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ArmorDetector.py
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ArmorDetector.py
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# -*- coding: UTF-8 -*-
# 开发作者 ：lau
# 开发时间 ：2022/6/21 18:11
# 文件名称 ：vision_main.PY
# 开发工具 ：vsc
# 文件说明 ：此文件用于封装摄像头处理函数
import time
# 导入外部库
import numpy as np
import math
import cv2
# 导入内部库
import DataParam
import TrackKF_2D
class ArmorDetector:
def __init__(self):
self.img = None
self.binary = None
self.dst = None
self.center = None
self.IfFire = "N"
self.center_y = int(0)
self.center_x = int(0)
self.center_result = [0,0]
self.DataParam = DataParam.DataParameter() # 程序参数
self.kalman = TrackKF_2D.KalmanFilter() # 卡尔曼滤波器初始化
self.distance = 0
self.InfoPlate = np.zeros((640, 480))
def read_morphology(self,binary):
"""
对图片进行形态学处理
输入：二值化图片
输出：进行形态学处理后的二值化图片
"""
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, self.DataParam.OpenRect) # 去除多余噪声
dst = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 9)) # 提取垂直直线,去除横向噪声
# dst = cv2.morphologyEx(dst, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, self.DataParam.CloseRect) # 填充物体中的小洞
dst = cv2.morphologyEx(dst, cv2.MORPH_CLOSE, kernel)
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# dst = cv2.erode(dst, kernel)
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# dst = cv2.dilate(dst, kernel)
return dst
def img_process(self,img, colour):
"""
对图片进行通道过滤，二值化处理，形态学处理
输入：原图像，敌方队伍颜色
输出：处理后的二值化图像
"""
blue, green, red = cv2.split(img) # 分离通道，在opencv中图片的存储通道为BGR非RBG
if colour == 'blue':
input_colour = blue
elif colour == 'red':
input_colour = red
ret2, binary = cv2.threshold(input_colour, self.DataParam.ThresholdValue, 255, 0) # 二值化
dst = self.read_morphology(binary)
self.center_y = img.shape[0]/2
self.center_x = img.shape[1]/2
return binary, dst
def find_contours(self,binary, frame): # find contours and main screening section
'''寻找装甲板的轮廓
寻找距离和角度合适的灯条，然后寻找装甲板，对装甲板中心进行标定，并在原图中绘出。
参数：binary：形态学处理过后的图片 frame：原始图像
返回值： None
'''
contours, heriachy = cv2.findContours(
binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 首先找到处理后图像的所有轮廓
length = len(contours)
data_list = []
first_data = []
second_data1 = []
second_data2 = []
self.InfoPlate = np.zeros((640, 480))
c = 0
d = 0
if length > 0:
print("---founding---")
for i, contour in enumerate(contours):
data_dict = dict()
# print("countour", contour)
area = cv2.contourArea(contour)
rect = cv2.minAreaRect(contour) # 将轮廓转化为矩形，获取特征
rx, ry = rect[0] # 中心坐标
# cv2.circle(frame, (int(rx),int(ry)), 2, (0, 0, 255), -1)
rw = rect[1][0]
rh = rect[1][1] # 宽高
if rw == 0:
rw = 1
if rh == 0:
rh = 1
z = rect[2]
coor = cv2.boxPoints(rect)
x1 = coor[0][0]
y1 = coor[0][1]
x2 = coor[1][0]
y2 = coor[1][1]
x3 = coor[2][0]
y3 = coor[2][1]
x4 = coor[3][0]
y4 = coor[3][1] # 矩形4个点的坐标
# if i >= 1:
data_dict["area"] = area
data_dict["rx"] = rx
data_dict["ry"] = ry
data_dict["rh"] = rh
data_dict["rw"] = rw
data_dict["z"] = z
data_dict["x1"] = x1
data_dict["y1"] = y1
data_dict["x2"] = x2
data_dict["y2"] = y2
data_dict["x3"] = x3
data_dict["y3"] = y3
data_dict["x4"] = x4
data_dict["y4"] = y4 # 用字典存入矩形的各个信息
