前言

这一篇博客用于记录camera RGB测心率的相关知识
一、心率与呼吸率？

心率和呼吸率是不同的：
心率：安静状态下每分钟心跳的次数。一般为60～100次/分左右
呼吸率：每分钟呼吸的次数。一般为12~20次/分左右
二、心率测量常见的方法

1.心电图ECG

这是比较常见的方法，在身上放置许多电极来测量，测量的就很准
2.光电容积脉搏波描记法PPG

这利用光学原理实现的，PPG有两种模式，透射式和反射式，医院的那种夹在手指上的就是投射式的，我们常用的手环是反射式的。我们戴的智能手环基于下面这个原理测量：贴着手腕的地方将LED光射向皮肤，透过皮肤组织反射回的光被光敏传感器接受并转换成电信号再经过AD转换成数字信号，即获得心率。
3.血红

三、代码实现

环境要求：基本的Python相关环境（这是摄像头版）
模型文件：链接：https://pan.baidu.com/s/1ULd0qixdwDyN9C-q2tdUxQ
提取码：vt48
# -*- coding: utf-8 -*-
"""
Created on Thu Apr  8 18:26:20 2021

@author: S
"""

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):
    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, cap, window):
    roi_avg_values = []
    graph_values = []
    times = []
    last_bpm = 0
    graph_height = 200
    graph_width = 0
    bpm_display_width = 0
    i=0
    bpm=0
    bpm_value=0
    bpm_value_flag=0
    
    while cv2.getWindowProperty(window, 0) == 0:
        ret_val, frame = cap.read()
        if not ret_val:
            shut_down(cap) 
        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)
                i+=1
                if i==10:
                    bpm_value_flag=bpm_value//10
                    bpm_value=0
                    i=0
                    print("i=10lalalalalalalalallalalalalalalalalallalalalalalallalalalalallalalalalallalal")
                    print(bpm_value_flag)
                else:
                    bpm_value+=bpm
                bpm_display = draw_bpm(str(int(round(bpm_value_flag))), 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) 
        cv2.waitKey(30)
# Clean up
def shut_down(cap):
    cap.release()
    cv2.destroyAllWindows()
    exit(0)  
def xinlv():
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor('shape_predictor_81_face_landmarks.dat')
    cap = cv2.VideoCapture(0)
    cv2.namedWindow(WINDOW_TITLE)
    run_pulse_observer(detector, predictor, cap, WINDOW_TITLE)

    # run_pulse_observer() returns when the user has closed the window.  Time to shut down.
    shut_down(cap)
if __name__ == '__main__':
    xinlv()
参考文献

参考：https://blog.csdn.net/Richard_LiuJH/article/details/49615395
https://codechina.csdn.net/mirrors/codeniko/shape_predictor_81_face_landmarks?utm_source=csdn_github_accelerator
python心率检测

目录
