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all_together_trap_bridge_stairs_lab_changed.py
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import cv2
import cv2 as cv
import numpy as np
import time
import threading
import math
import RobotControl.Serial_Servo_Running as SSR
import signal
#import PWMServo
import copy
from CameraCalibration.CalibrationConfig import *
# from cross_trap_mine_2 import go
############################### calibration #######################
# 加载参数
param_data = np.load(calibration_param_path + '.npz')
# 获取参数
dim = tuple(param_data['dim_array'])
k = np.array(param_data['k_array'].tolist())
d = np.array(param_data['d_array'].tolist())
print('加载参数完成')
print('dim:\n', dim)
print('k:\n', k)
print('d:\n', d)
# 截取区域，1表示完全截取
scale = 1
# 优化内参和畸变参数
p = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(k, d, dim, None)
Knew = p.copy()
if scale: # change fov
Knew[(0, 1), (0, 1)] = scale * Knew[(0, 1), (0, 1)]
map1, map2 = cv2.fisheye.initUndistortRectifyMap(k, d, np.eye(3), Knew, dim, cv2.CV_16SC2)
color_range = {'yellow_door': [(10, 43, 46), (34, 255, 255)],
'red_floor1': [(0, 43, 46), (10, 255, 255)],
'red_floor2': [(156, 43, 46), (180, 255, 255)],
'green_bridge': [(35, 43, 20), (100, 255, 255)],
'yellow_hole': [(10, 70, 46), (34, 255, 255)],
'black_hole': [(0, 0, 0), (180, 255, 80)],
'black_gap': [(0, 0, 0), (180, 255, 100)],
'black_dir': [(0, 0, 0), (180, 255, 46)],
# 'blue': [(110, 43, 46), (124, 255, 255)],
'blue': [(95, 43, 90), (130, 255, 255)],
'black_door': [(0, 0, 0), (180, 255, 46)],
'white': [(0, 0, 160), (180, 30, 255)]
}
color_dict = {'red': {'Lower': np.array([0, 60, 60]), 'Upper': np.array([6, 255, 255])},
'blue': {'Lower': np.array([100, 80, 46]), 'Upper': np.array([124, 255, 255])},
# [(110, 43, 46), (124, 255, 255)]
'green': {'Lower': np.array([35, 43, 46]), 'Upper': np.array([77, 255, 255])},
'yellow_door': {'Lower': np.array([10, 43, 46]), 'Upper': np.array([34, 255, 255])},
'black': {'Lower': np.array([0, 0, 0]), 'Upper': np.array([180, 255, 46])},
'black_obstacle': {'Lower': np.array([0, 0, 0]), 'Upper': np.array([180, 255, 90])},
'black_dir': {'Lower': np.array([0, 0, 0]), 'Upper': np.array([180, 255, 46])}
}
lab_range = {
# 'yellow_door': [(11, 63, 66), (34, 255, 255)],
# 'black_door': [(0, 0, 0), (80, 255, 5)],
#
# 'blue': [(0, 0, 0), (255, 175, 94)], # dark blue
# 'blue_bridge': [(137, 93, 0), (255, 122, 121)], # light blue
#
# 'red': [(0, 154, 130), (255, 255, 255)],
#
# 'green_bridge': [(47, 0, 135), (255, 110, 255)],
# 'black_hole': [(0, 0, 0), (180, 255, 80)],
# 'black_gap': [(0, 0, 0), (180, 255, 100)],
# 'black_dir': [(0, 0, 0), (180, 255, 46)],
# 'blue_old': [(0, 92, 52), (255, 178, 111)],
# 'blue_floor' : [(0, 0, 0), (255, 154, 102)],
# 'white': [(215, 0, 0), (255, 255, 255)],
'red': [(0, 154, 130), (255, 255, 255)],
'green': [(47, 0, 125), (255, 110, 255)],
'blue': [(0, 0, 0), (255, 175, 104)],#94
'yellow': [(11, 63, 66), (34, 255, 255)],
'white': [(215, 0, 0), (255, 255, 255)],
'black': [(0, 0, 0), (50, 255, 255)]
}
lab_red = [(0, 154, 130), (255, 255, 255)]
########################## 参数设置 ##########################
# video = "orgin_video.mp4"
# cap = cv2.VideoCapture(video)
cap = cv2.VideoCapture(-1)
Running = True
org_img = None # 全局变量，原始图像
ret = False # 读取图像标志位
debug = True
key = -1 # waitKey
isstop = True
# level2 parameters
step = 1
# ret = False
move_count = 0
forward_count = 0 # cross mine
end = 0
door_cnt = 0
door_end = 0
r_w = 640
r_h = 480
# should_chose = False
blue_persent = 0
################################################读取图像线程#################################################
def get_img():
global cap, org_img, Running, debug, key, ret
while True:
if cap.isOpened():
ret, org_img_fish = cap.read()
# org_img = cv2.remap(org_img_fish.copy(), map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
if ret:
org_img = cv2.remap(org_img_fish.copy(), map1, map2, interpolation=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT)
if not debug:
# cv2.imshow('orginal frame', org_img)
# time.sleep(0.5)
time.sleep(0.6)
key = cv2.waitKey(3)
else:
time.sleep(0.01)
else:
time.sleep(0.01)
# 读取图像线程
th1 = threading.Thread(target=get_img)
th1.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
th1.start()
########################## 控制线程 ##########################
