通过摄像头识别运动频率，比如步频。
打开摄像头
循环读取视频帧
灰度转换
统计中间一行数值和
人在摄像头前运动，该数值会呈现周期变化
通过快速傅里叶转换，将和步频相似频率显示出来。
（尝试人脸检测，跟着人脸位置变化计算频率。
这个对机器算力要求较高，视频帧处理能力不能满足需求）
import pyqtgraph as pg
import numpy as np
import cv2
from scipy.fftpack import fft, fftfreq
import time
timestamp = time.time()
print("当前时间戳：", timestamp)
print("cv2", cv2)
# 训练一组人脸
face_detector = cv2.CascadeClassifier("C:\\Users\\13361\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\cv2\\data\\haarcascade_frontalface_alt2.xml")
data_list = []
fcount = 0
f_periods = []
interval = 0.0
infos = 'TEXT ON VIDEO'
def show_info():
global fcount, interval
global timestamp
fcount += 1
res = []
if fcount == 100:
fcount = 0
new_time = time.time()
interval = new_time - timestamp
timestamp = new_time
interval /= 100.0
print('show_info interval ', interval)
# pos_mask = f_periods[np.where(f_periods < 10)]
if len(f_periods) :
for period in f_periods:
res.append(int(60/(period*interval)))
# res = res[np.where(res > 100)]
return res
def get_data():
global data_list, interval, fcount
global f_periods, last_y, infos
ret, frame = vid.read()
# conversion of BGR to grayscale is necessary to apply this operation
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# # print(gray.shape)
# # 检测人脸（用灰度图检测，返回人脸矩形坐标(4个角)）
# # param: 灰度图 图像尺寸缩小比例 至少检测次数（若为3，表示一个目标至少检测到3次才是真正目标）
# ret = face_detector.detectMultiScale(gray, 1.05, 5)
# ww = 0
# for x, y, w, h in ret:
# cv2.rectangle(gray, (x, y), (x + w, y + h), (0, 0, 255), 3) #画出矩形框
# # print("rect ", x, y, w, h)
# if w > 100 and w > ww:
# ww = w
# last_y = y
# # print("pend ", last_y)
# data_list.append(last_y)
data = np.sum(gray, axis=1)
data_list.append(float(data[200]))
# print(float(data[200]))
if len(data_list) > 200:
data_list = data_list[1:]
f_periods = do_fft(data_list)
plot.setData(data_list,pen='g')
res = show_info()
if len(res) :
infos = ''
for info in res:
if info > 100:
infos += str(info)
infos += ' '
# infos = str(res)
if fcount % 30 == 29:
print(f"fft_periods: {f_periods}", interval)
print(f"freq : {res}")
print(f"infos : {infos}")
# describe the type of font to be used.
font = cv2.FONT_HERSHEY_SIMPLEX
# Use putText() method for inserting text on video
cv2.putText(gray, infos, (50, 50), font, 1,
(0, 255, 255), 2, cv2.LINE_4)
# adaptive thresholding to use different threshold values on different regions of the frame.
# Thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
# cv2.THRESH_BINARY_INV, 11, 2)
cv2.imshow('Thresh', gray)
def do_fft(dl):
# print(data.shape)
# print(len(dl))
# print(dl[:5])
fft_series = fft(dl) # fft-返回复数数组
power = np.abs(fft_series) # 取模
sample_freq = fftfreq(fft_series.size)
pos_mask = np.where(sample_freq > 0)
freqs = sample_freq[pos_mask]
powers = power[pos_mask]
top_k_seasons = 3
# top K=3 index
top_k_idxs = np.argpartition(powers, -top_k_seasons)[-top_k_seasons:]
top_k_power = powers[top_k_idxs]
fft_periods = (1 / freqs[top_k_idxs]).astype(int)
# pos_mask = fft_periods[np.where(fft_periods > 100)]
# print(f"fft_periods: {fft_periods}")
# print(f"pos_mask: {pos_mask}")
# print(pos_mask)
return fft_periods
if __name__ == '__main__':
import sys
sys.setrecursionlimit(10000)
# threading.stack_size(200000000)
# thread = threading.Thread(target=your_code)
# thread.start()
vid = cv2.VideoCapture(0)
# pyqtgragh初始化
app = pg.mkQApp() # 建立app
win = pg.GraphicsLayoutWidget(show=True) # 建立窗口
win.setWindowTitle(u'pyqtgraph 实时波形显示工具')
win.resize(800, 500) # 小窗口大小
# 创建图表
historyLength = 200 # 横坐标长度
p = win.addPlot() # 把图p加入到窗口中
p.showGrid(x=True, y=True) # 把X和Y的表格打开
p.setRange(xRange=[0, historyLength], yRange=[50000, 150000], padding=0)
# p.setRange(xRange=[0, historyLength], yRange=[0, 500], padding=0)
p.setLabel(axis='left', text='gray') # 靠左
p.setLabel(axis='bottom', text='时间')
p.setTitle('gray graph') # 表格的名字
plot = p.plot()
timer = pg.QtCore.QTimer()
timer.timeout.connect(get_data) # 定时刷新数据显示
timer.start(10) # 多少ms调用一次
app.exec_()
vid.release()
cv2.destroyAllWindows()