226 lines
10 KiB
Python
226 lines
10 KiB
Python
import math
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import time
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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.mp_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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from MCamera.mn_algorithm import MoveNetAlgorithmPlugin
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class PullUp_1(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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# 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.body_state = 0
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self.direction = 0
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self.is_storard = 1
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# 动作持续帧数
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self._time = 0
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self.time = 0
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self.t = 0
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self.starttime = 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.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 = "引体向上"
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self.end_test = 0
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self.had_done = False
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# 摆动状态
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self.state = 0
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# 初始化
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self.initial_height_left = 0
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self.initial_height_right = 0
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self.initial_wrist_1_y = 0
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self.initial_wrist_2_y = 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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self.a = time.time()
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self.b = 0
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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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score = get_fin_score.Military(gender, int(age),
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integratedProjectResult=int(self.count)).IntegratedProjectScoreEvaluation()
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if 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": int(self.count),
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"score": score,
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"had_done": self.had_done,
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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):
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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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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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self.detector.drawPoint_more(img, [NOSE, LEFT_SHOULDER, LEFT_ELBOW, LEFT_WRIST, LEFT_KNEE], bias_x=self.corner[2], bias_y=self.corner[0])
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self.detector.drawPoint_more(img, [NOSE, RIGHT_SHOULDER, RIGHT_ELBOW, RIGHT_WRIST, RIGHT_KNEE], bias_x=self.corner[2], bias_y=self.corner[0])
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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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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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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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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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hip_1_x = self.detector.findPosition(img, False)[LEFT_HIP]['key_points'][0]
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hip_1_y = self.detector.findPosition(img, False)[LEFT_HIP]['key_points'][1]
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hip_2_x = self.detector.findPosition(img, False)[RIGHT_HIP]['key_points'][0]
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hip_2_y = self.detector.findPosition(img, False)[RIGHT_HIP]['key_points'][1]
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# 成功概率
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self.per = np.interp(elbow_1, (157, 100), (0, 100))
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# 显示进度栏
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self.bar = np.interp(elbow_1, (157, 100), (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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self.starttime = time.time()
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self.initial_wrist_1_y = wrist_1_y
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self.initial_wrist_2_y = wrist_2_y
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self.initial_height_left = math.sqrt(
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math.pow(shoulder_2_x - hip_2_x, 2) + math.pow(shoulder_2_y - hip_2_y, 2)) * math.cos(
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math.pi / 6)
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self.initial_height_right = math.sqrt(
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math.pow(shoulder_1_x - hip_1_x, 2) + math.pow(shoulder_1_y - hip_1_y, 2)) * math.cos(
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math.pi / 6)
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elif self.pre_pos == 1 and self.form == 1:
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height_left = math.sqrt(math.pow(shoulder_2_x - hip_2_x, 2) + math.pow(shoulder_2_y - hip_2_y, 2))
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height_right = math.sqrt(math.pow(shoulder_1_x - hip_1_x, 2) + math.pow(shoulder_1_y - hip_1_y, 2))
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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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h_range = head_1_y - hand_1_y
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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:
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self.end_test += 1
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if self.end_test > 2:
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self.speak_driver.add_speak("双手已离开单杠，考试结束")
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self.pre_pos = 2
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self.had_done = True
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if height_left < self.initial_height_left and height_right < self.initial_height_right:
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self.t += 1
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if self.t > 4:
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self.state = 1
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self.t = 0
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if self.body_state == 0:
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self.speak_driver.add_speak('摆幅过大')
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self.body_state = 1
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if elbow_1 > 157 and elbow_2 > 157 and head_1_y > hand_1_y and head_2_y > hand_2_y:
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self.feedback = "上升"
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self.state = 0
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if self.direction == 0:
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self._time += 1
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if self._time > 3:
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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.body_state = 0
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if head_1_y < hand_1_y and head_2_y < hand_2_y and not self.had_done:
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self.feedback = "下落"
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if self.state == 0:
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if self.direction == 1:
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self._time += 1
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if self._time > 2:
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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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if self.count % 1 == 0:
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self.speak_driver.add_speak("{}".format(int(self.count)))
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if self.count == 1:
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self.initial_wrist_1_y = wrist_1_y
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self.initial_wrist_2_y = wrist_2_y
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if h_range < 20 and self.is_storard == 1:
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self.time += 1
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if self.time > 3:
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self.is_storard = 2
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self.time = 0
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elif head_1_y < hand_1_y and head_2_y < hand_2_y and self.is_storard == 2:
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self.time += 1
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if self.time > 3:
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self.is_storard = 3
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self.time = 0
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elif h_range > 30 and self.is_storard == 2:
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self.time += 1
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if self.time > 3:
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self.is_storard = 1
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self.speak_driver.add_speak('未过杠')
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self.time = 0
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elif h_range > 30 and self.is_storard == 3:
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self.time += 1
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if self.time > 3:
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self.is_storard = 1
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self.time = 0
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