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Copy pathSPO2_Pred.py
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SPO2_Pred.py
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import csv
import pandas as pd
import cv2
import numpy as np
from imutils import face_utils
import matplotlib.pyplot as plt
import dlib
import math
from numpy import mean
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import KFold
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
blue, green, red, yellow, purple = (255, 0, 0), (0, 255, 0), (0, 0, 255), (0, 255, 255), (255, 0, 255)
font = cv2.FONT_HERSHEY_SIMPLEX
from scipy import stats
df = pd.DataFrame()
# def CalSpo21(video_file, bvp_file, sub_folder_path):
#
# cap = cv2.VideoCapture(video_file)
# frame_count = 0
#
# with open("C:/Users/YF/Desktop/train_W.csv", 'a',
# newline=''
# ) as csvfile:
# writer = csv.writer(csvfile)
#
# while cap.isOpened():
# ret, frame = cap.read()
# # frame = cv2.rotate(frame, cv2.ROTATE_90_COUNTERCLOCKWISE)
# frame_count += 1
# if not ret:
# break
#
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# faces = detector(gray)
#
# for face in faces:
# # Get the Cheek ROI of Face获取面部的Cheek ROI
# landmarks = predictor(gray, face)
# landmarks = face_utils.shape_to_np(landmarks)
#
# start_face = (face.left(), face.top())
# end_face = (face.right(), face.bottom())
# start_cheekl = (landmarks[4][0], landmarks[29][1])
# end_cheekl = (landmarks[48][0], landmarks[33][1])
# start_cheekr = (landmarks[54][0], landmarks[29][1])
# end_cheekr = (landmarks[12][0], landmarks[33][1])
# start_cheekw = (landmarks[49][0], landmarks[7][1])
# end_cheekw = (landmarks[55][0], landmarks[11][1])
#
# cv2.rectangle(frame, start_face, end_face, green, 2)
# cv2.rectangle(frame, start_cheekl, end_cheekl, green, 1)
# cv2.rectangle(frame, start_cheekr, end_cheekr, green, 1)
# cv2.rectangle(frame, start_cheekw, end_cheekw, green, 1)
#
# Ka = []
#
# # Calculate Ka left cheek
# image = frame[start_cheekl[1]:end_cheekl[1], start_cheekl[0]:end_cheekl[0]]
# (B, G, R) = cv2.split(image)
# lDCB, lACB, lDCR, lACR, lDCG, lACG = np.mean(B), np.std(B), np.mean(R), np.std(R), np.mean(G), np.std(G)
#
# # Calculate Ka right cheek
# image = frame[start_cheekr[1]:end_cheekr[1], start_cheekr[0]:end_cheekr[0]]
# (B, G, R) = cv2.split(image)
# rDCB, rACB, rDCR, rACR, rDCG, rACG = np.mean(B), np.std(B), np.mean(R), np.std(R), np.mean(G), np.std(G)
#
# # Calculate Ka w cheek
# image = frame[start_cheekw[1]:end_cheekw[1], start_cheekw[0]:end_cheekw[0]]
# (B, G, R) = cv2.split(image)
# wDCB, wACB, wrDCR, wACR, wDCG, wACG = np.mean(B), np.std(B), np.mean(R), np.std(R), np.mean(G), np.std(G)
#
# writer.writerow([lDCB, lACB, lDCR, lACR, lDCG, lACG, rDCB, rACB, rDCR, rACR, rDCG, rACG, wDCB, wACB, wrDCR, wACR, wDCG, wACG])
#
# print(frame_count - 1)
def CalSpo22(video_file
# , bvp_file
, sub_folder_path
):
cap = cv2.VideoCapture(video_file)
frame_count = 0
a = sub_folder_path.replace("\\", "")
b = a.replace(".avi", '')
