浅析Python+OpenCV使用摄像头追踪人脸面部血液变化实现脉搏评估


Posted in Python onOctober 17, 2019

使用摄像头追踪人脸由于血液流动引起的面部色素的微小变化实现实时脉搏评估。

效果如下(演示视频):

浅析Python+OpenCV使用摄像头追踪人脸面部血液变化实现脉搏评估

浅析Python+OpenCV使用摄像头追踪人脸面部血液变化实现脉搏评估

 由于这是通过比较面部色素的变化评估脉搏所以光线、人体移动、不同角度、不同电脑摄像头等因素均会影响评估效果,实验原理是面部色素对比,识别效果存在一定误差,各位小伙伴且当娱乐,代码如下:

import cv2
import numpy as np
import dlib
import time
from scipy import signal
# Constants
WINDOW_TITLE = 'Pulse Observer'
BUFFER_MAX_SIZE = 500  # Number of recent ROI average values to store
MAX_VALUES_TO_GRAPH = 50 # Number of recent ROI average values to show in the pulse graph
MIN_HZ = 0.83  # 50 BPM - minimum allowed heart rate
MAX_HZ = 3.33  # 200 BPM - maximum allowed heart rate
MIN_FRAMES = 100 # Minimum number of frames required before heart rate is computed. Higher values are slower, but
     # more accurate.
DEBUG_MODE = False
# Creates the specified Butterworth filter and applies it.
def butterworth_filter(data, low, high, sample_rate, order=5):
 nyquist_rate = sample_rate * 0.5
 low /= nyquist_rate
 high /= nyquist_rate
 b, a = signal.butter(order, [low, high], btype='band')
 return signal.lfilter(b, a, data)
# Gets the region of interest for the forehead.
def get_forehead_roi(face_points):
 # Store the points in a Numpy array so we can easily get the min and max for x and y via slicing
 points = np.zeros((len(face_points.parts()), 2))
 for i, part in enumerate(face_points.parts()):
  points[i] = (part.x, part.y)
 min_x = int(points[21, 0])
 min_y = int(min(points[21, 1], points[22, 1]))
 max_x = int(points[22, 0])
 max_y = int(max(points[21, 1], points[22, 1]))
 left = min_x
 right = max_x
 top = min_y - (max_x - min_x)
 bottom = max_y * 0.98
 return int(left), int(right), int(top), int(bottom)
# Gets the region of interest for the nose.
def get_nose_roi(face_points):
 points = np.zeros((len(face_points.parts()), 2))
 for i, part in enumerate(face_points.parts()):
  points[i] = (part.x, part.y)
 # Nose and cheeks
 min_x = int(points[36, 0])
 min_y = int(points[28, 1])
 max_x = int(points[45, 0])
 max_y = int(points[33, 1])
 left = min_x
 right = max_x
 top = min_y + (min_y * 0.02)
 bottom = max_y + (max_y * 0.02)
 return int(left), int(right), int(top), int(bottom)
# Gets region of interest that includes forehead, eyes, and nose.
# Note: Combination of forehead and nose performs better. This is probably because this ROI includes eyes,
# and eye blinking adds noise.
def get_full_roi(face_points):
 points = np.zeros((len(face_points.parts()), 2))
 for i, part in enumerate(face_points.parts()):
  points[i] = (part.x, part.y)
 # Only keep the points that correspond to the internal features of the face (e.g. mouth, nose, eyes, brows).
 # The points outlining the jaw are discarded.
 min_x = int(np.min(points[17:47, 0]))
 min_y = int(np.min(points[17:47, 1]))
 max_x = int(np.max(points[17:47, 0]))
 max_y = int(np.max(points[17:47, 1]))
 center_x = min_x + (max_x - min_x) / 2
 left = min_x + int((center_x - min_x) * 0.15)
 right = max_x - int((max_x - center_x) * 0.15)
 top = int(min_y * 0.88)
 bottom = max_y
 return int(left), int(right), int(top), int(bottom)
def sliding_window_demean(signal_values, num_windows):
 window_size = int(round(len(signal_values) / num_windows))
