Gap Framework - Computer Vision for Image Data

VISION MODULE

High Precision Image Processing
Technical Specification, Gap v0.9.4

1 Images

1.1 Images Overview

The Images CV preprocessor contains the following primary classes, and their relationships:

Images - This is the base class for the representation of a Computer Vision (CV) preprocessed list of images. The constructor optionally takes as parameters a list of images (paths), and corresponding labels, and flags for CV preprocessing the images into machine learning ready data.

images = Images([<list of images>], [<list_of_labels>], flags …)

Alternately, the list of images can be a list of directories which contain images.
Alternately, the list of images can be a list of URLs of remotely stored images.
Alternately, the list of images can be a multi-dimensional numpy array (where the first dimension is the number of images).
Alternately, the list of images can be a list of multi-dimensional numpy arrays.
Alternately, the list of labels maybe a single value; in which case, the label applies to all the images.
Alternately, the list of labels maybe a numpy 1D (not one-hot encoded) vector or 2D (one-hot encoded) matrix.

Image – This is the base class for the representation of a single Computer Vision (CV). The constructor optionally takes as parameters an image (path), corresponding label, and flags for CV preprocessing the image.

Alternately, the image can be an URL of a remotely stored image.

Fig. 1a High Level view of Images Class Object Relationships

1.2 Images Initializer (Constructor)

Synopsis

Images(images=None, labels= None, dir=’./’, name=None, ehandler=None, config=None)

Parameters

images: If not None, a list of either:

local image files.
remote image files (i.e., http[s]://….).
directories of local image files.
or a multi-dimensional numpy array.
or a list of multi-dimensional numpy arrays.

For a single multi-dimensional numpy array, the first dimension are the individual images. For example, the Tensorflow training set for MNIST data is a numpy array of shape (55000, 784). When passed as the images parameter it would be treated as 55,000 images of a 1D vector size 784 pixels.

labels: If not None, either:

A single integer value (i.e., label) which corresponds to all the images.
A list of the same size as images parameter list of integer values; where the index of each value is the label for the corresponding index in the images parameter.
A numpy 1D vector of the same size as images parameter list of integer values; where the index of each value is the label for the corresponding index in the images parameter.
A numpy 2D vector where the first dimension is of the same size as images parameter list, and the second dimension is a one-hot encoded 1D vector.

dir: If not ./, the directory where to store the machine learning ready data.

name: If not None, a name (string) for the collection.

ehandler: If not None, the processing of the images into machine learning ready data will be asynchronous, and the value of the parameter is the function (or method) that is the event handler when processing is complete.

The event handler takes the form:

def myHandler(images): 
 	 	# Where images is the Images object that was preprocessed.

config: If not None, a list of one or more configuration settings as strings:

    grayscale               | gray  
    flatten                 | flat  
    resize=(height,width)   | resize=height,width  
    thumb=(height,width)    | thumb=height,width  
    float16                 | float32 | float64
    nostore
    raw
nlabels=(n)

Usage

When specified with no parameters, an empty Images object is created. The Images object may then be used to subsequent load (retrieve) previously stored preprocessed machine learning ready data (see load()).

Otherwise, both images and labels parameters must be specified. The labels parameter corresponds to the labels of the images. Each image specified by the images parameter will be preprocessed according to the optional parameters and configuration settings (i.e., config parameter).

By default, the images will be preprocessed as follows:

An Image object is created for each image.
The config parameter passed to the Image initializer (constructor) will have the ‘nostore’ setting implicitly added, which instructs each Image object to not separately store the generated preprocessed machine learning ready data.
Upon completion, the preprocessed machine learning data for each image is stored as a single HDF5 file in the current working directory, unless the config parameter 'nostore' was specified. If either the 'raw' or 'thumb' configuration settings are specified, the corresponding raw pixel and thumbnail data for each image are stored in the HDF5 file. The root name of the file will be the root name of the first image, preprended with 'collection'. For example, if the first image was cat.jpg, then the root name of the HDF5 will be:

collection.cat.h5

If either or both the dir and config options are not None, they are passed down to each Image object.

If the name parameter is specified, the value will be the root name of the HDF5 stored file (overriding the above described default behavior. For example, if the parameter name is set to 'foobar', then the root name of the HDF5 will be:

foobar.h5

If the ehandler parameter is not None, then the above will occur asynchronously, and when completed, the corresponding event handler will be called with the Images object passed as a parameter. The ehandler parameter may also be specified as a tuple, where the first item in the tuple is the event handler and the remaining items are arguments to pass to the event handler.

