def __init__(self, controller=None):
QMainWindow.__init__(self)
self.setWindowTitle("HeartRateAnalyser")
# self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowSystemMenuHint)
# self.setWindowOpacity(0.97)
self.setMouseTracking(True)
self.setAutoFillBackground(True)
self.draw_background()
self.cascade = cv.Load("C:\Python27\haarcascade_frontalface_alt2.xml")
# -------------------------------------------------------------------------------------------------------------
# Initialization variable
# -------------------------------------------------------------------------------------------------------------
self.controller = controller
self.framesTreated = []
self.frames = []
self.np_result = []
self.results = []
self.capture = cv.CaptureFromCAM(0)
self.min_size = (15, 15)
self.heartRate = 0
# self.capture = 0
self.frameMaximumNumber = 180
self.image_scale = 2
self.haar_scale = 1.2
self.min_neighbors = 2
self.haar_flags = 0
self.processingTime = 0
self.faces_number = 0
self.counter = 0
self.faceDetectingCapacity = 1
self.testCounter = 0
self.needToBeRestart = False
self.acquisitionFlag = True
self.frameToBeDisplayed = None
self.acquisitionTask = None
self.timestamps = [x for x in range(self.frameMaximumNumber)]
self.timer = QTimer(self)
# menuBar
# self.fileMenu = self.menuBar().addMenu("&File")
# self.editMenu = self.menuBar().addMenu("&Edit")
# self.helpMenu = self.menuBar().addMenu("&Help")
# define the menu bar's action
file_new_action = self.create_action("&New...", self.file_new, QKeySequence.New, "filenew", "Create an image file")
file_open_action = self.create_action("&Open...", self.file_open, QKeySequence.Open, "fileopen", "Open an existing image file")
file_save_action = self.create_action("&Save", self.file_save, QKeySequence.Save, "filesave", "Save the image")
file_save_as_action = self.create_action("Save &As...", self.start_acquisition_process, icon="filesaveas",tip="Save the image using a new name")
# add the tool bar
# file_tool_bar = self.addToolBar("File")
# edit_tool_bar = self.addToolBar("Edit")
# self.add_actions(file_tool_bar, (file_new_action, file_open_action, file_save_action, file_save_as_action))
# self.add_actions(edit_tool_bar, ())
# add the action into the menu
# self.add_actions(self.fileMenu, (file_new_action, file_open_action, file_save_action, file_save_as_action))
self.patientImageViewer = PatientImageViewer(self)
self.displayWindow = self.patientImageViewer.get_video_player_reference()
self.windowsContainer = QWidget()
central_layout = QHBoxLayout(self.windowsContainer)
central_layout.addWidget(self.patientImageViewer)
central_layout.setSpacing(0)
central_layout.setMargin(0)
self.setCentralWidget(self.windowsContainer)
self.setFixedSize(1200, 800)
# set connections
self.connect(self.timer, SIGNAL("timeout()"), self.do_signals_processing)
class AnalyserMainWindow(QMainWindow):
def __init__(self, controller=None):
QMainWindow.__init__(self)
self.setWindowTitle("HeartRateAnalyser")
# self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowSystemMenuHint)
# self.setWindowOpacity(0.97)
self.setMouseTracking(True)
self.setAutoFillBackground(True)
self.draw_background()
self.cascade = cv.Load("C:\Python27\haarcascade_frontalface_alt2.xml")
# -------------------------------------------------------------------------------------------------------------
# Initialization variable
# -------------------------------------------------------------------------------------------------------------
self.controller = controller
self.framesTreated = []
self.frames = []
self.np_result = []
self.results = []
self.capture = cv.CaptureFromCAM(0)
self.min_size = (15, 15)
self.heartRate = 0
# self.capture = 0
self.frameMaximumNumber = 180
self.image_scale = 2
self.haar_scale = 1.2
self.min_neighbors = 2
self.haar_flags = 0