 demeaned = np.zeros(signal_values.shape)
 for i in range(0, len(signal_values), window_size):
  if i + window_size > len(signal_values):
   window_size = len(signal_values) - i
  curr_slice = signal_values[i: i + window_size]
  if DEBUG_MODE and curr_slice.size == 0:
   print ('Empty Slice: size={0}, i={1}, window_size={2}'.format(signal_values.size, i, window_size))
   print (curr_slice)
  demeaned[i:i + window_size] = curr_slice - np.mean(curr_slice)
 return demeaned
# Averages the green values for two arrays of pixels
def get_avg(roi1, roi2):
 roi1_green = roi1[:, :, 1]
 roi2_green = roi2[:, :, 1]
 avg = (np.mean(roi1_green) + np.mean(roi2_green)) / 2.0
 return avg
# Returns maximum absolute value from a list
def get_max_abs(lst):
 return max(max(lst), -min(lst))
# Draws the heart rate graph in the GUI window.
def draw_graph(signal_values, graph_width, graph_height):
 graph = np.zeros((graph_height, graph_width, 3), np.uint8)
 scale_factor_x = float(graph_width) / MAX_VALUES_TO_GRAPH
 # Automatically rescale vertically based on the value with largest absolute value
 max_abs = get_max_abs(signal_values)
 scale_factor_y = (float(graph_height) / 2.0) / max_abs
 midpoint_y = graph_height / 2
 for i in range(0, len(signal_values) - 1):
  curr_x = int(i * scale_factor_x)
  curr_y = int(midpoint_y + signal_values[i] * scale_factor_y)
  next_x = int((i + 1) * scale_factor_x)
  next_y = int(midpoint_y + signal_values[i + 1] * scale_factor_y)
  cv2.line(graph, (curr_x, curr_y), (next_x, next_y), color=(0, 255, 0), thickness=1)
 return graph
# Draws the heart rate text (BPM) in the GUI window.
def draw_bpm(bpm_str, bpm_width, bpm_height):
 bpm_display = np.zeros((bpm_height, bpm_width, 3), np.uint8)
 bpm_text_size, bpm_text_base = cv2.getTextSize(bpm_str, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=2.7,
             thickness=2)
 bpm_text_x = int((bpm_width - bpm_text_size[0]) / 2)
 bpm_text_y = int(bpm_height / 2 + bpm_text_base)
 cv2.putText(bpm_display, bpm_str, (bpm_text_x, bpm_text_y), fontFace=cv2.FONT_HERSHEY_DUPLEX,
    fontScale=2.7, color=(0, 255, 0), thickness=2)
 bpm_label_size, bpm_label_base = cv2.getTextSize('BPM', fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=0.6,
              thickness=1)
 bpm_label_x = int((bpm_width - bpm_label_size[0]) / 2)
 bpm_label_y = int(bpm_height - bpm_label_size[1] * 2)
 cv2.putText(bpm_display, 'BPM', (bpm_label_x, bpm_label_y),
    fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=0.6, color=(0, 255, 0), thickness=1)
 return bpm_display
# Draws the current frames per second in the GUI window.
def draw_fps(frame, fps):
 cv2.rectangle(frame, (0, 0), (100, 30), color=(0, 0, 0), thickness=-1)
 cv2.putText(frame, 'FPS: ' + str(round(fps, 2)), (5, 20), fontFace=cv2.FONT_HERSHEY_PLAIN,
    fontScale=1, color=(0, 255, 0))
 return frame
# Draw text in the graph area
def draw_graph_text(text, color, graph_width, graph_height):
 graph = np.zeros((graph_height, graph_width, 3), np.uint8)
 text_size, text_base = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=1, thickness=1)
 text_x = int((graph_width - text_size[0]) / 2)
 text_y = int((graph_height / 2 + text_base))