# invoke without arguments
def done(images):
	print(images.time)

images = Images(list, labels, ehandler=done)

# invoke with arguments
def done2(images, val):
	print(images.time, val)

images = Images(list, labels, ehandler=(done, 10))

If an exception is raised during aysnchronous processing of the image, then the exception is passed to the event handler instead of an Image object.

def done(image):
	# An exception occurred
	if isinstance(image, Exception):
		pass
	# Processing was successful
	else:
		pass

If the path to an image file is remote (i.e., starts with http), an HTTP request will be made to fetch the contents of the file from the remote location.

By default, when one-hot encoding of the labels, the Images object uses np.max() to calculate the total number of labels in the collection. The nlabels=n, where n is the number of labels, configuration setting will override the internal calculation.

Preprocessing Errors

During preprocessing of each individual image, if the preprocessing of the image fails, its corresponding Image object in the Images collection will be None, and are not written to HDF5 storage. For example, if ten images are to be preprocessed and two failed, then only eight Image objects are written to the HDF5 storage. The number of images that failed to be preprocessed is obtainable from the property fail.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A AttributeError is raised if an invalid configuration setting is specified.
A IndexError is raised if the size of the labels list does not match the size of the images list.

1.3 Images Properties

1.3.1 dir

Synopsis

# Getter
path = images.dir

# Setter
images.dir = path

Usage

When used as a getter, the property returns the path where the HDF5 file is stored.

When used as a setter, it is only applicable when used in conjunction with the load() or store() methods, indicating where the path where the HDF5 file is found. Otherwise, it is ignored.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A FileNotFoundError is raised if the directory does not exist.

1.3.2 name

Synopsis

# Getter
collection = images.name

Usage

When used as a getter the property returns the root name of the HDF5 stored file (also referred to as the name of the collection).

1.3.3 images

Synopsis

# Getter
images = images.images

Usage

When used as a getter the property returns the list of Image objects generated for the collection.

1.3.4 labels

Synopsis

# Getter
labels = images.labels

Usage

When used as a getter the property returns the label or list of labels for the collection.

1.3.5 time

Synopsis

# Getter
secs = images.time

Usage

When used as a getter the property returns the amount of time (in seconds) it took to preprocess the collection into machine learning ready data.

1.3.6 elapsed

Synopsis

# Getter
elapsed = images.elapsed

Usage

When used as a getter the property returns the amount of time it took to preprocess the collection into machine learning ready data, in the form HH:MM:SS.

1.3.7 split

Synopsis

# Getter
x_train, x_test, y_train, y_test = images.split

# Setter
images.split = percent [,seed]

Usage

When used as a setter, a training and test dataset is generated. The percent parameter specifies the percent that is test data. The data is first randomized before the split. By default, the seed for the split is 0. A seed may be optional specified as a second value.

When repeated, the property will re-split the data and re-randomize it.

When used as a getter, the split training, test, and corresponding labels are returned as lists converted to numpy arrays, and the labels are one-hot encoded (if not already). This is typically used in conjunction with next() operator or minibatch property.

When the percent is 0, the data is not split. All the data will be returned in x_train and y_train, but will still be randomized; x_test and y_test will be None.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A ValueError is raised if a parameter is out of range.
A AttributeError is raised if the number of parameters passed to the setter property is incorrect.

1.3.8 minibatch

Synopsis

# Getter
generator = images.minibatch

# Setter
images.minibatch = batch_size

Usage

When used as a setter, the mini-batch size is set.

When used as a getter, a generator is returned. The generator will iterate sequentially through the mini-batches of the training set.

If the augment property is set to True, for each image in the training set, an additional image is generated by rotating the image a random value between -90 and 90 degrees. Thus, if the mini-batch size is 100 images, the minibatch getter will build a generator for 200 images. See augment for more variations of image augmentation.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A ValueError is raised if the batch_size is out of range.

1.3.9 augment

Synopsis

# Getter
augment = images.augment

# Setter
images.augment = True | False

images.augment = (min, max[, n])

Usage

When used as a setter and set to True, image augmentation for rotation is enabled during batch generation (see minibatch and next()). In this mode, for each image, an additional image will be generated that is randomly rotated between -90 and 90 degrees.

When used as a setter and set to a tuple, the min and max boundaries for degree rotation are specified, and optionally the number of augmented images to generate per original image.

When used as a getter, the property returns whether image augmentation is enabled.

The parameter to the augment property may also be a tuple. The tuple specifies the rotation range and optionally the number of agumented images to generate per image; otherwise defaults to one.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.

1.3.10 flatten

images.flatten = True | False

Usage

When used as a setter and set to True, the machine learning ready data is flatten to a 1D vector.