self.processingTime = 0
self.faces_number = 0
self.counter = 0
self.faceDetectingCapacity = 1
self.testCounter = 0
self.needToBeRestart = False
self.acquisitionFlag = True
self.frameToBeDisplayed = None
self.acquisitionTask = None
self.timestamps = [x for x in range(self.frameMaximumNumber)]
self.timer = QTimer(self)
# menuBar
# self.fileMenu = self.menuBar().addMenu("&File")
# self.editMenu = self.menuBar().addMenu("&Edit")
# self.helpMenu = self.menuBar().addMenu("&Help")
# define the menu bar's action
file_new_action = self.create_action("&New...", self.file_new, QKeySequence.New, "filenew", "Create an image file")
file_open_action = self.create_action("&Open...", self.file_open, QKeySequence.Open, "fileopen", "Open an existing image file")
file_save_action = self.create_action("&Save", self.file_save, QKeySequence.Save, "filesave", "Save the image")
file_save_as_action = self.create_action("Save &As...", self.start_acquisition_process, icon="filesaveas",tip="Save the image using a new name")
# add the tool bar
# file_tool_bar = self.addToolBar("File")
# edit_tool_bar = self.addToolBar("Edit")
# self.add_actions(file_tool_bar, (file_new_action, file_open_action, file_save_action, file_save_as_action))
# self.add_actions(edit_tool_bar, ())
# add the action into the menu
# self.add_actions(self.fileMenu, (file_new_action, file_open_action, file_save_action, file_save_as_action))
self.patientImageViewer = PatientImageViewer(self)
self.displayWindow = self.patientImageViewer.get_video_player_reference()
self.windowsContainer = QWidget()
central_layout = QHBoxLayout(self.windowsContainer)
central_layout.addWidget(self.patientImageViewer)
central_layout.setSpacing(0)
central_layout.setMargin(0)
self.setCentralWidget(self.windowsContainer)
self.setFixedSize(1200, 800)
# set connections
self.connect(self.timer, SIGNAL("timeout()"), self.do_signals_processing)
def create_action(self, text, slot=None, shortcut=None, icon=None, tip=None, checkable=False, signal="triggered()"):
action = QAction(text, self)
if icon is not None:
action.setIcon(QIcon(":/%s.png" % icon))
if shortcut is not None:
action.setShortcut(shortcut)
if tip is not None:
action.setToolTip(tip)
action.setStatusTip(tip)
if slot is not None:
self.connect(action, SIGNAL(signal), slot)
if checkable:
action.setCheckable(True)
return action
def add_actions(self, target, actions):
for action in actions:
if action is None:
target.addSeparator()
else:
target.addAction(action)
def file_new(self):
return
def file_open(self):
return
def load_file(self, fname=None):
return
def add_recent_file(self, fname):
return
def file_save(self):
return
def stop_video_capture_task(self):
return
def apply_on_the_page(self):
# process after display finished.....
curve = self.generate_peoples_results_files()
self.patientImageViewer.get_plot_table_reference().showData(self.timestamps, curve)
self.patientImageViewer.display_result(self.heartRate)
self.stop_video_capture_task()
def face_detecting(self, frame):
# start = time.time()
self.processingTime += 1
image_size = cv.GetSize(frame)
gray = cv.CreateImage(image_size, 8, 1)
small_img = cv.CreateImage(
(cv.Round(frame.width / self.image_scale), cv.Round(frame.height / self.image_scale)), 8, 1)
cv.CvtColor(frame, gray, cv.CV_BGR2GRAY)
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
faces = cv.HaarDetectObjects(small_img, self.cascade, cv.CreateMemStorage(0), self.haar_scale,
self.min_neighbors, self.haar_flags,
self.min_size)
self.faces_number = len(faces)
if self.processingTime is 1:
if self.faces_number < 1:
self.needToBeRestart = True
self.processingTime = 0
return None
faces_count = 0
if self.faces_number is self.faceDetectingCapacity:
for ((x, y, w, h), n) in faces:
sum_pixlevalue_b = 0
sum_pixlevalue_g = 0
sum_pixlevalue_r = 0
if self.processingTime is 1:
