Drowning_Person_Detection/AI20230801_caogao.py

'''
这个版本增加了船舶过滤功能
'''
import time
import sys
from core.models.bisenet import BiSeNet
from models.AIDetector_pytorch import Detector
from models.AIDetector_pytorch import plot_one_box,Colors
from utils.postprocess_utils import center_coordinate,fourcorner_coordinate,remove_simivalue,remove_sameeleme_inalist
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
from models.model_stages import BiSeNet
import cv2
import torch
import torch.nn.functional as F
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
from utils.segutils import colour_code_segmentation
from utils.segutils import get_label_info
os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
sys.path.append("../")  # 为了导入上级目录的，添加一个新路径


def AI_postprocess(preds,_mask_cv,pars,_img_cv):
    '''考虑船上人过滤'''
    '''输入：落水人员的结果（类别+坐标）、原图、mask图像
       过程：获得mask的轮廓，判断人员是否在轮廓内。
            在，则保留且绘制；不在，舍弃。
       返回：最终绘制的结果图、最终落水人员（坐标、类别、置信度），
    '''
    '''1、最大分割水域作为判断依据'''
    zoom_factor=4 #缩小因子设置为4，考虑到numpy中分别遍历xy进行缩放耗时大。
    original_height = _mask_cv.shape[0]
    original_width=_mask_cv.shape[1]
    zoom_height=int(original_height/zoom_factor)
    zoom_width=int(original_width/zoom_factor)

    _mask_cv = cv2.resize(_mask_cv, (zoom_width,zoom_height))  #缩小原图,宽在前，高在后
    t4 = time.time()
    img_gray = cv2.cvtColor(_mask_cv, cv2.COLOR_BGR2GRAY)  if len(_mask_cv.shape)==3 else _mask_cv    #
    t5 = time.time()
    contours, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    # 寻找轮廓(多边界)
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, 2)
    contour_info = []
    for c in contours:
        contour_info.append((
            c,
            cv2.isContourConvex(c),
            cv2.contourArea(c),
        ))
    contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
    t6 = time.time()

    '''新增模块：：如果水域为空，则返回原图、无落水人员等。'''
    if contour_info==[]:
        # final_img=_img_cv
        final_head_person_filterwater=[]
        timeInfos=0
        # return final_img, final_head_person_filterwater
        return final_head_person_filterwater,timeInfos
    else:
        max_contour = contour_info[0]
        max_contour1=max_contour[0]
        max_contour_X=max_contour1[0][0][:]
        max_contour=max_contour[0]*zoom_factor# contours恢复原图尺寸
        # max_contour=max_contour[0]*zoom_factor# contours恢复原图尺寸
        print(max_contour)
    t7 = time.time()


    '''2.1、preds中head+person取出，boat取出。'''
    init_head_person=[]
    init_boat = []
    for i in range(len(preds)):
        if preds[i][4]=='head' or preds[i][4]=='person':
            init_head_person.append(preds[i])
        else:
            init_boat.append(preds[i])
    t8 = time.time()

    '''新增模块：2.2、preds中head+person取出，过滤掉head与person中指向同一人的部分，保留同一人的person标签。'''
    init_head=[]
    init_person=[]
    #head与person标签分开
    for i in range(len(init_head_person)):
        if init_head_person[i][4]=='head':
            init_head.append(init_head_person[i])
        else:
            init_person.append(init_head_person[i])
    # person的框形成contours
    person_contour=[]
    for i in range(len(init_person)):
        boundbxs_temp=[init_person[i][0],init_person[i][1],init_person[i][2],init_person[i][3]]
        contour_temp_person=fourcorner_coordinate(boundbxs_temp) #得到person预测框的顺序contour
        contour_temp_person=np.array(contour_temp_person)
        contour_temp_person=np.float32(contour_temp_person)
        person_contour.append(np.array(contour_temp_person))
    # head是否在person的contours内，在说明是同一人，过滤掉。
    list_head=[]
    for i in range(len(init_head)):
        for j in range(len(person_contour)):
            center_x, center_y=center_coordinate(init_head[i])
            flag = cv2.pointPolygonTest(person_contour[j], (center_x, center_y), 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:
                pass
            else:
                list_head.append(init_head[i])
    # person和最终head合并起来
    init_head_person_temp=init_person+list_head

