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3a61b02 7817b5d 3a61b02 36b8bfd 3a61b02 1d966f6 3a61b02 1d966f6 3a61b02 1d966f6 36b8bfd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | import os
from box.exceptions import BoxValueError
import yaml
from cnnClassifier import logger
import joblib
import json
from box import ConfigBox
from ensure import ensure_annotations
from pathlib import Path
from typing import Any
import base64
import tensorflow as tf
@ensure_annotations
def read_yaml (path_to_yaml: Path) -> ConfigBox:
"""reads yaml file and returns
Args:
path_to_yaml (str): path like input
Raises:
ValueError: if yaml file is empty
e: empty file
Returns:
ConfigBox: ConfigBox type
"""
try:
with open(path_to_yaml) as yaml_file:
content = yaml.safe_load(yaml_file)
logger.info(f"yaml file: {path_to_yaml} loaded successfully")
return ConfigBox(content)
except BoxValueError:
raise ValueError ("yaml file is empty")
except Exception as e:
raise e
@ensure_annotations
def create_directories (path_to_directories: list, verbose=True):
"""create list of directories
Args:
path_to_directories (list): list of path of directories
ignore_log (bool, optional): ignore if multiple dirs is to be created. Defaults to False
"""
for path in path_to_directories:
os.makedirs(path, exist_ok=True)
if verbose:
logger.info(f"created directory at: {path}")
@ensure_annotations
def save_json(path: Path, data: dict):
"""save json data
Args:
path (Path): path to json file
data (dict): data to be saved in json file
"""
with open(path, "w") as f:
json.dump(data, f, indent=4)
logger.info(f"json file saved at: {path}")
@ensure_annotations
def load_json(path: Path) -> ConfigBox:
"""load json files data
Args:
path (Path): path to json file
Returns:
ConfigBox data as class attribute instead of dict
"""
with open(path) as f:
content = json.load(f)
logger.info(f"json file loaded successfully from {path}")
return ConfigBox(content)
@ensure_annotations
def save_bin(data: Any, path: Path):
"""save binary file
Args:
data (Any): data to be saved as binary
path (Path): path to binary file
"""
joblib.dump(value=data, filename=path)
logger.info(f"binary file saved at {path}")
@ensure_annotations
def load_bin(path: Path) -> Any:
"""load binary data
Args:
path (Path): path to binary file
Returns:
Any: object stored in the file
"""
data = joblib.load(path)
logger.info(f"binary file loaded from {path}")
return data
@ensure_annotations
def get_size(path: Path) -> str:
"""get size in KB
Args:
path (Path): path of the file
Returns:
str: size in KB
"""
size_in_kb = round(os.path.getsize(path)/1024)
return f"~(size_in_kb) KB"
def decodeImage(imgstring, fileName):
imgdata = base64.b64decode(imgstring)
with open(fileName, 'wb') as f:
f.write(imgdata)
f.close()
def encodeImageIntoBase64 (croppedImagePath):
with open(croppedImagePath, "rb") as f:
return base64.b64encode(f.read())
PREPROCESSING_MAP = {
"VGG16": None,
"EfficientNetV2B3": tf.keras.applications.efficientnet_v2.preprocess_input,
"DenseNet121": tf.keras.applications.densenet.preprocess_input,
"ResNet50V2": tf.keras.applications.resnet_v2.preprocess_input,
}
def build_datagenerator_kwargs(model_name: str) -> dict:
preprocess_fn = PREPROCESSING_MAP.get(model_name)
if preprocess_fn:
return dict(
preprocessing_function=preprocess_fn,
validation_split=0.20
)
return dict(
rescale=1./255,
validation_split=0.20
)
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