SSR.start_action_thread()
# SSR.change_action_value("1",0) # 静止
########################## 必要函数 ##########################
# 得到最大轮廓和对应的最大面积
def getAreaMaxContour(contours, area_min=4000):
contour_area_temp = 0
contour_area_max = 0
area_max_contour = None
# area_max_contour = np.array([[[0, 0]], [[0, 1]], [[1, 1]], [[1, 0]]])
for c in contours: # 遍历所有轮廓
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 > area_min: # 只有在面积大于 area_min 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 getAreaSecondMaxContour(contours, max_area):
contour_area_temp = 0
contour_area_max = 0
area_max_contour = None
# area_max_contour = np.array([[[0, 0]], [[0, 1]], [[1, 1]], [[1, 0]]])
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 max_area == contour_area_temp:
continue
elif contour_area_temp > contour_area_max:
contour_area_max = contour_area_temp
if contour_area_temp > 1500: # 只有在面积大于4000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 getLineSumContour(contours, area=1):
contours_sum = None
for c in contours:
area_temp = math.fabs(cv2.contourArea(c))
rect = cv2.minAreaRect(c) # 最小外接矩形
box = np.int0(cv2.boxPoints(rect)) # 最小外接矩形的四个顶点 int0 <=> int64
edge1 = math.sqrt(math.pow(box[3, 1] - box[0, 1], 2) + math.pow(box[3, 0] - box[0, 0], 2))
edge2 = math.sqrt(math.pow(box[3, 1] - box[2, 1], 2) + math.pow(box[3, 0] - box[2, 0], 2))
ratio = edge1 / edge2
if (area_temp > area) and (ratio > 3 or ratio < 0.33):
contours_sum = c
break
for c in contours:
area_temp = math.fabs(cv2.contourArea(c))
rect = cv2.minAreaRect(c) # 最小外接矩形
box = np.int0(cv2.boxPoints(rect)) # 最小外接矩形的四个顶点 int0 <=> int64
edge1 = math.sqrt(math.pow(box[3, 1] - box[0, 1], 2) + math.pow(box[3, 0] - box[0, 0], 2))
edge2 = math.sqrt(math.pow(box[3, 1] - box[2, 1], 2) + math.pow(box[3, 0] - box[2, 0], 2))
ratio = edge1 / edge2
if (area_temp > area) and (ratio > 3 or ratio < 0.33):
contours_sum = np.concatenate((contours_sum, c), axis=0) # 将所有轮廓点拼接到一起
return contours_sum
# 得到全部的总的轮廓
def getSumContour(contours, area=1):
contours_sum = None
for c in contours:
area_temp = math.fabs(cv2.contourArea(c))
if (area_temp > area):
contours_sum = c
break
for c in contours:
area_temp = math.fabs(cv2.contourArea(c))
if (area_temp > area):
contours_sum = np.concatenate((contours_sum, c), axis=0) # 将所有轮廓点拼接到一起
return contours_sum
def direction_only_angle(resize_width, resize_height):
global org_img, reset
angle_flag = False
angle = 90
dis = 0
see = False
# PWMServo.setServo(1, 1800, 500)
# PWMServo.setServo(2, 850, 500)
time.sleep(0.5)
# SSR.change_action_value("00", 1) # stand
while True:
if reset == 1: # 跳关或者重置
cv2.destroyAllWindows()
break
OrgFrame = org_img
frame = cv2.resize(OrgFrame, (resize_width, resize_height), interpolation=cv2.INTER_LINEAR)
# cv2.imshow('init', frame)
# 获取图像中心点坐标x, y
center = []
# 开始处理图像
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
hsv = cv2.GaussianBlur(hsv, (3, 3), 3)
Imask = cv2.inRange(hsv, color_dict['black_dir']['Lower'], color_dict['black_dir']['Upper'])
Imask = cv2.erode(Imask, None, iterations=2)
Imask = cv2.dilate(Imask, np.ones((3, 3), np.uint8), iterations=2)
# cv2.imshow('black', Imask)
_, cnts, hierarchy = cv2.findContours(Imask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1) # 找出所有轮廓
cnt_sum = getLineSumContour(cnts, area=1)
cv2.drawContours(frame, cnt_sum, -1, (255, 0, 255), 3)
if cnt_sum is not None:
see = True
rect = cv2.minAreaRect(cnt_sum) # 最小外接矩形
box = np.int0(cv2.boxPoints(rect)) # 最小外接矩形的四个顶点
if math.sqrt(math.pow(box[3, 1] - box[0, 1], 2) + math.pow(box[3, 0] - box[0, 0], 2)) > math.sqrt(
math.pow(box[3, 1] - box[2, 1], 2) + math.pow(box[3, 0] - box[2, 0], 2)):
if box[3, 0] - box[0, 0] == 0:
angle = 90
else:
angle = - math.atan((box[3, 1] - box[0, 1]) / (box[3, 0] - box[0, 0])) * 180.0 / math.pi
if box[3, 1] + box[0, 1] > box[2, 1] + box[1, 1]:
edge_y1, edge_y2 = box[3, 1], box[0, 1]
dis = int((edge_y1 + edge_y2) / 2)
cv2.circle(OrgFrame, (int((box[3, 0] + box[0, 0]) / 2), int((box[3, 1] + box[0, 1]) / 2)), 10,
(0, 0, 255), -1) # 画出中心点
else:
edge_y1, edge_y2 = box[2, 1], box[1, 1]
dis = int((edge_y1 + edge_y2) / 2)
cv2.circle(OrgFrame, (int((box[2, 0] + box[1, 0]) / 2), int((box[2, 1] + box[1, 1]) / 2)), 10,
(0, 0, 255), -1) # 画出中心点
else:
if box[3, 0] - box[2, 0] == 0:
angle = 90
else:
angle = - math.atan(