self.faceDetectingCapacity = self.faces_number
rgb = {'green': [], 'red': [], 'blue': []}
self.results.append(rgb)
x1, y1 = (int(x * self.image_scale), int(y * self.image_scale))
x2, y2 = (int((x + w) * self.image_scale), int((y + h) * self.image_scale))
x11 = int((8 * x1 + 2 * x2) / 10)
y11 = int(y1)
x22 = int((2 * x1 + 8 * x2) / 10)
y22 = int(y2)
pt1 = (x11, y11)
pt2 = (x22, y22)
cv.Rectangle(frame, pt1, pt2, cv.RGB(0, 255, 0), 3, 8, 0)
# end = time.time()
# print (start - end)
Rectangle_width = x22 - x11 + 1
Rectangle_height = y22 - y11 + 1
sum_pixleNumber = Rectangle_width * Rectangle_height
for i in range(y11, y22):
for j in range(x11, x22):
tmpbgr = cv.Get2D(frame, i, j)
sum_pixlevalue_b += tmpbgr[0]
sum_pixlevalue_g += tmpbgr[1]
sum_pixlevalue_r += tmpbgr[2]
self.results[faces_count]['blue'].append(sum_pixlevalue_b / sum_pixleNumber)
self.results[faces_count]['green'].append(sum_pixlevalue_g / sum_pixleNumber)
self.results[faces_count]['red'].append(sum_pixlevalue_r / sum_pixleNumber)
faces_count += 1
else:
self.needToBeRestart = True
self.processingTime = 0
return frame
def generate_peoples_results_files(self):
self.np_result = np.c_[self.results[0]['blue'], self.results[0]['green'], self.results[0]['red']]
list_number = len(self.results[0]['blue'])
# ICA
ica = FastICA(n_components=3, fun='logcosh', max_iter=2000)
ica_transformed = ica.fit_transform(self.np_result)
component_all = ica_transformed.ravel([1])
component_1 = component_all[:list_number]
component_2 = component_all[list_number:(2 * list_number)]
component_3 = component_all[(2 * list_number):(3 * list_number)]
# butter_smooth
N = 8
Wn = [1.6 / 30, 4.0 / 30]
t = np.linspace(1 / 30, list_number / 30, list_number)
b, a = signal.butter(N, Wn, 'bandpass', analog=False)
filter_1 = signal.filtfilt(b, a, component_1)
filter_2 = signal.filtfilt(b, a, component_2)
filter_3 = signal.filtfilt(b, a, component_3)
lowess_1 = sm.nonparametric.lowess(filter_1, t, frac=10.0 / list_number)
lowess_2 = sm.nonparametric.lowess(filter_2, t, frac=10.0 / list_number)
lowess_3 = sm.nonparametric.lowess(filter_3, t, frac=10.0 / list_number)
smooths = []
smooth_1 = lowess_1[:, 1]
smooth_2 = lowess_2[:, 1]
smooth_3 = lowess_3[:, 1]
smooths.append(smooth_1)
smooths.append(smooth_2)
smooths.append(smooth_3)
# FFT and spectrum
fft_1 = np.fft.fft(smooth_1, 256)
fft_2 = np.fft.fft(smooth_2, 256)
fft_3 = np.fft.fft(smooth_3, 256)
spectrum_1 = list(np.abs(fft_1) ** 2)
spectrum_2 = list(np.abs(fft_2) ** 2)
spectrum_3 = list(np.abs(fft_3) ** 2)
max1 = max(spectrum_1)
max2 = max(spectrum_2)
max3 = max(spectrum_3)
num_spec1 = spectrum_1.index(max(spectrum_1))
if num_spec1 > (list_number / 2):
num_spec1 = 256 - num_spec1
num_spec2 = spectrum_2.index(max(spectrum_2))
if num_spec2 > (list_number / 2):
num_spec2 = 256 - num_spec2
num_spec3 = spectrum_3.index(max(spectrum_3))
if num_spec3 > (list_number / 2):
num_spec3 = 256 - num_spec3
num_spec = [num_spec1, num_spec2, num_spec3]
max_all = [max1, max2, max3]
max_num = max_all.index(max(max_all))
self.heartRate = int(num_spec[max_num] * 1800 / 256) + 1
return smooths[max_num]
def do_signals_processing(self):
if self.acquisitionFlag:
if self.frameToBeDisplayed is not None:
self.displayWindow.update_image(QImage(self.frameToBeDisplayed.tostring(),
self.frameToBeDisplayed.width,
self.frameToBeDisplayed.height,
QImage.Format_RGB888).rgbSwapped())
else:
self.timer.stop()
cv2.VideoCapture(0).release()
self.apply_on_the_page()
self.testCounter += 1
def do_images_acquisition(self):
self.counter = 0
while self.acquisitionFlag:
self.patientImageViewer.set_advancement(self.counter)
frame = cv.QueryFrame(self.capture)
self.frameToBeDisplayed = self.face_detecting(frame)
self.frames.append(self.frameToBeDisplayed)
if self.needToBeRestart:
self.counter = -1
self.frames = []
self.faces_number = 0
