import math import time import numpy as np from MCamera.mn_camera import MN_RESULT from PureBackend.general import * from MCamera.mp_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 from MCamera.mn_algorithm import MoveNetAlgorithmPlugin class PullUp_1(BaseExercise): def __init__(self, info, statistic_time=120, 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.body_state = 0 self.direction = 0 self.is_storard = 1 # 动作持续帧数 self._time = 0 self.time = 0 self.t = 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 = "引体向上" self.end_test = 0 self.had_done = False # 摆动状态 self.state = 0 # 初始化 self.initial_height_left = 0 self.initial_height_right = 0 self.initial_wrist_1_y = 0 self.initial_wrist_2_y = 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) 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() 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) self.detector.drawPoint_more(img, [NOSE, LEFT_SHOULDER, LEFT_ELBOW, LEFT_WRIST, LEFT_KNEE], bias_x=self.corner[2], bias_y=self.corner[0]) self.detector.drawPoint_more(img, [NOSE, RIGHT_SHOULDER, RIGHT_ELBOW, RIGHT_WRIST, RIGHT_KNEE], 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[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) 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] shoulder_1_x = self.detector.findPosition(img, False)[LEFT_SHOULDER]['key_points'][0] shoulder_2_x = self.detector.findPosition(img, False)[RIGHT_SHOULDER]['key_points'][0] 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] hip_1_x = self.detector.findPosition(img, False)[LEFT_HIP]['key_points'][0] hip_1_y = self.detector.findPosition(img, False)[LEFT_HIP]['key_points'][1] hip_2_x = self.detector.findPosition(img, False)[RIGHT_HIP]['key_points'][0] hip_2_y = self.detector.findPosition(img, False)[RIGHT_HIP]['key_points'][1] # 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.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") self.starttime = time.time() self.initial_wrist_1_y = wrist_1_y self.initial_wrist_2_y = wrist_2_y self.initial_height_left = math.sqrt( math.pow(shoulder_2_x - hip_2_x, 2) + math.pow(shoulder_2_y - hip_2_y, 2)) * math.cos( math.pi / 6) self.initial_height_right = math.sqrt( math.pow(shoulder_1_x - hip_1_x, 2) + math.pow(shoulder_1_y - hip_1_y, 2)) * math.cos( math.pi / 6) elif self.pre_pos == 1 and self.form == 1: height_left = math.sqrt(math.pow(shoulder_2_x - hip_2_x, 2) + math.pow(shoulder_2_y - hip_2_y, 2)) height_right = math.sqrt(math.pow(shoulder_1_x - hip_1_x, 2) + math.pow(shoulder_1_y - hip_1_y, 2)) 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 h_range = head_1_y - hand_1_y if wrist_1_y - self.initial_wrist_1_y > 50 and wrist_2_y - self.initial_wrist_2_y > 50 and self.pre_pos == 1: self.end_test += 1 if self.end_test > 2: self.speak_driver.add_speak("双手已离开单杠，考试结束") self.pre_pos = 2 self.had_done = True if height_left < self.initial_height_left and height_right < self.initial_height_right: self.t += 1 if self.t > 4: self.state = 1 self.t = 0 if self.body_state == 0: self.speak_driver.add_speak('摆幅过大') self.body_state = 1 if elbow_1 > 157 and elbow_2 > 157 and head_1_y > hand_1_y and head_2_y > hand_2_y: self.feedback = "上升" self.state = 0 if self.direction == 0: self._time += 1 if self._time > 3: self.count += 0.5 self.direction = 1 self._time = 0 self.body_state = 0 if head_1_y < hand_1_y and head_2_y < hand_2_y and not self.had_done: self.feedback = "下落" if self.state == 0: if self.direction == 1: self._time += 1 if self._time > 2: self.count += 0.5 self.direction = 0 self._time = 0 if self.count % 1 == 0: self.speak_driver.add_speak("{}".format(int(self.count))) if self.count == 1: self.initial_wrist_1_y = wrist_1_y self.initial_wrist_2_y = wrist_2_y if h_range < 20 and self.is_storard == 1: self.time += 1 if self.time > 3: self.is_storard = 2 self.time = 0 elif head_1_y < hand_1_y and head_2_y < hand_2_y and self.is_storard == 2: self.time += 1 if self.time > 3: self.is_storard = 3 self.time = 0 elif h_range > 30 and self.is_storard == 2: self.time += 1 if self.time > 3: self.is_storard = 1 self.speak_driver.add_speak('未过杠') self.time = 0 elif h_range > 30 and self.is_storard == 3: self.time += 1 if self.time > 3: self.is_storard = 1 self.time = 0