学习日记

接触了DELF模型，用来找图片之间的局部相似图片(注重语义上的)，从github上下的源码，找图片相似性分为两步：

1 抽取特征

2 匹配图片

为将其改成适用于自己任务，对代码进行了修改

    txt文件的命令行参数换成了图片的文件路径

parser.add_argument( '--full_images', type=str, default='list_images.txt', help=""" """) parser.add_argument( '--part_images', type=str, default='list_images.txt', help=""" Path to list of images whose DELF features will be extracted. """)

    相应的解析参数的代码也进行了修改

def _ReadImageList(full,part): """Helper function to read image paths. Args: list_path: Path to list of images, one image path per line. Returns: image_paths: List of image paths. """ image_paths = [full,part] return image_paths

脚本文件批量处理图片：

import os full_images = [] part_images = [] for path in os.listdir('full_path'):     full_images.append(path) for path in os.listdir('part_path'):     part_images.append(path) full_images = sorted(full_images) part_images = sorted(part_images) assert(len(full_images)==len(part_images)) assert(full_images[0].split('_')[1]==part_images[0].split('_')[1]) for i in range(133,len(full_images)):     first_command = "python3.5 extract_features.py \     --config_path delf_config_example.pbtxt \     --full_images imgs/full/"+full_images[i]+" \     --part_images imgs/part/"+part_images[i]+" \     --output_dir data/"     print('start '+full_images[i])     print('start '+part_images[i])     os.system(first_command)     second_command = "python3.5 match_images.py \     --image_1_path imgs/full/"+full_images[i]+" \     --image_2_path imgs/part/"+part_images[i]+" \     --features_1_path data/"+full_images[i].split('.')[0]+".delf \     --features_2_path data/"+part_images[i].split('.')[0]+".delf \     --output_image output/"+full_images[i].split('_')[1].split('.')[0]+'.png'     os.system(second_command)其中遇到几个问题，图片不清晰，是因为matplotlib分辨率过低。 plt.rcParams['savefig.dpi']=300 plt.rcParams['figure.dpi']=300

gpu内存使用过高导致tf.ssesion开启失败。

sudo fuser -v /dev/nvidia* #查找占用GPU资源的PID kill -9 pid