183 lines
7.8 KiB
Python
183 lines
7.8 KiB
Python
from threading import Thread
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import numpy as np
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from PureBackend.general import *
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from MCamera.mn_camera import *
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from MCamera.camera import *
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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 Exercise_mn.base_exercise import BaseExercise
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class Overhang(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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# 计时
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self.bar = None
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self.per = None
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self.costtime = 0
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self.endtime = None
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self.starttime = None
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# 动作持续帧数
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self._time = 0
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# 开始标志
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self.form = 0
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# 状态反馈
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self.state = 0
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self.pre_pos = 0
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self.sta_time = time.time()
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self.feedback = "开始"
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self.exercise_type = "屈臂悬垂"
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self.end_test = 0
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self.had_start = False
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self.had_done = False
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# 计时
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self.chronograph = 0
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# 引体向上参数
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self.corner = (0, 480, 260, 380)
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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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if self.costtime < 0:
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score = 0
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else:
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score = get_fin_score.Military(gender, int(age),
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integratedProjectResult=round(self.costtime,
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1)).IntegratedProjectScoreEvaluation()
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if gender != "woman":
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score = 0
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if self.starttime and self.starttime != 0:
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this_time = time.time()
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count_time = this_time - self.starttime
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else:
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count_time = 0
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result = {
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"count": self.costtime,
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"score": score,
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"had_done": self.had_done,
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"time": self.chronograph,
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'countdown': count_time
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}
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return result
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def speak_counting(self, counting_times, name, gender):
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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 gender != "女":
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self.speak_driver.add_speak(f"注意，该考生性别为男性，{self.exercise_type}项目成绩无效")
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def timekeeping(self):
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start_time = time.time()
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while self.had_start:
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now_time = time.time()
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self.chronograph = int(now_time - start_time)
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time.sleep(0.1)
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if self.had_done:
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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[GENDER])
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# 清除历史记录
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# 记录开始时间
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self.start_time = time.time()
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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 = self.cap.get_frame()
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self.img = self.display(_img)
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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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# 绘制状态条
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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_output = self.cv2_img_add_text(img, self.feedback, 640 - 120, 5, (255, 255, 255), 38)
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return img_output
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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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elbow_1 = self.detector.findAngle(img, LEFT_SHOULDER, LEFT_ELBOW, LEFT_WRIST)
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shoulder_1 = self.detector.findAngle(img, LEFT_ELBOW, LEFT_SHOULDER, LEFT_HIP)
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elbow_2 = self.detector.findAngle(img, RIGHT_SHOULDER, RIGHT_ELBOW, RIGHT_WRIST)
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shoulder_2 = self.detector.findAngle(img, RIGHT_ELBOW, RIGHT_SHOULDER, RIGHT_HIP)
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eye_1_y = self.detector.findPosition(img, False)[LEFT_EYE]['key_points'][1]
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eye_2_y = self.detector.findPosition(img, False)[RIGHT_EYE]['key_points'][1]
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nose_y = self.detector.findPosition(img, False)[NOSE]['key_points'][1]
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wrist_1_y = self.detector.findPosition(img, False)[LEFT_WRIST]['key_points'][1]
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wrist_2_y = self.detector.findPosition(img, False)[RIGHT_WRIST]['key_points'][1]
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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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# 成功概率
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self.per = np.interp(elbow_1, (90, 160), (0, 100))
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# 显示进度栏
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self.bar = np.interp(elbow_1, (90, 160), (280, 62))
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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 time.time() - self.sta_time > 1.5:
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if elbow_1 > 150 and elbow_2 > 150 and shoulder_1 > 100 and shoulder_2 > 100 and eye_1_y > wrist_1_y and eye_2_y > wrist_2_y:
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self.form = 1
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self.speak_driver.add_speak("请开始考试")
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elif self.pre_pos == 1 and self.form == 1:
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hand_1_y = wrist_1_y - (shoulder_1_y - wrist_1_y) / 3
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hand_2_y = wrist_2_y - (shoulder_2_y - wrist_2_y) / 3
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head_1_y = nose_y + (nose_y - eye_1_y) * 1.3
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head_2_y = nose_y + (nose_y - eye_2_y) * 1.3
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if elbow_1 > 120 and elbow_2 > 120 and head_1_y > hand_1_y and head_2_y > hand_2_y:
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if self.state == 1:
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self.end_test += 1
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if self.end_test > 3:
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self.endtime = time.time()
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self.costtime = round(self.endtime - self.starttime, 3)
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self.speak_driver.add_speak("考试结束")
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Thread(target=self.speak_driver.add_speak,
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args=(f"总耗时为{round(self.costtime, 2)}秒",)).start()
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self.pre_pos = 2
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self.had_done = True
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if elbow_1 < 90 and elbow_2 < 90 and head_1_y < hand_1_y and head_2_y < hand_2_y:
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if self.state == 0:
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self.feedback = "悬垂"
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self._time += 1
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if self._time > 3:
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self._time = 0
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self.starttime = time.time()
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self.had_start = True
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self.state = 1
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self.speak_driver.add_speak("计时开始")
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threading.Thread(target=self.timekeeping).start()
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