from pyqtgraph.Qt import QtGui, QtCore
import pyqtgraph as pg
from pandas import DataFrame
if __name__ == "__main__":
video = True
if video == False:
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture("WIN_20210325_11_08_24_Pro.mp4")
fu = Face_utilities()
sp = Signal_processing()
i=0
last_rects = None
last_shape = None
last_age = None
last_gender = None
face_detect_on = False
age_gender_on = False
t = time.time()
#for signal_processing
BUFFER_SIZE = 100
cv2.rectangle(aligned_face, (aligned_shape[4][0], aligned_shape[29][1]),
(aligned_shape[48][0], aligned_shape[33][1]), (0, 255, 0), 0)
#assign to last params
last_rects = rects
last_age = age
last_gender = gender
last_shape = shape
return display_frame, aligned_face
if __name__ == "__main__":
cap = cv2.VideoCapture("1.mp4")
fu = Face_utilities()
i = 0
last_rects = None
last_shape = None
last_age = None
last_gender = None
face_detect_on = False
age_gender_on = False
t = time.time()
while True:
# grab a frame -> face detection -> crop the face -> 68 facial landmarks -> get mask from those landmarks
# calculate time for each loop
import cv2
import face_utilities as fu
from imutils import face_utils
from face_utilities import Face_utilities
fu = Face_utilities()
cap = cv2.VideoCapture(0)
# def get_landmarks(self, frame, type):
# '''
# Get all facial landmarks in a face
#
# Args:
# frame (cv2 image): the original frame. In RGB format.
# type (str): 5 or 68 facial landmarks
#
# Outputs:
# shape (array): facial landmarks' co-ords in format of of tuples (x,y)
# '''
# if self.predictor is None:
# print("[INFO] load " + type + " facial landmarks model ...")
# self.predictor = dlib.shape_predictor("../shape_predictor_" + type + "_face_landmarks.dat")
# print("[INFO] Load model - DONE!")
#
# if frame is None:
# return None, None
# # all face will be resized to a fix size, e.g width = 200
# # face = imutils.resize(face, width=200)
# # face must be gray
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
def bpmrpm():
count = 0
rates = []
video = True
if video == False:
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture("video.webm")
fu = Face_utilities()
sp = Signal_processing()
i = 0
last_rects = None
last_shape = None
last_age = None
last_gender = None
face_detect_on = False
age_gender_on = False
t = time.time()
#for signal_processing
BUFFER_SIZE = 100
fps = 0 #for real time capture
video_fps = cap.get(cv2.CAP_PROP_FPS) # for video capture
#print(video_fps)
times = []
data_buffer = []
# data for plotting
filtered_data = []
fft_of_interest = []
freqs_of_interest = []
bpm = 0
def update():
p1.clear()
p1.plot(np.column_stack((freqs_of_interest, fft_of_interest)), pen='g')
p2.clear()
p2.plot(filtered_data[20:], pen='g')
app.processEvents()
timer = QtCore.QTimer()
timer.timeout.connect(update)
timer.start(300)
while True:
# grab a frame -> face detection -> crop the face -> 68 facial landmarks -> get mask from those landmarks
# calculate time for each loop
t0 = time.time()
if (i % 1 == 0):
face_detect_on = True
if (i % 10 == 0):
age_gender_on = True
else:
age_gender_on = False
else:
face_detect_on = False
ret, frame = cap.read()
#frame_copy = frame.copy()
if frame is None:
