IllegalParkingHandover/predict_lunkuo.py

#!/usr/bin/python
# -*- encoding: utf-8 -*-
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
from logger import setup_logger
from models.model_stages import BiSeNet
from predict_city.heliushuju2 import Heliushuju
import cv2
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.distributed as dist


import os.path as osp
import logging
import time
import numpy as np
from tqdm import tqdm
import math
import pandas as pd
# from cv2 import getTickCount, getTickFrequency
class MscEvalV0(object):
    def __init__(self, scale=0.5, ignore_label=255):
        self.ignore_label = ignore_label
        self.scale = scale
    def __call__(self, net, dl, n_classes):
        ## evaluate
        label_info = get_label_info('./class_dict.csv')
        hist = torch.zeros(n_classes, n_classes).cuda().detach()
        diter = enumerate(tqdm(dl))
        for i, (imgs, img_n, img_tt) in diter:
            
            loop_start = cv2.getTickCount()
            time0 = time.time()#################
            img_n = img_n[0]
            img_n = np.array(img_n.cpu())
            #img_n = 
            #print(img_n.dtype,img_n.shape)
            #N, _, H, W = label.shape
            #label = label.squeeze(1).cuda()
            #size = label.size()[-2:]
            imgs = imgs.cuda()
            #print('img',imgs.shape)
            N, C, H, W = imgs.size()
            # new_hw = [360, 640]
            new_hw = [int(H * self.scale), int(W * self.scale)]
            print(new_hw)
            imgs = F.interpolate(imgs, new_hw, mode='bilinear', align_corners=True)
            logits = net(imgs)[0]
            #print('logits:', logits.shape)
            time1 = time.time()#######################
            tm = (time1-time0)*1000
            print('##precess:%.1f'%(tm))
            loop_time = cv2.getTickCount() - loop_start
            tool_time = loop_time/(cv2.getTickFrequency())
            running_fps = int(1/tool_time)
            print('running_fps:', running_fps)
            #print('imgs.size():', imgs.size()[-2:])
            logits = F.interpolate(logits, size=(H,W),
                    mode='bilinear', align_corners=True)
            probs = torch.softmax(logits, dim=1)
            preds = torch.argmax(probs, dim=1)
            #print('preds:', preds.shape)
            preds_squeeze = preds.squeeze(0)
            preds_squeeze[preds_squeeze != 0] = 255
            preds_squeeze = np.array(preds_squeeze.cpu())
            preds_squeeze = np.uint8(preds_squeeze)
            #print('preds_squeeze:', preds_squeeze.shape)
            _, binary = cv2.threshold(preds_squeeze,220,255,cv2.THRESH_BINARY)
            contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
            img2 = cv2.drawContours(img_n,contours,-1,(0,0,255),8)
            save_path = './demo/' + img_tt[0] + '.png'
            cv2.imwrite(save_path,img2)          

            

def colour_code_segmentation(image, label_values):
    label_values = [label_values[key] for key in label_values]
    colour_codes = np.array(label_values)
    x = colour_codes[image.astype(int)]
    return x
def get_label_info(csv_path):
    ann = pd.read_csv(csv_path)
    label = {}
    for iter, row in ann.iterrows():
        label_name = row['name']
        r = row['r']
        g = row['g']
        b = row['b']
        label[label_name] = [int(r), int(g), int(b)]
    return label
def evaluatev0(respth='', dspth='', backbone='', scale=0.75, use_boundary_2=False,
               use_boundary_4=False, use_boundary_8=False, use_boundary_16=False, use_conv_last=False):
    print('scale', scale)
    ## dataset
    batchsize = 1
    n_workers = 0
    dsval = Heliushuju(dspth, mode='val')
    dl = DataLoader(dsval,
                    batch_size = batchsize,
                    shuffle = False,
                    num_workers = n_workers,
                    drop_last = False)

    n_classes = 2####################################################################
    print("backbone:", backbone)
    net = BiSeNet(backbone=backbone, n_classes=n_classes,
     use_boundary_2=use_boundary_2, use_boundary_4=use_boundary_4, 
     use_boundary_8=use_boundary_8, use_boundary_16=use_boundary_16, 
     use_conv_last=use_conv_last)
    net.load_state_dict(torch.load(respth))
    net.cuda()
    net.eval()
    with torch.no_grad():
        single_scale = MscEvalV0(scale=scale)
        single_scale(net, dl, 2)



if __name__ == "__main__":
    #STDC2-Seg75 mIoU 0.7704
    evaluatev0('./checkpoints_1648/wurenji_train_STDC1-Seg/pths/model_maxmIOU75_0.934_1024.pth', dspth='/home/data/lijiwen/wurenjiqifei/images/', backbone='STDCNet813', scale=0.75,
    use_boundary_2=False, use_boundary_4=False, use_boundary_8=True, use_boundary_16=False)