(box[3, 1] - box[2, 1]) / (box[3, 0] - box[2, 0])) * 180.0 / math.pi # 负号是因为坐标原点的问题
if box[3, 1] + box[2, 1] > box[0, 1] + box[1, 1]:
edge_y1, edge_y2 = box[3, 1], box[2, 1]
dis = int((edge_y1 + edge_y2) / 2)
cv2.circle(OrgFrame, (int((box[3, 0] + box[2, 0]) / 2), int((box[3, 1] + box[2, 1]) / 2)), 10,
(0, 0, 255), -1) # 画出中心点
else:
edge_y1, edge_y2 = box[0, 1], box[1, 1]
dis = int((edge_y1 + edge_y2) / 2)
cv2.circle(OrgFrame, (int((box[0, 0] + box[1, 0]) / 2), int((box[0, 1] + box[1, 1]) / 2)), 10,
(0, 0, 255), -1) # 画出中心点
else:
see = False
cv2.putText(frame, "angle:" + str(angle),
(10, frame.shape[0] - 35), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
# cv2.imshow('frame', frame)
# cv2.waitKey(1)
if not see: # not see the edge
cv2.destroyAllWindows()
break
else:
print(dis, angle)
if dis < 80:
if angle > -13:
# SSR.change_action_value("202", 1) # 左转小
print("left angle small")
elif angle < -16:
# SSR.change_action_value("203", 1) # 右转小
print("right angle small")
else:
break
elif dis > 400:
if angle > -24:
# SSR.change_action_value("202", 1) # 左转小
print("left angle small")
elif angle < -28:
# SSR.change_action_value("203", 1) # 右转小
print("right angle small")
else:
break
else:
if angle > -18:
# SSR.change_action_value("202", 1) # 左转小
print("left angle small")
elif angle < -22:
# SSR.change_action_value("203", 1) # 右转小
print("right angle small")
else:
break
time.sleep(0.2)
def judge_color():
global org_img
color = 'blue' # blue
border = cv2.copyMakeBorder(org_img, 12, 12, 16, 16, borderType=cv2.BORDER_CONSTANT,
value=(255, 255, 255)) # 扩展白边，防止边界无法识别
org_img_copy = cv2.resize(org_img, (r_w, r_h), interpolation=cv2.INTER_CUBIC) # 将图片缩放
frame_gauss = cv2.GaussianBlur(org_img_copy, (3, 3), 0) # 高斯模糊
frame_hsv = cv2.cvtColor(frame_gauss, cv2.COLOR_BGR2Lab) # 将图片转换到HSV空间
# frame_temp_1=cv2.inRange(frame_hsv, lab_range['blue'][0], lab_range['blue'][1]) # 对原图像和掩模(颜色的字典)进行位运算
frame_green = cv2.inRange(frame_hsv, lab_range['green'][0],
lab_range['green'][1]) # 对原图像和掩模(颜色的字典)进行位运算
opened_green = cv2.morphologyEx(frame_green, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8)) # 开运算 去噪点
closed_green = cv2.morphologyEx(opened_green, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
rgb_image = cv2.cvtColor(org_img_copy, cv2.COLOR_BGR2RGB)
result_green = cv2.bitwise_and(rgb_image, rgb_image, mask=closed_green)
frame_blue = cv2.inRange(frame_hsv, lab_range['blue'][0], lab_range['blue'][1]) # 对原图像和掩模(颜色的字典)进行位运算
opened_blue = cv2.morphologyEx(frame_blue, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8)) # 开运算 去噪点
closed_blue = cv2.morphologyEx(opened_blue, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
# rgb_image = cv2.cvtColor(org_img_copy, cv2.COLOR_BGR2RGB)
result_blue = cv2.bitwise_and(rgb_image, rgb_image, mask=closed_blue)
(image, contours_blue, hierarchy) = cv2.findContours(closed_blue, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
(image, contours_green, hierarchy) = cv2.findContours(closed_green, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
areaMaxContour, area_max_blue = getAreaMaxContour(contours_blue)
areaMaxContour, area_max_green = getAreaMaxContour(contours_green)
if area_max_blue > area_max_green:
color = 'blue'
else:
color = 'green'
return color
def check_stick():
global org_img
org_img_copy = cv2.resize(org_img.copy()[:,0:580], (r_w, r_h), interpolation=cv2.INTER_CUBIC) # 将图片缩放
frame_gauss = cv2.GaussianBlur(org_img_copy, (3, 3), 0) # 高斯模糊
frame_hsv = cv2.cvtColor(frame_gauss, cv2.COLOR_BGR2Lab) # 将图片转换到HSV空间
frame_door = cv2.inRange(frame_hsv[20:120, 0:640], lab_range['blue'][0],
lab_range['blue'][1]) # 对原图像和掩模(颜色的字典)进行位运算
opened_door = cv2.morphologyEx(frame_door, cv2.MORPH_OPEN, np.ones(( ), np.uint8)) # 开运算 去噪点
closed_door = cv2.morphologyEx(opened_door, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
(image_door, contours_door, hierarchy_door) = cv2.findContours(closed_door, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE) # 找出轮廓cv2.CHAIN_APPROX_NONE
cv2.imshow('check', closed_door)
areaMaxContour_door_1, area_max_door = getAreaMaxContour(contours_door, 1000)
if areaMaxContour_door_1 is None:
print('stick done with no stick')
SSR.change_action_value("go_middle_change_3", 5) # gogogo
time.sleep(10)
return
left_count = 0
SSR.serial_setServo(19, 600, 500)
time.sleep(3)