data_dict["coor"] = coor
data_list.append(data_dict)
# =============== 装甲板灯条筛选==================================================
for i in range(len(data_list)):
data_rh = data_list[i].get("rh", 0)
data_rw = data_list[i].get("rw", 0)
data_area = data_list[i].get("area", 0)
data_angle = data_list[i].get("z", 0)
# print(data_rh,data_rw,data_area,data_angle)
# time.sleep(3)
light_conditions1 = (float(data_rw / data_rh) >= self.DataParam.LgRatio) \
and (data_area >= self.DataParam.LgArea) \
and (abs(abs(data_angle) - 90) < self.DataParam.LgAngle)
light_conditions2 = (float(data_rh / data_rw) >= self.DataParam.LgRatio) \
and (data_area >= self.DataParam.LgArea) \
and (abs(abs(data_angle) - 0) < self.DataParam.LgAngle)
"""
灯条筛选条件：
1.长边/短边（实测大概在7.5左右）
2.灯条面积，一般用于筛选过远装甲板
3.灯条与垂直方向的角度
"""
if light_conditions1:
first_data.append(data_list[i])
cv2.line(frame, (int(data_list[i].get("x1", 0)), int(data_list[i].get("y1", 0))),
(int(data_list[i].get("x3", 0)), int(data_list[i].get("y3", 0))), (0, 255, 0), 2, 1)
elif light_conditions2: # 通过矩阵的形状大小角度等进行判断，删除不合理的矩阵
data_list[i].update(rh=data_rw)
data_list[i].update(rw=data_rh)
first_data.append(data_list[i])
cv2.line(frame, (int(data_list[i].get("x1", 0)), int(data_list[i].get("y1", 0))),
(int(data_list[i].get("x3", 0)), int(data_list[i].get("y3", 0))), (0, 255, 0), 2, 1)
else:
# print("装甲板灯条检测不通过")
pass
# =============== 装甲板灯条筛选==================================================
# print("装甲板数量"+str(len(first_data)))
for i in range(len(first_data)):
# cv.circle(frame, (int(first_data[i].get("rx")), int(first_data[i].get("ry"))), 5, (0, 0, 255), -1)
# cv.circle(frame, (int(first_data[i]["x1"]), int(first_data[i]["y1"])), 2, (0, 0, 255), -1)
# cv.circle(frame, (int(first_data[i]["x2"]), int(first_data[i]["y2"])), 2, (0, 0, 255), -1)
# cv.circle(frame, (int(first_data[i]["x3"]), int(first_data[i]["y3"])), 2, (0, 0, 255), -1)
# cv.circle(frame, (int(first_data[i]["x4"]), int(first_data[i]["y4"])), 2, (0, 0, 255), -1)
c = i + 1
while c < len(first_data):
# 判断两个矩形是否平行，如果平行则说明很可能是装甲板两边的灯条
data_ryi = float(first_data[i].get("ry", 0))
data_ryc = float(first_data[c].get("ry", 0))
data_rhi = float(first_data[i].get("rh", 0))
data_rhc = float(first_data[c].get("rh", 0))
data_rxi = float(first_data[i].get("rx", 0))
data_rxc = float(first_data[c].get("rx", 0))
data_rwi = float(first_data[i].get("rw", 0)) # i装甲板-长边
data_rwc = float(first_data[c].get("rw", 0)) # c装甲板-长边
data_rai = float(first_data[i].get("z", 0)) # angle-i
data_rac = float(first_data[c].get("z", 0)) # angle-c
# print("------")
# print(data_rai)
# rint("------")
print("装甲板间距:" + str(abs(data_rxi - data_rxc)))
print("灯条长度:" + str((data_rwi + data_rwc) / 2))
if (abs(data_ryi - data_ryc) <= self.DataParam.highRshort * ((data_rhi + data_rhc) / 2)) \
and ((abs(data_rxi - data_rxc) <= self.DataParam.XRlongMaxB * ((data_rwi + data_rwc) / 2)) and (abs(data_rxi - data_rxc) >= self.DataParam.XRlongMinB * ((data_rwi + data_rwc) / 2))
or (abs(data_rxi - data_rxc) <= self.DataParam.XRlongMaxS * ((data_rwi + data_rwc) / 2)) and (abs(data_rxi - data_rxc) >= self.DataParam.XRlongMinS * ((data_rwi + data_rwc) / 2))) \
