Skip to main content

image preprocessing

Project description

imagepreprocessing

A small library for speeding up the dataset preparation and model testing steps for deep learning on various frameworks. (mostly for me)

PyPI version fury.io PyPI download month Downloads GitHub license

What can it do

  • Creates all the required files for darknet-yolo3,4 training including cfg file with default parameters and class calculations in a single line. (example usage)
  • Creates train ready data for image classification tasks for keras in a single line. (example usage)
  • Makes multiple image prediction process easier with using keras model from both array and directory.(example usage)
  • Predicts and saves multiple images from a directory with using darknet. (example usage)
  • Includes a simple annotation tool for darknet-yolo style annotation. (example usage)
  • Auto annotation by given random points for yolo. (example usage)
  • Draws bounding boxes of the images from annotation files for preview.
  • Plots training history graph from keras history object. (example usage)
  • Plots confusion matrix. (example usage)
  • (More)

This dataset structure is required for most of the operations

my_dataset
   |----class1
   |     |---image1.jpg
   |     |---image2.jpg
   |     |---image3.jpg
   |     ...
   |----class2
   |----class3
         ...

Install

pip install imagepreprocessing

Create required files for training on darknet-yolo

from imagepreprocessing.darknet_functions import create_training_data_yolo
main_dir = "datasets/food_5class"
create_training_data_yolo(main_dir)

# other options
# create_training_data_yolo(main_dir, yolo_version=4, train_machine_path_sep = "/", percent_to_use = 1, validation_split = 0.2, create_cfg_file = True)

output

File name: apple - 1/5  Image:10/10
File name: melon - 2/5  Image:10/10
File name: orange - 3/5  Image:10/10
File name: beef - 4/5  Image:10/10
File name: bread - 5/5  Image:10/10

file saved -> yolo-custom.cfg
file saved -> train.txt
file saved -> test.txt
file saved -> obj.names
file saved -> obj.data

Download darknet53.conv.74 and move it to darknets root directory.(there are download links on https://github.com/AlexeyAB/darknet)
Also move your dataset file to darknet/data/food_5class
Run the command below in the darknets root directory to start training.
Your train command with map is: ./darknet detector train data/food_5class/obj.data data/food_5class/yolo-custom.cfg darknet53.conv.74 -map
Your train command for multi gpu is: ./darknet detector train data/food_5class/obj.data data/food_5class/yolo-custom.cfg darknet53.conv.74 -gpus 0,1 -map

Create training data for keras

from  imagepreprocessing.keras_functions import create_training_data_keras
source_path = "datasets/my_dataset"
train_x, train_y = create_training_data_keras(source_path)

# other options
# train_x, train_y, valid_x, valid_y = create_training_data_keras(source_path, save_path = "5000images_on_one_file", image_size = (299,299), validation_split=0.1, percent_to_use=0.5, grayscale = True)

Predict all images in a directory with keras model

from  imagepreprocessing.keras_functions import make_prediction_from_directory_keras

images_path = "datasets/my_dataset/class1"

# give the path
model = "model.h5"

# or model itself
# model.fit(...)

# predict
predictions = make_prediction_from_array_keras(images_path, model, image_size = (224,224), print_output=True, show_images=True)

Create training history graph for keras

from  imagepreprocessing.keras_functions import create_history_graph_keras

# training
# history = model.fit(...)

create_history_graph_keras(history)

trainig_histyory_example

Make prediction from test array and create the confusion matrix with keras model

from  imagepreprocessing.keras_functions import create_training_data_keras, make_prediction_from_array_keras
from  imagepreprocessing.utilities import create_confusion_matrix, train_test_split

images_path = "datasets/my_dataset"

# Create training data split the data
x, y, x_val, y_val = create_training_data_keras(images_path, save_path = None, validation_split=0.2, percent_to_use=0.5)