    '''3、preds中head+person，通过1中水域过滤'''
    init_head_person_filterwater=init_head_person_temp
    final_head_person_filterwater=[]
    for i in range(len(init_head_person_filterwater)):
        center_x, center_y=center_coordinate(init_head_person_filterwater[i])
        flag = cv2.pointPolygonTest(max_contour, (center_x, center_y), 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:
            final_head_person_filterwater.append(init_head_person_filterwater[i])
        else:
            pass
    t9 = time.time()

    '''4、水域过滤后的head+person，再通过船舶范围过滤'''
    init_head_person_filterboat=final_head_person_filterwater
    # final_head_person_filterboat=[]
    #获取船舶范围
    boat_contour=[]
    for i in range(len(init_boat)):
        boundbxs1=[init_boat[i][0],init_boat[i][1],init_boat[i][2],init_boat[i][3]]
        contour_temp=fourcorner_coordinate(boundbxs1) #得到boat预测框的顺序contour
        contour_temp_=np.array(contour_temp)
        contour_temp_=np.float32(contour_temp_)
        boat_contour.append(np.array(contour_temp_))
    t10 = time.time()
    # 遍历船舶范围，取出在船舶范围内的head和person（可能有重复元素）
    list_headperson_inboat=[]
    for i in range(len(init_head_person_filterboat)):
        for j in range(len(boat_contour)):
            center_x, center_y=center_coordinate(init_head_person_filterboat[i])
            # yyyyyyyy=boat_contour[j]
            flag = cv2.pointPolygonTest(boat_contour[j], (center_x, center_y), 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:
                list_headperson_inboat.append(init_head_person_filterboat[i])
            else:
                pass
    print('list_headperson_inboat',list_headperson_inboat)
    if len(list_headperson_inboat)==0:
        pass
    else:
        list_headperson_inboat=remove_sameeleme_inalist(list_headperson_inboat) #将重复嵌套列表元素删除
    # 过滤船舶范围内的head和person
    final_head_person_filterboat=remove_simivalue(init_head_person_filterboat,list_headperson_inboat)
    t11 = time.time()

    '''5、输出最终落水人员，并绘制保存检测图'''
    colors = Colors()
    if final_head_person_filterwater is not None:
        for i in range(len(final_head_person_filterboat)):
            # lbl = self.names[int(cls_id)]
            lbl = final_head_person_filterboat[i][4]
            xyxy=[final_head_person_filterboat[i][0],final_head_person_filterboat[i][1],final_head_person_filterboat[i][2],final_head_person_filterboat[i][3]]
            c = int(5)
            plot_one_box(xyxy, _img_cv, label=lbl, color=colors(c, True), line_thickness=3)
    final_img=_img_cv
    t12 = time.time()
    # cv2.imwrite('final_result.png', _img_cv)
    t13 = time.time()

    print('存图:%s, 过滤标签:%s ,遍历船舶范围:%s,水域过滤后的head+person:%s,水域过滤:%s,head+person、boat取出:%s，新增如果水域为空:%s，找contours:%s，图像改变:%s'
          %((t13-t12) * 1000,(t12-t11) * 1000,(t11-t10) * 1000,(t10-t9) * 1000,(t9-t8) * 1000,(t8-t7) * 1000,(t7-t6) * 1000,(t6-t5) * 1000,(t5-t4) * 1000 ) )
    timeInfos=('存图:%s, 过滤标签:%s ,遍历船舶范围:%s,水域过滤后的head+person:%s,水域过滤:%s,head+person、boat取出:%s，新增如果水域为空:%s，找contours:%s，图像改变:%s'
          %((t13-t12) * 1000,(t12-t11) * 1000,(t11-t10) * 1000,(t10-t9) * 1000,(t9-t8) * 1000,(t8-t7) * 1000,(t7-t6) * 1000,(t6-t5) * 1000,(t5-t4) * 1000 ) )
    return final_head_person_filterwater,timeInfos  #返回最终绘制的结果图、最终落水人员（坐标、类别、置信度）


def AI_process(model, segmodel, args1,path1):
    '''对原图进行目标检测和水域分割'''
    '''输入：检测模型、分割模型、配置参数、路径
       返回：返回目标检测结果、原图像、分割图像，
    '''
    '''检测图片'''
    t21=time.time()
    _img_cv = cv2.imread(path1)  # 将这里的送入yolov5
    t22 = time.time()

