kafka_yolov5/segutils/seg_detect.py

import torch
from core.models.bisenet import BiSeNet
from torchvision import transforms
import cv2,os
import numpy as np
from core.models.dinknet import DinkNet34     
import matplotlib.pyplot as plt           

import matplotlib.pyplot as plt           
import time
class SegModel(object):
    def __init__(self, nclass=2,weights=None,modelsize=512,device='cuda:3'):
        #self.args = args       
        self.model = BiSeNet(nclass)
        #self.model = DinkNet34(nclass)
        checkpoint = torch.load(weights)
        self.modelsize = modelsize
        self.model.load_state_dict(checkpoint['model'])
        self.device = device
        self.model= self.model.to(self.device)
        '''self.composed_transforms = transforms.Compose([
            
            transforms.Normalize(mean=(0.335, 0.358, 0.332), std=(0.141, 0.138, 0.143)),
            transforms.ToTensor()])   '''
        self.mean = (0.335, 0.358, 0.332)
        self.std =   (0.141, 0.138, 0.143)  
    def eval(self,image,outsize=None):
        imageW,imageH,imageC = image.shape
        time0 = time.time()
        image = self.preprocess_image(image)
        time1 = time.time()
        self.model.eval()
        image  = image.to(self.device)
        with torch.no_grad():
            output = self.model(image,outsize=outsize)
            
        time2 = time.time()    
        pred = output.data.cpu().numpy()
        pred = np.argmax(pred, axis=1)[0]#得到每行
        time3 = time.time()           
        pred = cv2.resize(pred.astype(np.uint8),(imageW,imageH))
        time4 = time.time()
        print('pre-precess:%.1f ,infer:%.1f ,post-precess:%.1f ,post-resize:%.1f '%( self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3) ))
        return pred 
    def get_ms(self,t1,t0):
        return (t1-t0)*1000.0            
    def preprocess_image(self,image):
        
        time0 = time.time()
        image = cv2.resize(image,(self.modelsize,self.modelsize))
        
        time1 = time.time()
        image = image.astype(np.float32)        
        image /= 255.0
        
        time2 = time.time()
        #image -= self.mean
        image[:,:,0] -=self.mean[0]
        image[:,:,1] -=self.mean[1]
        image[:,:,2] -=self.mean[2]
        
        time3 = time.time()
        #image /= self.std
        
        image[:,:,0] /= self.std[0]
        image[:,:,1] /= self.std[1]
        image[:,:,2] /= self.std[2]
        
        
        time4 = time.time()
        image = np.transpose(image, ( 2, 0, 1))
        time5 = time.time()
        image = torch.from_numpy(image).float()
        image = image.unsqueeze(0)
        print('resize:%.1f  norm:%.1f   mean:%.1f  std:%.1f trans:%.f '%(self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3) ,self.get_ms(time5,time4)     )   )
        
        return image
        
            
    
def get_ms(t1,t0):
    return (t1-t0)*1000.0 
             
if __name__=='__main__':


    
    #os.environ["CUDA_VISIBLE_DEVICES"] = str('4')
    '''
    image_url = '../../data/landcover/corp512/test/images/N-33-139-C-d-2-4_169.jpg'
    nclass = 5
    weights = 'runs/landcover/DinkNet34_save/experiment_wj_loss-10-10-1/checkpoint.pth'
    '''
    
    
    image_url = 'temp_pics/DJI_0645.JPG'
    nclass = 2
    #weights = '../weights/segmentation/BiSeNet/checkpoint.pth'
    weights = 'runs/THriver/BiSeNet/train/experiment_0/checkpoint.pth'
    #weights = 'runs/segmentation/BiSeNet_test/experiment_10/checkpoint.pth'
    
    
    
    segmodel = SegModel(nclass=nclass,weights=weights,device='cuda:4')
    for i in range(10):
        image_array0 = cv2.imread(image_url)
        imageH,imageW,_ = image_array0.shape
        #print('###line84:',image_array0.shape)
        image_array = cv2.cvtColor( image_array0,cv2.COLOR_RGB2BGR)        
        #image_in =  segmodel.preprocess_image(image_array)       
        pred = segmodel.eval(image_array,outsize=None)
        time0=time.time()
        binary = pred.copy()
        time1=time.time()
        contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        time2=time.time() 
        print(pred.shape,' time copy:%.1f  finccontour:%.1f '%(get_ms(time1,time0),get_ms(time2,time1)  ))
        
        
    ##计算findconturs时间与大小的关系
    binary0 = binary.copy()
    for ii,ss in enumerate([22,256,512,1024,2048]):       
        time0=time.time() 
        image = cv2.resize(binary0,(ss,ss))
        time1=time.time()
        if ii ==0:
            contours, hierarchy = cv2.findContours(binary0,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        else:
            contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        time2=time.time()
        print('size:%d  resize:%.1f ,findtime:%.1f  '%(ss, get_ms(time1,time0),get_ms(time2,time1)))