org_img_copy = cv2.resize(org_img.copy()[:,0:580], (r_w, r_h), interpolation=cv2.INTER_CUBIC) # 将图片缩放
frame_gauss = cv2.GaussianBlur(org_img_copy, (3, 3), 0) # 高斯模糊
frame_hsv = cv2.cvtColor(frame_gauss, cv2.COLOR_BGR2Lab) # 将图片转换到HSV空间
frame_door = cv2.inRange(frame_hsv[20:120, 0:640], lab_range['blue'][0],
lab_range['blue'][1]) # 对原图像和掩模(颜色的字典)进行位运算
opened_door = cv2.morphologyEx(frame_door, cv2.MORPH_OPEN, np.ones(( ), np.uint8)) # 开运算 去噪点
closed_door = cv2.morphologyEx(opened_door, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
(image_door, contours_door, hierarchy_door) = cv2.findContours(closed_door, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE) # 找出轮廓cv2.CHAIN_APPROX_NONE
cv2.imshow('check', closed_door)
SSR.serial_setServo(19, 500, 500)
time.sleep(1)
if contours_door is not None:
areaMaxContour_door_1, area_max_door = getAreaMaxContour(contours_door, 500)
if areaMaxContour_door_1 is not None:
if len(contours_door) > 1:
areaMaxContour_door_2, area_max_door = getAreaMaxContour(contours_door, 500)
if areaMaxContour_door_2 is not None:
print('get to left')
SSR.change_action_value("left_move_new_8", 1)
time.sleep(1)
left_count = 1
return
if left_count == 0:
SSR.serial_setServo(19, 400, 500)
time.sleep(3)
org_img_copy = cv2.resize(org_img.copy()[:,0:580], (r_w, r_h), interpolation=cv2.INTER_CUBIC) # 将图片缩放
frame_gauss = cv2.GaussianBlur(org_img_copy, (3, 3), 0) # 高斯模糊
frame_hsv = cv2.cvtColor(frame_gauss, cv2.COLOR_BGR2Lab) # 将图片转换到HSV空间
frame_door = cv2.inRange(frame_hsv[20:120, 0:640], lab_range['blue'][0],
lab_range['blue'][1]) # 对原图像和掩模(颜色的字典)进行位运算
opened_door = cv2.morphologyEx(frame_door, cv2.MORPH_OPEN, np.ones(( ), np.uint8)) # 开运算 去噪点
closed_door = cv2.morphologyEx(opened_door, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
(image_door, contours_door, hierarchy_door) = cv2.findContours(closed_door, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE) # 找出轮廓cv2.CHAIN_APPROX_NONE
SSR.serial_setServo(19, 500, 500)
time.sleep(1)
if contours_door is not None:
areaMaxContour_door_1, area_max_door = getAreaMaxContour(contours_door, 300)
if areaMaxContour_door_1 is not None:
if len(contours_door) > 1:
areaMaxContour_door_2, area_max_door = getAreaMaxContour(contours_door, 300)
if areaMaxContour_door_2 is not None:
print('get to right')
SSR.change_action_value("right_move_new_4", 1)
time.sleep(0.8)
left_count = 1
return
print('stick done ')
SSR.change_action_value("go_middle_change_3", 10) # gogogo
time.sleep(10)
########################## 关卡 ##########################
# 第1关：起点
def start_door(width, height):
global org_img, debug, isstop
# SSR.serial_setServo(20, 450, 500)
SSR.serial_setServo(20, 450, 500)
time.sleep(1)
door_count = 0
no_door_count = 0
print('进入start_door')
while True:
t1 = cv2.getTickCount()
border = cv2.copyMakeBorder(org_img, 12, 12, 16, 16, borderType=cv2.BORDER_CONSTANT,
value=(255, 255, 255)) # 扩展白边，防止边界无法识别
org_img_copy = cv2.resize(border, (width, height), interpolation=cv2.INTER_CUBIC) # 将图片缩放
frame_gauss = cv2.GaussianBlur(org_img_copy, (3, 3), 0) # 高斯模糊
frame_hsv = cv2.cvtColor(frame_gauss, cv2.COLOR_BGR2HSV) # 将图片转换到HSV空间
frame_door1 = cv2.inRange(frame_hsv, color_dict['yellow_door']['Lower'],
color_dict['yellow_door']['Upper']) # 对原图像和掩模(颜色的字典)进行位运算
frame_door2 = cv2.inRange(frame_hsv, color_dict['black']['Lower'],
color_dict['black']['Upper']) # 对原图像和掩模(颜色的字典)进行位运算
frame_door = cv2.add(frame_door1, frame_door2)
opened = cv2.morphologyEx(frame_door, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8)) # 开运算 去噪点
closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
# (image, contours, hierarchy) = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # 找出轮廓
_, contours, hierarchy = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # 找出轮廓
areaMaxContour, area_max = getAreaMaxContour(contours, 25) # 找出最大轮廓
percent = round(100 * area_max / (width * height), 2) # 最大轮廓的百分比
if areaMaxContour is not None:
rect = cv2.minAreaRect(areaMaxContour) # 矩形框选
box = np.int32(cv2.boxPoints(rect)) # 点的坐标
if debug:
cv2.drawContours(org_img_copy, [box], 0, (153, 200, 0), 2) # 将最小外接矩形画在图上
if debug:
# cv2.imshow('orginal frame', org_img)
# cv2.imshow('closed', closed) # 显示图像
cv2.putText(org_img_copy, 'area: ' + str(percent) + '%', (10, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
t2 = cv2.getTickCount()
time_r = (t2 - t1) / cv2.getTickFrequency()
