get_miou.py

import os

from PIL import Image
from tqdm import tqdm

from unet import Unet
from utils.utils_metrics import compute_mIoU, show_results

if __name__ == "__main__":
    miou_mode = 0
    num_classes = 21
    name_classes = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
                    "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train",
                    "tvmonitor"]
    # name_classes    = ["_background_","cat","dog"]
    VOCdevkit_path = 'VOCdevkit'

    image_ids = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"), 'r').read().splitlines()
    gt_dir = os.path.join(VOCdevkit_path, "VOC2007/SegmentationClass/")
    miou_out_path = "miou_out"
    pred_dir = os.path.join(miou_out_path, 'detection-results')

    if miou_mode == 0 or miou_mode == 1:
        if not os.path.exists(pred_dir):
            os.makedirs(pred_dir)

        print("Load model.")
        unet = Unet()
        print("Load model done.")

        print("Get predict result.")
        for image_id in tqdm(image_ids):
            image_path = os.path.join(VOCdevkit_path, "VOC2007/JPEGImages/" + image_id + ".jpg")
            image = Image.open(image_path)
            image = unet.get_miou_png(image)
            image.save(os.path.join(pred_dir, image_id + ".png"))
        print("Get predict result done.")

    if miou_mode == 0 or miou_mode == 2:
        print("Get miou.")
        hist, IoUs, PA_Recall, Precision = compute_mIoU(gt_dir, pred_dir, image_ids, num_classes,
                                                        name_classes)  # 执行计算mIoU的函数
        print("Get miou done.")
        show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)