When used as a setter and set to False, the machine learning ready data is unflatten back to a 2D (gray) or 3D (color) matrix.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.

1.3.11 resize

images.resize = (height, width)

Usage

When used as a setter, the machine learning ready data is resized to the specified height and width.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type. A AttributeError is raised if the parameter is not a tuple of length 2.

1.3.12 fail

nfailed = images.fail

Usage

When used as a getter, the property returns the number of images in the collection that failed to be preprocessed into machine learning ready data.

1.4 Images Overridden Operators

1.4.1 len()

Synopsis

n_images = len(images)

Usage

The len() (__len__) operator is overridden to return the number of Image objects in the collection.

1.4.2 []

Synopsis

image = images[n]

Usage

The [] (__getitem__) operator is overridden to return the Image object at the specified index.

Exceptions

A IndexError is raised if the index is out of range.

1.4.3 next()

Synopsis

data, label = next(images)

Usage

The next() operator is overridden and is used in conjunction with the split property. Once the collection has been split in training and test data, the next() operator will iterate through the training dataset one image, and corresponding label, at a time.

Once the training set has been fully iterated, the next() operator returns None, and will reset and start with the first element. Additionally, the training set will be randomly reshuffled.

If the augment property is not False, for each image in the training set, one or more additional images are generated by rotating the image a random value between a predetermined min and max bound degrees. For example, for a training set of a 1000 images, if the parameter to the property augment is True, the next() operator will iterate through 2000 images. If the parameter was a tuple and the number of augmentations per image was set to 2, the next() operator will iterate through 3000 images.

1.4.4 +=

Synopsis

images += image

images += images2

Parameters

image: A single Image object

images2: A single Images object (i.e., collection of Image objects).

Usage

The [] (__iadd__) operator is overridden to either add a single Image object or a Images object (i.e., collection) to an existing Images object. If the configuration setting 'nostore' is set for the parent Images object, the updated Images object is not stored to the corresponding HDF5 file, in which case one must explicity issue the store() method; otherwise ('nostore' is not set), the updated Images object is stored to the corresponding HDF5 file.

The accumaltive time (see propeties time and elapsed) will be the time of the pre-existing Images and the add Images collection.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.

1.5 Images Public Methods

1.5.1 load()

Synopsis

images.load(name, dir=None)

Parameters

name: The name of the collection.

Usage

This method will load into memory a preprocessed machine learning ready data from an HDF5 file specified by the collection name. The method will load the HDF5 by the filename <collection>.h5. If dir is None, then it will look for the file where the current value for dir is defined (either locally or reset by the dir property). Otherwise, it will look for the file under the directory specified by the dir parameter.

Once loaded, the Images object will have the same characteristics as when the Images object was created.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A ValueError is raised if the name parameter is None.

1.5.2 store()

Synopsis

images.store()

Usage

This method will store the machine learning ready data (and corresponding metadata) in a HDF5 file.

2 Image

2.1 Image Overview

The Image CV preprocessor contains the following primary classes, and their relationships:

Image - This is the base class for the representation of a Computer Vision (CV) preprocessed image. The constructor optionally takes as parameters an image (path), and corresponding label, and flags for CV preprocessing of the image.

image = Image(<image_path>, <label>, flags …)

The image path maybe a local path, an URL to a remote location, or raw pixel data as a numpy array. For remote location, a HTTP request is made to obtain the image data.

2.2 Image Initializer (Constructor)

Synopsis

Image(image=None, label=0, dir=’./’, ehandler=None, config=None)

Parameters

image: If not None, a string of either:

local path to an image file.
remote location of an image file (i.e., http[s]://….).
or raw pixel data as a numpy array.

label: An integer value which is the label corresponding to the image, or a numpy 1D vector which is one-hot encoded.

dir: If not './', the directory where to store the machine learning ready data.

ehandler: If the ehandler parameter is not None, then the above will occur asynchronously, and when completed, the corresponding event handler will be called with the Image object passed as a parameter. The ehandler parameter may also be specified as a tuple, where the first item in the tuple is the event handler and the remaining items are arguments to pass to the event handler.

# invoke without arguments
def done(image):
	print(image.time)

image = Image(path, label, ehandler=done)

# invoke with arguments
def done2(image, val):
	print(image.time, val)

image = Images(path, label, ehandler=(done, 10))

Usage

When specified with no parameters, an empty Image object is created. The Image object may then be used to subsequent load previously stored preprocessed machine learning ready data (see load()).

