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#!/usr/bin/python3
# -*- coding: UTF-8 -*-
import time
import os
import sys
import math
from pyzbar import pyzbar
import numpy as np
import cv2
import threading
import img_pro
import datetime
import Serial_Servo_Running as SSR
import LeCmd
import getUsedSpace
from PIL import Image, ImageDraw, ImageFont
print('''
**********************************************************
*******功能:所有玩法的集合，可通过不同指令进行调用********
**********************************************************
----------------------------------------------------------
Official website:http://www.lobot-robot.com/pc/index/index
Online mall:https://lobot-zone.taobao.com/
----------------------------------------------------------
以下指令均需在LX终端使用，LX终端可通过ctrl+alt+t打开，或点
击上栏的黑色LX终端图标。
----------------------------------------------------------
Usage:
-1 | --启动颜色识别玩法
-2 | --启动人脸检测玩法
-3 | --启动智能巡线玩法
-4 | --启动手指个数识别玩法
-5 | --启动人脸个数识别玩法
-6 | --启动二维码识别玩法
-7 | --启动智能监控玩法
-8 | --启动数字计算玩法
----------------------------------------------------------
Example #1:
显示图像,识别红绿蓝三种颜色
python3 PC_function.py -41
----------------------------------------------------------
Example #2:
显示图像，将识别到的人脸框起来
python3 PC_function.py -42
----------------------------------------------------------
Version: --V2.1 2019/06/03
----------------------------------------------------------
Tips:
* 按下Ctrl+C可中断此次程序运行
----------------------------------------------------------
''')
orgFrame = None
ret = False
stream = "http://127.0.0.1:8080/?action=stream?dummy=param.mjpg"
cap = cv2.VideoCapture(stream)
width, height = 480, 360
font = cv2.FONT_HERSHEY_SIMPLEX
# 数值映射
# 将一个数从一个范围映射到另一个范围
def leMap(x, in_min, in_max, out_min, out_max):
return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min
#找出面积最大的轮廓
#参数为要比较的轮廓的列表
def getAreaMaxContour(contours) :
contour_area_temp = 0
contour_area_max = 0
area_max_contour = None;
for c in contours : #历遍所有轮廓
contour_area_temp = math.fabs(cv2.contourArea(c)) #计算轮廓面积
if contour_area_temp > contour_area_max :
contour_area_max = contour_area_temp
if contour_area_temp > 300: #只有在面积大于300时，最大面积的轮廓才是有效的，以过滤干扰
area_max_contour = c
return area_max_contour, contour_area_max#返回最大的轮廓
def get_image():
global orgFrame
global ret
global cap
while True:
if cap.isOpened():
ret, orgFrame = cap.read()
else:
time.sleep(0.01)
#################################################
# 颜色识别
def cv_color(frame):
global width, height
#颜色的字典
color_range = {'red': [(0,43,46), (6, 255, 255)],
'blue': [(110,43,46), (124, 255,255)],
'green': [(35,43,46), (77, 255, 255)],
}
range_rgb = {'red': (0, 0, 255),
'blue': (255, 0,0),
'green': (0, 255, 0),
}
wd,hg = 320, 240
dispose_frame = cv2.resize(frame, (wd, hg), interpolation = cv2.INTER_CUBIC) #将图片缩放
gs_frame = cv2.GaussianBlur(dispose_frame, (3,3), 0)#高斯模糊
hsv = cv2.cvtColor(gs_frame, cv2.COLOR_BGR2HSV)#将图片转换到HSV空间
#分离出各个HSV通道
h, s, v = cv2.split(hsv)
#直方图化
v = cv2.equalizeHist(v)
#合并三个通道
hsv = cv2.merge((h,s,v))
color_list = []
rad = 0
areaMaxContour = 0
max_area = 0
area_max = 0
centerX = 0
centerY = 0