 cv2.putText(graph, text, (text_x, text_y), fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=1, color=color,
    thickness=1)
 return graph
# Calculate the pulse in beats per minute (BPM)
def compute_bpm(filtered_values, fps, buffer_size, last_bpm):
 # Compute FFT
 fft = np.abs(np.fft.rfft(filtered_values))
 # Generate list of frequencies that correspond to the FFT values
 freqs = fps / buffer_size * np.arange(buffer_size / 2 + 1)
 # Filter out any peaks in the FFT that are not within our range of [MIN_HZ, MAX_HZ]
 # because they correspond to impossible BPM values.
 while True:
  max_idx = fft.argmax()
  bps = freqs[max_idx]
  if bps < MIN_HZ or bps > MAX_HZ:
   if DEBUG_MODE:
    print ('BPM of {0} was discarded.'.format(bps * 60.0))
   fft[max_idx] = 0
  else:
   bpm = bps * 60.0
   break
 # It's impossible for the heart rate to change more than 10% between samples,
 # so use a weighted average to smooth the BPM with the last BPM.
 if last_bpm > 0:
  bpm = (last_bpm * 0.9) + (bpm * 0.1)
 return bpm
def filter_signal_data(values, fps):
 # Ensure that array doesn't have infinite or NaN values
 values = np.array(values)
 np.nan_to_num(values, copy=False)
 # Smooth the signal by detrending and demeaning
 detrended = signal.detrend(values, type='linear')
 demeaned = sliding_window_demean(detrended, 15)
 # Filter signal with Butterworth bandpass filter
 filtered = butterworth_filter(demeaned, MIN_HZ, MAX_HZ, fps, order=5)
 return filtered
# Get the average value for the regions of interest. Will also draw a green rectangle around
# the regions of interest, if requested.
def get_roi_avg(frame, view, face_points, draw_rect=True):
 # Get the regions of interest.
 fh_left, fh_right, fh_top, fh_bottom = get_forehead_roi(face_points)
 nose_left, nose_right, nose_top, nose_bottom = get_nose_roi(face_points)
 # Draw green rectangles around our regions of interest (ROI)
 if draw_rect:
  cv2.rectangle(view, (fh_left, fh_top), (fh_right, fh_bottom), color=(0, 255, 0), thickness=2)
  cv2.rectangle(view, (nose_left, nose_top), (nose_right, nose_bottom), color=(0, 255, 0), thickness=2)
 # Slice out the regions of interest (ROI) and average them
 fh_roi = frame[fh_top:fh_bottom, fh_left:fh_right]
 nose_roi = frame[nose_top:nose_bottom, nose_left:nose_right]
 return get_avg(fh_roi, nose_roi)
# Main function.
def run_pulse_observer(detector, predictor, webcam, window):
 roi_avg_values = []
 graph_values = []
 times = []
 last_bpm = 0
 graph_height = 200
 graph_width = 0
 bpm_display_width = 0
 # cv2.getWindowProperty() returns -1 when window is closed by user.
 while cv2.getWindowProperty(window, 0) == 0:
  ret_val, frame = webcam.read()
  # ret_val == False if unable to read from webcam
  if not ret_val:
   print ("ERROR: Unable to read from webcam. Was the webcam disconnected? Exiting.")
   shut_down(webcam)
  # Make copy of frame before we draw on it. We'll display the copy in the GUI.
  # The original frame will be used to compute heart rate.
  view = np.array(frame)
  # Heart rate graph gets 75% of window width. BPM gets 25%.
  if graph_width == 0:
   graph_width = int(view.shape[1] * 0.75)
   if DEBUG_MODE:
    print ('Graph width = {0}'.format(graph_width))
  if bpm_display_width == 0:
   bpm_display_width = view.shape[1] - graph_width
  # Detect face using dlib
  faces = detector(frame, 0)
  if len(faces) == 1:
   face_points = predictor(frame, faces[0])
   roi_avg = get_roi_avg(frame, view, face_points, draw_rect=True)
   roi_avg_values.append(roi_avg)
   times.append(time.time())
   # Buffer is full, so pop the value off the top to get rid of it
   if len(times) > BUFFER_MAX_SIZE:
    roi_avg_values.pop(0)
    times.pop(0)
   curr_buffer_size = len(times)
   # Don't try to compute pulse until we have at least the min. number of frames
   if curr_buffer_size > MIN_FRAMES:
    # Compute relevant times
    time_elapsed = times[-1] - times[0]
    fps = curr_buffer_size / time_elapsed # frames per second
    # Clean up the signal data
    filtered = filter_signal_data(roi_avg_values, fps)
    graph_values.append(filtered[-1])
    if len(graph_values) > MAX_VALUES_TO_GRAPH:
     graph_values.pop(0)
    # Draw the pulse graph
    graph = draw_graph(graph_values, graph_width, graph_height)
    # Compute and display the BPM
    bpm = compute_bpm(filtered, fps, curr_buffer_size, last_bpm)
    bpm_display = draw_bpm(str(int(round(bpm))), bpm_display_width, graph_height)
    last_bpm = bpm
    # Display the FPS
    if DEBUG_MODE:
     view = draw_fps(view, fps)
   else:
    # If there's not enough data to compute HR, show an empty graph with loading text and
    # the BPM placeholder
    pct = int(round(float(curr_buffer_size) / MIN_FRAMES * 100.0))
    loading_text = 'Computing pulse: ' + str(pct) + '%'
    graph = draw_graph_text(loading_text, (0, 255, 0), graph_width, graph_height)
    bpm_display = draw_bpm('--', bpm_display_width, graph_height)
  else:
   # No faces detected, so we must clear the lists of values and timestamps. Otherwise there will be a gap
   # in timestamps when a face is detected again.
   del roi_avg_values[:]
   del times[:]
   graph = draw_graph_text('No face detected', (0, 0, 255), graph_width, graph_height)
   bpm_display = draw_bpm('--', bpm_display_width, graph_height)
  graph = np.hstack((graph, bpm_display))
  view = np.vstack((view, graph))
  cv2.imshow(window, view)
  key = cv2.waitKey(1)
  # Exit if user presses the escape key
  if key == 27:
   shut_down(webcam)
# Clean up
def shut_down(webcam):
 webcam.release()
 cv2.destroyAllWindows()
 exit(0)
def main():
 detector = dlib.get_frontal_face_detector()
 # Predictor pre-trained model can be downloaded from:
 # http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
 try:
  predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
 except RuntimeError as e:
  print ('ERROR: \'shape_predictor_68_face_landmarks.dat\' was not found in current directory. ' \
    'Download it from http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2')
  return
 webcam = cv2.VideoCapture(0)
 if not webcam.isOpened():
  print ('ERROR: Unable to open webcam. Verify that webcam is connected and try again. Exiting.')
  webcam.release()
  return
 cv2.namedWindow(WINDOW_TITLE)
 run_pulse_observer(detector, predictor, webcam, WINDOW_TITLE)
 # run_pulse_observer() returns when the user has closed the window. Time to shut down.
 shut_down(webcam)
if __name__ == '__main__':
 main()

