119 lines
5.7 KiB
Python
119 lines
5.7 KiB
Python
|
from loguru import logger
|
|||
|
|
import cv2, time
|
|||
|
|
import numpy as np
|
|||
|
|
|
|||
|
|
from DrGraph.util.drHelper import *
|
|||
|
|
from .Bussiness import BussinessBase
|
|||
|
|
|
|||
|
|
class Bussiness_IllParking(BussinessBase):
|
|||
|
|
def __init__(self, opt):
|
|||
|
|
logger.info("create AlAlg_IllParking")
|
|||
|
|
super().__init__(opt)
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def postProcess(pred, cvMask, pars):
|
|||
|
|
#pred:直接预测结果，不要原图。预测结果[0,1,2,...],不是[车、T角点，L角点]
|
|||
|
|
#mask_cv:分割结果图，numpy格式(H,W),结果是int，[0,1,2,...]
|
|||
|
|
#pars: 其它参数,dict格式
|
|||
|
|
'''三个标签：车、T角点，L角点'''
|
|||
|
|
'''输入：落水人员的结果（类别+坐标）、原图
|
|||
|
|
|
|||
|
|
过程：将车辆识别框外扩，并按contours形成区域。
|
|||
|
|
T角点与L角点的坐标合并为列表。
|
|||
|
|
判断每个车辆contours区域内有几个角点，少于2个则判断违停。
|
|||
|
|
返回：最终违停车辆标记结果图、违停车辆信息（坐标、类别、置信度）。
|
|||
|
|
'''
|
|||
|
|
#输入的是[cls,x0,y0,x1,y1,score]---> [x0,y0,x1,y1,cls,score]
|
|||
|
|
#输出的也是[cls,x0,y0,x1,y1,score]
|
|||
|
|
#pred = [ [ int(x[4]) ,*x[1:5], x[5] ] for x in pred]
|
|||
|
|
|
|||
|
|
#pred = [[ *x[1:5],x[0], x[5] ] for x in pred]
|
|||
|
|
pred = [[ *x[0:4],x[5], x[4] ] for x in pred]
|
|||
|
|
|
|||
|
|
##统一格式
|
|||
|
|
imgSize=pars['imgSize']
|
|||
|
|
'''1、pred中车辆识别框形成列表，T角点与L角点形成列表'''
|
|||
|
|
tW1=time.time()
|
|||
|
|
init_vehicle=[]
|
|||
|
|
init_corner = []
|
|||
|
|
|
|||
|
|
for i in range(len(pred)):
|
|||
|
|
#if pred[i][4]=='TCorner' or pred[i][4]=='LCorner': #vehicle、TCorner、LCorner
|
|||
|
|
if pred[i][4]==1 or pred[i][4]==2: #vehicle、TCorner、LCorner
|
|||
|
|
init_corner.append(pred[i])
|
|||
|
|
else:
|
|||
|
|
init_vehicle.append(pred[i])
|
|||
|
|
|
|||
|
|
'''2、init_corner中心点坐标计算，并形成列表。'''
|
|||
|
|
tW2 = time.time()
|
|||
|
|
center_corner=[]
|
|||
|
|
for i in range(len(init_corner)):
|
|||
|
|
center_corner.append(mathHelper.center_coordinate(init_corner[i]))
|
|||
|
|
|
|||
|
|
|
|||
|
|
'''3、遍历每个车辆识别框，扩充矩形区域，将矩形区域形成contours，判断扩充区域内的。'''
|
|||
|
|
tW3 = time.time()
|
|||
|
|
final_weiting=[] #违停车辆列表
|
|||
|
|
'''遍历车辆列表，扩大矩形框形成contours'''
|
|||
|
|
for i in range(len(init_vehicle)):
|
|||
|
|
boundbxs1=[init_vehicle[i][0],init_vehicle[i][1],init_vehicle[i][2],init_vehicle[i][3]]
|
|||
|
|
width_boundingbox=init_vehicle[i][2]-init_vehicle[i][0] #框宽度
|
|||
|
|
height_boundingbox=init_vehicle[i][2] - init_vehicle[i][0] #框长度
|
|||
|
|
#当框长大于宽，则是水平方向车辆；否则认为是竖向车辆
|
|||
|
|
if width_boundingbox>=height_boundingbox:
|
|||
|
|
