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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.vision</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .data import (
get_data_aug,
images_from_array,
images_from_csv,
images_from_fname,
images_from_folder,
preprocess_csv,
preview_data_aug,
show_image,
show_random_images,
)
from .models import (
image_classifier,
image_regression_model,
print_image_classifiers,
print_image_regression_models,
)
from .predictor import ImagePredictor
__all__ = [
"image_classifier",
"image_regression_model",
"print_image_classifiers",
"print_image_regression_models",
"images_from_folder",
"images_from_csv",
"images_from_array",
"images_from_fname",
"get_data_aug",
"preprocess_csv",
"ImagePredictor",
"show_image",
"show_random_images",
"preview_data_aug",
]</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="ktrain.vision.caption" href="caption/index.html">ktrain.vision.caption</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision.data" href="data.html">ktrain.vision.data</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision.learner" href="learner.html">ktrain.vision.learner</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision.models" href="models.html">ktrain.vision.models</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision.object_detection" href="object_detection/index.html">ktrain.vision.object_detection</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision.predictor" href="predictor.html">ktrain.vision.predictor</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision.preprocessor" href="preprocessor.html">ktrain.vision.preprocessor</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.vision.wrn" href="wrn.html">ktrain.vision.wrn</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.vision.get_data_aug"><code class="name flex">
<span>def <span class="ident">get_data_aug</span></span>(<span>rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=False, vertical_flip=False, featurewise_center=True, featurewise_std_normalization=True, samplewise_center=False, samplewise_std_normalization=False, rescale=None, **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>This function is simply a wrapper around ImageDataGenerator
with some reasonable defaults for data augmentation.
Returns the default image_data_generator to support
data augmentation and data normalization.
Parameters can be adjusted by caller.
Note that the ktrain.vision.model.image_classifier
function may adjust these as needed.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_data_aug(
rotation_range=40,
zoom_range=0.2,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=False,
vertical_flip=False,
featurewise_center=True,
featurewise_std_normalization=True,
samplewise_center=False,
samplewise_std_normalization=False,
rescale=None,
**kwargs
):
"""
```
This function is simply a wrapper around ImageDataGenerator
with some reasonable defaults for data augmentation.
Returns the default image_data_generator to support
data augmentation and data normalization.
Parameters can be adjusted by caller.
Note that the ktrain.vision.model.image_classifier
function may adjust these as needed.
```
"""
data_aug = keras.preprocessing.image.ImageDataGenerator(
rotation_range=rotation_range,
zoom_range=zoom_range,
width_shift_range=width_shift_range,
height_shift_range=height_shift_range,
horizontal_flip=horizontal_flip,
vertical_flip=vertical_flip,
featurewise_center=featurewise_center,
featurewise_std_normalization=featurewise_std_normalization,
samplewise_center=samplewise_center,
samplewise_std_normalization=samplewise_std_normalization,
rescale=rescale,
**kwargs
)
return data_aug</code></pre>
</details>
</dd>
<dt id="ktrain.vision.image_classifier"><code class="name flex">
<span>def <span class="ident">image_classifier</span></span>(<span>name, train_data, val_data=None, freeze_layers=None, metrics=None, optimizer_name=<keras.optimizers.optimizer_v2.adam.Adam object>, multilabel=None, pt_fc=[], pt_ps=[], verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns a pre-defined/pre-trained model ready to be trained/fine-tuned
for multi-class classification. By default, all layers are
trainable/unfrozen.
Args:
name (string): one of model shown on ktrain.vision.print_image_classifiers
train_data (image.Iterator): train data. Note: Will be manipulated here!
val_data (image.Iterator): validation data. Note: Will be manipulated here!
freeze_layers (int): number of beginning layers to make untrainable
If None, then all layers except new Dense layers
will be frozen/untrainable.
metrics (list): metrics to use
metrics(list): List of metrics to use. If None: 'accuracy' is used for binar/multiclass,
'binary_accuracy' is used for multilabel classification, and 'mae' is used for regression
optimizer_name(str|obj): name of Keras optimizer (e.g., 'adam', 'sgd') or instance of keras Optimizer
multilabel(bool): If True, model will be build to support
multilabel classification (labels are not mutually exclusive).
