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

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")
   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()