总结

以上所述是小编给大家介绍的浅析Python+OpenCV使用摄像头追踪人脸面部血液变化实现脉搏评估,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对三水点靠木网站的支持!
如果你觉得本文对你有帮助,欢迎转载,烦请注明出处,谢谢!

Python 相关文章推荐
python str与repr的区别
Mar 23 Python
Python使用设计模式中的责任链模式与迭代器模式的示例
Mar 02 Python
Python多层嵌套list的递归处理方法(推荐)
Jun 08 Python
python机器学习之神经网络(三)
Dec 20 Python
Python Flask前后端Ajax交互的方法示例
Jul 31 Python
pygame游戏之旅 调用按钮实现游戏开始功能
Nov 21 Python
用Python实现最速下降法求极值的方法
Jul 10 Python
wxPython实现带颜色的进度条
Nov 19 Python
pytorch1.0中torch.nn.Conv2d用法详解
Jan 10 Python
python判断正负数方式
Jun 03 Python
什么是python类属性
Jun 10 Python
Python爬虫实战案例之爬取喜马拉雅音频数据详解
Dec 07 Python
Python 3.8正式发布重要新功能一览
Oct 17 #Python
Python 装饰器@,对函数进行功能扩展操作示例【开闭原则】
Oct 17 #Python
python实现复制文件到指定目录
Oct 16 #Python
如何解决django-celery启动后迅速关闭
Oct 16 #Python
Python发送邮件的实例代码讲解
Oct 16 #Python
python运用sklearn实现KNN分类算法
Oct 16 #Python
python sklearn常用分类算法模型的调用
Oct 16 #Python
You might like
简单采集了yahoo的一些数据
2007/02/14 PHP
判断是否为指定长度内字符串的php函数
2010/02/16 PHP
php学习之数据类型之间的转换介绍
2011/06/09 PHP
提高define性能的php扩展hidef的安装和使用
2011/06/14 PHP
php学习笔记 面向对象中[接口]与[多态性]的应用
2011/06/16 PHP
php 网上商城促销设计实例代码
2012/02/17 PHP
PHP模板解析类实例
2015/07/09 PHP
百度Popup.js弹出框进化版 拖拽小框架发布 兼容IE6/7/8,Firefox,Chrome
2010/04/13 Javascript
Javascript变量函数浅析
2011/09/02 Javascript
JavaScript定时器和优化的取消定时器方法
2015/07/03 Javascript
JS响应鼠标点击实现两个滑块区间拖动效果
2015/10/26 Javascript
关于Bootstrap弹出框无法调用问题的解决办法
2016/03/10 Javascript
JavaScript的字符串方法汇总
2016/07/31 Javascript
纯js实现倒计时功能
2017/01/06 Javascript
AngularJS解决ng-if中的ng-model值无效的问题
2017/06/21 Javascript
node.js + socket.io 实现点对点随机匹配聊天
2017/06/30 Javascript
利用jQuery异步上传文件的插件用法详解
2017/07/19 jQuery
[04:50]DOTA2亚洲邀请赛小组赛第四日 TOP10精彩集锦
2015/02/02 DOTA
在Python的Django框架中显示对象子集的方法
2015/07/21 Python
Python实现 PS 图像调整中的亮度调整
2019/06/28 Python
Pytorch 的损失函数Loss function使用详解
2020/01/02 Python
基于python实现破解滑动验证码过程解析
2020/05/28 Python
Pycharm导入anaconda环境的教程图解
2020/07/31 Python
HTML5的postMessage的使用手册
2018/12/19 HTML / CSS
应届生法律顾问求职信
2013/11/19 职场文书
省优秀教师事迹材料
2014/01/30 职场文书
新品发布会主持词
2014/04/02 职场文书
保险专业求职信
2014/07/07 职场文书
开展党的群众路线教育实践活动工作总结
2014/11/05 职场文书
2014年工人工作总结
2014/11/25 职场文书
商业门面租房协议书
2014/11/25 职场文书
百年校庆感言
2015/08/01 职场文书
工作汇报材料难写?方法都在这里了!
2019/07/01 职场文书
Vue Element UI自定义描述列表组件
2021/05/18 Vue.js
科普 | 业余无线电知识-波段篇
2022/02/18 无线电
MySql数据库触发器使用教程
2022/06/01 MySQL