self.faceDetectingCapacity = 1
self.needToBeRestart = False
self.results = []
self.counter += 1
if self.counter == self.frameMaximumNumber:
self.acquisitionFlag = False
# do_images_acquisition()
def init_camera(self):
# self.capture = cv.CaptureFromCAM(0)
cv.SetCaptureProperty(self.capture, cv.CV_CAP_PROP_FPS, 30)
fps = cv.GetCaptureProperty(self.capture, cv.CV_CAP_PROP_FPS)
def start_acquisition_process(self):
self.counter = 0
self.frames = []
self.faces_number = 0
self.processingTime = 0
self.faceDetectingCapacity = 1
self.needToBeRestart = False
self.results = []
self.acquisitionFlag = True
self.init_camera()
self.timer.start(50)
acquisitionTask = threading.Thread(None, self.do_images_acquisition)
acquisitionTask.start()
def file_print(self):
return
def draw_background(self):
pixmap = QPixmap(":/pagebackground.png")
palette = self.palette()
palette.setBrush(QPalette.Background, QBrush(
pixmap.scaled(QSize(self.width(), self.height()), Qt.IgnoreAspectRatio, Qt.SmoothTransformation)))
self.setPalette(palette)
self.setMask(pixmap.mask())
self.setAutoFillBackground(True)
def __init__(self, controller=None):
QMainWindow.__init__(self)
self.setWindowTitle("HeartRateAnalyser")
# self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowSystemMenuHint)
# self.setWindowOpacity(0.97)
self.setMouseTracking(True)
self.setAutoFillBackground(True)
self.draw_background()
self.cascade = cv.Load("C:\Python27\haarcascade_frontalface_alt2.xml")
# -------------------------------------------------------------------------------------------------------------
# Initialization variable
# -------------------------------------------------------------------------------------------------------------
self.controller = controller
self.framesTreated = []
self.frames = []
self.np_result = []
self.results = []
self.capture = cv.CaptureFromCAM(0)
self.min_size = (15, 15)
self.heartRate = 0
# self.capture = 0
self.frameMaximumNumber = 180
self.image_scale = 2
self.haar_scale = 1.2
self.min_neighbors = 2
self.haar_flags = 0
self.processingTime = 0
self.faces_number = 0
self.counter = 0
self.faceDetectingCapacity = 1
self.testCounter = 0
self.needToBeRestart = False
self.acquisitionFlag = True
self.frameToBeDisplayed = None
self.acquisitionTask = None
self.timestamps = [x for x in range(self.frameMaximumNumber)]
self.timer = QTimer(self)
# menuBar
# self.fileMenu = self.menuBar().addMenu("&File")
# self.editMenu = self.menuBar().addMenu("&Edit")
# self.helpMenu = self.menuBar().addMenu("&Help")
# define the menu bar's action
file_new_action = self.create_action("&New...", self.file_new,
QKeySequence.New, "filenew",
"Create an image file")
file_open_action = self.create_action("&Open...", self.file_open,
QKeySequence.Open, "fileopen",
"Open an existing image file")
file_save_action = self.create_action("&Save", self.file_save,
QKeySequence.Save, "filesave",
"Save the image")
file_save_as_action = self.create_action(
"Save &As...",
self.start_acquisition_process,
icon="filesaveas",
tip="Save the image using a new name")
# add the tool bar
# file_tool_bar = self.addToolBar("File")
# edit_tool_bar = self.addToolBar("Edit")
# self.add_actions(file_tool_bar, (file_new_action, file_open_action, file_save_action, file_save_as_action))
# self.add_actions(edit_tool_bar, ())
# add the action into the menu
# self.add_actions(self.fileMenu, (file_new_action, file_open_action, file_save_action, file_save_as_action))
self.patientImageViewer = PatientImageViewer(self)
self.displayWindow = self.patientImageViewer.get_video_player_reference(
)
self.windowsContainer = QWidget()
central_layout = QHBoxLayout(self.windowsContainer)
central_layout.addWidget(self.patientImageViewer)
central_layout.setSpacing(0)
central_layout.setMargin(0)
self.setCentralWidget(self.windowsContainer)
self.setFixedSize(1200, 800)