for i in color_range:
mask = cv2.inRange(hsv, color_range[i][0], color_range[i][1])#对原图像和掩模进行位运算
opened = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((3,3),np.uint8))#开运算
closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, np.ones((3,3),np.uint8))#闭运算
(image, contours, hierarchy) = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)#找出轮廓
areaMaxContour, area_max = getAreaMaxContour(contours)#找出最大轮廓
if areaMaxContour is not None:
if area_max > max_area:#找最大面积
max_area = area_max
color_max = i
areaMaxContour_max = areaMaxContour
if max_area != 0:
((centerX, centerY), rad) = cv2.minEnclosingCircle(areaMaxContour_max) # 获取最小外接圆
centerX = int(leMap(centerX, 0.0, wd, 0.0, width)) # 将数据从0-160 映射到 0-480
centerY = int(leMap(centerY, 0.0, hg, 0.0, height)) # 将数据从0-120 映射到 0-360
rad = int(leMap(rad, 0.0, wd/2, 0.0, 240.0)) # 将数据从0-160 映射到 0-480
if rad >= 10:
cv2.circle(frame, (centerX, centerY), rad, (0, 255, 0), 2)#画出圆心
if color_max == 'red': #红色最大
#print("red")
SSR.thread_runActing('wag_tail', 1)
Color_BGR = range_rgb["red"]
elif color_max == 'green': #绿色最大
SSR.thread_runActing('sit_down', 1)
Color_BGR = range_rgb["green"]
#print("green")
elif color_max == 'blue': #蓝色最大
SSR.thread_runActing('37', 1)
Color_BGR = range_rgb["blue"]
#print("blue")
else:
Color_BGR = (0, 0, 0)
color_max = "Other"
else:
Color_BGR = (0, 0, 0)
color_max = "None"
cv2.putText(frame, "Color: " + color_max, (10, frame.shape[0] - 35), cv2.FONT_HERSHEY_SIMPLEX, 0.65, Color_BGR, 2)
return color_max
#################################################
#################################################
# 人脸检测
face_cascade = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml')
face_detection_count = 0
def face_detection(frame):
global face_detection_count
dispose_frame = cv2.resize(frame, (160, 120), interpolation=cv2.INTER_LINEAR)
gray = cv2.cvtColor(dispose_frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.2, 8)
for (x, y, w, h) in faces:
cX = int(leMap(x, 0.0, 160.0, 0.0, 480.0)) #0-480
cY = int(leMap(y, 0.0, 120.0, 0.0, 360.0)) #0-360
cW = int(leMap(w, 0.0, 160.0, 0.0, 480.0))
cH = int(leMap(h, 0.0, 120.0, 0.0, 360.0))
cv2.rectangle(frame, (cX, cY), (cX + cW, cY + cH), (255, 0, 0), 2)
if len(faces) >= 1:
face_detection_count += 1
else:
face_detection_count = 0
if face_detection_count >= 5:
# print ('hello')
SSR.thread_runActing('36', 1)
face_detection_count = 0
#################################################
#################################################
# 智能巡线
def turn_left_right(angle):
'''
左转或者右转的动作组，根据实际需要转动的角度转动， angle 为 负数 左转 ， 正数 右转
:param angle: 转向的角度
:return:
'''
pwm = int(angle * 4.167 + 500) # 1000(pwm 范围) / 240(舵机角度范围) = 4.166667； 500 中位位置
LeCmd.cmd_i001([50, 8, 1, 500, 2, 775, 3, 500, 4, 125, 5, 500, 6, 775, 7, 500, 8, 125])
time.sleep(0.05)
LeCmd.cmd_i001([50, 8, 1, pwm, 2, 775, 3, 500, 4, 125, 5, pwm, 6, 775, 7, 500, 8, 125])
time.sleep(0.05)
LeCmd.cmd_i001([50, 8, 1, pwm, 2, 875, 3, 500, 4, 125, 5, pwm, 6, 875, 7, 500, 8, 125])
time.sleep(0.05)
LeCmd.cmd_i001([50, 8, 1, 500, 2, 875, 3, 500, 4, 255, 5, 500, 6, 875, 7, 500, 8, 225])
