276 lines
9.9 KiB
Python
276 lines
9.9 KiB
Python
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'''
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这个版本增加了船舶过滤功能
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'''
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import time
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import numpy as np
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import cv2
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def center_coordinate(boundbxs):
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'''
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输入：两个对角坐标xyxy
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输出：矩形框重点坐标xy
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'''
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boundbxs_x1=boundbxs[0]
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boundbxs_y1=boundbxs[1]
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boundbxs_x2=boundbxs[2]
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boundbxs_y2=boundbxs[3]
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center_x=0.5*(boundbxs_x1+boundbxs_x2)
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center_y=0.5*(boundbxs_y1+boundbxs_y2)
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return center_x,center_y
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def fourcorner_coordinate(boundbxs):
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'''
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输入：两个对角坐标xyxy
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输出：矩形框四个角点坐标，以contours顺序。
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'''
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boundbxs_x1=boundbxs[0]
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boundbxs_y1=boundbxs[1]
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boundbxs_x2=boundbxs[2]
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boundbxs_y2=boundbxs[3]
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wid=boundbxs_x2-boundbxs_x1
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hei=boundbxs_y2-boundbxs_y1
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boundbxs_x3=boundbxs_x1+wid
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boundbxs_y3=boundbxs_y1
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boundbxs_x4=boundbxs_x1
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boundbxs_y4 = boundbxs_y1+hei
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contours_rec=[[boundbxs_x1,boundbxs_y1],[boundbxs_x3,boundbxs_y3],[boundbxs_x2,boundbxs_y2],[boundbxs_x4,boundbxs_y4]]
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return contours_rec
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def expand_rectangle(rec,imgSize,ex_width,ex_height):
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'''
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矩形框外扩，且不超过图像范围
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输入：矩形框xyxy（左上和右下坐标），图像，外扩宽度大小，外扩高度大小
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输出：扩后的矩形框坐标xyxy
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'''
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#img_height=img.shape[0];img_width=img.shape[1]
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img_width,img_height = imgSize[0:2]
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#print('高、宽',img_height,img_width)
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x1=rec[0]
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y1=rec[1]
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x3=rec[2]
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y3=rec[3]
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x1=x1-ex_width if x1-ex_width >= 0 else 0
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y1=y1-ex_height if y1-ex_height >= 0 else 0
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x3=x3+ex_width if x3+ex_width <= img_width else img_width
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y3=y3+ex_height if y3+ex_height <=img_height else img_height
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xyxy=[x1,y1,x3,y3]
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return xyxy
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def remove_simivalue(list1,list2):
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'''
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将list1中属于list2的元素都删除。
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输入：两个嵌套列表
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返回：嵌套列表
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'''
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list33=list1.copy()
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for i in range(len(list1)):
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for j in range(len(list2)):
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if list2[j] == list1[i]:
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# list33.pop(list1[i])
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list33.remove(list1[i])
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return list33
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def remove_sameeleme_inalist(list3):
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'''
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将list3中重复嵌套列表元素删除。
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输入：嵌套列表
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返回：嵌套列表
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'''
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list3=list3
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list4=[]
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list4.append(list3[0])
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for dict in list3:
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k=0
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for item in list4:
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if dict!=item:
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k=k+1
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else:
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break
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if k==len(list4):
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list4.append(dict)
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return list4
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def order_points(pts):
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''' sort rectangle points by clockwise '''
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sort_x = pts[np.argsort(pts[:, 0]), :]
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Left = sort_x[:2, :]
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Right = sort_x[2:, :]
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# Left sort
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Left = Left[np.argsort(Left[:, 1])[::-1], :]
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# Right sort
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Right = Right[np.argsort(Right[:, 1]), :]
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return np.concatenate((Left, Right), axis=0)
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def ms(t2,t1):
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return '%.1f' %( (t2-t1)*1000.0)
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def illParking_postprocess(pred,cvMask,pars):
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#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]
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#mask_cv:分割结果图，numpy格式(H,W),结果是int，[0,1,2,...]
