diff --git a/bob/pad/face/extractor/Chrom.py b/bob/pad/face/extractor/Chrom.py
index 28a017826b460b1c248e6732e93b84dc7644ae95..7cafa8720581305f4e0012e9cd2ffc41c25bd323 100644
--- a/bob/pad/face/extractor/Chrom.py
+++ b/bob/pad/face/extractor/Chrom.py
@@ -15,8 +15,6 @@ import bob.ip.skincolorfilter
import logging
logger = logging.getLogger("bob.pad.face")
-
-
from bob.rppg.base.utils import crop_face
from bob.rppg.base.utils import build_bandpass_filter
@@ -26,7 +24,6 @@ from bob.rppg.chrom.extract_utils import compute_gray_diff
from bob.rppg.chrom.extract_utils import select_stable_frames
-
class Chrom(Extractor, object):
"""
Extract pulse signal according to the CHROM algorithm
@@ -52,8 +49,11 @@ class Chrom(Extractor, object):
The percentage of frames you want to select where the
signal is "stable". 0 mean all the sequence.
+ debug: boolean
+ Plot some stuff
+
"""
- def __init__(self, skin_threshold=0.5, skin_init=False, framerate=25, bp_order=32, window_size=0, motion=0.0, **kwargs):
+ def __init__(self, skin_threshold=0.5, skin_init=False, framerate=25, bp_order=32, window_size=0, motion=0.0, debug=False, **kwargs):
super(Chrom, self).__init__()
@@ -63,6 +63,7 @@ class Chrom(Extractor, object):
self.bp_order = bp_order
self.window_size = window_size
self.motion = motion
+ self.debug = debug
self.skin_filter = bob.ip.skincolorfilter.SkinColorFilter()
@@ -78,24 +79,23 @@ class Chrom(Extractor, object):
see ``bob.bio.video.utils.FrameContainer`` for further details.
If string, the name of the file to load the video data from is
defined in it. String is possible only when empty preprocessor is
- used. In this case video data is loaded directly from the database.
+ used. In this case video data is loaded directly from the database
+ and not using any high or low-level db packages (so beware).
**Returns:**
- pulse: FrameContainer
- Quality Measures for each frame stored in the FrameContainer.
+ pulse: numpy.array
+ The pulse signal
"""
-
- # load video based on the filename
- assert isinstance(frames, six.string_types)
if isinstance(frames, six.string_types):
video_loader = VideoDataLoader()
video = video_loader(frames)
-
- video = video.as_array()
+ else:
+ video = frames
+ video = video.as_array()
nb_frames = video.shape[0]
- output_data = numpy.zeros(nb_frames, dtype='float64')
+
chrom = numpy.zeros((nb_frames, 2), dtype='float64')
# build the bandpass filter one and for all
@@ -104,7 +104,14 @@ class Chrom(Extractor, object):
counter = 0
previous_bbox = None
for i, frame in enumerate(video):
+
+ logger.debug("Processing frame {}/{}".format(counter, nb_frames))
+ if self.debug:
+ from matplotlib import pyplot
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(frame, 2),2))
+ pyplot.show()
+
try:
bbox, quality = bob.ip.facedetect.detect_single_face(frame)
except:
@@ -118,9 +125,10 @@ class Chrom(Extractor, object):
face = crop_face(frame, bbox, bbox.size[1])
- #from matplotlib import pyplot
- #pyplot.imshow(numpy.rollaxis(numpy.rollaxis(face, 2),2))
- #pyplot.show()
+ if self.debug:
+ from matplotlib import pyplot
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(face, 2),2))
+ pyplot.show()
# skin filter
if counter == 0 or self.skin_init:
@@ -128,13 +136,13 @@ class Chrom(Extractor, object):
logger.debug("Skin color parameters:\nmean\n{0}\ncovariance\n{1}".format(self.skin_filter.mean, self.skin_filter.covariance))
skin_mask = self.skin_filter.get_skin_mask(face, self.skin_threshold)
- #from matplotlib import pyplot
- #skin_mask_image = numpy.copy(face)
- #skin_mask_image[:, skin_mask] = 255
- #pyplot.imshow(numpy.rollaxis(numpy.rollaxis(skin_mask_image, 2),2))
- #pyplot.show()
+ if self.debug:
+ from matplotlib import pyplot
+ skin_mask_image = numpy.copy(face)
+ skin_mask_image[:, skin_mask] = 255
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(skin_mask_image, 2),2))
+ pyplot.show()
- logger.debug("Processing frame {}/{}".format(counter, nb_frames))