#print("End of video")
cv2.destroyAllWindows()
timer.stop()
#sys.exit()
break
#display_frame, aligned_face = flow_process(frame)
ret_process = fu.no_age_gender_face_process(frame, "68")
if ret_process is None:
cv2.putText(frame, "No face detected", (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.imshow("frame", frame)
#print(time.time()-t0)
cv2.destroyWindow("face")
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
timer.stop()
#sys.exit()
break
continue
rects, face, shape, aligned_face, aligned_shape = ret_process
(x, y, w, h) = face_utils.rect_to_bb(rects[0])
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
#overlay_text = "%s, %s" % (gender, age)
#cv2.putText(frame, overlay_text ,(x,y-15), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,0,0),2,cv2.LINE_AA)
if (len(aligned_shape) == 68):
cv2.rectangle(
aligned_face,
(aligned_shape[54][0], aligned_shape[29][1]
), #draw rectangle on right and left cheeks
(aligned_shape[12][0], aligned_shape[33][1]),
(0, 255, 0),
0)
cv2.rectangle(aligned_face,
(aligned_shape[4][0], aligned_shape[29][1]),
(aligned_shape[48][0], aligned_shape[33][1]),
(0, 255, 0), 0)
else:
#print(shape[4][1])
#print(shape[2][1])
#print(int((shape[4][1] - shape[2][1])))
cv2.rectangle(
aligned_face,
(aligned_shape[0][0],
int((aligned_shape[4][1] + aligned_shape[2][1]) / 2)),
(aligned_shape[1][0], aligned_shape[4][1]), (0, 255, 0), 0)
cv2.rectangle(
aligned_face,
(aligned_shape[2][0],
int((aligned_shape[4][1] + aligned_shape[2][1]) / 2)),
(aligned_shape[3][0], aligned_shape[4][1]), (0, 255, 0), 0)
for (x, y) in aligned_shape:
cv2.circle(aligned_face, (x, y), 1, (0, 0, 255), -1)
#for signal_processing
ROIs = fu.ROI_extraction(aligned_face, aligned_shape)
green_val = sp.extract_color(ROIs)
#print(green_val)
data_buffer.append(green_val)
if (video == False):
times.append(time.time() - t)
else:
times.append((1.0 / video_fps) * i)
L = len(data_buffer)
#print("buffer length: " + str(L))
if L > BUFFER_SIZE:
data_buffer = data_buffer[-BUFFER_SIZE:]
times = times[-BUFFER_SIZE:]
#bpms = bpms[-BUFFER_SIZE//2:]
L = BUFFER_SIZE
#print(times)
if L == 100:
fps = float(L) / (times[-1] - times[0])
cv2.putText(frame, "fps: {0:.2f}".format(fps),
(30, int(frame.shape[0] * 0.95)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
#
detrended_data = sp.signal_detrending(data_buffer)
#print(len(detrended_data))
#print(len(times))
interpolated_data = sp.interpolation(detrended_data, times)
normalized_data = sp.normalization(interpolated_data)
fft_of_interest, freqs_of_interest = sp.fft(normalized_data, fps)
max_arg = np.argmax(fft_of_interest)
bpm = freqs_of_interest[max_arg]
cv2.putText(
frame, "HR: {0:.2f}".format(bpm),
(int(frame.shape[1] * 0.8), int(frame.shape[0] * 0.95)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
#print(detrended_data)
filtered_data = sp.butter_bandpass_filter(interpolated_data,
(bpm - 20) / 60,
(bpm + 20) / 60,
fps,
order=3)
#print(fps)
#filtered_data = sp.butter_bandpass_filter(interpolated_data, 0.8, 3, fps, order = 3)
#write to txt file
with open("a.txt", mode="a+") as f:
rates.append(bpm)
f.write("time: {0:.4f} ".format(times[-1]) +
", HR: {0:.2f} ".format(bpm) + "\n")