c = b.replace("C:/Users/YF/Desktop/", "")
with open(f"C:/Users/YF/Desktop/subject/DDMPFV/{c}.csv", 'a',
# with open(f"C:/Users/YF/Desktop/subject/test/12345.csv", 'w',
newline=''
) as csvfile:
writer = csv.writer(csvfile)
writer.writerow(
['var1', 'var2', 'var3', 'var4', 'var5', 'var6', 'var7', 'var8', 'var9', 'var10', 'var11', 'var12', 'var13', 'var14', 'var15', 'var16', "var17", "var18", 'ppg',
'spo2']
)
while cap.isOpened():
ret, frame = cap.read()
# frame = cv2.rotate(frame, cv2.ROTATE_90_COUNTERCLOCKWISE)
frame_count += 1
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for face in faces:
# Get the Cheek ROI of Face获取面部的Cheek ROI
landmarks = predictor(gray, face)
landmarks = face_utils.shape_to_np(landmarks)
start_face = (face.left(), face.top())
end_face = (face.right(), face.bottom())
start_cheekl = (landmarks[4][0], landmarks[29][1])
end_cheekl = (landmarks[48][0], landmarks[33][1])
start_cheekr = (landmarks[54][0], landmarks[29][1])
end_cheekr = (landmarks[12][0], landmarks[33][1])
start_cheekw = (landmarks[49][0], landmarks[7][1])
end_cheekw = (landmarks[55][0], landmarks[11][1])
cv2.rectangle(frame, start_face, end_face, green, 2)
cv2.rectangle(frame, start_cheekl, end_cheekl, green, 1)
cv2.rectangle(frame, start_cheekr, end_cheekr, green, 1)
cv2.rectangle(frame, start_cheekw, end_cheekw, green, 1)
Ka = []
# Calculate Ka left cheek
image = frame[start_cheekl[1]:end_cheekl[1], start_cheekl[0]:end_cheekl[0]]
(B, G, R) = cv2.split(image)
lDCB, lACB, lDCR, lACR, lDCG, lACG = np.mean(B), np.std(B), np.mean(R), np.std(R), np.mean(
G), np.std(G)
# Calculate Ka right cheek
image = frame[start_cheekr[1]:end_cheekr[1], start_cheekr[0]:end_cheekr[0]]
(B, G, R) = cv2.split(image)
rDCB, rACB, rDCR, rACR, rDCG, rACG = np.mean(B), np.std(B), np.mean(R), np.std(R), np.mean(
G), np.std(G)
# Calculate Ka F cheek
image = frame[start_face[1]:end_face[1], start_face[0]:end_face[0]]
(B, G, R) = cv2.split(image)
fDCB, fACB, fDCR, fACR, fDCG, fACG = np.mean(B), np.std(B), np.mean(R), np.std(R), np.mean(G), np.std(G)
writer.writerow([lDCB, lACB, lDCR, lACR, lDCG, lACG, rDCB, rACB, rDCR, rACR, rDCG, rACG, fDCB, fACB, fDCR, fACR, fDCG, fACG])
print(frame_count - 1)
# with open(bvp_file, "r") as f:
# str1 = f.read()
# str1 = str1.split("\n")
# spo2 = [float(x) for x in str1[1].split()]
# print(len(spo2))
# with open("C:/Users/YF/Desktop/11.csv", 'a',
# newline=''
# ) as csvfile:
# writer = csv.writer(csvfile)
# writer.writerow(spo2)
# data = pd.read_csv('C:/Users/YF/Desktop/456.csv')
# data['spo2'] = spo2
# data.to_csv('C:/Users/YF/Desktop/456.csv', index=False)
# with open(bvp_file, "r") as f:
# str1 = f.read()
# str1 = str1.split("\n")
# ppg = [float(x) for x in str1[0].split()]
# return spo2, ppg
# with open("C:/Users/YF/Desktop/11.csv", 'a',
# newline=''
# ) as csvfile:
# writer = csv.writer(csvfile)
# writer.writerow(ppg)
# data = pd.read_csv('C:/Users/YF/Desktop/456.csv')
# data['ppg'] = ppg
# data.to_csv('C:/Users/YF/Desktop/456.csv', index=False)