time.sleep(0.05)
LeCmd.cmd_i001([50, 8, 1, 500, 2, 875, 3, pwm, 4, 255, 5, 500, 6, 875, 7, pwm, 8, 225])
time.sleep(0.05)
LeCmd.cmd_i001([50, 8, 1, 500, 2, 875, 3, pwm, 4, 125, 5, 500, 6, 875, 7, pwm, 8, 125])
time.sleep(0.05)
LeCmd.cmd_i001([50, 8, 1, 500, 2, 875, 3, 500, 4, 125, 5, 500, 6, 875, 7, 500, 8, 125])
time.sleep(0.05)
def get_x(img):
'''
范围区域图像内色块的中心坐标X
:param img:
:return:
'''
# 要识别的颜色字典
color_dist = {'red': {'Lower': np.array([0, 50, 50]), 'Upper': np.array([6, 255, 255])},
'blue': {'Lower': np.array([100, 80, 46]), 'Upper': np.array([124, 255, 255])},
'green': {'Lower': np.array([35, 43, 46]), 'Upper': np.array([77, 255, 255])},
'black': {'Lower': np.array([0, 0, 0]), 'Upper': np.array([180, 255, 46])},
}
x = None
# 高斯模糊
gs_frame = cv2.GaussianBlur(img, (5, 5), 0)
# 转换颜色空间
hsv = cv2.cvtColor(gs_frame, cv2.COLOR_BGR2HSV)
# 查找颜色
mask = cv2.inRange(hsv, color_dist['black']['Lower'], color_dist['black']['Upper'])
# 腐蚀
mask = cv2.erode(mask, None, iterations=2)
# 膨胀
mask = cv2.dilate(mask, None, iterations=2)
# 查找轮廓
# cv2.imshow('mask', mask)
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
if len(cnts):
c = max(cnts, key=cv2.contourArea) # 找出最大的区域
area = cv2.contourArea(c)
# 获取最小外接矩形
rect = cv2.minAreaRect(c)
if area >= 500:
xy = rect[0]
xy = int(xy[0]), int(xy[1])
cv2.circle(img, (xy[0], xy[1]), 3, (0, 255, 0), -1)
x = xy[0]
box = cv2.boxPoints(rect)
# 数据类型转换
box = np.int0(box)
# 绘制轮廓
cv2.drawContours(img, [box], 0, (0, 255, 255), 1)
return x
# 三个区域的加权值 从上到下
line_weight = [0.1, 0.35, 0.55]
line_weight_sum = 0
for w in range(len(line_weight)):
line_weight_sum += line_weight[w]
# 机器人应该转的角度
line_deflection_angle = 0
line_cv_ok = False
Exit_thread = False
def run_line():
global line_cv_ok, line_deflection_angle
while True:
if line_cv_ok:
if -25 <= line_deflection_angle <= 25:
SSR.running_action_group('1', 2)
else:
turn_left_right(line_deflection_angle)
time.sleep(0.15)
line_cv_ok = False
else:
time.sleep(0.01)
line_th = threading.Thread(target=run_line)
line_th.setDaemon(True) # 设置为后台线程，这里默认是False，设置为True之后则主线程不用等待子线程
line_th.start()
# 智能巡线
def line_patrol(f):
global line_cv_ok, line_weight, line_weight_sum, line_deflection_angle
# 获取总图像的大小
img_h, img_w = f.shape[:2]
# cv2.imshow('f', f)
up_frame = f[0:65, 0:480]
center_frame = f[145:210, 0:480]
down_frame = f[290:355, 0:480]
up_x = get_x(up_frame)
center_x = get_x(center_frame)
down_x = get_x(down_frame)
if up_x is not None and center_x is not None and down_x is not None and line_cv_ok is False:
centroid_sum = up_x * line_weight[0] + center_x * line_weight[1] + down_x * line_weight[2]
center_pos = (centroid_sum / line_weight_sum) # 求出三个点的加权平均X
deflection_angle = math.atan((center_pos - (img_w / 2)) / (img_h / 2)) # 求出弧度
line_deflection_angle = -math.degrees(deflection_angle) # 转换成角度
line_cv_ok = True
elif center_x is not None and down_x is not None and line_cv_ok is False:
centroid_sum = center_x * line_weight[1] + down_x * line_weight[2]
center_pos = (centroid_sum / (line_weight[1] + line_weight[2])) # 求出三个点的加权平均X
