276 lines
13 KiB
Python
276 lines
13 KiB
Python
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import threading
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import time
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from copy import deepcopy
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import numpy as np
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from MCamera.mn_camera import MN_RESULT
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from PureBackend.general import *
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from MCamera.mn_algorithm import MoveNetAlgorithmPlugin
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from MCamera.mp_camera import MP_RESULT
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from MCamera.camera import *
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from Speaker.speak_base import beep
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from Database.manager_database import *
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from score_doc import get_fin_score
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from .base_exercise import BaseExercise
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class PushUp_3(BaseExercise):
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def __init__(self, info, statistic_time=120, camera=None):
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super().__init__(info, statistic_time, camera=camera)
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self.countdown_flag = threading.Event() # 计时线程
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# 个数统计
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self.bar = None
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self.per = None
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self.count = 0
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# 当前状态
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self.pre_pos = 0
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self.state = 0
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self.speak_state = 0
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self.direction = 1
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# 动作持续帧数
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self.time = 0
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self.last_time = 0
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self.interval = 0.5
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# 开始标志
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self.form = 0
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# 初始化
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self.countdown = 120
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self.shoulder_angle = 23
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# 状态反馈
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self.feedback = "开始"
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self.had_done = False
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self.sign = 0
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self.time_1 = 0
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self.time_2 = 0
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self.dir = 0
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self.exercise_type = "俯卧撑"
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# 俯卧撑参数
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self.corner = (180, 430, 80, 560)
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self.config = (0.7, 50, 55, 60)
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MoveNetAlgorithmPlugin.set_corner(corner=self.corner)
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MoveNetAlgorithmPlugin.set_config(config=self.config)
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def get_result(self):
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# 人员年龄
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age = self.info.get(AGE)
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# 人员性别
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gender = "woman" if self.info.get(GENDER) == "女" else "man"
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count = self.count
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score = get_fin_score.Military(gender, int(age),
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integratedProjectResult=int(count)).IntegratedProjectScoreEvaluation()
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if age < 40:
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score = 0
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result = {
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"count": int(count),
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"score": score,
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"countdown": self.countdown,
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"had_done": self.had_done
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}
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return result
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def retail_counting(self):
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retail_counting = 3
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counting = 0
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first_time = time.time()
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while True:
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this_time = time.time()
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if this_time - first_time > counting:
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if retail_counting > 0:
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self.speak_driver.add_speak(f"{retail_counting}！")
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self.speak_driver.wait_4_speak()
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counting += 1
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retail_counting -= 1
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else:
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break
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threading.Thread(target=beep, daemon=True).start()
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self.countdown_flag.set()
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self.start_time = time.time()
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def speak_counting(self, counting_times, name, age):
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self.speak_driver.start()
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self.speak_driver.speed_control(200)
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self.speak_driver.volume_control(1)
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self.speak_driver.add_speak("考试人员{}".format(name))
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self.speak_driver.add_speak(f"考试项目{self.exercise_type}")
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if age < 40:
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self.speak_driver.add_speak(f"注意，该考生年龄小于40岁，{self.exercise_type}项目成绩无效")
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def count_down(self):
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seconds = 120
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while True:
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self.countdown_flag.wait()
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this_time = time.time()
