get_miou_prediction.py

import os

import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from tqdm import tqdm

from unet import Unet


class miou_Unet(Unet):
    def detect_image(self, image):
        orininal_h = np.array(image).shape[0]
        orininal_w = np.array(image).shape[1]

        image, nw, nh = self.letterbox_image(image,(self.model_image_size[1],self.model_image_size[0]))
        images = [np.array(image)/255]
        images = np.transpose(images,(0,3,1,2))

        with torch.no_grad():
            images = torch.from_numpy(images).type(torch.FloatTensor)
            if self.cuda:
                images =images.cuda()

            pr = self.net(images)[0]
            pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
            pr = pr[int((self.model_image_size[0]-nh)//2):int((self.model_image_size[0]-nh)//2+nh), int((self.model_image_size[1]-nw)//2):int((self.model_image_size[1]-nw)//2+nw)]

        image = Image.fromarray(np.uint8(pr)).resize((orininal_w,orininal_h),Image.NEAREST)
        return image

unet = miou_Unet()

image_ids = open("VOCdevkit/VOC2007/ImageSets/Segmentation/val.txt",'r').read().splitlines()

if not os.path.exists("./miou_pr_dir"):
    os.makedirs("./miou_pr_dir")

for image_id in tqdm(image_ids):
    image_path = "VOCdevkit/VOC2007/JPEGImages/"+image_id+".jpg"
    image = Image.open(image_path)
    image = unet.detect_image(image)
    image.save("miou_pr_dir/" + image_id + ".png")