# set connections
self.connect(self.timer, SIGNAL("timeout()"),
self.do_signals_processing)
__author__ = 'XuHaoshen'
class AnalyserMainWindow(QMainWindow):
def __init__(self, controller=None):
QMainWindow.__init__(self)
self.setWindowTitle("HeartRateAnalyser")
# self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowSystemMenuHint)
# self.setWindowOpacity(0.97)
self.setMouseTracking(True)
self.setAutoFillBackground(True)
self.draw_background()
self.cascade = cv.Load("C:\Python27\haarcascade_frontalface_alt2.xml")
# -------------------------------------------------------------------------------------------------------------
# Initialization variable
# -------------------------------------------------------------------------------------------------------------
self.controller = controller
self.framesTreated = []
self.frames = []
self.np_result = []
self.results = []
self.capture = cv.CaptureFromCAM(0)
self.min_size = (15, 15)
self.heartRate = 0
# self.capture = 0
self.frameMaximumNumber = 180
self.image_scale = 2
self.haar_scale = 1.2
self.min_neighbors = 2
self.haar_flags = 0
self.processingTime = 0
self.faces_number = 0
self.counter = 0
self.faceDetectingCapacity = 1
self.testCounter = 0
self.needToBeRestart = False
self.acquisitionFlag = True
self.frameToBeDisplayed = None
self.acquisitionTask = None
self.timestamps = [x for x in range(self.frameMaximumNumber)]
self.timer = QTimer(self)
# menuBar
# self.fileMenu = self.menuBar().addMenu("&File")
# self.editMenu = self.menuBar().addMenu("&Edit")
# self.helpMenu = self.menuBar().addMenu("&Help")
# define the menu bar's action
file_new_action = self.create_action("&New...", self.file_new,
QKeySequence.New, "filenew",
"Create an image file")
file_open_action = self.create_action("&Open...", self.file_open,
QKeySequence.Open, "fileopen",
"Open an existing image file")
file_save_action = self.create_action("&Save", self.file_save,
QKeySequence.Save, "filesave",
"Save the image")
file_save_as_action = self.create_action(
"Save &As...",
self.start_acquisition_process,
icon="filesaveas",
tip="Save the image using a new name")
# add the tool bar
# file_tool_bar = self.addToolBar("File")
# edit_tool_bar = self.addToolBar("Edit")
# self.add_actions(file_tool_bar, (file_new_action, file_open_action, file_save_action, file_save_as_action))
# self.add_actions(edit_tool_bar, ())
# add the action into the menu
# self.add_actions(self.fileMenu, (file_new_action, file_open_action, file_save_action, file_save_as_action))
self.patientImageViewer = PatientImageViewer(self)
self.displayWindow = self.patientImageViewer.get_video_player_reference(
)
self.windowsContainer = QWidget()
central_layout = QHBoxLayout(self.windowsContainer)
central_layout.addWidget(self.patientImageViewer)
central_layout.setSpacing(0)
central_layout.setMargin(0)
self.setCentralWidget(self.windowsContainer)
self.setFixedSize(1200, 800)
# set connections
self.connect(self.timer, SIGNAL("timeout()"),
self.do_signals_processing)
def create_action(self,
text,
slot=None,
shortcut=None,
icon=None,
tip=None,
checkable=False,
signal="triggered()"):
action = QAction(text, self)
if icon is not None:
action.setIcon(QIcon(":/%s.png" % icon))
if shortcut is not None:
action.setShortcut(shortcut)
if tip is not None:
action.setToolTip(tip)
action.setStatusTip(tip)
if slot is not None:
self.connect(action, SIGNAL(signal), slot)
if checkable:
action.setCheckable(True)
return action
def add_actions(self, target, actions):
for action in actions:
if action is None:
target.addSeparator()
else:
target.addAction(action)
def file_new(self):
return
def file_open(self):
return
def load_file(self, fname=None):
return
def add_recent_file(self, fname):
return
def file_save(self):
return
def stop_video_capture_task(self):
return
def apply_on_the_page(self):