If False, binary/multiclassification model will be returned.
If None, multilabel status will be inferred from data.
pt_fc (list of ints): number of hidden units in extra Dense layers
before final Dense layer of pretrained model.
Only takes effect if name in PRETRAINED_MODELS
pt_ps (list of floats): dropout probabilities to use before
each extra Dense layer in pretrained model.
Only takes effect if name in PRETRAINED_MODELS
verbose (int): verbosity
Return:
model(Model): the compiled model ready to be fine-tuned/trained
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def image_classifier(
name,
train_data,
val_data=None,
freeze_layers=None,
metrics=None,
optimizer_name=U.DEFAULT_OPT,
multilabel=None,
pt_fc=[],
pt_ps=[],
verbose=1,
):
"""
```
Returns a pre-defined/pre-trained model ready to be trained/fine-tuned
for multi-class classification. By default, all layers are
trainable/unfrozen.
Args:
name (string): one of model shown on ktrain.vision.print_image_classifiers
train_data (image.Iterator): train data. Note: Will be manipulated here!
val_data (image.Iterator): validation data. Note: Will be manipulated here!
freeze_layers (int): number of beginning layers to make untrainable
If None, then all layers except new Dense layers
will be frozen/untrainable.
metrics (list): metrics to use
metrics(list): List of metrics to use. If None: 'accuracy' is used for binar/multiclass,
'binary_accuracy' is used for multilabel classification, and 'mae' is used for regression
optimizer_name(str|obj): name of Keras optimizer (e.g., 'adam', 'sgd') or instance of keras Optimizer
multilabel(bool): If True, model will be build to support
multilabel classification (labels are not mutually exclusive).
If False, binary/multiclassification model will be returned.
If None, multilabel status will be inferred from data.
pt_fc (list of ints): number of hidden units in extra Dense layers
before final Dense layer of pretrained model.
Only takes effect if name in PRETRAINED_MODELS
pt_ps (list of floats): dropout probabilities to use before
each extra Dense layer in pretrained model.
Only takes effect if name in PRETRAINED_MODELS
verbose (int): verbosity
Return:
model(Model): the compiled model ready to be fine-tuned/trained
```
"""
return image_model(
name,
train_data,
val_data=val_data,
freeze_layers=freeze_layers,
metrics=metrics,
optimizer_name=optimizer_name,
multilabel=multilabel,
pt_fc=pt_fc,
pt_ps=pt_ps,
verbose=verbose,
)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.image_regression_model"><code class="name flex">
<span>def <span class="ident">image_regression_model</span></span>(<span>name, train_data, val_data=None, freeze_layers=None, metrics=['mae'], optimizer_name=<keras.optimizers.optimizer_v2.adam.Adam object>, pt_fc=[], pt_ps=[], verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns a pre-defined/pre-trained model ready to be trained/fine-tuned
for multi-class classification. By default, all layers are
trainable/unfrozen.
Args:
name (string): one of model shown on ktrain.vision.print_image_regression_models
train_data (image.Iterator): train data. Note: Will be manipulated here!
val_data (image.Iterator): validation data. Note: Will be manipulated here!
freeze_layers (int): number of beginning layers to make untrainable
If None, then all layers except new Dense layers
will be frozen/untrainable.
metrics (list): metrics to use
optimizer_name(str): name of Keras optimizer (e.g., 'adam', 'sgd')
multilabel(bool): If True, model will be build to support
multilabel classificaiton (labels are not mutually exclusive).
If False, binary/multiclassification model will be returned.
If None, multilabel status will be inferred from data.
pt_fc (list of ints): number of hidden units in extra Dense layers
before final Dense layer of pretrained model.
Only takes effect if name in PRETRAINED_MODELS
pt_ps (list of floats): dropout probabilities to use before
each extra Dense layer in pretrained model.
