img2feat 0.3.0

Convert image to feature based on convolutional neural network (CNN).

It is an image feature extractor based on a convolutional neural network.

Github PyPI

Installation

% pip install img2feat

Required libraries: numpy, torch, torchvision, opencv-python

class CNN

The CNN converts a list of numpy images to features, where numpy image is assumed opencv format, or [Height, Width, BGR]. The shape of the output features is [ length of the list of the input images, dim_feature of the CNN].

Available networks: alexnet, vgg11, vgg13, vgg16, vgg19, resnet18, resnet34, resnet101, resnet152, densenet121, densenet161, densenet169, densenet201 googlenet, mobilenet vit_b_16

from img2feat import BuildNet
net = BuildNet.build('vgg11')
x = net( [img] )

Methods

• available_networks() -> list of string

  Return the list of names of available networks.

• init( network=‘vgg11’, gpu=False, img_size=(224,224) )

  Constructor

  network should be one of available_networks()

  gpu is set True, if the GPU is available.

  img_size is the image size which is input to the network, (width, height)

• call( imgs ) -> feature (numpy float32)

  It converts the list of images to the features.

  imgs is thee list of images. The image is should be the opencv format, or [Height, Width, BGR].

  feature is the converted features where [ len(imgs), dim_feature].

Variables

• dim_feature (int)

  It is the dimension of the output feature.

class PixelFeature

The PixelFeature converts images to per-pixel features. The feature is the numpy array of [Height, Width, Dim Feature].

Available networks: vgg11, vgg13, vgg16, vgg19,

from img2feat import CNN
net = CNN('vgg11')
x = net( [img] )

Methods

• available_networks() -> list of string

  Return the list of names of available networks.

• init( network=‘vgg11’, gpu=False )

  Constructor

  network should be one of available_networks()

  gpu is set True, if the GPU is available.

• call( imgs ) -> list of feature (numpy float32)

  It converts the list of images to the features.

  imgs is thee list of images. The image is should be the opencv format, or [Height, Width, BGR].

  feature is the converted features where [ height, width, dim_featute]. The height and width are same as the input image.

Variables

• dim_feature (int)

  It is the dimension of the output feature.

class Mirror

The Mirror provide a data augmentation of mirroring.

Methods

• call( imgs ) -> augmented images

  It return the augmented images. The output is the list of images. The odd is the original images and the even is the mirrored images.

Variables

• nb_aug int

  It return 2.

class TenCrop

The TenCrop provide a typical 10-crop data augmentation. First, images are resized so that the shorter side is a setting scale. Then, center, top-left, top-right, bottom-left, and bottom-right are cropped.

Methods

• init( scales=[224, 256, 384, 480, 640], mirror=True, img_size=(224,224) )

  Constructor.

  scales is a list of scales. Images are resized so that the shorter side is scale.

  If mirror is True, the mirroring augmentation is also applied.

  img_size is cropping size.

• call( imgs ) -> augmented images

  It returns the augmented images.

Variables

• img_size

  It is the cropping size. [Width, Height]

• nb_aug

  It is the number of augmentation fro a single image. It is the multiplication of len(scales) * 5 * 2, if mirror is True

package antbee

It is utility package for the dataset of ants and bees in Transfer Learning for Computer Vision Tutorial.

Methods

• load( squared=True, root=None ) -> ( Itrain, Ytrain ), ( Itest, Ytest )

  root is the root directory of the data. If it is None, the root directory is set as the package directory.

  If squared is True, only squared images are loaded. If squared is False, all images are loaded.

  Itrain, Itest are lists of images.

  Ytrain, Ytest are numpy array of the label. 0: ant, 1: bee.

• load_squared_npy( name, root=None ) -> ( Xtrain, Ytrain ), ( Xtest, Ytest )

  root is the root directory of the data. If it is None, the root directory is set as the package directory.

  name is the name of CNN network.

  Xtrain, Xtest are numpy array of extracted features.

  Ytrain, Ytest are numpy array of the label. 0: ant, 1: bee.

Variables

• str

  str[0]: ‘ant’, str[1]: ‘bee’

Sample Codes

sample1.py: Linear regression.

sample2.py: Data augmentation.

Network References

AlexNet: One weird trick for parallelizing convolutional neural networks

VGG: Very Deep Convolutional Networks for Large-Scale Image Recognition

ResNet: Deep Residual Learning for Image Recognition

DenseNet: Densely Connected Convolutional Networks

MobileNet: Inverted Residuals and Linear Bottlenecks

GoogLeNet: Going Deeper with Convolutions