kafka_yolov5/segutils/segmodel.py

import torch
import sys,os
sys.path.extend(['/home/thsw2/WJ/src/yolov5/segutils'])
from core.models.bisenet import BiSeNet
from torchvision import transforms
import cv2,glob
import numpy as np
from core.models.dinknet import DinkNet34     
import matplotlib.pyplot as plt           
import time
class SegModel(object):
    def __init__(self, nclass=2,weights=None,modelsize=512,device='cuda:0'):
        #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):	
        time0 = time.time()
        imageH,imageW,imageC = image.shape        
        image = self.preprocess_image(image)
        time1 = time.time()
        self.model.eval()
        image  = image.to(self.device)
        with torch.no_grad():
            output = self.model(image)

        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()
        outstr= 'pre-precess:%.1f ,infer:%.1f ,post-precess:%.1f ,post-resize:%.1f, total:%.1f \n '%( self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3),self.get_ms(time4,time0) )

        #print('pre-precess:%.1f ,infer:%.1f ,post-precess:%.1f ,post-resize:%.1f, total:%.1f '%( self.get_ms(time1,time0),self.get_ms(time2,time1),self.get_ms(time3,time2),self.get_ms(time4,time3),self.get_ms(time4,time0) ))
        return pred,outstr 
    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))
        time0 = time.time()
        image = image.astype(np.float32)        
        image /= 255.0

        image[:,:,0] -=self.mean[0]
        image[:,:,1] -=self.mean[1]
        image[:,:,2] -=self.mean[2]

        image[:,:,0] /= self.std[0]
        image[:,:,1] /= self.std[1]
        image[:,:,2] /= self.std[2]
        image = cv2.cvtColor( image,cv2.COLOR_RGB2BGR)
        #image -= self.mean
        #image /= self.std
        image = np.transpose(image, ( 2, 0, 1))
        
        image = torch.from_numpy(image).float()
        image = image.unsqueeze(0)
        
        
        return image
        
def get_ms(t1,t0):
    return (t1-t0)*1000.0                  
    
     
def get_largest_contours(contours):
    areas = [cv2.contourArea(x) for x in contours]
    max_area = max(areas)
    max_id = areas.index(max_area)

    return max_id
    
if __name__=='__main__':
    image_url = '/home/thsw2/WJ/data/THexit/val/images/DJI_0645.JPG'
    nclass = 2
    weights = '../weights/segmentation/BiSeNet/checkpoint.pth'

    segmodel = SegModel(nclass=nclass,weights=weights)

    image_urls=glob.glob('/home/thsw2/WJ/data/THexit/val/images/*')
    out_dir ='../runs/detect/exp2-seg';os.makedirs(out_dir,exist_ok=True)
    for image_url in image_urls[0:1]:
        image_url = '/home/thsw2/WJ/data/THexit/val/images/54(199).JPG'
        image_array0 = cv2.imread(image_url)       
        pred = segmodel.eval(image_array0 )

        #plt.figure(1);plt.imshow(pred);
        #plt.show()
        binary0 = pred.copy()

        
        time0 = time.time()
        contours, hierarchy = cv2.findContours(binary0,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        max_id = -1
        if len(contours)>0:
            max_id = get_largest_contours(contours)
            binary0[:,:] = 0
            print(contours[0].shape,contours[1].shape,contours[0])
            cv2.fillPoly(binary0, [contours[max_id][:,0,:]], 1) 
      
        time1 = time.time()
        
        #num_labels,_,Areastats,centroids = cv2.connectedComponentsWithStats(binary0,connectivity=4) 
        time2 = time.time()
        
        cv2.drawContours(image_array0,contours,max_id,(0,255,255),3)
        time3 = time.time()
        out_url='%s/%s'%(out_dir,os.path.basename(image_url))
        ret = cv2.imwrite(out_url,image_array0)
        time4 = time.time()
        
        print('image:%s findcontours:%.1f ms , connect:%.1f ms ,draw:%.1f save:%.1f'%(os.path.basename(image_url),get_ms(time1,time0),get_ms(time2,time1), get_ms(time3,time2),get_ms(time4,time3), ) )
        plt.figure(0);plt.imshow(pred)
        plt.figure(1);plt.imshow(image_array0)
        plt.figure(2);plt.imshow(binary0)
        plt.show()
        
        #print(out_url,ret)