deflection_angle = math.atan((center_pos - (img_w / 2)) / (img_h / 2)) # 求出弧度
line_deflection_angle = -math.degrees(deflection_angle) # 转换成角度
line_cv_ok = True
elif down_x is not None and line_cv_ok is False:
centroid_sum = down_x * line_weight[2]
center_pos = (centroid_sum / line_weight[2]) # 求出三个点的加权平均X
deflection_angle = math.atan((center_pos - (img_w / 2)) / (img_h / 2)) # 求出弧度
line_deflection_angle = -math.degrees(deflection_angle) # 转换成角度
line_cv_ok = True
else:
line_cv_ok = False
#################################################
#################################################
# 手指个数识别
cv_hand_last_hand_num = -1
cv_hand_two_last_hand_num = 0
cv_hand_count = 0
cv_hand_run_one = False
def cv_hand_action(hand_num):
global cv_hand_last_hand_num, cv_hand_count, cv_hand_two_last_hand_num, cv_hand_run_one
if hand_num == cv_hand_last_hand_num:
cv_hand_count += 1
if cv_hand_count >= 8:
cv_hand_count = 0
cv_hand_last_hand_num = -1
if cv_hand_run_one is False:
if hand_num == 0:
#print('run0')
SSR.thread_runActing('0', 1)
if hand_num == 1:
#print('run1')
SSR.thread_runActing('front_back', 1)
if hand_num == 2:
#print('run2')
SSR.thread_runActing('right_left', 1)
if hand_num == 3:
#print('run3')
SSR.thread_runActing('rotate', 1)
if hand_num == 4:
#print('run4')
SSR.thread_runActing('wag_tail', 1)
if hand_num == 5:
#print('run5')
SSR.thread_runActing('37', 1)
cv_hand_run_one = True
cv_hand_two_last_hand_num = hand_num
else:
cv_hand_run_one = False
cv_hand_count = 0
cv_hand_last_hand_num = hand_num
def cv_hand_number(frame):
binary = img_pro.image_process(frame)
# 获取手指个数
hand_num = img_pro.get_hand_number(binary, frame)
# 执行相应的动作组
if hand_num is not None:
cv_hand_action(hand_num)
return hand_num
#################################################
#################################################
# 人脸个数识别
def faces_number(frame):
dispose_frame = cv2.resize(frame, (160, 120), interpolation=cv2.INTER_LINEAR) # 将图片缩放到
gray = cv2.cvtColor(dispose_frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.25, 3)
for (x, y, w, h) in faces:
cX = int(leMap(x, 0.0, 160.0, 0.0, 480.0)) # 将数据从0-160 映射到 0-480
cY = int(leMap(y, 0.0, 120.0, 0.0, 360.0)) # 将数据从0-120 映射到 0-360
cW = int(leMap(w, 0.0, 160.0, 0.0, 480.0))
cH = int(leMap(h, 0.0, 120.0, 0.0, 360.0))
cv2.rectangle(frame, (cX, cY), (cX + cW, cY + cH), (255, 0, 0), 2)
cv2.putText(frame, str(len(faces)), (100, 100), font, 3, (0, 0, 255), 2)
#################################################
#################################################
# 二维码识别
def Qc_code(frame):
global orgframe
barcodeData = None
min_frame = cv2.resize(frame, (160, 120), interpolation=cv2.INTER_LINEAR) # 将图片缩放到
gray_min = cv2.cvtColor(min_frame, cv2.COLOR_BGR2GRAY)
barcodes = pyzbar.decode(gray_min)
for barcode in barcodes:
(x, y, w, h) = barcode.rect
cX = int(leMap(x, 0.0, 160.0, 0.0, 480.0)) # 将数据从0-160 映射到 0-480
cY = int(leMap(y, 0.0, 120.0, 0.0, 360.0)) # 将数据从0-120 映射到 0-360
cW = int(leMap(w, 0.0, 160.0, 0.0, 480.0))
cH = int(leMap(h, 0.0, 120.0, 0.0, 360.0))
cv2.rectangle(frame, (cX, cY), (cX + cW, cY + cH), (0, 0, 255), 2)