# display
#cv2.imshow("mask",mask)
i = i + 1
#print("time of the loop number "+ str(i) +" : " + str(time.time()-t0))
count = count + 1
rates = [i for i in rates if i != 0]
#print(rates)
avg = sum(rates) / len(rates)
resp = avg / 4.5
print("Heart Rate: ", avg)
print("Respiratory Rate: ", resp)
l = []
l.append(avg)
l.append(resp)
return l
def __init__(self, heartrate, *args, **kwargs):
cap = cv2.VideoCapture(path)
fu = Face_utilities()
sp = Signal_processing()
i = 0
last_rects = None
last_shape = None
last_age = None
last_gender = None
face_detect_on = False
age_gender_on = False
t = time.time()
#for signal_processing
BUFFER_SIZE = 100
fps = 0 #for real time capture
video_fps = cap.get(cv2.CAP_PROP_FPS) # for video capture
print(video_fps)
times = []
data_buffer = []
# data for plotting
filtered_data = []
fft_of_interest = []
freqs_of_interest = []
bpm = 0
#plotting
app = QtGui.QApplication([])
win = pg.GraphicsWindow(title="plotting")
p1 = win.addPlot(title="FFT")
p2 = win.addPlot(title="Signal")
win.resize(1200, 600)
def update():
p1.clear()
p1.plot(np.column_stack((freqs_of_interest, fft_of_interest)),
pen='g')
p2.clear()
p2.plot(filtered_data[20:], pen='g')
app.processEvents()
timer = QtCore.QTimer()
timer.timeout.connect(update)
timer.start(300)
while True:
# grab a frame -> face detection -> crop the face -> 68 facial landmarks -> get mask from those landmarks
# calculate time for each loop
t0 = time.time()
if (i % 1 == 0):
face_detect_on = True
if (i % 10 == 0):
age_gender_on = True
else:
age_gender_on = False
else:
face_detect_on = False
ret, frame = cap.read()
#frame_copy = frame.copy()
if frame is None:
print("End of video")
cv2.destroyAllWindows()
timer.stop()
#sys.exit()
break
#display_frame, aligned_face = flow_process(frame)
ret_process = fu.no_age_gender_face_process(frame, "68")
if ret_process is None:
cv2.putText(frame, "No face detected", (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.imshow("frame", frame)
print(time.time() - t0)
cv2.destroyWindow("face")
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
timer.stop()
#sys.exit()
break
continue
rects, face, shape, aligned_face, aligned_shape = ret_process
(x, y, w, h) = face_utils.rect_to_bb(rects[0])
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
#overlay_text = "%s, %s" % (gender, age)
#cv2.putText(frame, overlay_text ,(x,y-15), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,0,0),2,cv2.LINE_AA)
if (len(aligned_shape) == 68):
cv2.rectangle(
aligned_face,
(aligned_shape[54][0], aligned_shape[29][1]
), #draw rectangle on right and left cheeks
(aligned_shape[12][0], aligned_shape[33][1]),
(0, 255, 0),
0)
cv2.rectangle(aligned_face,
(aligned_shape[4][0], aligned_shape[29][1]),
(aligned_shape[48][0], aligned_shape[33][1]),
(0, 255, 0), 0)
else:
#print(shape[4][1])
#print(shape[2][1])
#print(int((shape[4][1] - shape[2][1])))
cv2.rectangle(
aligned_face,
(aligned_shape[0][0],
int((aligned_shape[4][1] + aligned_shape[2][1]) / 2)),
(aligned_shape[1][0], aligned_shape[4][1]), (0, 255, 0), 0)
cv2.rectangle(
aligned_face,
(aligned_shape[2][0],
int((aligned_shape[4][1] + aligned_shape[2][1]) / 2)),
(aligned_shape[3][0], aligned_shape[4][1]), (0, 255, 0), 0)