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#pars: 其它参数,dict格式
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'''三个标签：车、T角点，L角点'''
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'''输入：落水人员的结果（类别+坐标）、原图
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过程：将车辆识别框外扩，并按contours形成区域。
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T角点与L角点的坐标合并为列表。
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判断每个车辆contours区域内有几个角点，少于2个则判断违停。
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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）。
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'''
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#输入的是[cls,x0,y0,x1,y1,score]---> [x0,y0,x1,y1,cls,score]
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#输出的也是[cls,x0,y0,x1,y1,score]
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#pred = [ [ int(x[4]) ,*x[1:5], x[5] ] for x in pred]
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#pred = [[ *x[1:5],x[0], x[5] ] for x in pred]
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pred = [[ *x[0:4],x[5], x[4] ] for x in pred]
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##统一格式
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imgSize=pars['imgSize']
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'''1、pred中车辆识别框形成列表，T角点与L角点形成列表'''
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tW1=time.time()
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init_vehicle=[]
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init_corner = []
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for i in range(len(pred)):
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#if pred[i][4]=='TCorner' or pred[i][4]=='LCorner': #vehicle、TCorner、LCorner
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if pred[i][4]==1 or pred[i][4]==2: #vehicle、TCorner、LCorner
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init_corner.append(pred[i])
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else:
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init_vehicle.append(pred[i])
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'''2、init_corner中心点坐标计算，并形成列表。'''
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tW2 = time.time()
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center_corner=[]
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for i in range(len(init_corner)):
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center_corner.append(center_coordinate(init_corner[i]))
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'''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。'''
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tW3 = time.time()
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final_weiting=[] #违停车辆列表
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'''遍历车辆列表，扩大矩形框形成contours'''
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for i in range(len(init_vehicle)):
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boundbxs1=[init_vehicle[i][0],init_vehicle[i][1],init_vehicle[i][2],init_vehicle[i][3]]
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width_boundingbox=init_vehicle[i][2]-init_vehicle[i][0] #框宽度
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height_boundingbox=init_vehicle[i][2] - init_vehicle[i][0] #框长度
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#当框长大于宽，则是水平方向车辆；否则认为是竖向车辆
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if width_boundingbox>=height_boundingbox:
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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。
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ex_height=0.2*(init_vehicle[i][2]-init_vehicle[i][0]) #矩形扩充宽度，取车长0.2倍
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boundbxs1 = expand_rectangle(boundbxs1, imgSize, ex_width, ex_height) # 扩充后矩形对角坐标
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else:
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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）
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ex_height=0.4*(init_vehicle[i][2]-init_vehicle[i][0]) #
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boundbxs1 = expand_rectangle(boundbxs1, imgSize, ex_width, ex_height) # 扩充后矩形对角坐标
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contour_temp=fourcorner_coordinate(boundbxs1) #得到扩充后矩形框的contour
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contour_temp_=np.array(contour_temp)#contour转为array
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contour_temp_=np.float32(contour_temp_)
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'''遍历角点识别框中心坐标是否在contours内，在则计1'''
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zzz=0
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for j in range(len(center_corner)):
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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)。
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if flag==+1:
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zzz+=1
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'''contours框内小于等于1个角点，认为不在停车位内'''
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# if zzz<=1:
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if zzz<1:
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final_weiting.append(init_vehicle[i])
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#print('t7-t6',t7-t6)
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#print('final_weiting',final_weiting)
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'''4、绘制保存检违停车辆图像'''
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tW4=time.time()
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'''
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colors = Colors()
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if final_weiting is not None:
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for i in range(len(final_weiting)):
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lbl='illegal park'
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xyxy=[final_weiting[i][0],final_weiting[i][1],final_weiting[i][2],final_weiting[i][3]]
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c = int(5)
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plot_one_box(xyxy, _img_cv, label=lbl, color=colors(c, True), line_thickness=3)
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final_img=_img_cv
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'''
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tW5=time.time()
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# cv2.imwrite('final_result.png', _img_cv)
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timeStr = ' step1:%s step2:%s step3:%s save:%s'%( ms(tW2,tW1), ms(tW3,tW2),ms(tW4,tW3), ms(tW5,tW4) )
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#final_weiting-----[x0,y0,x1,y1,cls,score]
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#输出的也是outRe----[cls,x0,y0,x1,y1,score]
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#outRes = [ [ 3 ,*x[0:4], x[5] ] for x in final_weiting]###违停用3表示
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outRes = [ [ *x[0:4], x[5],3 ] for x in final_weiting]###违停用3表示
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return outRes,timeStr #返回最终绘制的结果图、违停车辆（坐标、类别、置信度）
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def illParking_postprocess_N(predList,pars):
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pred=predList[0]
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cvMask=None
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return illParking_postprocess(pred,cvMask,pars)
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def AI_process(model, args1,path1):
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'''对原图进行目标检测'''
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'''输入：检测模型、配置参数、路径
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返回：返回目标检测结果、原图像，
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'''
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'''检测图片'''
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t3=time.time()
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_img_cv = cv2.imread(path1) # 将这里的送入yolov5
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t4 = time.time()
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pred = model.detect(_img_cv) # 检测结果
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t5 = time.time()
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#print('t5-t4', t5-t4)
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#print('t4-t3', t4-t3)
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return pred, _img_cv #返回目标检测结果、原图像
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def main():
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'''配置参数'''
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args1={'cuda':'0','input_dir':'input_dir','output_dir':'output_dir'}
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dete_weights='weights/weiting20230727.pt'
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'''分割模型权重路径'''
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'''初始化目标检测模型'''
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model = Detector(dete_weights)
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names=['vehicle', 'TCorner', 'LCorner']
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t1=time.time()
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'''图像测试'''
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folders = os.listdir(args1['input_dir'])
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for i in range(len(folders)):
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path1 = args1['input_dir'] + '/' + folders[i]
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print('-'*100,path1)
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'''对原图进行目标检测'''
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pred, _img_cv=AI_process(model, args1,path1)
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H,W = _img_cv.shape[0:2]
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imgSize = (W,H);pars={'imgSize':imgSize}
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#preds = [[ names.index(x[4]),*x[0:4], float(x[5].cpu()) ] for x in pred[1]]
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preds = [[ *x[0:4], names.index(x[4]),float(x[5].cpu()) ] for x in pred[1]]
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# print('pred', pred)
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final_weiting,timeStr = illParking_postprocess(preds,None,pars)
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'''进入后处理，判断是否有违章停车'''
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#final_img,final_weiting=AI_postprocess(pred, _img_cv)
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#cv2.imwrite('./outdir/final_result'+str(i)+'.png', final_img)
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t2=time.time()
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print('耗时',t2-t1)
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if __name__ == "__main__":
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main()
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