    # _img_cv=_img_cv.numpy()
    pred = model.detect(_img_cv)  # 检测结果
    #对pred处理，处理成list嵌套
    pred=[[*x[0:4],x[4],x[5].cpu().tolist()] for x in pred[1]]
    # pred=[[x[0],*x[1:5],x[5].cpu().float()] for x in pred[1]]
    print('pred', pred)

    t23 = time.time()
    '''分割图片'''
    img = Image.open(path1).convert('RGB')
    t231 = time.time()
    transf1 = transforms.ToTensor()
    transf2 = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
    imgs = transf1(img)
    imgs = transf2(imgs)
    print(path1)  # numpy数组格式为（H,W,C）

    size = [360, 640]
    imgs = imgs.unsqueeze(0)
    imgs = imgs.cuda()
    N, C, H, W = imgs.size()

    self_scale = 360 / H
    new_hw = [int(H * self_scale), int(W * self_scale)]
    print("line50", new_hw)
    imgs = F.interpolate(imgs, new_hw, mode='bilinear', align_corners=True)
    t24 = time.time()
    with torch.no_grad():
        logits = segmodel(imgs)[0]
    t241 = time.time()
    logits = F.interpolate(logits, size=size, mode='bilinear', align_corners=True)
    probs = torch.softmax(logits, dim=1)
    preds = torch.argmax(probs, dim=1)
    preds_squeeze = preds.squeeze(0)
    preds_squeeze_predict = colour_code_segmentation(np.array(preds_squeeze.cpu()), args1['label_info'])
    preds_squeeze_predict = cv2.resize(np.uint8(preds_squeeze_predict), (W, H))
    predict_mask = cv2.cvtColor(np.uint8(preds_squeeze_predict), cv2.COLOR_RGB2BGR)
    _mask_cv =predict_mask
    t25 = time.time()
    cv2.imwrite('seg_result.png', _mask_cv)
    t26 = time.time()
    print('存分割图:%s, 分割后处理:%s ,分割推理:%s ,分割图变小:%s,分割图读图:%s,检测模型推理:%s,读图片:%s'
          %((t26-t25) * 1000,(t25-t241) * 1000,(t241-t24) * 1000,(t24-t231) * 1000,(t231-t23) * 1000,(t23-t22) * 1000,(t22-t21) * 1000 ) )

    return pred, _img_cv, _mask_cv   #返回目标检测结果、原图像、分割图像

def main():

    '''配置参数'''
    label_info = get_label_info('utils/class_dict.csv')
    pars={'cuda':'0','crop_size':512,'input_dir':'input_dir','output_dir':'output_dir','workers':16,'label_info':label_info,
           'dspth':'./data/','backbone':'STDCNet813','use_boundary_2':False, 'use_boundary_4':False, 'use_boundary_8':True, 'use_boundary_16':False,'use_conv_last':False}


    dete_weights='weights/best_luoshui20230608.pt'
    '''分割模型权重路径'''
    seg_weights = 'weights/model_final.pth'

    '''初始化目标检测模型'''
    model = Detector(dete_weights)


    '''初始化分割模型2'''
    n_classes = 2
    segmodel = BiSeNet(backbone=pars['backbone'], n_classes=n_classes,
     use_boundary_2=pars['use_boundary_2'], use_boundary_4=pars['use_boundary_4'],
     use_boundary_8=pars['use_boundary_8'], use_boundary_16=pars['use_boundary_16'],
     use_conv_last=pars['use_conv_last'])
    segmodel.load_state_dict(torch.load(seg_weights))
    segmodel.cuda()
    segmodel.eval()


    '''图像测试'''
    folders = os.listdir(pars['input_dir'])
    for i in range(len(folders)):
        path1 = pars['input_dir'] + '/' + folders[i]

        t1=time.time()

        '''对原图进行目标检测和水域分割'''
        pred, _img_cv, _mask_cv=AI_process(model,segmodel, pars,path1)

        t2 = time.time()

        '''进入后处理，判断水域内有落水人员'''
        haha,zzzz=AI_postprocess(pred, _mask_cv,pars,_img_cv )
        t3 = time.time()

        print('总时间分布：前处理t2-t1,后处理t3-t2',(t2-t1)*1000,(t3-t2)*1000)

if __name__ == "__main__":
    main()