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estimation.py
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"""
哑铃动作标准化计数
"""
import math
import time
from collections import OrderedDict
import cv2
import matplotlib.pyplot as plt
import numpy as np
import paddlehub as hub
from PIL import Image
pose_estimation = hub.Module(name="human_pose_estimation_resnet50_mpii")
count = 0
det_1 = 0
angle_last = 90.0
train_content = input("请问您训练的是左臂还是右臂：") #左臂还是右臂
def Cal_Ang(point_1, point_2, point_3):
"""
根据三点坐标计算夹角
:param point_1: 点1坐标
:param point_2: 点2坐标
:param point_3: 点3坐标
:return: 点2的夹角(中心点)
"""
a=math.sqrt((point_2[0]-point_3[0]) * (point_2[0]-point_3[0])+(point_2[1]-point_3[1]) * (point_2[1]-point_3[1]))
b=math.sqrt((point_1[0]-point_3[0]) * (point_1[0]-point_3[0])+(point_1[1]-point_3[1]) * (point_1[1]-point_3[1]))
c=math.sqrt((point_1[0]-point_2[0]) * (point_1[0]-point_2[0])+(point_1[1]-point_2[1]) * (point_1[1]-point_2[1]))
B=math.degrees(math.acos((b*b-a*a-c*c)/(-2*a*c)))
return B
def aitrainer(picture):
consequence = []
result = pose_estimation.keypoint_detection(images=[cv2.imread(picture)])
predict = dict(result[0]["data"])
right_wrist = predict['right_wrist']
consequence.append(right_wrist)
right_elbow = predict['right_elbow']
consequence.append(right_elbow)
right_shoulder = predict['right_shoulder']
consequence.append(right_shoulder)
left_shoulder = predict['left_shoulder']
consequence.append(left_shoulder)
left_elbow = predict['left_elbow']
consequence.append(left_elbow)
left_wrist = predict['left_wrist']
consequence.append(left_wrist)
return consequence
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture("AiTrainer/curls.mp4")
print("训练将在5秒后进行，请您做好准备！")
for i in range(5):
time.sleep(1)
m = 5 - i
print(m)
while(1):
# get a frame
ret, frame = cap.read()
frame = cv2.resize(frame, (1280, 720))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.imwrite("camera.jpg", frame)
point = aitrainer('camera.jpg')
if train_content == '左臂':
left_wrist = point[5]
left_elbow = point[4]
left_shoulder = point[3]
angle_left = Cal_Ang(left_wrist,left_elbow,left_shoulder)
diff = angle_left - angle_last
if diff >=0 and det_1 == 0:
count += 0.5
det_1 = 1
if diff <=0 and det_1 ==1:
count += 0.5
det_1 = 2
cv2.line(frame, tuple(left_wrist), tuple(left_elbow),(0, 0, 255), 3)
cv2.line(frame, tuple(left_elbow), tuple(left_shoulder),(0, 0, 225), 3)
if count % 1.0 == 0.0:
det_1 = 0
print("count:%.2f"%count)
cv2.rectangle(frame, (0, 450), (250, 720), (0, 255, 0), cv2.FILLED)
cv2.putText(frame, str(int(count)), (45, 670), cv2.FONT_HERSHEY_PLAIN, 10,
(255, 0, 0), 20)
cv2.imshow('OpenPose using OpenCV', frame)
if train_content == '右臂':
right_wrist = point[0]
right_elbow = point[1]
right_shoulder = point[2]
angle_right = Cal_Ang(right_wrist,right_elbow,right_shoulder)
diff = angle_right - angle_last
if diff >=0 and det_1 == 0:
count += 0.5
det_1 = 1
if diff <=0 and det_1 ==1:
count += 0.5
det_1 = 2
cv2.line(frame, tuple(right_wrist), tuple(right_elbow),(0, 0, 255), 3)
cv2.line(frame, tuple(right_elbow), tuple(right_shoulder),(0, 0, 225), 3)
if count % 1.0 == 0.0:
det_1 = 0
print("count:%.2f"%count)
cv2.rectangle(frame, (0, 450), (250, 720), (0, 255, 0), cv2.FILLED)
cv2.putText(frame, str(int(count)), (45, 670), cv2.FONT_HERSHEY_PLAIN, 10,
(255, 0, 0), 20)
cv2.imshow('OpenPose using OpenCV', frame)
cap.release()