and (abs(data_rhi - data_rhc) <= self.DataParam.DiffRMaxS * max(data_rhi, data_rhc)) \
and (abs(data_rwi - data_rwc) <= self.DataParam.DiffRMaxL * max(data_rwi, data_rwc)) \
and ((abs(data_rai - data_rac) <= self.DataParam.DiffAngle) or (abs((abs(data_rai - data_rac) - 90)) <= self.DataParam.DiffAngle)):
'''
y1-y2 （两个灯条的高度差） <= h1，h2均值（灯条短边）
x1-x2 （两个灯条的间距） <= w1，w2均值（灯条长边） big/small amor experience:big=38/169
x1-x2 （两个灯条的间距） <= w1，w2均值（灯条长边） big/small amor
h1-h2 (两个灯条的短边之差) <= 4（h1,h2的最大值）两个灯条的短边最大值
w1-w2 (两个灯条的长边之差) <= 0.5（w1,w2的最大值）两个灯条的长边最大值
ra1-ra2 (两个灯条的角度差) <=6 or(两个灯条距离90度的角度差--主要为了防止0度和90度跳变)
'''
second_data1.append(first_data[i])
second_data2.append(first_data[c]) # 将平行的矩形成对存入
# print("装甲板矩形测试通过")
c = c + 1
for i in range(len(second_data2)):
cv2.circle(frame, (int(second_data2[i]["rx"]), int(second_data2[i]["ry"])), 5, (0, 0, 255), -1)
cv2.circle(frame, (int(second_data2[i]["x1"]), int(second_data2[i]["y1"])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(second_data2[i]["x2"]), int(second_data2[i]["y2"])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(second_data2[i]["x3"]), int(second_data2[i]["y3"])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(second_data2[i]["x4"]), int(second_data2[i]["y4"])), 2, (0, 0, 255), -1)
dataList_c = []
dataList_d = [] # 距离
if len(second_data1): # second_data1中储存了所有可能的的装甲板
dataRange = []
dataList_c.clear() # 用于储存筛选后的装甲板位置中心
for i in range(len(second_data1)):
rectangle_x1 = int(second_data1[i]["x1"])
rectangle_y1 = int(second_data1[i]["y1"])
rectangle_x2 = int(second_data2[i]["x3"])
rectangle_y2 = int(second_data2[i]["y3"])
if abs(rectangle_y1 - rectangle_y2) <= self.DataParam.HeightRWidth * (abs(rectangle_x1 - rectangle_x2)):
# 判断所认为的装甲板高宽比
# TODO: 可能需要删掉
# global point1_1x, point1_1y, point1_2x, point1_2y, point1_3x, point1_3y, point1_4x, point1_4y
# global point2_1x, point2_1y, point2_2x, point2_2y, point2_3x, point2_3y, point2_4x, point2_4y
point1_1x = second_data1[i]["x1"]
point1_1y = second_data1[i]["y1"]
point1_2x = second_data1[i]["x2"]
point1_2y = second_data1[i]["y2"]
point1_3x = second_data1[i]["x3"]
point1_3y = second_data1[i]["y3"]
point1_4x = second_data1[i]["x4"]
point1_4y = second_data1[i]["y4"]
point2_1x = second_data2[i]["x1"]
point2_1y = second_data2[i]["y1"]
point2_2x = second_data2[i]["x2"]
point2_2y = second_data2[i]["y2"]
point2_3x = second_data2[i]["x3"]
point2_3y = second_data2[i]["y3"]
point2_4x = second_data2[i]["x4"]
point2_4y = second_data2[i]["y4"]
midpoint1, midpoint2 = self.Rectangle2Light((point1_1x,point1_1y), (point1_2x,point1_2y), (point1_3x,point1_3y), (point1_4x,point1_4y))
midpoint3, midpoint4 = self.Rectangle2Light((point2_1x,point2_1y), (point2_2x,point2_2y), (point2_3x,point2_3y), (point2_4x,point2_4y))
print(midpoint1, midpoint2)
cv2.line(frame, midpoint1, midpoint2, (0, 225, 100), 2, 1)
cv2.line(frame, midpoint3, midpoint4, (0, 225, 100), 2, 1)
self.plot_armor(frame, second_data1[i]["coor"], second_data2[i]["coor"])
cv2.putText(frame, "target1:", (rectangle_x2, rectangle_y2 - 5), cv2.FONT_HERSHEY_SIMPLEX,
