from threading import Thread import numpy as np from PureBackend.general import * from MCamera.mn_camera import * from MCamera.camera import * from Database.manager_database import * from score_doc import get_fin_score from Exercise_mn.base_exercise import BaseExercise class Overhang(BaseExercise): def __init__(self, info, statistic_time=120, camera=None): super().__init__(info, statistic_time, camera=camera) # 计时 self.bar = None self.per = None self.costtime = 0 self.endtime = None self.starttime = None # 动作持续帧数 self._time = 0 # 开始标志 self.form = 0 # 状态反馈 self.state = 0 self.pre_pos = 0 self.sta_time = time.time() self.feedback = "开始" self.exercise_type = "屈臂悬垂" self.end_test = 0 self.had_start = False self.had_done = False # 计时 self.chronograph = 0 # 引体向上参数 self.corner = (0, 480, 260, 380) MoveNetAlgorithmPlugin.set_corner(corner=self.corner) MoveNetAlgorithmPlugin.set_config(config=self.config) def get_result(self): # 人员年龄 age = self.info.get(AGE) # 人员性别 gender = "woman" if self.info.get(GENDER) == "女" else "man" if self.costtime < 0: score = 0 else: score = get_fin_score.Military(gender, int(age), integratedProjectResult=round(self.costtime, 1)).IntegratedProjectScoreEvaluation() if gender != "woman": score = 0 if self.starttime and self.starttime != 0: this_time = time.time() count_time = this_time - self.starttime else: count_time = 0 result = { "count": self.costtime, "score": score, "had_done": self.had_done, "time": self.chronograph, 'countdown': count_time } return result def speak_counting(self, counting_times, name, gender): self.speak_driver.start() self.speak_driver.speed_control(200) self.speak_driver.volume_control(1) self.speak_driver.add_speak("考试人员{}".format(name)) self.speak_driver.add_speak(f"考试项目{self.exercise_type}") if gender != "女": 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") def timekeeping(self): start_time = time.time() while self.had_start: now_time = time.time() self.chronograph = int(now_time - start_time) time.sleep(0.1) if self.had_done: break def run(self) -> None: self.speak_counting(5, self.info[NAME], self.info[GENDER]) # 清除历史记录 # 记录开始时间 self.start_time = time.time() self.is_start = True # 不停更新计算和更新画面 threading.Thread(target=self.thread_counting_streaming, daemon=True).start() while not self.is_done(): _img = self.cap.get_frame() self.img = self.display(_img) self.is_start = False def display(self, img): cv2.rectangle(img, (self.corner[2], self.corner[0]), (self.corner[3], self.corner[1]), (0, 255, 0), 2) # 绘制状态条 cv2.rectangle(img, (650 - 100, 60), (650 - 80, 282), (255, 255, 255), 2) if self.bar and self.per: cv2.rectangle(img, (650 - 98, int(self.bar)), (650 - 82, 280), (102, 106, 233), cv2.FILLED) cv2.putText(img, f'{int(self.per)}%', (650 - 125, 320), cv2.FONT_HERSHEY_PLAIN, 2, (255, 255, 255), 2) # # 绘制计数器 # cv2.rectangle(img, (0, 480 - 120), (120, 480), (102, 106, 233), cv2.FILLED) # cv2.putText(img, str(int(self.count)), (10, 480 - 35), cv2.FONT_HERSHEY_PLAIN, 5, # (255, 255, 255), 5) # 展示状态反馈 cv2.rectangle(img, (640 - 160, 0), (640, 50), (102, 106, 233), cv2.FILLED) img_output = self.cv2_img_add_text(img, self.feedback, 640 - 120, 5, (255, 255, 255), 38) return img_output def analysis(self, frame): catch_time = frame[CATCH_TIME] if catch_time < self.start_cal_time: return img = frame[FRAME_DAT] lm_list = frame[MN_RESULT] self.detector.set_result(lm_list) if len(lm_list) != 0: elbow_1 = self.detector.findAngle(img, LEFT_SHOULDER, LEFT_ELBOW, LEFT_WRIST) shoulder_1 = self.detector.findAngle(img, LEFT_ELBOW, LEFT_SHOULDER, LEFT_HIP) elbow_2 = self.detector.findAngle(img, RIGHT_SHOULDER, RIGHT_ELBOW, RIGHT_WRIST) shoulder_2 = self.detector.findAngle(img, RIGHT_ELBOW, RIGHT_SHOULDER, RIGHT_HIP) eye_1_y = self.detector.findPosition(img, False)[LEFT_EYE]['key_points'][1] eye_2_y = self.detector.findPosition(img, False)[RIGHT_EYE]['key_points'][1] nose_y = self.detector.findPosition(img, False)[NOSE]['key_points'][1] wrist_1_y = self.detector.findPosition(img, False)[LEFT_WRIST]['key_points'][1] wrist_2_y = self.detector.findPosition(img, False)[RIGHT_WRIST]['key_points'][1] shoulder_1_y = self.detector.findPosition(img, False)[LEFT_SHOULDER]['key_points'][1] shoulder_2_y = self.detector.findPosition(img, False)[RIGHT_SHOULDER]['key_points'][1] # 成功概率 self.per = np.interp(elbow_1, (90, 160), (0, 100)) # 显示进度栏 self.bar = np.interp(elbow_1, (90, 160), (280, 62)) if self.pre_pos == 0: self.pre_pos = 1 self.speak_driver.add_speak("请进入准备状态") if self.form == 0: if time.time() - self.sta_time > 1.5: 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: self.form = 1 self.speak_driver.add_speak("请开始考试") elif self.pre_pos == 1 and self.form == 1: hand_1_y = wrist_1_y - (shoulder_1_y - wrist_1_y) / 3 hand_2_y = wrist_2_y - (shoulder_2_y - wrist_2_y) / 3 head_1_y = nose_y + (nose_y - eye_1_y) * 1.3 head_2_y = nose_y + (nose_y - eye_2_y) * 1.3 if elbow_1 > 120 and elbow_2 > 120 and head_1_y > hand_1_y and head_2_y > hand_2_y: if self.state == 1: self.end_test += 1 if self.end_test > 3: self.endtime = time.time() self.costtime = round(self.endtime - self.starttime, 3) self.speak_driver.add_speak("考试结束") Thread(target=self.speak_driver.add_speak, args=(f"总耗时为{round(self.costtime, 2)}秒",)).start() self.pre_pos = 2 self.had_done = True if elbow_1 < 90 and elbow_2 < 90 and head_1_y < hand_1_y and head_2_y < hand_2_y: if self.state == 0: self.feedback = "悬垂" self._time += 1 if self._time > 3: self._time = 0 self.starttime = time.time() self.had_start = True self.state = 1 self.speak_driver.add_speak("计时开始") threading.Thread(target=self.timekeeping).start()