barcodeData = barcode.data.decode("utf-8")
frame_cv = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_pil = Image.fromarray(frame_cv)
draw = ImageDraw.Draw(frame_pil)
fontText = ImageFont.truetype("/usr/share/fonts/chinese/simsun.ttc", 40, encoding='utf-8')
draw.text((cX, cY - 40), barcodeData, (0, 0, 255), font=fontText)
orgframe = cv2.cvtColor(np.asarray(frame_pil), cv2.COLOR_RGB2BGR)
if barcodeData == 'left':
SSR.thread_runActing('7', 6) # 左转
elif barcodeData == 'right':
SSR.thread_runActing('9', 6) # 右转
elif barcodeData == 'straight':
SSR.thread_runActing('1', 5) # 直行
elif barcodeData == 'retreat': # 后退
SSR.thread_runActing('4', 5) # 后退
else:
pass
return barcodeData
#################################################
#################################################
# 智能监控
# 第一帧，用于比较
moving_last_gray = None
moving_first_flag = False # 第一帧标志位
image_number = len(os.listdir("/home/pi/AlienbotPi/video"))
four_cc = cv2.VideoWriter_fourcc(*'XVID')
out_video = cv2.VideoWriter('video/' + str(image_number) + '.avi', four_cc, 20.0, (480, 360))
def moving_objiect_tracking(frame):
global moving_last_gray, moving_first_flag
text = "Unoccupied"
# 转换成灰度图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 高斯模糊
gray = cv2.GaussianBlur(gray, (21, 21), 0)
if moving_first_flag and moving_last_gray is not None:
# 计算当前帧和第一帧的不同
cv2.accumulateWeighted(gray, moving_last_gray, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(moving_last_gray))
# cv2.imshow('frameDelta', frameDelta)
thresh = cv2.threshold(frameDelta, 5, 255, cv2.THRESH_BINARY)[1]
# 扩展阀值图像填充孔洞，然后找到阀值图像上的轮廓
thresh = cv2.dilate(thresh, None, iterations=2)
(_,cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# 遍历轮廓
for c in cnts:
if cv2.contourArea(c) < 5000:
continue
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
moving_first_flag = False
# 保存上一帧
moving_last_gray = gray.copy().astype("float")
moving_first_flag = True
# 在当前帧上写文字以及时间戳
cv2.putText(frame, "Monitor Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
if text == "Occupied":
space = getUsedSpace.getDiskSpace()
if space > 100.0:
out_video.write(frame) # 如果有画面有动，录制视频
#################################################
#################################################
# 简单数字计算
last_hand_num = -1
count = 0
number_list = []
bit_count = 0
run_one = False
operator = 1 # 运算符 1：+ 2：- 3： * 4：/
operator_list = ['add', 'sub', 'mul', 'div']
bit_time = 0
count_finish = False
def hand_num_action(hand_num):
global last_hand_num, count, bit_count, run_one
global bit_time
if hand_num == last_hand_num:
count += 1
if count >= 5: # 判断五次， 确认手指个数
count = 0
if count_finish is False:
if run_one is False: # 判断是否切换手指
bit_count += 1
number_list.append(hand_num)
# print number_list
run_one = True
else:
if bit_count == 1: # 判断第二个数是否还是一样
bit_time += 1 # 相同手指计时 ，1秒后，手指个数还是相同
if bit_time >= 8: # 大概1.5秒
bit_time = 0
run_one = False
else:
run_one = False
count = 0
bit_time = 0
last_hand_num = hand_num
def digital_computation(frame, key):
global operator, count_finish, number_list, bit_count
global last_hand_num, run_one, count, bit_time