Otherwise, both image and label parameters must be specified. The label parameter corresponds to the label of the image. The image specified by the image parameter will be preprocessed according to the optional parameters and configuration settings. By default, the image will be preprocessed as follows:

Decompressed into raw pixel data.
Converted to RGB, if not already.
The pixel values are normalized, if not already (e.g., pixel integer values 0..255 converted to floating point values between 0.0 and 1.0).
Upon completion, the preprocessed machine learning data for the image is stored as a single HDF5 file in the current working directory. The root name of the file will be the root name of the image.
If the config setting 'raw' is specified, the raw pixel data for the image is additionally stored in the HDF5 file.
If the config setting 'thumb' is specified, the thumb data for the image is additionally stored in the HDF5 file.
Attributes of the raw and preprocessed image are stored in the HDF5 file.

If the path to an image file is remote (i.e., starts with http), an HTTP request will be made to fetch the contents of the file from the remote location.

If the parameter image is raw pixel data as a numpy array, the image is processed according to the shape of the numpy array. For example, a shape (100, 50, 3) would be processed as a 3-channel image (i.e., RGB) of height 100 and width 50

If the raw pixel data is a uint8 (8-bit pixels), the pixel data will be normalized by dividing it by 255.0 to convert to floating point values between 0.0 and 1.0. If the raw pixel data is a uint16 (i.e., 16-bit pixels), the pixel data will be normalized by dividing it by 65535.0 to convert to floating point values between 0.0 and 1.0.

By default, the normalized pixels will be of np.float32 data type (single precision float). If the config setting 'float16' or 'float64' are specified, the normalized pixels will be of np.float16 (half float) or np.float64 (double precision float) data type, respectively.

If the data type of the raw pixel data is already a float, the raw pixel data is assumed to be already normalized.

If the parameter dir is specified, then the generated HDF5 file is stored in the specified directory. If the directory does not exist, it is created.

If the ehandler parameter is not None, then the above will occur asynchronously, and when completed, the corresponding event handler will be called with the Image object passed as a parameter. The ehandler parameter may also be specified as a tuple, where the first item in the tuple is the event handler and the remaining items are arguments to pass to the event handler.

If the configuration setting grayscale (may be shortened to gray) is specified, then the image is converted to a single channel grayscale image, if not already.

If the configuration setting resize is specified, then the image is resized to the specified height and width.

If the configuration setting flatten (may be shortened to flat) is specified, the image is flattened into a single 1D vector (i.e., for input to a ANN).

If the configuration setting thumb is specified, then a thumbnail of the raw pixel data is generated to the specified height and width and stored in the HDF5 file.

If the configuration setting raw is specirfied, then the raw pixel image data is stored in the HDF5 file.

If the configuration setting nostore is specified, then the image data and corresponding metadata are not stored in the HDF5 file.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A AttributeError is raised if an invalid configuration setting is specified.
A IOError is raised if an error occurs reading in the image file.
A FileNotFoundError is raised if a local image is not found.
A TimeoutError is raised if a remote image is not retrieved.
A EOFError is raised if the image is not a valid image.

2.3 Image Properties

2.3.1 image

Synopsis

# Getter
path = image.image

# Setter
image.image = path

Usage

When used as a getter the property returns the path to the image file. If the image input was raw pixel data (i.e., numpy array), the property will return the string 'untitled'.

When used as a setter the property specifies the path of the image file to preprocess into machine learning ready data (see initializer).

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A FileNotFoundError is raised if the image file does not exist.
A IOError is raised if an error occurs reading in the image file.

2.3.2 name

Synopsis

# Getter
root = image.name

Usage

When used as a getter the property returns the root name of the image file (e.g., /mydir/myimage.jpg -> myimage). If the image input was raw pixel data (i.e., numpy array), the property will return the string 'untitled'.

2.3.3 type

Synopsis

# Getter
suffix = image.type

Usage

When used as a getter the property returns the file suffix of the image file (e.g., jpg). If the input was raw pixel data (i.e., numpy array), the property will return the string ‘raw’.

2.3.4 size

Synopsis

# Getter
size = image.size

Usage

When used as a getter the property returns the file size of the image file in bytes. If the input image was raw pixel data (i.e., numpy array), it will return the byte size of the raw pixel data.

2.3.5 raw

Synopsis

# Getter
pixels = image.raw

Usage

When used as a getter the property returns the raw pixel data of the uncompressed image.

2.3.6 thumb

Synopsis

# Getter
pixels = image.thumb

Usage

When used as a getter the property returns the pixel data for the thumbnail image.

2.3.7 label

Synopsis

# Getter
label = image.label

# Setter
image.label = label

Usage

When used as a getter the property returns the (integer) label specified for the image.

When used as a setter the property sets the label of the image to the specified integer value.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.