Only takes effect if name in PRETRAINED_MODELS
verbose (int): verbosity
Return:
model(Model): the compiled model ready to be fine-tuned/trained
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def image_regression_model(
name,
train_data,
val_data=None,
freeze_layers=None,
metrics=["mae"],
optimizer_name=U.DEFAULT_OPT,
pt_fc=[],
pt_ps=[],
verbose=1,
):
"""
```
Returns a pre-defined/pre-trained model ready to be trained/fine-tuned
for multi-class classification. By default, all layers are
trainable/unfrozen.
Args:
name (string): one of model shown on ktrain.vision.print_image_regression_models
train_data (image.Iterator): train data. Note: Will be manipulated here!
val_data (image.Iterator): validation data. Note: Will be manipulated here!
freeze_layers (int): number of beginning layers to make untrainable
If None, then all layers except new Dense layers
will be frozen/untrainable.
metrics (list): metrics to use
optimizer_name(str): name of Keras optimizer (e.g., 'adam', 'sgd')
multilabel(bool): If True, model will be build to support
multilabel classificaiton (labels are not mutually exclusive).
If False, binary/multiclassification model will be returned.
If None, multilabel status will be inferred from data.
pt_fc (list of ints): number of hidden units in extra Dense layers
before final Dense layer of pretrained model.
Only takes effect if name in PRETRAINED_MODELS
pt_ps (list of floats): dropout probabilities to use before
each extra Dense layer in pretrained model.
Only takes effect if name in PRETRAINED_MODELS
verbose (int): verbosity
Return:
model(Model): the compiled model ready to be fine-tuned/trained
```
"""
return image_model(
name,
train_data,
val_data=val_data,
freeze_layers=freeze_layers,
metrics=metrics,
optimizer_name=optimizer_name,
multilabel=False,
pt_fc=pt_fc,
pt_ps=pt_ps,
verbose=verbose,
)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.images_from_array"><code class="name flex">
<span>def <span class="ident">images_from_array</span></span>(<span>x_train, y_train, validation_data=None, val_pct=0.1, random_state=None, data_aug=None, classes=None, class_names=None, is_regression=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns image generator (Iterator instance) from training
and validation data in the form of NumPy arrays.
This function only supports image classification.
For image regression, please use images_from_df.
Args:
x_train(numpy.ndarray): training gdata
y_train(numpy.ndarray): labels must either be:
1. one-hot (or multi-hot) encoded arrays
2. integer values representing the label
validation_data (tuple): tuple of numpy.ndarrays for validation data.
labels should be in one of the formats listed above.
val_pct(float): percentage of training data to use for validaton if validation_data is None
random_state(int): random state to use for splitting data
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
classes(str): old name for class_names - should no longer be used
class_names(str): list of strings to use as class names
is_regression(bool): If True, task is treated as regression.
Used when there is single column of numeric values and
numeric values should be treated as numeric targets as opposed to class labels
Returns:
batches: a tuple of two image.Iterator - one for train and one for test and ImagePreprocessor instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def images_from_array(
x_train,
y_train,
validation_data=None,
val_pct=0.1,
random_state=None,
data_aug=None,
classes=None,
class_names=None,
is_regression=False,
):
"""
```
Returns image generator (Iterator instance) from training
and validation data in the form of NumPy arrays.
This function only supports image classification.
For image regression, please use images_from_df.
Args:
x_train(numpy.ndarray): training gdata
y_train(numpy.ndarray): labels must either be:
1. one-hot (or multi-hot) encoded arrays
2. integer values representing the label
validation_data (tuple): tuple of numpy.ndarrays for validation data.
labels should be in one of the formats listed above.
val_pct(float): percentage of training data to use for validaton if validation_data is None
random_state(int): random state to use for splitting data
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
classes(str): old name for class_names - should no longer be used
class_names(str): list of strings to use as class names
is_regression(bool): If True, task is treated as regression.
