212 lines
8.7 KiB
Python
212 lines
8.7 KiB
Python
import time
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from copy import deepcopy
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from threading import Lock
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from threading import Thread
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from MCamera.mn_algorithm import MoveNetAlgorithmPlugin
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from MCamera.mn_camera import MN_RESULT
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from MCamera.mp_camera import MP_RESULT
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from MCamera.camera import *
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from Speaker.speak_base import beep
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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 Runaround(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.countdown_flag = threading.Event() # 计时线程
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self.costtime = 0
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self.countdown = 0
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# 当前状态
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self.direction = 0
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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.feedback = "请开始"
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self.had_leaf = False
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self.had_start = False
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self.had_done = False
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self.had_countdown = False
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self.countdown_signal = True
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self.pre_frame = None
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self.retail_counting = 5
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self.preempt_lock = Lock()
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self.Preempt = 0
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self.pretime = 0
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self.racetime = 0
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self.starttime = 0
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self.endtime = 0
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self.fix = 0
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self.timefix = 0
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self.catch_time = 0
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self.exercise_type = "蛇形跑"
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# 蛇形跑参数
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self.corner = (0, 480, 200, 400)
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self.config = (0.7, 50, 55, 60)
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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 == '未完成':
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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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serpentine60RunResult=round(self.costtime,
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1)).Serpentine60RunScoreEvaluation()
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result = {
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"count": self.costtime,
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"score": score,
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"countdown": self.countdown,
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"had_done": self.had_done,
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'preempt': self.Preempt
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}
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return result
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def get_info(self):
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self.preempt_lock.acquire()
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result = self.Preempt
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self.preempt_lock.release()
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return result
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def is_valid(self):
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return True
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def _speak_counting(self):
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threading.Thread(target=self.count_down).start()
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counting = 0
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first_time = self.catch_time
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while True:
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this_time = self.catch_time
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if this_time - first_time > counting:
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if not self.countdown_signal and not self.had_countdown:
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self.speak_driver.add_speak(f"抢跑！")
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self.pretime = self.catch_time
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self.countdown_flag.set()
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self.had_countdown = True
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if self.retail_counting > 0:
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if self.countdown_signal:
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self.speak_driver.speed_control(350)
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self.speak_driver.add_speak(f"{self.retail_counting}")
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self.speak_driver.wait_4_speak()
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counting += 1
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self.retail_counting -= 1
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else:
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if self.countdown_signal:
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threading.Thread(target=beep, args=(300, 640), daemon=True).start()
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self.had_countdown = True
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self.countdown_flag.set()
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self.pretime = self.catch_time
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break
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self.had_start = True
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self.starttime = self.catch_time
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def count_down(self):
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while True:
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self.countdown_flag.wait()
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self.costtime = self.catch_time - self.pretime
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self.countdown = int(self.costtime)
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time.sleep(0.1)
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def run(self) -> None:
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self.speak_driver.add_speak("考试人员{}".format(self.info[NAME]))
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self.speak_driver.add_speak(f"考试项目{self.exercise_type}")
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self.speak_driver.wait_4_speak()
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self.cap.clear_cache()
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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 = deepcopy(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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if not self.had_countdown:
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cv2.putText(img, str(self.retail_counting), (230, 320), cv2.FONT_HERSHEY_PLAIN, 20, (255, 255, 255), 8)
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return img
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def analysis(self, frame):
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self.catch_time = frame[CATCH_TIME]
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if self.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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img_input = img[self.corner[0]:self.corner[1], self.corner[2]:self.corner[3]]
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if self.timefix == 0:
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self.timefix = 1
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Thread(target=self._speak_counting, daemon=True).start()
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if not self.had_countdown:
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self.detector.set_result(lm_list)
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# self.starttime = time.time()
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if len(lm_list) == 0:
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self.time = self.time + 1
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if self.fix == 0 and self.time > 3:
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self.fix += 1
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self.countdown_signal = False
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self.preempt_lock.acquire()
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self.racetime = self.catch_time
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self.preempt_lock.release()
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self.valid_mes()
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self.is_valid()
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# 如果没有背景图像就将当前帧当作背景图片
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else:
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# 计算抢跑时间
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if self.starttime - self.racetime <= 5:
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self.Preempt = round(self.starttime - self.racetime, 3)
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elif 10 > self.starttime - self.racetime > 5:
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self.Preempt = 5
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else:
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self.Preempt = 0
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if self.had_start and not self.had_leaf and not self.had_done:
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# 一直等待到目标完全离开检测区
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if self.catch_time - self.pretime > 14:
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self.had_leaf = True
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else:
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# 转灰度图
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gray_pic = cv2.cvtColor(img_input, cv2.COLOR_BGR2GRAY)
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# 用高斯滤波进行模糊处理
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gray_pic = cv2.GaussianBlur(gray_pic, (21, 21), 0)
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if self.pre_frame is None:
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self.pre_frame = gray_pic
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else:
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# absdiff把两幅图的差的绝对值输出到另一幅图上面来
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img_delta = cv2.absdiff(self.pre_frame, gray_pic)
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# threshold阈值函数(原图像应该是灰度图,对像素值进行分类的阈值,当像素值高于（有时是小于）阈值时应该被赋予的新的像素值,阈值方法)
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thresh = cv2.threshold(img_delta, 30, 255, cv2.THRESH_BINARY)[1]
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# 用一下腐蚀与膨胀
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thresh = cv2.dilate(thresh, None, iterations=2)
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# findContours检测物体轮廓(寻找轮廓的图像,轮廓的检索模式,轮廓的近似办法)
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contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for c in contours:
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# 设置敏感度
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if cv2.contourArea(c) < 7500:
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continue
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else:
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if self.had_start and self.had_leaf and not self.had_done:
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self.endtime = self.catch_time
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self.had_done = True
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self.costtime = round(self.endtime - self.starttime + self.Preempt, 3)
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self.countdown_flag.clear()
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# print("总耗时 = {}".format(self.costtime))
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self.speak_driver.speed_control(200)
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Thread(
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target=self.speak_driver.add_speak,
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args=(f"抢跑时间为{round(self.Preempt, 2)}秒,总耗时为{round(self.costtime, 2)}秒",)
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).start()
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break
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self.pre_frame = gray_pic
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