import sys from pathlib import Path import math import cv2 import numpy as np import torch FILE = Path(__file__).absolute() #sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path ''' 修改说明： 1、pars中增加了recScale参数。船舶判断是否悬挂国旗时，需将船舶检测框乘以扩大系数imgScale后，与国旗中心点坐标比较。 pars={'imgSize':(imgwidth,imgheight),'wRation':1/6.0,'hRation':1/6.0,'smallId':0,'bigId':3,'newId':4,'recScale':1.2} 2、增加expand_rect(preds_boat, recScale, imgSize)函数，在图像范围内，将矩形框扩大recScale倍数。 3、增加或修改以下两行： preds_boat_flag_expand=expand_rect(preds_boat_flag[i],pars['recScale'],pars['imgSize']) #新增！ if point_in_rectangle(preds_flag,preds_boat_flag_expand)>=1: #新增后修改！ ''' def channel2_post_process(predsList,pars): print('----line24:',predsList) #pars={'imgSize':(imgwidth,imgheight),'wRation':1/6.0,'hRation':1/6.0,'smallId':0,'bigId':3,'newId':4,'recScale':1.2} ''' 后处理程序，将检测结果中未悬挂国旗的船只，其类别改为4，即'unflagged_ship' 最终类别汇总如下， ['flag', 'buoy', 'shipname', 'ship','unflagged_ship','uncover']=[0,1,2,3,4,5] 输入： preds 一张图像的检测结果，为嵌套列表，tensor，包括x_y_x_y_conf_class imgwidth,imgheight 图像的原始宽度及长度 输出：检测结果（将其中未悬挂国旗的显示） ''' preds = torch.tensor(predsList[0]) preds=preds.tolist() preds = filter_detection_results(preds,pars) preds=[[*sublist[:-1], int(sublist[-1])] for sublist in preds] #类别从浮点型转为整型 #设置空的列表 output_detection=[] #存放往接口传的类别 #1、判断类别中哪些有船？取出船检测结果，并取出国旗检测结果。 # output_detection.append[] 这里将船和国旗以外的类别加进去 preds_boat=[] preds_flag=[] #jcq： 增加封仓 preds_uncover = [] # 1、处理未封仓 preds = filter_detection_results(preds,pars) for i in range(len(preds)): if preds[i][5]==pars['boatId']: #识别为船 preds_boat.append(preds[i]) elif preds[i][5]==pars['flagId']: #识别为国旗 preds_flag.append(preds[i]) # output_detection.append(preds[i]) #jcq: elif preds[i][5]==pars['uncoverId']: #未封仓 preds_uncover.append(preds[i]) # output_detection.append(preds[i]) else: output_detection.append(preds[i]) # pass # return output_detection #2、船尺寸与图像比较，其中长或宽有一个维度超过图像宽高平均值的1/3，启动国旗检测 #①if 判断：判断超过1/3的，则取出这些大船，进一步判断是否悬挂国旗 #不超过1/3的，则output_detection.append[] boat_uncover = preds_boat+preds_uncover for i in range(len(boat_uncover)): length_bbx,width_bbx=get_rectangle_dimensions(boat_uncover[i]) length_bbx, width_bbx=int(length_bbx),int(width_bbx) if length_bbx>(pars['imgSize'][0]+pars['imgSize'][1])* pars['hRation'] or width_bbx>(pars['imgSize'][0]+pars['imgSize'][1])*pars['wRation']: boat_uncover[i] = unflag(boat_uncover[i], preds_flag, pars) return output_detection + boat_uncover def unflag(boat_uncover,preds_flag,pars): preds_boat_flag_expand = expand_rect(boat_uncover, pars['recScale'], pars['imgSize']) # 新增！ if not point_in_rectangle(preds_flag, preds_boat_flag_expand) >= 1: # 新增后修改！ if boat_uncover[5] == pars['uncoverId']: boat_uncover[5] = pars['unflagAndcoverId'] # 将类别标签改为6，未挂国旗且未封仓 else: boat_uncover[5] = pars['unflagId'] # 将类别标签改为4，即为未悬挂国旗的船只 return boat_uncover def center_coordinate(boundbxs): ''' 根据检测矩形框，得到其矩形长度和宽度 输入：两个对角坐标xyxy 输出：矩形框重点坐标xy ''' boundbxs_x1 = boundbxs[0] boundbxs_y1 = boundbxs[1] boundbxs_x2 = boundbxs[2] boundbxs_y2 = boundbxs[3] center_x = 0.5 * (boundbxs_x1 + boundbxs_x2) center_y = 0.5 * (boundbxs_y1 + boundbxs_y2) return center_x, center_y def get_rectangle_dimensions(boundbxs): ''' 根据检测矩形框，得到其矩形长度和宽度 输入：两个对角坐标xyxy 输出：矩形框四个角点坐标，以contours顺序。 ''' # 计算两点之间的水平距离 width = math.fabs(boundbxs[2] - boundbxs[0]) # 计算两点之间的垂直距离 height = math.fabs(boundbxs[3]- boundbxs[1]) return width, height def fourcorner_coordinate(boundbxs): ''' 通过矩形框对角xyxy坐标，得到矩形框轮廓 输入：两个对角坐标xyxy 输出：矩形框四个角点坐标，以contours顺序。 ''' boundbxs_x1 = boundbxs[0] boundbxs_y1 = boundbxs[1] boundbxs_x2 = boundbxs[2] boundbxs_y2 = boundbxs[3] wid = boundbxs_x2 - boundbxs_x1 hei = boundbxs_y2 - boundbxs_y1 boundbxs_x3 = boundbxs_x1 + wid boundbxs_y3 = boundbxs_y1 boundbxs_x4 = boundbxs_x1 boundbxs_y4 = boundbxs_y1 + hei contours_rec = [[boundbxs_x1, boundbxs_y1], [boundbxs_x3, boundbxs_y3], [boundbxs_x2, boundbxs_y2], [boundbxs_x4, boundbxs_y4]] return contours_rec def point_in_rectangle(preds_flag,preds_boat_flag): ''' 遍历所有国旗坐标，判断落在检测框中的数量 输入： preds_flag 国旗类别的检测结果列表 preds_boat_flag 待判定船只的检测结果（单个船只） 输出：落入检测框的国旗数量 ''' iii=0 boat_contour=fourcorner_coordinate(preds_boat_flag) boat_contour=np.array(boat_contour,dtype=np.float32) for i in range(len(preds_flag)): center_x, center_y = center_coordinate(preds_flag[i]) if cv2.pointPolygonTest(boat_contour, (center_x, center_y), False)==1: iii+=1 else: pass return iii def expand_rect(preds_boat, recScale, imgSize): ''' 在图像范围内，将矩形框扩大recScale倍数。 输入： preds_boat 国旗类别的检测结果列表 xyxy_conf_class imgSize 从pars传来的元组 输出：调整后的preds_boat ''' # preds_boat_1=preds_boat preds_boat_1=[x for x in preds_boat] x1, y1 = preds_boat[0],preds_boat[1] x2, y2 = preds_boat[2],preds_boat[3] width = x2 - x1 height = y2 - y1 # 计算新的宽度和高度 new_width = width * recScale new_height = height * recScale # 计算新的对角坐标 new_x1 = max(x1 - (new_width - width) / 2, 0) # 确保不会超出左边界 new_y1 = max(y1 - (new_height - height) / 2, 0) # 确保不会超出上边界 new_x2 = min(x2 + (new_width - width) / 2, imgSize[0]) # 图像宽度是imgSize[0] new_y2 = min(y2 + (new_height - height) / 2, imgSize[1]) # 图像高度是imgSize[1] preds_boat_1[0]=new_x1 preds_boat_1[1]=new_y1 preds_boat_1[2]=new_x2 preds_boat_1[3]=new_y2 return preds_boat_1 ###jcq : 增加封仓后处理函数 def filter_detection_results(results, par): target_cls = par['target_cls'] # 船只 filter_cls = par['filter_cls'] # 非封仓 # 分离处理与非处理的结果 non_process = [box for box in results if box[5] not in {target_cls, filter_cls}] to_process = [box for box in results if box[5] in {target_cls, filter_cls}] # 提取目标类别和过滤类别的检测框 class_target = [box for box in to_process if box[5] == target_cls] # 船只 class_filter = [box for box in to_process if box[5] == filter_cls] # 非封仓 # 处理过滤类别（映射条件） for i in range(len(class_target)): t_box = class_target[i] if any( # 检查是否在任意目标框内部 (t_box[0] <= f_box[0] and t_box[1] <= f_box[1] and t_box[2] >= f_box[2] and t_box[3] >= f_box[3]) for f_box in class_filter ): class_target[i][5] = par['uncoverId'] # 映射类别4->5 # 合并结果（保留非处理类别） return non_process + class_target def filter_detection_results_uncover(results, par): target_cls = par['target_cls'] filter_cls = par['filter_cls'] # 分离处理与非处理的结果 non_process = [box for box in results if box[5] not in {target_cls, filter_cls}] to_process = [box for box in results if box[5] in {target_cls, filter_cls}] # 提取目标类别和过滤类别的检测框 class_target = [box for box in to_process if box[5] == target_cls] class_filter = [box for box in to_process if box[5] == filter_cls] processed = [] # 处理过滤类别（映射条件） for f_box in class_filter: if any( # 检查是否在任意目标框内部 (f_box[0] >= t_box[0] and f_box[1] >= t_box[1] and f_box[2] <= t_box[2] and f_box[3] <= t_box[3]) for t_box in class_target ): new_box = f_box.copy() new_box[5] = 5 # 映射类别4->5 processed.append(new_box) # 保留所有目标类别检测框 processed += class_target # 合并结果（保留非处理类别） return non_process + processed if __name__ == "__main__": # 对应DJI_20230306140129_0001_Z_165.jpg检测结果 # preds=[[6.49000e+02, 2.91000e+02, 1.07900e+03, 7.33000e+02, 9.08165e-01, 3.00000e+00], # [8.11000e+02, 2.99000e+02, 1.31200e+03, 7.65000e+02, 8.61268e-01, 3.00000e+00], # [7.05000e+02, 1.96000e+02, 7.19000e+02, 2.62000e+02, 5.66877e-01, 0.00000e+00]] # 对应DJI_20230306152702_0001_Z_562.jpg检测结果 preds=[[7.62000e+02, 7.14000e+02, 1.82800e+03, 9.51000e+02, 9.00902e-01, 3.00000e+00], [2.00000e+01, 3.45000e+02, 1.51300e+03, 6.71000e+02, 8.81440e-01, 3.00000e+00], [8.35000e+02, 8.16000e+02, 8.53000e+02, 8.30000e+02, 7.07651e-01, 0.00000e+00], [1.35600e+03, 4.56000e+02, 1.42800e+03, 4.94000e+02, 6.70549e-01, 2.00000e+00]] print('before :\n ',preds) #preds=torch.tensor(preds) #返回的预测结果 imgwidth=1920 imgheight=1680 pars={'imgSize':(imgwidth,imgheight),'wRation':1/6.0,'hRation':1/6.0,'smallId':0,'bigId':3,'newId':4,'recScale':1.2} # 'smallId':0(国旗)，'bigId':3(船只),wRation和hRation表示判断的阈值条件，newId--新目标的id yyy=channel2_post_process([preds],pars) #送入后处理函数 print('after :\n ',yyy)