for (x, y) in aligned_shape:
cv2.circle(aligned_face, (x, y), 1, (0, 0, 255), -1)
#for signal_processing
ROIs = fu.ROI_extraction(aligned_face, aligned_shape)
green_val = sp.extract_color(ROIs)
print(green_val)
data_buffer.append(green_val)
times.append(time.time() - t)
L = len(data_buffer)
#print("buffer length: " + str(L))
if L > BUFFER_SIZE:
data_buffer = data_buffer[-BUFFER_SIZE:]
times = times[-BUFFER_SIZE:]
#bpms = bpms[-BUFFER_SIZE//2:]
L = BUFFER_SIZE
#print(times)
if L == 100:
fps = float(L) / (times[-1] - times[0])
cv2.putText(frame, "fps: {0:.2f}".format(fps),
(30, int(frame.shape[0] * 0.95)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
#
detrended_data = sp.signal_detrending(data_buffer)
#print(len(detrended_data))
#print(len(times))
interpolated_data = sp.interpolation(detrended_data, times)
normalized_data = sp.normalization(interpolated_data)
fft_of_interest, freqs_of_interest = sp.fft(
normalized_data, fps)
max_arg = np.argmax(fft_of_interest)
bpm = freqs_of_interest[max_arg]
cv2.putText(
frame, "HR: {0:.2f}".format(bpm),
(int(frame.shape[1] * 0.8), int(frame.shape[0] * 0.95)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
#print(detrended_data)
filtered_data = sp.butter_bandpass_filter(interpolated_data,
(bpm - 20) / 60,
(bpm + 20) / 60,
fps,
order=3)
#print(fps)
#filtered_data = sp.butter_bandpass_filter(interpolated_data, 0.8, 3, fps, order = 3)
#write to txt file
with open("dbalt_hr_output.txt", mode="a+") as f:
f.write("time: {0:.4f} ".format(times[-1]) +
", HR: {0:.2f} ".format(bpm) + "\n")
# display
cv2.imshow("frame", frame)
cv2.imshow("face", aligned_face)
#cv2.imshow("mask",mask)
i = i + 1
print("time of the loop number " + str(i) + " : " +
str(time.time() - t0))
# waitKey to show the frame and break loop whenever 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
timer.stop()
#sys.exit()
break
cap.release()
cv2.destroyAllWindows()
print("total running time: " + str(time.time() - t))
output_conv = pd.read_csv("dbalt_hr_output.txt")
output_conv.to_csv('dbalt_hr_output.csv', index=None)
def run(self):
i = 0
last_rects = None
last_shape = None
last_age = None
last_gender = None
face_detect_on = False
age_gender_on = False
times = []
data_buffer = []
fft_of_interest = []
freqs_of_interest = []
valori = []
bpm = 0
# Cattura ed apertura del video
path = self.record()
cap = cv2.VideoCapture(path)
# Se cap e' nullo vuol dire che il video non e' stato aperto correttamente
if cap is None:
print "Errore nell'apertura del video"
return
fu = Face_utilities()
sp = Signal_processing()
t = time.time()
BUFFER_SIZE = 100
fps = 0
video_fps = cap.get(cv2.CAP_PROP_FPS)
'''
Loop infinito. Ogni ciclo equivale alla lettura di un frame del video.
'''
while True:
t0 = time.time()
if i % 1 == 0:
face_detect_on = True
if i % 10 == 0:
age_gender_on = True
else:
age_gender_on = False
else:
face_detect_on = False
# Lettura del frame
ret, frame = cap.read()
# Se frame e' nullo vuol dire che il video e' finito. Stop al programma.
if frame is None:
print "Video terminato - Nessun frame disponibile"
cv2.destroyAllWindows()
break
# Face detection con i 68 landmarks.
ret_process = fu.no_age_gender_face_process(frame, u"68")
