import numpy as np from MCamera.mp_camera import * from MCamera.camera import * from Database.manager_database import * from score_doc import get_fin_score from .base_exercise import BaseExercise class Tricep_dip_1(BaseExercise): def __init__(self, info, statistic_time=99999, camera=None): super().__init__(info, statistic_time, camera=camera) # 个数统计 self.bar = None self.per = None self.count = 0 # 当前状态 self.heel = 0 # 动作持续帧数 self.time_0 = 0 self.time_2 = 0 self.time_1 = 0 self.time = 0 self.starttime = 0 # 开始标志 self.form = 0 # 状态反馈 self.pre_pos = 0 self.sta_time = time.time() self.feedback = "开始" self.exercise_type = "双杠臂屈伸" self.end_test = 0 self.had_done = False self.sign = 0 self.readiness_state = 0 # 摆动状态 self.state = 1 # 初始化 self.initial_wrist_1_y = 0 self.initial_wrist_2_y = 0 # 双杠臂屈伸参数 self.corner = (0, 480, 230, 410) MediapipeAlgorithmPlugin.set_corner(corner=self.corner) MediapipeAlgorithmPlugin.set_config(config=self.config) self.a = time.time() self.b = 0 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" # score = get_fin_score.Military(gender, int(age), # integratedProjectResult=int(self.count)).IntegratedProjectScoreEvaluation() score = int(self.count) if self.starttime != 0: this_time = time.time() count_time = this_time - self.starttime else: count_time = 0 result = { "count": int(self.count), "score": score, "had_done": self.had_done, 'countdown': count_time } return result def speak_counting(self, counting_times, name): 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}") 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.dir == 0: self.detector.drawPoint_more(img, [9, 11, 13, 19, 25], bias_x=self.corner[2], bias_y=self.corner[0]) self.detector.drawPoint_more(img, [10, 12, 14, 20, 26], bias_x=self.corner[2], bias_y=self.corner[0]) elif self.dir == 1: self.detector.drawPoint_more(img, [9, 11, 13, 19, 25], bias_x=self.corner[2], bias_y=self.corner[0]) elif self.dir == 2: self.detector.drawPoint_more(img, [10, 12, 14, 20, 26], bias_x=self.corner[2], bias_y=self.corner[0]) 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[MP_RESULT] self.detector.set_result(lm_list) if len(lm_list) != 0: elbow_1 = self.detector.findAngle(img, 11, 13, 15) elbow_2 = self.detector.findAngle(img, 12, 14, 16) shoulder_1 = self.detector.findAngle(img, 13, 11, 23) shoulder_2 = self.detector.findAngle(img, 14, 12, 24) eye_1_y = self.detector.findPosition(img, False)[3][2] eye_2_y = self.detector.findPosition(img, False)[6][2] elbow_1_x = self.detector.findPosition(img, False)[13][1] elbow_1_y = self.detector.findPosition(img, False)[13][2] elbow_2_x = self.detector.findPosition(img, False)[14][1] elbow_2_y = self.detector.findPosition(img, False)[14][2] wrist_1_x = self.detector.findPosition(img, False)[15][1] wrist_2_x = self.detector.findPosition(img, False)[16][1] wrist_1_y = self.detector.findPosition(img, False)[15][2] wrist_2_y = self.detector.findPosition(img, False)[16][2] heel_y = self.detector.findPosition(img, False)[27][2] shoulder_1_x = self.detector.findPosition(img, False)[11][1] shoulder_2_x = self.detector.findPosition(img, False)[12][1] shoulder_1_y = self.detector.findPosition(img, False)[11][2] shoulder_2_y = self.detector.findPosition(img, False)[12][2] vis_shoulder_1 = self.detector.findPosition(img, False)[11][4] vis_shoulder_2 = self.detector.findPosition(img, False)[12][4] # 成功概率 self.per = np.interp(elbow_1, (157, 100), (0, 100)) # 显示进度栏 self.bar = np.interp(elbow_1, (157, 100), (280, 62)) if self.pre_pos == 0: self.speak_driver.add_speak("双手握杠后，进入准备状态") self.pre_pos = 1 if self.form == 0 and self.pre_pos == 1: if self.readiness_state == 0: if shoulder_1 > 70 and shoulder_2 > 70: self.readiness_state = 1 self.heel = heel_y self.speak_driver.add_speak("上杠，听到提示声后开始考试") if self.readiness_state == 1: if (elbow_1 > 157 or elbow_2 > 157)and eye_1_y < wrist_1_y and eye_2_y < wrist_2_y and (shoulder_1 < 30 or shoulder_2 < 30) and heel_y < self.heel - 30: self.time_0 += 1 if self.time_0 > 15: if vis_shoulder_1 > 0.8 and vis_shoulder_1 > vis_shoulder_2: self.time_1 += 1 if self.time_1 > 3: self.dir = 1 self.sign = 1 self.time_1 = 0 self.time_2 = 0 elif vis_shoulder_2 > 0.8 and vis_shoulder_1 < vis_shoulder_2: self.time_2 += 1 if self.time_2 > 3: self.dir = 2 self.sign = 1 self.time_2 = 0 self.time_1 = 0 if self.sign == 1: self.form = 1 beep() self.starttime = time.time() self.initial_wrist_1_y = wrist_1_y self.initial_wrist_2_y = wrist_2_y if self.dir == 1 and self.pre_pos == 1 and self.form == 1: shoulder_angle = self.detector.findIncludedAngle(elbow_1_x, elbow_1_y, wrist_1_x, wrist_1_x) # if wrist_1_y - self.initial_wrist_1_y > 50 or heel_y > self.heel- 5: # self.end_test += 1 # if self.end_test > 10: # self.speak_driver.add_speak("双手已离开双杠，考试结束") # self.pre_pos = 2 # self.had_done = True if elbow_1 <= 95 and shoulder_1_y >= elbow_1_y and wrist_1_y - self.initial_wrist_1_y < 50: self.feedback = "上升" if self.state == 1: self.time += 1 if self.time > 2: self.count += 0.5 self.state = 0 self.time = 0 if elbow_1 >= 146 and shoulder_1_y < elbow_1_y and shoulder_1_y < self.initial_wrist_1_y - 60 and shoulder_angle > 60 and self.count > 0 and wrist_1_y - self.initial_wrist_1_y < 50: self.feedback = "下落" if self.state == 0: self.time += 1 if self.time > 2: self.count += 0.5 self.state = 1 self.end_test = 0 self.speak_driver.speed_control(400) self.speak_driver.add_speak("{}".format(int(self.count))) elif self.dir == 2 and self.pre_pos == 1 and self.form == 1: shoulder_angle = self.detector.findIncludedAngle(elbow_2_x, elbow_2_y, wrist_2_x, wrist_2_x) # if wrist_2_y - self.initial_wrist_2_y > 50 or heel_y > self.heel - 5: # self.end_test += 1 # if self.end_test > 10: # self.speak_driver.add_speak("双手已离开双杠，考试结束") # self.pre_pos = 2 # self.had_done = True if elbow_2 <= 93 and shoulder_2_y >= elbow_2_y and wrist_2_y - self.initial_wrist_2_y < 50: self.feedback = "上升" if self.state == 1: self.time += 1 if self.time > 2: self.count += 0.5 self.state = 0 self.time = 0 if elbow_2 >= 146 and shoulder_2_y < elbow_2_y and shoulder_2_y < self.initial_wrist_2_y - 60 and shoulder_angle > 60 and self.count > 0 and wrist_2_y - self.initial_wrist_2_y < 50: self.feedback = "下落" if self.state == 0: self.time += 1 if self.time > 2: self.count += 0.5 self.state = 1 self.end_test = 0 self.speak_driver.speed_control(400) self.speak_driver.add_speak("{}".format(int(self.count)))