0.5, [255, 255, 255])
# center = (int((point2_2x + point1_4x) / 2),
# int((point2_2y + point1_4y) / 2)) # 在装甲板中心标点。
center = (int((
point1_1x + point1_2x + point1_3x + point1_4x + point2_1x + point2_2x + point2_3x + point2_4x) / 8),
int((
point1_1y + point1_2y + point1_3y + point1_4y + point2_1y + point2_2y + point2_3y + point2_4y) / 8))
# print("中心像素坐标"+str(center))
cv2.circle(frame, center, 5, (0, 0, 255), -1) # 画出重心
cv2.circle(frame, (int(self.center_x), int(self.center_y)), 3, (0, 0, 255), -1) # 画出图像中心
dataList_c.append(center)
"""
这里要加灯条宽度======================这里距离不准确=========================
"""
print("面积", (second_data2[i]["rh"]*second_data2[i]["rw"]+second_data1[i]["rh"]*second_data1[i]["rw"])) # 取当前装甲板的灯条的面积作为判别距离的标准
# distance = (1 / (second_data2[i]["rh"]*second_data2[i]["rw"]+second_data1[i]["rh"]*second_data1[i]["rw"])) * 20000 / 80
s=(second_data2[i]["rh"]*second_data2[i]["rw"]+second_data1[i]["rh"]*second_data1[i]["rw"])
distance = 18.73*(s**(-0.3572))
print("距离", distance)
self.distance = round(distance, 2)
distance_rise = self.distance * self.DataParam.Distance_K
print(distance_rise)
if distance_rise >= self.DataParam.RiseValue:
distance_rise = self.DataParam.RiseValue
cv2.putText(frame, 'distance is' + str(distance) + " m",
(int((point2_2x + point1_4x) / 2), int((point2_2y + point1_4y) / 2) + 70),
cv2.FONT_HERSHEY_SIMPLEX,
1.5, [255, 255, 255])
# -============================= 这里距离不准确 ================================
dataList_d.append(distance_rise)
high = (rectangle_y1, rectangle_y2)
dataRange.append(high)
else: # 未通过装甲板检验的装甲板
center = '---not find---'
print(center)
cv2.circle(frame, (self.center_x, self.center_y), 3, (0, 255, 0), -1) # 画出图像中心
if not (dataList_c == []): # 最优装甲板选取与画图
center_list = []
center_list = dataList_c
best_point, best_point_rise = self.find_best_armor(center_list, dataList_d)
center = best_point
cv2.circle(frame, center, 20, (244, 244, 120), -1) # 未枪口抬升的装甲板中心
cv2.putText(frame, 'best armor point is' + str(best_point), best_point, cv2.FONT_HERSHEY_SIMPLEX,
1.5, [255, 255, 255])
# center = (center[0],int(center[1]-5)) # no rise
center = (center[0], int(center[1] - best_point_rise)) # bullet rise
cv2.putText(self.InfoPlate, str(len(dataList_c))+" armors was found", (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[255, 255, 255])
cv2.putText(self.InfoPlate, "distance : " + str(self.distance), (10, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[255, 255, 255])
cv2.putText(self.InfoPlate, "position of armor : " + str(self.center), (10, 100),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[255, 255, 255])
cv2.putText(self.InfoPlate, "Final position : " + str(self.center_result), (10, 140),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[255, 255, 255])
else: # 图像中未发现可能装甲板的情况
center = '---not find---'
print(center)
cv2.circle(frame, (int(self.center_x), int(self.center_y)), 3, (0, 255, 0), -1) # 画出图像中心
cv2.putText(self.InfoPlate, "No armor was found", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, [255, 255, 255])
dataList_c.clear()
data_list.clear()
return center