# sometimes skin is not detected !
if numpy.count_nonzero(skin_mask) != 0:
@@ -167,13 +175,14 @@ class Chrom(Extractor, object):
logger.info("Stable segment -> {0} - {1}".format(index, index + n_stable_frames_to_keep))
chrom = chrom[index:(index + n_stable_frames_to_keep),:]
- #from matplotlib import pyplot
- #f, axarr = pyplot.subplots(2, sharex=True)
- #axarr[0].plot(range(chrom.shape[0]), chrom[:, 0], 'k')
- #axarr[0].set_title("X value in the chrominance subspace")
- #axarr[1].plot(range(chrom.shape[0]), chrom[:, 1], 'k')
- #axarr[1].set_title("Y value in the chrominance subspace")
- #pyplot.show()
+ if self.debug:
+ from matplotlib import pyplot
+ f, axarr = pyplot.subplots(2, sharex=True)
+ axarr[0].plot(range(chrom.shape[0]), chrom[:, 0], 'k')
+ axarr[0].set_title("X value in the chrominance subspace")
+ axarr[1].plot(range(chrom.shape[0]), chrom[:, 1], 'k')
+ axarr[1].set_title("Y value in the chrominance subspace")
+ pyplot.show()
# now that we have the chrominance signals, apply bandpass
from scipy.signal import filtfilt
@@ -182,13 +191,14 @@ class Chrom(Extractor, object):
x_bandpassed = filtfilt(bandpass_filter, numpy.array([1]), chrom[:, 0])
y_bandpassed = filtfilt(bandpass_filter, numpy.array([1]), chrom[:, 1])
- #from matplotlib import pyplot
- #f, axarr = pyplot.subplots(2, sharex=True)
- #axarr[0].plot(range(x_bandpassed.shape[0]), x_bandpassed, 'k')
- #axarr[0].set_title("X bandpassed")
- #axarr[1].plot(range(y_bandpassed.shape[0]), y_bandpassed, 'k')
- #axarr[1].set_title("Y bandpassed")
- #pyplot.show()
+ if self.debug:
+ from matplotlib import pyplot
+ f, axarr = pyplot.subplots(2, sharex=True)
+ axarr[0].plot(range(x_bandpassed.shape[0]), x_bandpassed, 'k')
+ axarr[0].set_title("X bandpassed")
+ axarr[1].plot(range(y_bandpassed.shape[0]), y_bandpassed, 'k')
+ axarr[1].set_title("Y bandpassed")
+ pyplot.show()
# build the final pulse signal
alpha = numpy.std(x_bandpassed) / numpy.std(y_bandpassed)
@@ -206,11 +216,11 @@ class Chrom(Extractor, object):
sw *= numpy.hanning(window_size)
pulse[w:w+window_size] += sw
- #from matplotlib import pyplot
- #f, axarr = pyplot.subplots(1)
- #pyplot.plot(range(pulse.shape[0]), pulse, 'k')
- #pyplot.title("Pulse signal")
- #pyplot.show()
+ if self.debug:
+ from matplotlib import pyplot
+ f, axarr = pyplot.subplots(1)
+ pyplot.plot(range(pulse.shape[0]), pulse, 'k')
+ pyplot.title("Pulse signal")
+ pyplot.show()
- #output_data = pulse
return pulse
diff --git a/bob/pad/face/extractor/SSR.py b/bob/pad/face/extractor/SSR.py
index b52c5df682e48f79ac1582f925b5bb69f998a06f..da86bde64ef0f8502be2af185b083171a64894f9 100644
--- a/bob/pad/face/extractor/SSR.py
+++ b/bob/pad/face/extractor/SSR.py
@@ -15,7 +15,6 @@ import bob.ip.skincolorfilter
import logging
logger = logging.getLogger("bob.pad.face")
-
from bob.rppg.base.utils import crop_face
from bob.rppg.ssr.ssr_utils import get_eigen
@@ -23,7 +22,6 @@ from bob.rppg.ssr.ssr_utils import plot_eigenvectors
from bob.rppg.ssr.ssr_utils import build_P
-
class SSR(Extractor, object):
"""
Extract pulse signal according to the SSR algorithm
@@ -39,8 +37,11 @@ class SSR(Extractor, object):
stride: int
The temporal stride.
