imagepreprocessing 1.7.2 on PyPI

imagepreprocessing

Creates train ready data for keras or yolo in a single line
Makes prediction process easier with using keras model from both array and directory
Plots confusion matrix

Install

pip install imagepreprocessing

Usage

from imagepreprocessing import create_training_data_keras, create_training_data_yolo, make_prediction_from_directory, create_confusion_matrix

Create training data for keras

source_path = "datasets/deep_learning/food-101/only3"
save_path = "food10class1000sampleeach"
create_training_data(source_path, save_path, img_size = 299, validation_split=0.1, percent_to_use=0.1, grayscale = True, files_to_exclude=["excludemoe","hi.txt"])

File name: apple_pie - 1/3  Image:100/100
File name: baby_back_ribs - 2/3  Image:100/100
File name: baklava - 3/3  Image:100/100

validation x: 30 validation y: 30
train x: 270 train y: 270

shape of train x: (270, 299, 299, 1)
shape of train y: (270, 3)
shape of validate x: (30, 299, 299, 1)
shape of validate y: (30, 3)

file saved -> C:\Users\can\Desktop\food3class100sampleeach_x_train.pkl
file saved -> C:\Users\can\Desktop\food3class100sampleeach_y_train.pkl
file saved -> C:\Users\can\Desktop\food3class100sampleeach_x_validation.pkl
file saved -> C:\Users\can\Desktop\food3class100sampleeach_y_validation.pkl

Make prediction from directory with a keras model and plot confusion matrix

images_path = "deep_learning/test_images/food2"
model_path = "deep_learning/saved_models/alexnet.h5"

predictions = make_prediction_from_directory(images_path, model_path)

class_names = ["apple", "melon", "orange"]
labels = [0,0,0,1,1,1,2,2,2]
create_confusion_matrix(predictions, labels, class_names=class_names)

1.jpg : 0
2.jpg : 0
3.jpg : 0
4.jpg : 1
5.jpg : 1
6.jpg : 2
7.jpg : 2
8.jpg : 2
9.jpg : 1
Confusion matrix, without normalization
[[3 0 0]
 [0 2 1]
 [0 1 2]]

Create training data split the data and make prediction from test_x with a keras model finally create the confusion matrix

images_path = "deep_learning/test_images/food2"
save_path = "food"
model_path = "deep_learning/saved_models/alexnet.h5"


x, y, x_val, y_val = create_training_data_keras(images_path, save_path = save_path, validation_split=0.2, percent_to_use=0.5)

x, y, test_x, test_y =  train_test_split(x,y,save_path = save_path)

# ...
# training
# ...

class_names = ["apple", "melon", "orange"]

predictions = make_prediction_from_array(test_x, model_path, print_output=False)

create_confusion_matrix(predictions, test_y, class_names=class_names, one_hot=True)

imagepreprocessing
Release 1.7.2

Release 1.7.2

1.7.2

1.7.1

1.7.0

1.6.1

1.6.0

1.5.1

1.5.0

1.4.1

1.4.0

1.3.0

Documentation

imagepreprocessing

Install

Usage

Create training data for keras

Make prediction from directory with a keras model and plot confusion matrix

Create training data split the data and make prediction from test_x with a keras model finally create the confusion matrix

Stats

Development practices

Releases

Contributors

imagepreprocessing Release 1.7.2

Release 1.7.2 Toggle Dropdown 1.7.2 1.7.1 1.7.0 1.6.1 1.6.0 1.5.1 1.5.0 1.4.1 1.4.0 1.3.0

Documentation

imagepreprocessing

Install

Usage

Create training data for keras

Make prediction from directory with a keras model and plot confusion matrix

Create training data split the data and make prediction from test_x with a keras model finally create the confusion matrix

Stats

Development practices

Releases

Contributors

imagepreprocessing
Release 1.7.2

Release 1.7.2

1.7.2

1.7.1

1.7.0

1.6.1

1.6.0

1.5.1

1.5.0

1.4.1

1.4.0

1.3.0