2.3.7 dir

Synopsis

# Getter
subfolder = image.dir

# Setter
image.dir = subfolder

Usage

When used as a getter the property returns the directory path where the corresponding HDF5 file is stored.

When used as a setter, it is only applicable when used in conjunction with the load() method, indicating where the path where the HDF5 file is found. Otherwise, it is ignored.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A FileNotFoundError is raised if the directory does not exist.

2.3.8 data

Synopsis

# Getter
data = image.data

Usage

When used as a getter the property returns the preprocessed machine learning ready data.

2.3.9 shape

Synopsis

# Getter
shape = image.shape

Usage

When used as a getter the property returns the shape of the preprocessed machine learning ready data (e.g., (50, 50, 3)).

2.3.10 time

Synopsis

# Getter
secs = image.time

Usage

When used as a getter the property returns the amount of time (in seconds) it took to preprocess the image into machine learning ready data.

2.3.11 elapsed

Synopsis

# Getter
time_elapsed = image.elapsed

Usage

When used as a getter the property returns time (in hh:mm:ss format) it took to preprocess the image into machine learning ready data.

2.4 Image Overridden Operators

2.4.1 str()

Synopsis

label = str(image)

Usage

The str() (__str__) operator is overridden to return the label of the image as a string.

2.5 Image Public Methods

2.5.1 load()

Synopsis

image.load(name, dir=None)

Parameters

name: The filename of the stored HDF5 file.

dir: The directory where the HDF5 file is located.

Usage

This method will load into memory a preprocessed machine learning ready data from an HDF5 file specified by the parameter name. The method will load the HDF5 by the filename <name>.h5. If dir is None, then it will look for the file where the current value for dir is defined (either locally or reset by the dir property). Otherwise, it will look for the file under the directory specified by the dir parameter.

Once loaded, the Image object will have the same characteristics as when the Image object was created.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A ValueError is raised if the name parameter is None.

2.5.2 rotate()

Synopsis

image.rotate(degree)

Parameters

degree: The degree (angle) to rotate the image data.

Usage

This method generates a rotated copy of the raw image data. The parameter degree specifies the degree (angle) to rotate the image. The method uses the imutils module which will resize the image to prevent clipping prior to the rotation. Once rotated, the image is resized back to the target size.

Exceptions

A TypeError is raised if the type of the parameter is not the expected type.
A ValueError is raised if the degree is not between 0 and 360.

APPENDIX I: Updates

Pre-Gap (Epipog) v1.5

Created first instance of module

Gap v0.9 (alpha)

Added splitting collection into training and test data
Added iterating (next) through the training set
Added support for minibatch

Gap v0.9.1 (alpha)

Added support for Images to take list of directories of images.
Added support for Image for image path is an URL (http request).
Added image rotation.
Rewrote Specification.
Added support for Images for image parameters to be folders of images.
Added support for GIF.
Added support for image augmentation in next()/minibatch.
Added support for raw pixel input to Image class.

Gap v0.9.2 (alpha)

Added support for mix image size/shape in Images object.
Added support += overriden operator.
Added support for specifying (min,max,n) for Image Augmentation.

Gap v0.9.3 (alpha)

Added converting to numpy arrays and one hot encoding of labels for Image split getter.
Added raw setting to config parameter.
Added float setting to config parameter.
Added transformation property flatten.
Added support for numpy arrays as image collections to Images.
Added support for 16-bit pixels.

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Gap Framework - Computer Vision for Image Data

VISION MODULE

1 Images

1.1 Images Overview

1.2 Images Initializer (Constructor)

1.3 Images Properties

1.3.1 dir

1.3.2 name

1.3.3 images

1.3.4 labels

1.3.5 time

1.3.6 elapsed

1.3.7 split

1.3.8 minibatch

1.3.9 augment

1.3.10 flatten

1.3.11 resize

1.3.12 fail

1.4 Images Overridden Operators

1.4.1 len()

1.4.2 []

1.4.3 next()

1.4.4 +=

1.5 Images Public Methods

1.5.1 load()

1.5.2 store()

2 Image

2.1 Image Overview

2.2 Image Initializer (Constructor)

2.3 Image Properties

2.3.1 image

2.3.2 name

2.3.3 type

2.3.4 size

2.3.5 raw

2.3.6 thumb

2.3.7 label

2.3.7 dir

2.3.8 data

2.3.9 shape

2.3.10 time

2.3.11 elapsed

2.4 Image Overridden Operators

2.4.1 str()

2.5 Image Public Methods

2.5.1 load()

2.5.2 rotate()

APPENDIX I: Updates