+ debug: boolean
+ Plot some stuff
+
"""
- def __init__(self, skin_threshold=0.5, skin_init=False, stride=25, **kwargs):
+ def __init__(self, skin_threshold=0.5, skin_init=False, stride=25, debug=False, **kwargs):
super(SSR, self).__init__()
@@ -62,20 +63,20 @@ class SSR(Extractor, object):
see ``bob.bio.video.utils.FrameContainer`` for further details.
If string, the name of the file to load the video data from is
defined in it. String is possible only when empty preprocessor is
- used. In this case video data is loaded directly from the database.
+ used. In this case video data is loaded directly from the database
+ and not using any high or low-level db packages (so beware).
**Returns:**
- pulse: FrameContainer
- Quality Measures for each frame stored in the FrameContainer.
+ pulse: numpy.array
+ The pulse signal
"""
-
- # load video based on the filename
- assert isinstance(frames, six.string_types)
if isinstance(frames, six.string_types):
video_loader = VideoDataLoader()
video = video_loader(frames)
-
+ else:
+ video = frames
+
video = video.as_array()
nb_frames = video.shape[0]
@@ -86,16 +87,19 @@ class SSR(Extractor, object):
eigenvalues = numpy.zeros((3, nb_frames), dtype='float64')
eigenvectors = numpy.zeros((3, 3, nb_frames), dtype='float64')
- ### LET'S GO
-
- #XXX
- plot = True
-
counter = 0
previous_bbox = None
+ previous_skin_pixels = None
+
for i, frame in enumerate(video):
logger.debug("Processing frame %d/%d...", i, nb_frames)
+
+ if self.debug:
+ from matplotlib import pyplot
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(frame, 2),2))
+ pyplot.show()
+
try:
bbox, quality = bob.ip.facedetect.detect_single_face(frame)
except:
@@ -104,43 +108,54 @@ class SSR(Extractor, object):
face = crop_face(frame, bbox, bbox.size[1])
- #from matplotlib import pyplot
- #pyplot.imshow(numpy.rollaxis(numpy.rollaxis(face, 2),2))
- #pyplot.show()
+ if self.debug:
+ from matplotlib import pyplot
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(face, 2),2))
+ pyplot.show()
# skin filter
if counter == 0 or self.skin_init:
self.skin_filter.estimate_gaussian_parameters(face)
logger.debug("Skin color parameters:\nmean\n{0}\ncovariance\n{1}".format(self.skin_filter.mean, self.skin_filter.covariance))
+
skin_mask = self.skin_filter.get_skin_mask(face, self.skin_threshold)
skin_pixels = face[:, skin_mask]
-
- #from matplotlib import pyplot
- #skin_mask_image = numpy.copy(face)
- #skin_mask_image[:, skin_mask] = 255
- #pyplot.title("skin pixels in frame {0}".format(i))
- #pyplot.imshow(numpy.rollaxis(numpy.rollaxis(skin_mask_image, 2),2))
- #pyplot.show()
-
skin_pixels = skin_pixels.astype('float64') / 255.0
+ if self.debug:
+ from matplotlib import pyplot
+ skin_mask_image = numpy.copy(face)
+ skin_mask_image[:, skin_mask] = 255
+ pyplot.title("skin pixels in frame {0}".format(i))
+ pyplot.imshow(numpy.rollaxis(numpy.rollaxis(skin_mask_image, 2),2))
+ pyplot.show()
+
+ # nos skin pixels have ben detected ... using the previous ones
+ if skin_pixels.shape[1] == 0:
+ skin_pixels = previous_skin_pixels
+ logger.warn("No skin pixels detected, using the previous ones")
+
# build c matrix and get eigenvectors and eigenvalues
eigenvalues[:, counter], eigenvectors[:, :, counter] = get_eigen(skin_pixels)
- #plot_eigenvectors(skin_pixels, eigenvectors[:, :, counter])
+
+ if self.debug:
+ plot_eigenvectors(skin_pixels, eigenvectors[:, :, counter])
# build P and add it to the pulse signal
if counter >= self.stride:
tau = counter - self.stride
p = build_P(counter, self.stride, eigenvectors, eigenvalues)
output_data[tau:counter] += (p - numpy.mean(p))
-
+
+ previous_bbox = bbox
+ previous_skin_pixels = skin_pixels
counter += 1
- # plot the pulse signal
- #import matplotlib.pyplot as plt
- #fig = plt.figure()
- #ax = fig.add_subplot(111)
- #ax.plot(range(nb_frames), output_data)
- #plt.show()
+ if self.debug:
+ import matplotlib.pyplot as plt
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+ ax.plot(range(nb_frames), output_data)
+ plt.show()
return output_data