ex_width=0.4*(init_vehicle[i][2]-init_vehicle[i][0]) #矩形扩充宽度，取车宽0.4倍 #膨胀系数小一些。角点设成1个。
|
|||
|
|
ex_height=0.2*(init_vehicle[i][2]-init_vehicle[i][0]) #矩形扩充宽度，取车长0.2倍
|
|||
|
|
boundbxs1 = imgHelper.expand_rectangle(boundbxs1, imgSize, ex_width, ex_height) # 扩充后矩形对角坐标
|
|||
|
|
else:
|
|||
|
|
ex_width=0.2*(init_vehicle[i][2]-init_vehicle[i][0]) #竖向，不需要改变变量名称，将系数对换下就行。（坐标点顺序还是1234不变）
|
|||
|
|
ex_height=0.4*(init_vehicle[i][2]-init_vehicle[i][0]) #
|
|||
|
|
boundbxs1 = imgHelper.expand_rectangle(boundbxs1, imgSize, ex_width, ex_height) # 扩充后矩形对角坐标
|
|||
|
|
contour_temp = mathHelper.fourcorner_coordinate(boundbxs1) #得到扩充后矩形框的contour
|
|||
|
|
contour_temp_=np.array(contour_temp)#contour转为array
|
|||
|
|
contour_temp_=np.float32(contour_temp_)
|
|||
|
|
|
|||
|
|
'''遍历角点识别框中心坐标是否在contours内，在则计1'''
|
|||
|
|
zzz=0
|
|||
|
|
for j in range(len(center_corner)):
|
|||
|
|
flag = cv2.pointPolygonTest(contour_temp_, (center_corner[j][0], center_corner[j][1]), False) #若为False，会找点是否在内，外，或轮廓上(相应返回+1, -1, 0)。
|
|||
|
|
if flag==+1:
|
|||
|
|
zzz+=1
|
|||
|
|
'''contours框内小于等于1个角点，认为不在停车位内'''
|
|||
|
|
# if zzz<=1:
|
|||
|
|
if zzz<1:
|
|||
|
|
final_weiting.append(init_vehicle[i])
|
|||
|
|
#print('t7-t6',t7-t6)
|
|||
|
|
#print('final_weiting',final_weiting)
|
|||
|
|
|
|||
|
|
'''4、绘制保存检违停车辆图像'''
|
|||
|
|
|
|||
|
|
tW4=time.time()
|
|||
|
|
'''
|
|||
|
|
colors = Colors()
|
|||
|
|
if final_weiting is not None:
|
|||
|
|
for i in range(len(final_weiting)):
|
|||
|
|
lbl='illegal park'
|
|||
|
|
xyxy=[final_weiting[i][0],final_weiting[i][1],final_weiting[i][2],final_weiting[i][3]]
|
|||
|
|
c = int(5)
|
|||
|
|
plot_one_box(xyxy, _img_cv, label=lbl, color=colors(c, True), line_thickness=3)
|
|||
|
|
final_img=_img_cv
|
|||
|
|
'''
|
|||
|
|
tW5=time.time()
|
|||
|
|
# cv2.imwrite('final_result.png', _img_cv)
|
|||
|
|
|
|||
|
|
|
|||
|
|
timeStr = ' step1:%s step2:%s step3:%s save:%s'%(\
|
|||
|
|
timeHelper.deltaTimeString_MS(tW2,tW1), \
|
|||
|
|
timeHelper.deltaTimeString_MS(tW3,tW2), \
|
|||
|
|
timeHelper.deltaTimeString_MS(tW4,tW3), \
|
|||
|
|
timeHelper.deltaTimeString_MS(tW5,tW4) )
|
|||
|
|
|
|||
|
|
#final_weiting-----[x0,y0,x1,y1,cls,score]
|
|||
|
|
#输出的也是outRe----[cls,x0,y0,x1,y1,score]
|
|||
|
|
|
|||
|
|
#outRes = [ [ 3 ,*x[0:4], x[5] ] for x in final_weiting]###违停用3表示
|
|||
|
|
|
|||
|
|
outRes = [ [ *x[0:4], x[5],3 ] for x in final_weiting]###违停用3表示
|
|||
|
|
|
|||
|
|
return outRes,timeStr #返回最终绘制的结果图、违停车辆（坐标、类别、置信度）
|
|||
|
|
|