fps = 1.0 / time_r
# cv2.putText(org_img_copy, "fps:" + str(int(fps)), (30, 200), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
# cv2.moveWindow('orgFrame', img_center_x, 100) # 显示框位置
# cv2.imshow('org_img_copy', org_img_copy) # 显示图像
# cv2.waitKey(3)
# 根据比例得到是否前进的信息
if percent > 20:
print(percent)
print('stop')
isstop = True
# SSR.change_action_value("1",10) # 静止
time.sleep(0.1)
door_count += 1
no_door_count = 0
elif door_count >= 10:
no_door_count += 1
if no_door_count >= 10:
isstop = False
print('gogogo')
print(percent)
SSR.change_action_value("go_middle_stair", 5) # gogogo
time.sleep(6)
break
else:
pass
print("door_count = ", door_count, " no_door_count = ", no_door_count)
# cv2.destroyAllWindows()
# time.sleep(0.5)
# 第2关：过坑
def cross_trap_all():
if end == 0:
SSR.change_action_value("left_move_new_6", 2) # 左移
print("left")
time.sleep(1.2)
SSR.serial_setServo(20, 340, 500)
time.sleep(0.5)
SSR.serial_setServo(19, 500, 500)
time.sleep(0.5)
go()
# pass
# go()
def cross_trap():
global org_img, step, reset, end
# initialize
straight = False
left = False
left2 = False
right = False
right2 = False
# border = cv2.copyMakeBorder(org_img, 12, 12, 16, 16, borderType=cv2.BORDER_CONSTANT,
# value=(255, 255, 255)) # 扩展白边，防止边界无法识别
org_img_copy = cv2.resize(org_img[:,0:580], (r_w, r_h), interpolation=cv2.INTER_CUBIC) # 将图片缩放
frame_gauss = cv2.GaussianBlur(org_img_copy, (3, 3), 0) # 高斯模糊
frame_hsv = cv2.cvtColor(frame_gauss, cv2.COLOR_BGR2Lab) # 将图片转换到HSV空间
color = judge_color()
print(color)
# frame_temp_1=cv2.inRange(frame_hsv, lab_range['blue'][0], lab_range['blue'][1]) # 对原图像和掩模(颜色的字典)进行位运算
frame_green = cv2.inRange(frame_hsv, lab_range[color][0], lab_range[color][1]) # 对原图像和掩模(颜色的字典)进行位运算
opened = cv2.morphologyEx(frame_green, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8)) # 开运算 去噪点
closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
rgb_image = cv2.cvtColor(org_img_copy, cv2.COLOR_BGR2RGB)
result = cv2.bitwise_and(rgb_image, rgb_image, mask=closed)
# print(closed)
cv2.imshow('closed', closed)
(image, contours, hierarchy) = cv2.findContours(closed, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE) # 找出轮廓cv2.CHAIN_APPROX_NONE
image_filled = 255 * np.ones(closed.shape, np.uint8) # 用于识别色块的图像
areaMaxContour, area_max = getAreaMaxContour(contours)
image_line_detection = np.zeros(closed.shape, np.uint8)
image_line_detection = cv2.drawContours(image_line_detection, areaMaxContour, -1, 255, 2)
lines = cv2.HoughLinesP(image_line_detection, 1, np.pi / 180, 30, 300, 5)
################################调整转向部分##############################
tangent_list = []
length_list = []
image_copy = copy.copy(org_img)
if lines is not None:
for line in lines:
x1, y1, x2, y2 = line[0]
if y1 < 410 and x2 - x1 != 0:
tangent_abs = np.abs((y2 - y1) / (x2 - x1))
if tangent_abs < 0.5:
length_list.append(np.abs(line[0, 0] - line[0, 2]))
tangent_list.append([x1, y1, x2, y2])
if len(tangent_list) > 0:
[x1_f, y1_f, x2_f, y2_f] = tangent_list[np.where(length_list == np.max(length_list))[0][0]]
tangent_used = -(y2_f - y1_f) / (x2_f - x1_f) #####标准tangent
else:
tangent_used = 0
[x1_f, y1_f, x2_f, y2_f] = [0, 0, 0, 0]
print(tangent_used)
cv2.line(image_copy, (x1_f, y1_f), (x2_f, y2_f), (0, 255, 0), 1)
if 0.20 > tangent_used > 0.07:
print("should turn left little")
SSR.change_action_value("turn_left", 1)
time.sleep(0.7)
elif tangent_used > 0.20:
print("should turn left large")
SSR.change_action_value("turn_left", 1)
time.sleep(0.7)
elif -0.07 > tangent_used > -0.20:
print("should turn right little")
SSR.change_action_value("turn_right", 1)
time.sleep(0.7)
elif tangent_used < - 0.20:
print("should turn right large")
SSR.change_action_value("turn_right", 1)
time.sleep(0.7)
print(y1_f, y2_f, area_max)
# cv2.imshow("image_line1", image_copy)
if y1_f > 340 and y2_f > 340 and area_max < 45000:
print("get end")
time.sleep(1)
SSR.change_action_value("go_middle_change_3", 3) # 前进
time.sleep(2)
SSR.change_action_value("right_move_new_2", 5) # 右移
time.sleep(15)
SSR.change_action_value("turn_right", 1)
time.sleep(3)
SSR.change_action_value("go_middle_change_3", 3) # 前进
time.sleep(2)
end = 1
return
##########################开始避障检测
if areaMaxContour is not None:
cv2.fillPoly(image_filled, pts=[areaMaxContour], color=0)
if len(contours) > 1:
contours.remove(areaMaxContour)