Used when there is single column of numeric values and
numeric values should be treated as numeric targets as opposed to class labels
Returns:
batches: a tuple of two image.Iterator - one for train and one for test and ImagePreprocessor instance
```
"""
if classes is not None:
raise ValueError('Please use class_names argument instead of "classes".')
if class_names and is_regression:
warnings.warn(
"is_regression=True, but class_names is not empty. Task being treated as regression."
)
# split out validation set if necessary
if validation_data:
x_test = validation_data[0]
y_test = validation_data[1]
elif val_pct is not None and val_pct > 0:
x_train, x_test, y_train, y_test = train_test_split(
x_train, y_train, test_size=val_pct, random_state=random_state
)
else:
x_test = None
y_test = None
# transform labels
ytrans = U.YTransform(class_names=class_names if not is_regression else [])
y_train = ytrans.apply_train(y_train)
y_test = ytrans.apply_test(y_test)
class_names = ytrans.get_classes()
# train and test data generators
(train_datagen, test_datagen) = process_datagen(data_aug, train_array=x_train)
# Image preprocessor
preproc = ImagePreprocessor(
test_datagen, class_names, target_size=None, color_mode=None
)
# training data
batches_tr = train_datagen.flow(x_train, y_train, shuffle=True)
# validation data
batches_te = None
if x_test is not None and y_test is not None:
batches_te = test_datagen.flow(x_test, y_test, shuffle=False)
return (batches_tr, batches_te, preproc)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.images_from_csv"><code class="name flex">
<span>def <span class="ident">images_from_csv</span></span>(<span>train_filepath, image_column, label_columns=[], directory=None, suffix='', val_filepath=None, is_regression=False, target_size=(224, 224), color_mode='rgb', data_aug=None, val_pct=0.1, random_state=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns image generator (Iterator instance).
Assumes output will be 2D one-hot-encoded labels for categorization.
Note: This function preprocesses the input in preparation
for a ResNet50 model.
Args:
train_filepath (string): path to training dataset in CSV format with header row
image_column (string): name of column containing the filenames of images
If values in image_column do not have a file extension,
the extension should be supplied with suffix argument.
If values in image_column are not full file paths,
then the path to directory containing images should be supplied
as directory argument.
label_columns(list or str): list or str representing the columns that store labels
Labels can be in any one of the following formats:
1. a single column string string (or integer) labels
image_fname,label
-----------------
image01,cat
image02,dog
2. multiple columns for one-hot-encoded labels
image_fname,cat,dog
image01,1,0
image02,0,1
3. a single column of numeric values for image regression
image_fname,age
-----------------
image01,68
image02,18
directory (string): path to directory containing images
not required if image_column contains full filepaths
suffix(str): will be appended to each entry in image_column
Used when the filenames in image_column do not contain file extensions.
The extension in suffx should include ".".
val_filepath (string): path to validation dataset in CSV format
suffix(string): suffix to add to file names in image_column
is_regression(bool): If True, task is treated as regression.
Used when there is single column of numeric values and
numeric values should be treated as numeric targets as opposed to class labels
target_size (tuple): image dimensions
color_mode (string): color mode
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
for data augmentation
val_pct(float): proportion of training data to be used for validation
only used if val_filepath is None
random_state(int): random seed for train/test split
Returns:
batches: a tuple of two Iterators - one for train and one for test
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def images_from_csv(
train_filepath,
image_column,
label_columns=[],
directory=None,
suffix="",
val_filepath=None,
is_regression=False,
target_size=(224, 224),
color_mode="rgb",
data_aug=None,
val_pct=0.1,
random_state=None,
):
"""
```
Returns image generator (Iterator instance).
Assumes output will be 2D one-hot-encoded labels for categorization.
Note: This function preprocesses the input in preparation
for a ResNet50 model.
Args:
train_filepath (string): path to training dataset in CSV format with header row
image_column (string): name of column containing the filenames of images
If values in image_column do not have a file extension,
the extension should be supplied with suffix argument.