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count_time = this_time - self.start_time
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second = int(seconds - count_time)
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self.countdown = f"{second}"
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time.sleep(0.1)
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if second == 0:
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self.countdown_flag.clear()
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break
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def run(self) -> None:
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self.speak_counting(5, self.info[NAME], self.info[AGE])
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# 准备倒计时
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threading.Thread(target=self.count_down).start()
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self.is_start = True
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# 不停更新计算和更新画面
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threading.Thread(target=self.thread_counting_streaming, daemon=True).start()
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while not self.is_done():
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_img = deepcopy(self.cap.get_frame())
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self.img = self.display(_img)
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if self.countdown == 120:
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self.speak_driver.add_speak(f"长时间未进入准备状态，考试结束")
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elif int(self.countdown) <= 1:
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self.had_done = True
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self.speak_driver.add_speak(f"时间到，考试结束")
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elif 1 < int(self.countdown) < 120:
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self.speak_driver.add_speak(f"考试终止")
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self.is_start = False
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def display(self, img):
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cv2.rectangle(img, (self.corner[2], self.corner[0]), (self.corner[3], self.corner[1]), (0, 255, 0), 2)
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# 检查俯卧撑运动过程
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if self.sign == 1 and self.dir == 1 and self.countdown_flag.is_set():
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self.detector.drawPoint(img, LEFT_SHOULDER, LEFT_ELBOW, LEFT_ANKLE, bias_x=self.corner[2],
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bias_y=self.corner[0])
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if self.sign == 1 and self.dir == 2 and self.countdown_flag.is_set():
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self.detector.drawPoint(img, RIGHT_SHOULDER, RIGHT_ELBOW, RIGHT_ANKLE, bias_x=self.corner[2],
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bias_y=self.corner[0])
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# 绘制状态条
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if self.sign == 1:
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cv2.rectangle(img, (650 - 100, 60), (650 - 80, 282), (255, 255, 255), 2)
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if self.bar and self.per:
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cv2.rectangle(img, (650 - 98, int(self.bar)), (650 - 82, 280), (102, 106, 233),
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cv2.FILLED)
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cv2.putText(img, f'{int(self.per)}%', (650 - 125, 320), cv2.FONT_HERSHEY_PLAIN, 2,
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(255, 255, 255), 2)
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# 绘制计数器
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cv2.rectangle(img, (0, 480 - 120), (120, 480), (102, 106, 233), cv2.FILLED)
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cv2.putText(img, str(int(self.count)), (10, 480 - 35), cv2.FONT_HERSHEY_PLAIN, 5,
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(255, 255, 255), 5)
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# 展示状态反馈
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cv2.rectangle(img, (640 - 160, 0), (640, 50), (102, 106, 233), cv2.FILLED)
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img = self.cv2_img_add_text(img, self.feedback, 640 - 120, 5, (255, 255, 255), 38)
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return img
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def analysis(self, frame):
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catch_time = frame[CATCH_TIME]
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if catch_time < self.start_cal_time:
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return
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img = frame[FRAME_DAT]
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lm_list = frame[MN_RESULT]
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self.detector.set_result(lm_list)
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if len(lm_list) != 0:
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shoulder_1_x = self.detector.findPosition(img, False)[LEFT_SHOULDER]['key_points'][0]
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shoulder_2_x = self.detector.findPosition(img, False)[RIGHT_SHOULDER]['key_points'][0]
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shoulder_1_y = self.detector.findPosition(img, False)[LEFT_SHOULDER]['key_points'][1]
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shoulder_2_y = self.detector.findPosition(img, False)[RIGHT_SHOULDER]['key_points'][1]
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vis_elbow_1 = self.detector.findPosition(img, False)[LEFT_ELBOW]['confidence']
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vis_elbow_2 = self.detector.findPosition(img, False)[RIGHT_ELBOW]['confidence']
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vis_knee_1 = self.detector.findPosition(img, False)[LEFT_KNEE]['confidence']
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vis_knee_2 = self.detector.findPosition(img, False)[RIGHT_KNEE]['confidence']
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Concave_angle_1 = self.detector.findConcave(LEFT_SHOULDER, LEFT_HIP, LEFT_KNEE)
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Concave_angle_2 = self.detector.findConcave(RIGHT_SHOULDER, RIGHT_HIP, RIGHT_KNEE)
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knee_1_x = self.detector.findPosition(img, False)[LEFT_KNEE]['key_points'][0]
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knee_2_x = self.detector.findPosition(img, False)[RIGHT_KNEE]['key_points'][0]
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knee_1_y = self.detector.findPosition(img, False)[LEFT_KNEE]['key_points'][1]
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knee_2_y = self.detector.findPosition(img, False)[RIGHT_KNEE]['key_points'][1]
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ankle_1_x = self.detector.findPosition(img, False)[LEFT_ANKLE]['key_points'][0]
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ankle_2_x = self.detector.findPosition(img, False)[RIGHT_ANKLE]['key_points'][0]
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ankle_1_y = self.detector.findPosition(img, False)[LEFT_ANKLE]['key_points'][1]