areaMaxContour, area_max = getAreaMaxContour(contours, 4000)
# print(area_max)
if area_max > 4000:
# pass
# print(areaMaxContour)
cv2.fillPoly(image_filled, pts=[areaMaxContour], color=255)
else:
print('trap_end')
time.sleep(1)
SSR.change_action_value("go_middle_change_3", 3) # 前进
time.sleep(2)
SSR.change_action_value("right_move_new_2", 3) # 右移
time.sleep(8)
#SSR.change_action_value("turn_right", 1)
#time.sleep(3)
SSR.change_action_value("go_middle_change_3", 1) # 前进
time.sleep(2)
end = 1
return
image_line_detection = image_line_detection[15:465, 20:620]
image_line_detection = cv2.copyMakeBorder(image_line_detection, 15, 15, 20, 20, borderType=cv2.BORDER_CONSTANT,
value=0)
Imask = image_filled
n = -1
obscle_list = []
obscle_list_last = []
for r in roi:
n += 1
blobs = Imask[r[0]:r[1], r[2]:r[3]]
_, cnts, hierarchy = cv2.findContours(blobs, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnt_large, _ = getAreaMaxContour(cnts, 200)
#####################################change 80 to 200##########################
if cnt_large is not None:
# print(area_max)
lefttop_x = r[2]
lefttop_y = r[0]
rightbottom_x = r[3]
rightbottom_y = r[1]
cv2.rectangle(image_copy, (lefttop_x, lefttop_y), (rightbottom_x, rightbottom_y), (0, 255, 255), 2)
# cv2.imshow("what",org_img)
obscle_list.append([n // 9, n % 9])
s_straight = [[5, 4], [4, 4], [5, 3], [5, 5]]
# s_straight = [[5, 4], [4, 4]]
s_left_edge = [[4, 0], [3, 0], [2, 0], [1, 0]]
s_right_edge = [[4, 8], [3, 8], [2, 8], [1, 8]]
s_left_1 = [[4, 1], [3, 1], [2, 1], [4, 2]]
s_left_2 = [[3, 2], [2, 2], [1, 3], [2, 3]]
s_right_1 = [[4, 7], [3, 7], [2, 7], [1, 5]]
s_right_2 = [[4, 6], [3, 6], [2, 6], [2, 5]]
s_left_middle_1 = [[4, 4], [4, 3], [5, 4], [5, 3]]
s_left_middle_2 = [[5, 2], [5, 3], [5, 4]]
s_right_middle_1 = [[4, 4], [4, 5], [5, 4], [5, 5]]
s_right_middle_2 = [[5, 4], [5, 5], [5, 6]]
if all(s not in obscle_list for s in s_straight):
straight = True
left = False
left2 = False
right = False
right2 = False
# elif all(s in obscle_list for s in s_edge_left) and any(s in obscle_list for s in s1_2):
elif all(s in obscle_list for s in s_right_middle_1) or all(s in obscle_list for s in s_right_middle_2):
straight = False
left = False
left2 = True
right = False
right2 = False
elif all(s in obscle_list for s in s_left_middle_1) or all(s in obscle_list for s in s_left_middle_2):
straight = False
left = False
left2 = False
right = False
right2 = True
# elif all(s in obscle_list for s in s_edge_right) and any(s in obscle_list for s in s1_3):
elif all(s not in obscle_list for s in s_right_1) or all(s not in obscle_list_last for s in s_right_2):
straight = False
left = True
left2 = False
right = False
right2 = False
elif all(s not in obscle_list for s in s_left_1) or all(s not in obscle_list_last for s in s_left_2):
straight = False
left = False
left2 = False
right = True
right2 = False
# elif all(s not in obscle_list for s in s4_1) and all(s not in obscle_list_last for s in s2_1) and any(
# s in obscle_list for s in s4_2):
# straight = False
# left = True
# left2 = False
# right = False
# right2 = False
# elif all(s not in obscle_list for s in s5_1) and all(s not in obscle_list_last for s in s3_1) and any(
# s in obscle_list for s in s5_2):
# straight = False
# left = False
# left2 = False
# right = True
# right2 = False
s1 = [[5, 4], [5, 3], [5, 2], [5, 1], [5, 5], [5, 6], [5, 7], [5, 0], [5, 8]]
# s1 = [[5, 2], [5, 1], [5, 6], [5, 7]]
s1_1 = [[5, 4], [5, 3], [5, 2], [5, 1], [5, 5], [5, 6], [5, 7]]
# s1_2 = [[5, 4], [5, 3], [5, 2], [5, 1], [5, 5], [5, 6], [5, 7], [5, 8]]
s1_3 = [[5, 4], [5, 3], [5, 2], [5, 1], [5, 5], [5, 6], [5, 7], [5, 0]]
s2_1 = [[5, 1], [5, 0], [5, 3], [5, 2]]
s2_2 = [[5, 4], [5, 5], [5, 6]]
s3_1 = [[5, 5], [5, 6], [5, 7], [5, 8]]
s3_2 = [[5, 4], [5, 3], [5, 2]]
s4_1 = [[5, 1], [5, 0], [5, 3], [5, 2], [5, 4], [5, 5], [5, 6]]
s4_2 = [[5, 7], [5, 8]]
s5_1 = [[5, 5], [5, 6], [5, 7], [5, 8], [5, 3], [5, 2], [5, 4]]
s5_2 = [[5, 1], [5, 0]]
s_edge_left = [[4, 0], [3, 0], [2, 0], [1, 0]]
s_edge_right = [[4, 8], [3, 8], [2, 8], [1, 8]]
s_adjust = [[4, 4], [4, 3], [4, 2], [4, 1], [4, 5], [4, 6], [4, 7], [4, 0], [4, 8]]
s_under = [[5, 4], [5, 3], [5, 2], [5, 1], [5, 5], [5, 6], [5, 7], [5, 0], [5, 8], [4, 4], [4, 3], [4, 2],
[4, 1], [4, 5], [4, 6], [4, 7], [4, 0], [3, 8], [3, 4], [3, 3], [3, 2], [3, 1], [3, 5], [3, 6],
