Skip to main content

image preprocessing

Project description

imagepreprocessing

  • Creates train ready data for keras or yolo in a single line
  • Makes prediction with using keras model
  • Plots confusion matrix

Install

pip install imagepreprocessing

Usage

from imagepreprocessing import create_training_data_keras, create_training_data_yolo, create_only_path_files_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 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(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]]

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for imagepreprocessing, version 0.4.0
Filename, size File type Python version Upload date Hashes
Filename, size imagepreprocessing-0.4.0.tar.gz (7.9 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page