def find_best_armor(self,center_list, rise_diatance):
distance_list = []
for ii in range(len(center_list)):
distance_x = abs(center_list[ii][0] - self.center_x)
distance_y = abs(center_list[ii][1] - self.center_y)
distance_list.append(distance_x + distance_y)
best_ii = distance_list.index(min(distance_list))
best_point = center_list[best_ii]
best_point_rise = rise_diatance[best_ii]
return best_point, best_point_rise
def plot_armor(self,frame, coor0, coor1):
point = [coor0[0], coor0[1], coor0[2], coor0[3], coor1[0], coor1[1], coor1[2], coor1[3]]
for point_i in point:
for point_j in point:
cv2.line(frame, (int(point_i[0]),int(point_i[1])), (int(point_j[0]),int(point_j[1])), (0, 255, 0), 1, 1)
def ArmorDetectTask(self, img, enemycolor):
self.img = img
self.binary, self.dst = self.img_process(self.img, enemycolor)
self.center = self.find_contours(self.dst, self.img) # 寻找装甲板 返回最优的中心装甲板坐标
if not self.center == '---not find---':
delta_x, delta_y = self.kalman.track(self.center[0], self.center[1])
# 画出图像中心画出卡尔曼预测后的装甲板中心
cv2.circle(self.img, (int((delta_x) * self.DataParam.kalmanKP + self.center[0]),
int((delta_y) * self.DataParam.kalmanKP + self.center[1])),
20, (120, 120, 0),-1)
if self.DataParam.IfKalman:
self.center_result[0] = int(delta_x)*self.DataParam.kalmanKP + self.center[0]
self.center_result[1] = int(delta_y)*self.DataParam.kalmanKP + self.center[1]
else:
self.center_result[0] = self.center[0]
self.center_result[1] = self.center[1]
x_distance = self.center[0] - self.center_x
y_distance = -(self.center[1] - self.center_y)
self.distance = (x_distance, y_distance)
if math.sqrt(self.distance[0]**2 + self.distance[1]**2) <= self.DataParam.FireDistance:
self.IfFire = "F"
print("开火F")
cv2.putText(self.InfoPlate, "Fire or not : " + str(self.IfFire), (10, 180),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[255, 255, 255])
else:
self.IfFire = "N"
print("熄火N")
if self.center == '---not find---':
self.center_result[0] = self.center_x
self.center_result[1] = self.center_y
self.IfFire = "N"
print("熄火N")
def Rectangle2Light(self,Recpoint1,Recpoint2,Recpoint3,Recpoint4):
"""
此函数用来将灯条的四个点转换成长边的两点
输入：矩形的四个点的坐标（x，y）
输出：穿过矩形短边的长边两点
"""
def calculate_midpoint(point1, point2):
# 计算两点之间的中点
x1, y1 = point1
x2, y2 = point2
midpoint = (int((x1 + x2) / 2), int((y1 + y2) / 2))
return midpoint
# 计算边的长度
edge1_length = self.calculate_distance(Recpoint1, Recpoint2)
edge2_length = self.calculate_distance(Recpoint2, Recpoint3)
edge3_length = self.calculate_distance(Recpoint3, Recpoint4)
edge4_length = self.calculate_distance(Recpoint4, Recpoint1)
# 找到两条最短的边
shortest_edges = sorted([(edge1_length, Recpoint1, Recpoint2),
(edge2_length, Recpoint2, Recpoint3),
(edge3_length, Recpoint3, Recpoint4),
(edge4_length, Recpoint4, Recpoint1)])
# 找到两条最短边的中点
midpoint1 = calculate_midpoint(shortest_edges[0][1], shortest_edges[0][2])
midpoint2 = calculate_midpoint(shortest_edges[1][1], shortest_edges[1][2])
return midpoint1, midpoint2
def calculate_distance(self, point1, point2):
# 计算两点之间的距离（欧几里德距离）
x1, y1 = point1
x2, y2 = point2
distance = ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
return distance