luuil

"""
Manually download the ollama model
1. You need a software (Linux only): https://github.com/amirrezaDev1378/ollama-model-direct-download
2. Use the software to get the download links of the model, it will output 6 links. (If the file downloaded from the release page is not available, you need to install the Go environment and compile it yourself)
3. You can manually download them, or use this script to download all at once.
"""
import subprocess
import re
import json
import requests

luuil

"""
# @ Author: Lu Liu
# @ Create Time: 2024-12-05 14:28:43
# @ Modified by: Lu Liu
# @ Modified time: 2025-03-20 18:56:59
# @ Description: 从huggingface下载
#  - 整个repo
#  - 单个文件

# huya海聪平台外网加速器 https://ai.huya.com/docs/QA/common.html#%E9%80%9A%E7%94%A8%E5%A4%96%E7%BD%91%E4%B8%8B%E8%BD%BD%E5%8A%A0%E9%80%9F%E5%99%A8

luuil

'''
 # @ Author: Lu Liu
 # @ Create Time: 2024-02-28 18:02:05
 # @ Modified by: Lu Liu
 # @ Modified time: 2024-02-28 18:08:28
 # @ Description:
 '''
import time

luuil

import os
import tensorflow as tf
import numpy as np
from PIL import Image
import io


def bytes_feature(value):
  """Returns a bytes_list from a string / byte."""
  if isinstance(value, type(tf.constant(0))):

luuil

import numpy as np


class TopK(object):
  def __init__(self, k, key=None, reverse=False) -> None:
    """ Find k largest or smallest(reverse is True) elements from stream data.
    """
    super(TopK, self).__init__()
    self._heap = list()
    self._k = k

luuil

import os
import time
import subprocess
import shlex
import numpy as np


def run_process(tmpl, *args, **kwargs):
    _args = args
    cmd = tmpl.format(**kwargs)

luuil

import cv2
import os
import numpy as np
from PIL import Image, ImageFont, ImageDraw
from typing import Tuple, Optional, Union, List
from dataclasses import dataclass, InitVar
import sys
from pathlib import Path
from enum import Enum
import logging

luuil

def describe_graph(graph_def, show_nodes=False):
    from collections import Counter
    c = Counter(map(lambda n: n.op, graph_def.node))
    print(c)

    print('Input Nodes: {}'.format([node.name for node in graph_def.node if node.op == 'Placeholder']))

    output_nodes = [node.name for node in graph_def.node if (
            'predictions' in node.name or 'softmax' in node.name or 'concat' in node.name)]
    print(f'Output Nodes: {output_nodes}')

luuil

import cv2
import os


class ExtractFromVideo(object):
    def __init__(self, path, frame_range=None, debug=False):
        assert os.path.exists(path)

        self._p = path
        self._vc = cv2.VideoCapture(self._p)

luuil

def freeze_graph(model_dir, output_node_names):
    """Extract the sub graph defined by the output nodes and convert 
    all its variables into constant 
    Args:
        model_dir: the root folder containing the checkpoint state file
        output_node_names: a string, containing all the output node's names, 
                            comma separated
    """
    from os.path import join
    import tensorflow as tf