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ankle_2_y = self.detector.findPosition(img, False)[RIGHT_ANKLE]['key_points'][1]
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elbow_1 = self.detector.findAngle(img, LEFT_SHOULDER, LEFT_ELBOW, LEFT_WRIST, False)
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elbow_2 = self.detector.findAngle(img, RIGHT_SHOULDER, RIGHT_ELBOW, RIGHT_WRIST, False)
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if self.pre_pos == 0:
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self.pre_pos = 1
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self.speak_driver.add_speak("请进入准备状态")
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if self.form == 0:
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if ankle_1_y > shoulder_1_y or ankle_2_y > shoulder_2_y:
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if self.pre_pos == 1:
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if vis_elbow_1 > 0.8 and vis_elbow_1 > vis_elbow_2 and vis_knee_1 > 0.8:
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self.time_1 += 1
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if self.time_1 > 4:
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self.dir = 1
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self.sign = 1
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self.time_1 = 0
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self.form = 1
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elif vis_elbow_2 > 0.8 and vis_elbow_1 < vis_elbow_2 and vis_knee_2 > 0.8:
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self.time_2 += 1
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if self.time_2 > 4:
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self.dir = 2
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self.sign = 1
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self.time_2 = 0
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self.form = 1
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if self.sign == 1:
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self.speak_driver.add_speak("准备开始考试")
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threading.Thread(target=self.retail_counting).start()
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# 检查俯卧撑运动过程
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if self.sign == 1 and self.dir == 1 and self.countdown_flag.is_set():
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shoulder_angle = self.detector.findIncludedAngle(ankle_1_x, ankle_1_y, shoulder_1_x, shoulder_1_y)
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hip_angle = self.detector.findIncludedAngle(ankle_1_x, ankle_1_y, knee_1_x, knee_1_y)
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# 成功概率
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self.per = np.interp(elbow_1, (90, 150), (100, 0))
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# 显示进度栏
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self.bar = np.interp(elbow_1, (90, 150), (62, 280))
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if hip_angle < 70:
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if elbow_1 > 140 and 150 <= Concave_angle_1 <= 210:
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self.feedback = "下落"
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if self.direction == 0:
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self.time += 1
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if self.time > 1:
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self.count += 0.5
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self.direction = 1
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self.time = 0
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self.speak_state = 0
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self.shoulder_angle = shoulder_angle
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if self.count % 1 == 0:
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self.speak_driver.speed_control(200)
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self.speak_driver.add_speak("{}".format(int(self.count)))
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if self.shoulder_angle - shoulder_angle < 3 and elbow_1 < 115 and time.time() - self.last_time > self.interval:
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self.feedback = "上升"
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if self.direction == 1:
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self.time += 1
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if self.time > 1:
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self.count += 0.5
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self.direction = 0
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self.time = 0
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self.last_time = time.time()
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if self.sign == 1 and self.dir == 2 and self.countdown_flag.is_set():
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shoulder_angle = self.detector.findIncludedAngle(ankle_2_x, ankle_2_y, shoulder_2_x, shoulder_2_y)
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hip_angle = self.detector.findIncludedAngle(ankle_2_x, ankle_2_y, knee_2_x, knee_2_y)
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# 成功概率
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self.per = np.interp(elbow_2, (90, 150), (100, 0))
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# 显示进度栏
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self.bar = np.interp(elbow_2, (90, 150), (62, 280))
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if hip_angle < 70:
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if elbow_2 > 140 and 150 <= Concave_angle_2 <= 210:
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self.feedback = "下落"
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if self.direction == 0:
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self.time += 1
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if self.time > 1:
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self.count += 0.5
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self.direction = 1
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self.time = 0
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self.speak_state = 0
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self.shoulder_angle = shoulder_angle
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if self.count % 1 == 0:
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self.speak_driver.speed_control(200)
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self.speak_driver.add_speak("{}".format(int(self.count)))
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if self.shoulder_angle - shoulder_angle < 3 and elbow_1 < 115 and time.time() - self.last_time > self.interval:
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self.feedback = "上升"
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if self.direction == 1:
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self.time += 1
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if self.time > 1:
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self.count += 0.5
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self.direction = 0
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self.time = 0
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self.last_time = time.time()
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