[3, 7], [3, 0], [3, 8]]
cv2.putText(image_copy,
"straight:" + str(straight) + " left:" + str(left) + " right:" + str(right) + " left2:" + str(
left2) + " right2:" + str(right2),
(10, org_img.shape[0] - 35), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
cv2.putText(image_copy, "step:" + str(step),
(10, org_img.shape[0] - 55), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
# cv2.putText(OrgFrame,"finish:"+str(obscle_finish),(10,OrgFrame.shape[0]-75),cv2.FONT_HERSHEY_SIMPLEX,0.65,(0,0,255),2)
# cv2.imshow('frame', org_img)
# cv2.waitKey(3)
#########################正常避障部分 动作组换成move########################
isgo = False
count = 0
if step == 0:
if all(s not in obscle_list for s in s_under):
print("straight")
# SSR.change_action_value("239", 1) # 前进
count += 1
isgo = True
else:
if isgo:
pass
# SSR.change_action_value('157', 1)
isgo = False
step = 1
time.sleep(0.2)
elif step == 1:
if all(s not in obscle_list for s in s_under):
step = 0
elif (count >= 20) and any(s in obscle_list for s in s_adjust):
direction_only_angle(640, 480)
# PWMServo.setServo(1, 2090, 500)
# PWMServo.setServo(2, 1480, 500)
time.sleep(0.8)
count = 0
elif straight:
# SSR.change_action_value("go_forward", 1) # 前进
print("forward")
SSR.change_action_value("go_middle_change_3", 3) # 前进
time.sleep(1.5)
count += 1
elif left:
SSR.change_action_value("left_move_new_6", 1) # 左移
print("left")
time.sleep(1)
elif right:
SSR.change_action_value("right_move_new_2", 1) # 右移
print("right")
time.sleep(1)
elif left2 is True:
SSR.change_action_value("left_move_new_6", 1) # 左移
print("left*2")
time.sleep(1)
elif right2 is True:
SSR.change_action_value("right_move_new_2", 1) # 右移
print("right*2")
time.sleep(1)
else:
# SSR.change_action_value("1", 1) # 静止
print("stand_stand")
SSR.change_action_value("1", 1) # 右移
cv2.imshow("image_org", image_copy)
# cv2.imshow("image_filled", image_filled)
# cv2.waitKey(0)
cv2.imshow("image_line", image_line_detection)
# cv2.imshow("image1", result)
cv2.waitKey(3)
def go():
while True:
time1 = time.time()
cross_trap()
time2 = time.time()
print("spend", time2 - time1)
if end:
cv2.destroyAllWindows()
return
# lowThreshold = 0
# max_lowThreshold = 100
# ratio = 3
# kernel_size = 3
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv2.namedWindow('canny demo')
# cv2.createTrackbar('Min threshold', 'canny demo', lowThreshold, max_lowThreshold, CannyThreshold)
# CannyThreshold(0) # initialization
# if cv2.waitKey(0) == 27:
# cv2.destroyAllWindows()
# 第3关：地雷
roi = [(0, 80, 0, 70), (0, 80, 70, 140), (0, 80, 140, 210), (0, 80, 210, 240), (0, 80, 240, 400), (0, 80, 400, 430),
(0, 80, 430, 500),
(0, 80, 500, 570), (0, 80, 570, 640), (80, 160, 0, 70), (80, 160, 70, 140), (80, 160, 140, 210),
(80, 160, 210, 240),
(80, 160, 240, 400), (80, 160, 400, 430), (80, 160, 430, 500), (80, 160, 500, 570), (80, 160, 570, 640),
(160, 240, 0, 70),
(160, 240, 70, 140), (160, 240, 140, 210), (160, 240, 210, 240), (160, 240, 240, 400), (160, 240, 400, 430),
(160, 240, 430, 500),
(160, 240, 500, 570), (160, 240, 570, 640), (240, 320, 0, 70), (240, 320, 70, 140), (240, 320, 140, 210),
(240, 320, 210, 240),
(240, 350, 240, 400), (240, 320, 400, 430), (240, 320, 430, 500), (240, 320, 500, 570), (240, 320, 570, 640),
(320, 400, 0, 70), (320, 400, 70, 140), (320, 400, 140, 210), (320, 400, 210, 240), (350, 400, 240, 400),
(320, 400, 400, 430),
(320, 400, 430, 500), (320, 400, 500, 570), (320, 400, 570, 640), (400, 430, 0, 70), (400, 430, 70, 140),
(400, 430, 140, 210),
(400, 430, 210, 240), (400, 430, 240, 400), (400, 430, 400, 430), (400, 430, 430, 500), (400, 430, 500, 570),
(400, 430, 570, 640)]
# print(len(roi))
roi_mine = [(0, 80, 0, 70), (0, 80, 70, 140), (0, 80, 140, 210), (0, 80, 210, 280), (0, 80, 280, 360),
(0, 80, 360, 430),
(0, 80, 430, 500),
(0, 80, 500, 570), (0, 80, 570, 640), (80, 160, 0, 70), (80, 160, 70, 140), (80, 160, 140, 210),
(80, 160, 210, 280),
(80, 160, 280, 360), (80, 160, 360, 430), (80, 160, 430, 500), (80, 160, 500, 570), (80, 160, 570, 640),
(160, 240, 0, 70),
(160, 240, 70, 140), (160, 240, 140, 210), (160, 240, 210, 280), (160, 240, 280, 360), (160, 240, 360, 430),
(160, 240, 430, 500),
(160, 240, 500, 570), (160, 240, 570, 640), (240, 320, 0, 70), (240, 320, 70, 140), (240, 320, 140, 210),
(240, 320, 210, 280),
