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process.py
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process.py
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import cv2
import numpy as np
import time
from face_detection import FaceDetection
from scipy import signal
from face_utilities import Face_utilities
from signal_processing import Signal_processing
from imutils import face_utils
# from sklearn.decomposition import FastICA
class Process(object):
def __init__(self):
self.frame_in = np.zeros((10, 10, 3), np.uint8)
self.frame_ROI = np.zeros((10, 10, 3), np.uint8)
self.frame_out = np.zeros((10, 10, 3), np.uint8)
self.samples = []
self.buffer_size = 100
self.times = []
self.data_buffer = []
self.fps = 0
self.fft = []
self.freqs = []
self.t0 = time.time()
self.bpm = 0
self.fd = FaceDetection()
self.bpms = []
self.peaks = []
self.fu = Face_utilities()
self.sp = Signal_processing()
#self.red = np.zeros((256,256,3),np.uint8)
def extractColor(self, frame):
#r = np.mean(frame[:,:,0])
g = np.mean(frame[:,:,1])
#b = np.mean(frame[:,:,2])
#return r, g, b
return g
def run(self):
# frame, face_frame, ROI1, ROI2, status, mask = self.fd.face_detect(self.frame_in)
frame = self.frame_in
ret_process = self.fu.no_age_gender_face_process(frame, "5")
if ret_process is None:
return False
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)
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:
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)
ROIs = self.fu.ROI_extraction(aligned_face, aligned_shape)
green_val = self.sp.extract_color(ROIs)
self.frame_out = frame
self.frame_ROI = aligned_face
# g1 = self.extractColor(ROI1)
# g2 = self.extractColor(ROI2)
#g3 = self.extractColor(ROI3)
L = len(self.data_buffer)
#calculate average green value of 2 ROIs
#r = (r1+r2)/2
#g = (g1+g2)/2
#b = (b1+b2)/2
g = green_val
if(abs(g-np.mean(self.data_buffer))>10 and L>99): #remove sudden change, if the avg value change is over 10, use the mean of the data_buffer
g = self.data_buffer[-1]
self.times.append(time.time() - self.t0)
self.data_buffer.append(g)
#only process in a fixed-size buffer
if L > self.buffer_size:
self.data_buffer = self.data_buffer[-self.buffer_size:]
self.times = self.times[-self.buffer_size:]
self.bpms = self.bpms[-self.buffer_size//2:]
L = self.buffer_size
processed = np.array(self.data_buffer)
# start calculating after the first 10 frames
if L == self.buffer_size:
self.fps = float(L) / (self.times[-1] - self.times[0])#calculate HR using a true fps of processor of the computer, not the fps the camera provide
even_times = np.linspace(self.times[0], self.times[-1], L)
processed = signal.detrend(processed)#detrend the signal to avoid interference of light change
interpolated = np.interp(even_times, self.times, processed) #interpolation by 1
interpolated = np.hamming(L) * interpolated#make the signal become more periodic (advoid spectral leakage)
#norm = (interpolated - np.mean(interpolated))/np.std(interpolated)#normalization
norm = interpolated/np.linalg.norm(interpolated)
raw = np.fft.rfft(norm*30)#do real fft with the normalization multiplied by 10
self.freqs = float(self.fps) / L * np.arange(L / 2 + 1)
freqs = 60. * self.freqs
# idx_remove = np.where((freqs < 50) & (freqs > 180))
# raw[idx_remove] = 0
self.fft = np.abs(raw)**2#get amplitude spectrum
idx = np.where((freqs > 50) & (freqs < 180))#the range of frequency that HR is supposed to be within
pruned = self.fft[idx]
pfreq = freqs[idx]
self.freqs = pfreq
self.fft = pruned
idx2 = np.argmax(pruned)#max in the range can be HR
self.bpm = self.freqs[idx2]
self.bpms.append(self.bpm)
processed = self.butter_bandpass_filter(processed,0.8,3,self.fps,order = 3)
#ifft = np.fft.irfft(raw)
self.samples = processed # multiply the signal with 5 for easier to see in the plot
#TODO: find peaks to draw HR-like signal.
# if(mask.shape[0]!=10):
# out = np.zeros_like(aligned_face)
# mask = mask.astype(np.bool)
# out[mask] = aligned_face[mask]
# if(processed[-1]>np.mean(processed)):
# out[mask,2] = 180 + processed[-1]*10
# aligned_face[mask] = out[mask]
#cv2.imshow("face", face_frame)
#out = cv2.add(face_frame,out)
# else:
# cv2.imshow("face", face_frame)
return True
def reset(self):
self.frame_in = np.zeros((10, 10, 3), np.uint8)
self.frame_ROI = np.zeros((10, 10, 3), np.uint8)
self.frame_out = np.zeros((10, 10, 3), np.uint8)
self.samples = []
self.times = []
self.data_buffer = []
self.fps = 0
self.fft = []
self.freqs = []
self.t0 = time.time()
self.bpm = 0
self.bpms = []
def butter_bandpass(self, lowcut, highcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = signal.butter(order, [low, high], btype='band')
return b, a
def butter_bandpass_filter(self, data, lowcut, highcut, fs, order=5):
b, a = self.butter_bandpass(lowcut, highcut, fs, order=order)
y = signal.lfilter(b, a, data)
return y