63 lines
3.0 KiB
Python
63 lines
3.0 KiB
Python
import os
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from PIL import Image
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from tqdm import tqdm
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from segformer import SegFormer_Segmentation
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from utils.utils_metrics import compute_mIoU, show_results
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'''
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进行指标评估需要注意以下几点：
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1、该文件生成的图为灰度图，因为值比较小，按照PNG形式的图看是没有显示效果的，所以看到近似全黑的图是正常的。
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2、该文件计算的是验证集的miou，当前该库将测试集当作验证集使用，不单独划分测试集
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'''
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if __name__ == "__main__":
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#---------------------------------------------------------------------------#
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# miou_mode用于指定该文件运行时计算的内容
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# miou_mode为0代表整个miou计算流程，包括获得预测结果、计算miou。
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# miou_mode为1代表仅仅获得预测结果。
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# miou_mode为2代表仅仅计算miou。
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#---------------------------------------------------------------------------#
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miou_mode = 0
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#------------------------------#
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# 分类个数+1、如2+1
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#------------------------------#
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num_classes = 21
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#--------------------------------------------#
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# 区分的种类，和json_to_dataset里面的一样
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#--------------------------------------------#
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name_classes = ["background","aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
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# name_classes = ["_background_","cat","dog"]
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#-------------------------------------------------------#
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# 指向VOC数据集所在的文件夹
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# 默认指向根目录下的VOC数据集
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#-------------------------------------------------------#
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VOCdevkit_path = 'VOCdevkit'
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image_ids = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"),'r').read().splitlines()
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gt_dir = os.path.join(VOCdevkit_path, "VOC2007/SegmentationClass/")
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miou_out_path = "miou_out"
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pred_dir = os.path.join(miou_out_path, 'detection-results')
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if miou_mode == 0 or miou_mode == 1:
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if not os.path.exists(pred_dir):
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os.makedirs(pred_dir)
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print("Load model.")
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segformer = SegFormer_Segmentation()
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print("Load model done.")
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print("Get predict result.")
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for image_id in tqdm(image_ids):
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image_path = os.path.join(VOCdevkit_path, "VOC2007/JPEGImages/"+image_id+".jpg")
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image = Image.open(image_path)
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image = segformer.get_miou_png(image)
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image.save(os.path.join(pred_dir, image_id + ".png"))
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print("Get predict result done.")
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if miou_mode == 0 or miou_mode == 2:
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print("Get miou.")
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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
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print("Get miou done.")
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show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)
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