(240, 320, 280, 360), (240, 320, 360, 430), (240, 320, 430, 500), (240, 320, 500, 570),
(240, 320, 570, 640),
(320, 400, 0, 70), (320, 400, 70, 140), (320, 400, 140, 210), (320, 400, 210, 280), (320, 400, 280, 360),
(320, 400, 360, 430),
(320, 400, 430, 500), (320, 400, 500, 570), (320, 400, 570, 640), (400, 480, 0, 70), (400, 480, 70, 140),
(400, 480, 140, 210),
(400, 480, 210, 280), (400, 480, 280, 360), (400, 480, 360, 430), (400, 480, 430, 500),
(400, 480, 500, 570),
(400, 480, 570, 640)]
def get_angle(color):
global org_img, blue_persent
org_img_copy = copy.copy(org_img)
org_img_copy = cv2.resize(org_img_copy, (r_w, r_h), interpolation=cv2.INTER_CUBIC) # 将图片缩放
frame_gauss = cv2.GaussianBlur(org_img_copy, (3, 3), 0) # 高斯模糊
frame_hsv = cv2.cvtColor(frame_gauss, cv2.COLOR_BGR2Lab) # 将图片转换到HSV空间
frame_green = cv2.inRange(frame_hsv, lab_range[color][0], lab_range[color][1]) # 对原图像和掩模(颜色的字典)进行位运算
opened = cv2.morphologyEx(frame_green, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) # 开运算 去噪点
closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) # 闭运算 封闭连接
rgb_image = cv2.cvtColor(org_img_copy, cv2.COLOR_BGR2RGB)
#result = cv2.bitwise_and(rgb_image, rgb_image, mask=closed)
#cv2.imshow('closed', closed)
(image, contours, hierarchy) = cv2.findContours(closed, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE) # 找出轮廓cv2.CHAIN_APPROX_NONE
# image_filled = 255 * np.ones(closed.shape, np.uint8) # 用于识别色块的图像
areaMaxContour, area_max = getAreaMaxContour(contours, 25)
center = (1, 1)
if areaMaxContour is not None:
rect = cv2.minAreaRect(areaMaxContour)
center, w_h, angle = rect # 中心点 宽高 旋转角度
# box = np.int0(cv2.boxPoints(rect)) # 点的坐标
image_line_detection = np.zeros(closed.shape, np.uint8)
image_line_detection = cv2.drawContours(image_line_detection, areaMaxContour, -1, 255, 2)
lines = cv2.HoughLinesP(image_line_detection, 1, np.pi / 180, 30, 300, 5)
tangent_list = []
length_list = []
image_copy = copy.copy(org_img)
if lines is not None:
for line in lines:
x1, y1, x2, y2 = line[0]
if y1 < 410 and x2 - x1 != 0:
tangent_abs = np.abs((y2 - y1) / (x2 - x1))
if tangent_abs < 0.5:
length_list.append(np.abs(line[0, 0] - line[0, 2]))
tangent_list.append([x1, y1, x2, y2])
if len(tangent_list) > 0:
[x1_f, y1_f, x2_f, y2_f] = tangent_list[np.where(length_list == np.max(length_list))[0][0]]
tangent_used = -(y2_f - y1_f) / (x2_f - x1_f) #####标准tangent
else:
tangent_used = 0
[x1_f, y1_f, x2_f, y2_f] = [0, 0, 0, 0]
print('angle is: ', tangent_used)
blue_persent = round(area_max * 100 / (r_w * r_h), 2)
print('percent', blue_persent)
# cv2.line(image_copy, (x1_f, y1_f), (x2_f, y2_f), (0, 255, 0), 3)
# cv2.circle(image_copy, (int(center[0]), int(center[1])), 5, (255, 0, 0), 2)
# cv2.imshow("image_line", image_copy)
return tangent_used
def obscle():
global org_img, step, reset, move_count, forward_count, blue_persent
right_count = 0
left_count = 0
straight = False # 直行信号
left = False # 左移信号
left2 = False # 遇到右边缘且前方有障碍
right = False # 右移
right2 = False # 遇到左边缘且前方有障碍
SSR.change_action_value("turn_left", 1)
time.sleep(0.8)
if org_img is None:
print('No image')
time.sleep(5)
# frame = cv2.resize(OrgFrame, (r_w, r_h), interpolation=cv2.INTER_LINEAR)
while True:
# OrgFrame = copy.copy(org_img)
# cv2.imshow('init', OrgFrame)
# cv2.waitKey(3)3
tangent_used = get_angle('blue')
head_degree = 410 - forward_count * 3
if head_degree < 380:
head_degree = 380
if move_count % 6 == 0: # abled
judge = True
while judge:
print("head up")
time.sleep(0.5)
SSR.serial_setServo(20, head_degree, 1000)
time.sleep(1)
tangent_used = get_angle('blue')
# OrgFrame = org_img
# #print(org_img)
# #cv2.imshow('init', OrgFrame)
# hsv = cv2.cvtColor(OrgFrame, cv2.COLOR_BGR2HSV)
# hsv = cv2.GaussianBlur(hsv, (3, 3), 0)
# Imask = cv2.inRange(hsv, color_dict['black_obstacle']['Lower'], color_dict['black_obstacle']['Upper'])
# # Imask = cv2.erode(Imask, None, iterations=2)
# Imask = cv2.dilate(Imask, np.ones((3, 3), np.uint8), iterations=2)
# cv2.imshow('black', Imask)
#
# Imask2 = cv2.inRange(hsv, color_dict['blue']['Lower'], color_dict['blue']['Upper'])
# Imask2 = cv2.erode(Imask2, None, iterations=2)
# Imask2 = cv2.dilate(Imask2, np.ones((3, 3), np.uint8), iterations=2)
# _, cnts2, hierarchy2 = cv2.findContours(Imask2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cv2.imshow('blue', Imask2)