# process after display finished.....
curve = self.generate_peoples_results_files()
self.patientImageViewer.get_plot_table_reference().showData(
self.timestamps, curve)
self.patientImageViewer.display_result(self.heartRate)
self.stop_video_capture_task()
def face_detecting(self, frame):
# start = time.time()
self.processingTime += 1
image_size = cv.GetSize(frame)
gray = cv.CreateImage(image_size, 8, 1)
small_img = cv.CreateImage((cv.Round(frame.width / self.image_scale),
cv.Round(frame.height / self.image_scale)),
8, 1)
cv.CvtColor(frame, gray, cv.CV_BGR2GRAY)
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
faces = cv.HaarDetectObjects(small_img, self.cascade,
cv.CreateMemStorage(0), self.haar_scale,
self.min_neighbors, self.haar_flags,
self.min_size)
self.faces_number = len(faces)
if self.processingTime is 1:
if self.faces_number < 1:
self.needToBeRestart = True
self.processingTime = 0
return None
faces_count = 0
if self.faces_number is self.faceDetectingCapacity:
for ((x, y, w, h), n) in faces:
sum_pixlevalue_b = 0
sum_pixlevalue_g = 0
sum_pixlevalue_r = 0
if self.processingTime is 1:
self.faceDetectingCapacity = self.faces_number
rgb = {'green': [], 'red': [], 'blue': []}
self.results.append(rgb)
x1, y1 = (int(x * self.image_scale), int(y * self.image_scale))
x2, y2 = (int(
(x + w) * self.image_scale), int(
(y + h) * self.image_scale))
x11 = int((8 * x1 + 2 * x2) / 10)
y11 = int(y1)
x22 = int((2 * x1 + 8 * x2) / 10)
y22 = int(y2)
pt1 = (x11, y11)
pt2 = (x22, y22)
cv.Rectangle(frame, pt1, pt2, cv.RGB(0, 255, 0), 3, 8, 0)
# end = time.time()
# print (start - end)
Rectangle_width = x22 - x11 + 1
Rectangle_height = y22 - y11 + 1
sum_pixleNumber = Rectangle_width * Rectangle_height
for i in range(y11, y22):
for j in range(x11, x22):
tmpbgr = cv.Get2D(frame, i, j)
sum_pixlevalue_b += tmpbgr[0]
sum_pixlevalue_g += tmpbgr[1]
sum_pixlevalue_r += tmpbgr[2]
self.results[faces_count]['blue'].append(sum_pixlevalue_b /
sum_pixleNumber)
self.results[faces_count]['green'].append(sum_pixlevalue_g /
sum_pixleNumber)
self.results[faces_count]['red'].append(sum_pixlevalue_r /
sum_pixleNumber)
faces_count += 1
else:
self.needToBeRestart = True
self.processingTime = 0
return frame
def generate_peoples_results_files(self):
self.np_result = np.c_[self.results[0]['blue'],
self.results[0]['green'],
self.results[0]['red']]
list_number = len(self.results[0]['blue'])
# ICA
ica = FastICA(n_components=3, fun='logcosh', max_iter=2000)
ica_transformed = ica.fit_transform(self.np_result)
component_all = ica_transformed.ravel([1])
component_1 = component_all[:list_number]
component_2 = component_all[list_number:(2 * list_number)]
component_3 = component_all[(2 * list_number):(3 * list_number)]
# butter_smooth
N = 8
Wn = [1.6 / 30, 4.0 / 30]
t = np.linspace(1 / 30, list_number / 30, list_number)
b, a = signal.butter(N, Wn, 'bandpass', analog=False)
filter_1 = signal.filtfilt(b, a, component_1)
filter_2 = signal.filtfilt(b, a, component_2)
filter_3 = signal.filtfilt(b, a, component_3)