binary = img_pro.image_process(frame)
# 获取手指个数
hand_num = img_pro.get_hand_number(binary, frame)
# 执行相应的动作组
if hand_num is not None:
hand_num_action(hand_num)
else: # 清零
number_list = []
bit_count = 0
last_hand_num = -1
run_one = False
count = 0
bit_time = 0
count_finish = False
if len(number_list) > 0:
cv2.putText(frame, str(number_list[0]), (0, 100), font, 2, (0, 0, 255), 2)
if bit_count == 2:
cv2.putText(frame, str(number_list[1]), (100, 100), font, 2, (0, 0, 255), 2)
cv2.putText(frame, '=', (150, 100), font, 2, (0, 0, 255), 2)
if operator == 1:
cv2.putText(frame, '+', (50, 100), font, 2, (0, 0, 255), 2)
cv2.putText(frame, str(number_list[0] + number_list[1]), (200, 100), font, 2, (0, 0, 255), 2)
elif operator == 2:
cv2.putText(frame, '-', (50, 100), font, 2, (0, 0, 255), 2)
cv2.putText(frame, str(number_list[0] - number_list[1]), (200, 100), font, 2, (0, 0, 255), 2)
elif operator == 3:
cv2.putText(frame, '*', (50, 100), font, 2, (0, 0, 255), 2)
cv2.putText(frame, str(number_list[0] * number_list[1]), (200, 100), font, 2, (0, 0, 255), 2)
elif operator == 4:
if number_list[1] == 0: # 被除数不能为0
cv2.putText(frame, '/', (50, 100), font, 2, (0, 0, 255), 2)
cv2.putText(frame, 'The dividend cannot be 0', (0, 200), font, 1.1, (0, 0, 255), 2)
else:
cv2.putText(frame, '/', (50, 100), font, 2, (0, 0, 255), 2)
cv2.putText(frame, str(number_list[0] / (number_list[1] + 0.0)), (200, 100), font, 2, (0, 0, 255), 2)
count_finish = True
cv2.putText(frame, operator_list[operator - 1], (0, 320), font, 3, (0, 0, 255), 2)
if key == '1':
operator = 1
elif key == '2':
operator = 2
elif key == '3':
operator = 3
elif key == '4':
operator = 4
#################################################
operator = 1
if len(sys.argv) > 1:#对传参长度进行判断
mode = 0
para = sys.argv[1]
if para == "-1":
mode = 1
elif para == "-2":
mode = 2
elif para == "-3":
mode = 3
elif para == "-4":
mode = 4
elif para == "-5":
mode = 5
elif para == "-6":
mode = 6
elif para == "-7":
mode = 7
elif para == "-8":
mode = 8
else:
print("异常：参数输入错误！")
sys.exit()
print('''--程序正常运行中......
''')
th1 = threading.Thread(target = get_image)
th1.setDaemon(True)
th1.start()
while True:
if ret and orgFrame is not None:
try:
ret = False
t1 = cv2.getTickCount()
orgframe = cv2.resize(orgFrame, (480, 360), interpolation=cv2.INTER_LINEAR) # 将图片缩放到
if mode == 1:
cv_color(orgframe)
elif mode == 2:
face_detection(orgframe)
elif mode == 3:
line_patrol(orgframe)
elif mode == 4:
cv_hand_number(orgframe)
elif mode == 5:
faces_number(orgframe)
elif mode == 6:
Qc_code(orgframe)
elif mode == 7:
moving_objiect_tracking(orgframe)
elif mode == 8:
digital_computation(orgframe, operator)
t2 = cv2.getTickCount()
time_r = (t2 - t1) / cv2.getTickFrequency()
fps = 1.0/time_r
cv2.putText(orgframe, "fps:" + str(int(fps)),
(10, orgframe.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)#(0, 0, 255)BGR
cv2.imshow("orgframe", orgframe)
cv2.waitKey(1)
except BaseException as e:
print(e)
continue
else:
time.sleep(0.01)
else:
print("异常：请重新运行，并输入参数！")
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