# Se ret_process e' nullo vuol dire che i landmarks non sono stati applicati correttamente
# quindi nessun volto e' stato rilevato.
# Controllo inutile se non si guarda il pc durante l'esecuzione dell'algoritmo.
if ret_process is None:
cv2.putText(frame, u"Nessun volto rilevato", (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.imshow(u"Frequenza Cardiaca", frame)
print time.time() - t0
if cv2.waitKey(1) & 0xFF == ord(u'q'):
cv2.destroyAllWindows()
break
continue
rects, face, shape, aligned_face, aligned_shape = ret_process
(x, y, w, h) = face_utils.rect_to_bb(rects[0])
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
# Disegno dei rettangoli su guancia destra e sinistra
if len(aligned_shape) == 68:
cv2.rectangle(aligned_face,
(aligned_shape[54][0], aligned_shape[29][1]),
(aligned_shape[12][0], aligned_shape[33][1]),
(0, 255, 0), 0)
cv2.rectangle(aligned_face,
(aligned_shape[4][0], aligned_shape[29][1]),
(aligned_shape[48][0], aligned_shape[33][1]),
(0, 255, 0), 0)
else:
cv2.rectangle(
aligned_face,
(aligned_shape[0][0],
int((aligned_shape[4][1] + aligned_shape[2][1]) / 2)),
(aligned_shape[1][0], aligned_shape[4][1]), (0, 255, 0), 0)
cv2.rectangle(
aligned_face,
(aligned_shape[2][0],
int((aligned_shape[4][1] + aligned_shape[2][1]) / 2)),
(aligned_shape[3][0], aligned_shape[4][1]), (0, 255, 0), 0)
for (x, y) in aligned_shape:
cv2.circle(aligned_face, (x, y), 1, (0, 0, 255), -1)
# Estrazione delle caratteristiche
ROIs = fu.ROI_extraction(aligned_face, aligned_shape)
# Estrazione del valore di verde dalle ROI
green_val = sp.extract_color(ROIs)
# Inserimento del valore di verde in un data buffer
data_buffer.append(green_val)
times.append((1.0 / video_fps) * i)
L = len(data_buffer)
if L > BUFFER_SIZE:
data_buffer = data_buffer[-BUFFER_SIZE:]
times = times[-BUFFER_SIZE:]
# bpms = bpms[-BUFFER_SIZE//2:]
L = BUFFER_SIZE
# Non appena il buffer e' stato riempito con almeno 100 valori si inizia a stampare il battito cardiaco
# Quindi dopo il passaggio di 100 frames.
if L == 100:
fps = float(L) / (times[-1] - times[0])
cv2.putText(frame, u"fps: {0:.2f}".format(fps),
(30, int(frame.shape[0] * 0.95)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
detrended_data = sp.signal_detrending(data_buffer)
interpolated_data = sp.interpolation(detrended_data, times)
normalized_data = sp.normalization(interpolated_data)
fft_of_interest, freqs_of_interest = sp.fft(
normalized_data, fps)
max_arg = np.argmax(fft_of_interest)
bpm = freqs_of_interest[max_arg]
cv2.putText(
frame, u"HR: {0:.2f}".format(bpm),
(int(frame.shape[1] * 0.8), int(frame.shape[0] * 0.95)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
filtered_data = sp.butter_bandpass_filter(interpolated_data,
(bpm - 20) / 60,
(bpm + 20) / 60,
fps,
order=3)
# Apertura (o creazione) del file result.txt e scrittura del valore del battito
with open(u"result.txt", mode=u"a+") as f:
f.write(u"time: {0:.4f} ".format(times[-1]) +
u", HR: {0:.2f} ".format(bpm) + u"\n")
# Se il battito e' significativo quindi maggiore di 70, si inserisce il valore in un array che servira'
# per calcolare la media del battito finale
if bpm > 65:
valori.append(bpm)
i = i + 1
# Allo scorrere dei frame viene stampato il numero del frame corrente
print u"Frame numero " + unicode(i) + u" : " + unicode(
time.time() - t0)
if cv2.waitKey(1) & 0xFF == ord(u'q'):
cv2.destroyAllWindows()
break
cap.release()
cv2.destroyAllWindows()
# Pepper dice la media dei valori del battito cardiaco
self.tts.say("Your heart rate is " + format(np.mean(valori)))
print u"Tempo totale impiegato: " + unicode(time.time() - t)