lowess_1 = sm.nonparametric.lowess(filter_1,
t,
frac=10.0 / list_number)
lowess_2 = sm.nonparametric.lowess(filter_2,
t,
frac=10.0 / list_number)
lowess_3 = sm.nonparametric.lowess(filter_3,
t,
frac=10.0 / list_number)
smooths = []
smooth_1 = lowess_1[:, 1]
smooth_2 = lowess_2[:, 1]
smooth_3 = lowess_3[:, 1]
smooths.append(smooth_1)
smooths.append(smooth_2)
smooths.append(smooth_3)
# FFT and spectrum
fft_1 = np.fft.fft(smooth_1, 256)
fft_2 = np.fft.fft(smooth_2, 256)
fft_3 = np.fft.fft(smooth_3, 256)
spectrum_1 = list(np.abs(fft_1)**2)
spectrum_2 = list(np.abs(fft_2)**2)
spectrum_3 = list(np.abs(fft_3)**2)
max1 = max(spectrum_1)
max2 = max(spectrum_2)
max3 = max(spectrum_3)
num_spec1 = spectrum_1.index(max(spectrum_1))
if num_spec1 > (list_number / 2):
num_spec1 = 256 - num_spec1
num_spec2 = spectrum_2.index(max(spectrum_2))
if num_spec2 > (list_number / 2):
num_spec2 = 256 - num_spec2
num_spec3 = spectrum_3.index(max(spectrum_3))
if num_spec3 > (list_number / 2):
num_spec3 = 256 - num_spec3
num_spec = [num_spec1, num_spec2, num_spec3]
max_all = [max1, max2, max3]
max_num = max_all.index(max(max_all))
self.heartRate = int(num_spec[max_num] * 1800 / 256) + 1
return smooths[max_num]
def do_signals_processing(self):
if self.acquisitionFlag:
if self.frameToBeDisplayed is not None:
self.displayWindow.update_image(
QImage(self.frameToBeDisplayed.tostring(),
self.frameToBeDisplayed.width,
self.frameToBeDisplayed.height,
QImage.Format_RGB888).rgbSwapped())
else:
self.timer.stop()
cv2.VideoCapture(0).release()
self.apply_on_the_page()
self.testCounter += 1
def do_images_acquisition(self):
self.counter = 0
while self.acquisitionFlag:
self.patientImageViewer.set_advancement(self.counter)
frame = cv.QueryFrame(self.capture)
self.frameToBeDisplayed = self.face_detecting(frame)
self.frames.append(self.frameToBeDisplayed)
if self.needToBeRestart:
self.counter = -1
self.frames = []
self.faces_number = 0
self.faceDetectingCapacity = 1
self.needToBeRestart = False
self.results = []
self.counter += 1
if self.counter == self.frameMaximumNumber:
self.acquisitionFlag = False
# do_images_acquisition()
def init_camera(self):
# self.capture = cv.CaptureFromCAM(0)
cv.SetCaptureProperty(self.capture, cv.CV_CAP_PROP_FPS, 30)
fps = cv.GetCaptureProperty(self.capture, cv.CV_CAP_PROP_FPS)
def start_acquisition_process(self):
self.counter = 0
self.frames = []
self.faces_number = 0
self.processingTime = 0
self.faceDetectingCapacity = 1
self.needToBeRestart = False
self.results = []
self.acquisitionFlag = True
self.init_camera()
self.timer.start(50)
acquisitionTask = threading.Thread(None, self.do_images_acquisition)
acquisitionTask.start()
def file_print(self):
return
def draw_background(self):
pixmap = QPixmap(":/pagebackground.png")
palette = self.palette()
palette.setBrush(
QPalette.Background,
QBrush(
pixmap.scaled(QSize(self.width(), self.height()),
Qt.IgnoreAspectRatio, Qt.SmoothTransformation)))
self.setPalette(palette)
self.setMask(pixmap.mask())
self.setAutoFillBackground(True)
