faces_flag = detect_faces_flag,
**kwargs)
self.cropped_image_size = cropped_image_size
@@ -116,6 +126,7 @@ class VideoFaceCrop(Preprocessor, object):
self.min_face_size = min_face_size
self.use_local_cropper_flag = use_local_cropper_flag
self.rgb_output_flag = rgb_output_flag
+ self.detect_faces_flag = detect_faces_flag
# Save also the data stored in the kwargs:
for (k, v) in kwargs.items():
@@ -263,6 +274,10 @@ class VideoFaceCrop(Preprocessor, object):
Cropped faces stored in the FrameContainer.
"""
+ if self.detect_faces_flag:
+
+ annotations = detect_faces_in_video(frames)
+
if len(frames) != len(annotations): # if some annotations are missing
## Select only annotated frames:
diff --git a/bob/pad/face/utils/__init__.py b/bob/pad/face/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/bob/pad/face/utils/face_detection_utils.py b/bob/pad/face/utils/face_detection_utils.py
new file mode 100644
index 00000000..2214958d
--- /dev/null
+++ b/bob/pad/face/utils/face_detection_utils.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+
+"""
+This file contains face detection utils.
+"""
+#==============================================================================
+# Import here:
+
+import bob.ip.dlib # for face detection functionality
+
+
+#==============================================================================
+def detect_face_in_image(image):
+ """
+ This function detects a face in the input image.
+
+ **Parameters:**
+
+ ``image`` : 3D :py:class:`numpy.ndarray`
+ A color image to detect the face in.
+
+ **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)}``.
+ """
+
+ bounding_box, _ = bob.ip.dlib.FaceDetector().detect_single_face(image)
+
+ annotations = {}
+
+ if bounding_box is not None:
+
+ annotations['topleft'] = bounding_box.topleft
+
+ annotations['bottomright'] = bounding_box.bottomright
+
+ else:
+
+ annotations['topleft'] = (0, 0)
+
+ annotations['bottomright'] = (0, 0)
+
+ return annotations
+
+
+#==============================================================================
+def detect_faces_in_video(frame_container):
+ """
+ This function detects a face in each farme of the input video.
+
+ **Parameters:**
+
+ ``frame_container`` : FrameContainer
+ FrameContainer containing the frames data.
+
+ **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.
+ """
+
+ annotations = {}
+
+ for idx, frame in enumerate(frame_container):
+
+ image = frame[1]
+
+ frame_annotations = detect_face_in_image(image)
+
+ annotations[str(idx)] = frame_annotations
+
+ return annotations
+
+
+
+
+
+
+
+
+
diff --git a/setup.py b/setup.py
index 228505a4..0a0578e9 100644
--- a/setup.py
+++ b/setup.py
@@ -103,6 +103,7 @@ setup(
# registered preprocessors:
'bob.pad.preprocessor': [
'empty-preprocessor = bob.pad.face.config.preprocessor.filename:empty_preprocessor', # no preprocessing
+ 'preprocessor-rgb-face-detect = bob.pad.face.config.preprocessor.video_face_crop:preprocessor_rgb_face_detect', # detect faces locally replacing database annotations
],
# registered extractors:
--
GitLab