If values in image_column are not full file paths,
then the path to directory containing images should be supplied
as directory argument.
label_columns(list or str): list or str representing the columns that store labels
Labels can be in any one of the following formats:
1. a single column string string (or integer) labels
image_fname,label
-----------------
image01,cat
image02,dog
2. multiple columns for one-hot-encoded labels
image_fname,cat,dog
image01,1,0
image02,0,1
3. a single column of numeric values for image regression
image_fname,age
-----------------
image01,68
image02,18
directory (string): path to directory containing images
not required if image_column contains full filepaths
suffix(str): will be appended to each entry in image_column
Used when the filenames in image_column do not contain file extensions.
The extension in suffx should include ".".
val_filepath (string): path to validation dataset in CSV format
suffix(string): suffix to add to file names in image_column
is_regression(bool): If True, task is treated as regression.
Used when there is single column of numeric values and
numeric values should be treated as numeric targets as opposed to class labels
target_size (tuple): image dimensions
color_mode (string): color mode
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
for data augmentation
val_pct(float): proportion of training data to be used for validation
only used if val_filepath is None
random_state(int): random seed for train/test split
Returns:
batches: a tuple of two Iterators - one for train and one for test
```
"""
# convert to dataframes
train_df = pd.read_csv(train_filepath)
val_df = None
if val_filepath is not None:
val_df = pd.read_csv(val_filepath)
return images_from_df(
train_df,
image_column,
label_columns=label_columns,
directory=directory,
suffix=suffix,
val_df=val_df,
is_regression=is_regression,
target_size=target_size,
color_mode=color_mode,
data_aug=data_aug,
val_pct=val_pct,
random_state=random_state,
)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.images_from_fname"><code class="name flex">
<span>def <span class="ident">images_from_fname</span></span>(<span>train_folder, pattern='([^/]+)_\\d+.jpg$', val_folder=None, is_regression=False, target_size=(224, 224), color_mode='rgb', data_aug=None, val_pct=0.1, random_state=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns image generator (Iterator instance).
Args:
train_folder (str): directory containing images
pat (str): regular expression to extract class from file name of each image
Example: r'([^/]+)_\d+.jpg$' to match 'english_setter' in 'english_setter_140.jpg'
By default, it will extract classes from file names of the form:
<class_name>_<numbers>.jpg
val_folder (str): directory containing validation images. default:None
is_regression(bool): If True, task is treated as regression.
Used when there is single column of numeric values and
numeric values should be treated as numeric targets as opposed to class labels
target_size (tuple): image dimensions
color_mode (string): color mode
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
for data augmentation
val_pct(float): proportion of training data to be used for validation
only used if val_folder is None
random_state(int): random seed for train/test split
verbose(bool): verbosity
Returns:
batches: a tuple of two Iterators - one for train and one for test
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def images_from_fname(
train_folder,
pattern=r"([^/]+)_\d+.jpg$",
val_folder=None,
is_regression=False,
target_size=(224, 224),
color_mode="rgb",
data_aug=None,
val_pct=0.1,
random_state=None,
verbose=1,
):
"""
```
Returns image generator (Iterator instance).
Args:
train_folder (str): directory containing images
pat (str): regular expression to extract class from file name of each image
Example: r'([^/]+)_\d+.jpg$' to match 'english_setter' in 'english_setter_140.jpg'
By default, it will extract classes from file names of the form:
<class_name>_<numbers>.jpg
val_folder (str): directory containing validation images. default:None
is_regression(bool): If True, task is treated as regression.
Used when there is single column of numeric values and
numeric values should be treated as numeric targets as opposed to class labels
target_size (tuple): image dimensions
color_mode (string): color mode
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
for data augmentation
val_pct(float): proportion of training data to be used for validation
only used if val_folder is None
random_state(int): random seed for train/test split
verbose(bool): verbosity
Returns:
batches: a tuple of two Iterators - one for train and one for test
```
"""
image_column = "image_name"
label_column = "label"
train_df = _img_fnames_to_df(
train_folder,
pattern,
image_column=image_column,
label_column=label_column,
verbose=verbose,
)
val_df = None
if val_folder is not None:
val_df = _img_fnames_to_df(
val_folder,
pattern,
image_column=image_column,
label_column=label_column,
verbose=verbose,
)
return images_from_df(
train_df,
image_column,
label_columns=label_column,
directory=train_folder,
val_directory=val_folder,
val_df=val_df,
is_regression=is_regression,
target_size=target_size,
color_mode=color_mode,
data_aug=data_aug,
val_pct=val_pct,
random_state=random_state,
)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.images_from_folder"><code class="name flex">
<span>def <span class="ident">images_from_folder</span></span>(<span>datadir, target_size=(224, 224), classes=None, color_mode='rgb', train_test_names=['train', 'test'], data_aug=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns image generator (Iterator instance).
Assumes output will be 2D one-hot-encoded labels for categorization.
Note: This function preprocesses the input in preparation
for a ResNet50 model.
Args:
datadir (string): path to training (or validation/test) dataset
Assumes folder follows this structure:
├── datadir
│ ├── train
│ │ ├── class0 # folder containing documents of class 0
│ │ ├── class1 # folder containing documents of class 1
│ │ ├── class2 # folder containing documents of class 2
│ │ └── classN # folder containing documents of class N
│ └── test
│ ├── class0 # folder containing documents of class 0
│ ├── class1 # folder containing documents of class 1
│ ├── class2 # folder containing documents of class 2
│ └── classN # folder containing documents of class N
target_size (tuple): image dimensions
classes (list): optional list of class subdirectories (e.g., ['cats','dogs'])
color_mode (string): color mode
train_test_names(list): names for train and test subfolders
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
for data augmentation
verbose (bool): verbosity
Returns:
batches: a tuple of two Iterators - one for train and one for test
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def images_from_folder(
datadir,
target_size=(224, 224),
classes=None,
color_mode="rgb",
train_test_names=["train", "test"],
data_aug=None,
verbose=1,
):
"""
```
Returns image generator (Iterator instance).
Assumes output will be 2D one-hot-encoded labels for categorization.
Note: This function preprocesses the input in preparation
for a ResNet50 model.
Args:
datadir (string): path to training (or validation/test) dataset
Assumes folder follows this structure:
├── datadir
│ ├── train
│ │ ├── class0 # folder containing documents of class 0
│ │ ├── class1 # folder containing documents of class 1
│ │ ├── class2 # folder containing documents of class 2
│ │ └── classN # folder containing documents of class N
│ └── test
│ ├── class0 # folder containing documents of class 0
│ ├── class1 # folder containing documents of class 1
│ ├── class2 # folder containing documents of class 2
│ └── classN # folder containing documents of class N
target_size (tuple): image dimensions
classes (list): optional list of class subdirectories (e.g., ['cats','dogs'])
color_mode (string): color mode
train_test_names(list): names for train and test subfolders
data_aug(ImageDataGenerator): a keras.preprocessing.image.ImageDataGenerator
for data augmentation
verbose (bool): verbosity
Returns:
batches: a tuple of two Iterators - one for train and one for test
```
"""
# train/test names
train_str = train_test_names[0]
test_str = train_test_names[1]
train_dir = os.path.join(datadir, train_str)
test_dir = os.path.join(datadir, test_str)
# color mode warning
if PIL_INSTALLED:
inferred_color_mode = detect_color_mode(train_dir)
if inferred_color_mode is not None and (inferred_color_mode != color_mode):
U.vprint(
"color_mode detected (%s) different than color_mode selected (%s)"
% (inferred_color_mode, color_mode),
verbose=verbose,
)
# get train and test data generators
(train_datagen, test_datagen) = process_datagen(
data_aug,
train_directory=train_dir,
target_size=target_size,
color_mode=color_mode,
)
batches_tr = train_datagen.flow_from_directory(
train_dir,
target_size=target_size,
classes=classes,
class_mode="categorical",
shuffle=True,
interpolation="bicubic",
color_mode=color_mode,
)
batches_te = test_datagen.flow_from_directory(
test_dir,
target_size=target_size,
classes=classes,
class_mode="categorical",
shuffle=False,
interpolation="bicubic",
color_mode=color_mode,
)
# setup preprocessor
class_tup = sorted(batches_tr.class_indices.items(), key=operator.itemgetter(1))
preproc = ImagePreprocessor(
test_datagen,
[x[0] for x in class_tup],
target_size=target_size,
color_mode=color_mode,
)
return (batches_tr, batches_te, preproc)</code></pre>
</details>
</dd>
<dt id="ktrain.vision.preprocess_csv"><code class="name flex">
<span>def <span class="ident">preprocess_csv</span></span>(<span>csv_in, csv_out, x_col='filename', y_col=None, sep=',', label_sep=' ', suffix='', split_by=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Takes a CSV where the one column contains a file name and a column
containing a string representations of the class(es) like here:
image_name,tags
01, sunny|hot
02, cloudy|cold
03, cloudy|hot
.... and one-hot encodes the classes to produce a CSV as follows:
image_name, cloudy, cold, hot, sunny
01.jpg,0,0,1,1
02.jpg,1,1,0,0
03.jpg,1,0,1,0
Args:
csv_in (str): filepath to input CSV file
csv_out (str): filepath to output CSV file
x_col (str): name of column containing file names
y_col (str): name of column containing the classes
sep (str): field delimiter of entire file (e.g., comma fore CSV)
label_sep (str): delimiter for column containing classes
suffix (str): adds suffix to x_col values
split_by(str): name of column. A separate CSV will be
created for each value in column. Useful
for splitting a CSV based on whether a column
contains 'train' or 'valid'.
Return:
list : the list of clases (and csv_out will be new CSV file)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess_csv(
csv_in,
csv_out,
x_col="filename",
y_col=None,
sep=",",
label_sep=" ",
suffix="",
split_by=None,
):
"""
```
Takes a CSV where the one column contains a file name and a column
containing a string representations of the class(es) like here:
image_name,tags
01, sunny|hot
02, cloudy|cold
03, cloudy|hot
.... and one-hot encodes the classes to produce a CSV as follows:
image_name, cloudy, cold, hot, sunny
01.jpg,0,0,1,1
02.jpg,1,1,0,0
03.jpg,1,0,1,0
Args:
csv_in (str): filepath to input CSV file
csv_out (str): filepath to output CSV file
x_col (str): name of column containing file names
y_col (str): name of column containing the classes
sep (str): field delimiter of entire file (e.g., comma fore CSV)
label_sep (str): delimiter for column containing classes
suffix (str): adds suffix to x_col values
split_by(str): name of column. A separate CSV will be
created for each value in column. Useful
for splitting a CSV based on whether a column
contains 'train' or 'valid'.
Return:
list : the list of clases (and csv_out will be new CSV file)
```
"""
if not y_col and not suffix:
raise ValueError("one or both of y_col and suffix should be supplied")
df = pd.read_csv(csv_in, sep=sep)
f_csv_out = open(csv_out, "w")
writer = csv.writer(f_csv_out, delimiter=sep)
if y_col:
df[y_col] = df[y_col].apply(str)
# write header
if y_col:
classes = set()
for row in df.iterrows():
data = row[1]
tags = data[y_col].split(label_sep)
classes.update(tags)
classes = list(classes)
classes.sort()
writer.writerow([x_col] + classes)
else:
classes = df.columns[:-1]
write.writerow(df.columns)
# write rows
for row in df.iterrows():
data = row[1]
data[x_col] = data[x_col] + suffix
if y_col:
out = list(data[[x_col]].values)
tags = set(data[y_col].strip().split(label_sep))
for c in classes:
if c in tags:
out.append(1)
else:
out.append(0)
else:
out = data
writer.writerow(out)
f_csv_out.close()
return classes</code></pre>
</details>
</dd>
<dt id="ktrain.vision.preview_data_aug"><code class="name flex">
<span>def <span class="ident">preview_data_aug</span></span>(<span>img_path, data_aug, rows=1, n=4)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Preview data augmentation (ImageDatagenerator)
on a supplied image.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preview_data_aug(img_path, data_aug, rows=1, n=4):