# split training data
x, y, test_x, test_y =  train_test_split(x,y,save_path = save_path)

# ...
# training
# ...

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

# make prediction
predictions = make_prediction_from_array_keras(test_x, model, print_output=False)

# create confusion matrix
create_confusion_matrix(predictions, test_y, class_names=class_names, one_hot=True)
create_confusion_matrix(predictions, test_y, class_names=class_names, one_hot=True, cmap_color="Blues")

confusion_matrix_exampleconfusion_matrix_example

Annotation tool for derknet-yolo

from imagepreprocessing.darknet_functions import yolo_annotation_tool
yolo_annotation_tool("test_stuff/images", "test_stuff/obj.names")

Usage

  • "a" go backward
  • "d" go forward
  • "s" save selected annotations
  • "z" delete last annotation
  • "r" remove unsaved annotations
  • "c" clear all annotations including saved ones
  • "h" hide or show labels on the image

Predict all images in a directory with yolo model

This function uses shell commands to run darknet so you don't need to compile it as .so file but it is also slow because of that.
from imagepreprocessing.darknet_functions import make_prediction_from_directory_yolo

images_path = "datasets/my_dataset/class1"
darknet_path = "home/user/darknet"
save_path = "detection_results"

# your command has to have {0} on the position of image path
darknet_command = "./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights {0} -dont_show"

make_prediction_from_directory_yolo(images_path, darknet_path, save_path=save_path, darknet_command=darknet_command)

Create required files for training on darknet-yolo and auto annotate images by center

Auto annotation is for testing the dataset or just for using it for classification, detection won't work without proper annotations.
from imagepreprocessing.darknet_functions import create_training_data_yolo, auto_annotation_by_random_points
import os

main_dir = "datasets/my_dataset"

# auto annotating all images by their center points (x,y,w,h)
folders = sorted(os.listdir(main_dir))
for index, folder in enumerate(folders):
    auto_annotation_by_random_points(os.path.join(main_dir, folder), index, annotation_points=((0.5,0.5), (0.5,0.5), (1.0,1.0), (1.0,1.0)))

# creating required files
create_training_data_yolo(main_dir)

Delete a class and update all yolo annotation files in a directory

# function saves new annotation files on a different directory by default but you can pass the same directory to override old ones

# single directory
class_path = "datasets/my_dataset/class1"
remove_index = 2
remove_class_from_annotation_files(class_path, remove_index, new_annotations_path = "new_annotations")

# for multiple directories
import os
for path in os.listdir("datasets/my_dataset"):
    remove_class_from_annotation_files(path, remove_index, new_annotations_path = path + "_new")

Count class appearances in a directory for annotated yolo data

class_path = "datasets/my_dataset/class1"
names_path = "datasets/my_dataset/obj.names"
classes = count_classes_from_annotation_files(class_path, names_path, include_zeros=True)
print(classes)

output

{'apple': 3, 'melon': 2, 'orange': 0}

Make multi input model prediction and create the confusion matrix

from imagepreprocessing.keras_functions import create_training_data_keras
from  imagepreprocessing.utilities import create_confusion_matrix, train_test_split
import numpy as np

# Create training data split the data and split the data
source_path = "datasets/my_dataset"
x, y = create_training_data_keras(source_path, image_size=(28,28), validation_split=0, percent_to_use=1, grayscale=True, convert_array_and_reshape=False)
x, y, test_x, test_y = train_test_split(x,y)

# prepare the data for multi input training and testing
x1 = np.array(x).reshape(-1,28,28,1)
x2 = np.array(x).reshape(-1,28,28)
y = np.array(y)
x = [x1, x2]

test_x1 = np.array(test_x).reshape(-1,28,28,1)
test_x2 = np.array(test_x).reshape(-1,28,28)
test_y = np.array(test_y)
test_x = [test_x1, test_x2]

# ...
# training
# ...

# make prediction
predictions = make_prediction_from_array_keras(test_x, model, print_output=False, model_summary=False, show_images=False)

# create confusion matrix
create_confusion_matrix(predictions, test_y, class_names=["0","1","2","3","4","5","6","7","8","9"], one_hot=True)

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 1.7.2
Filename, size File type Python version Upload date Hashes
Filename, size imagepreprocessing-1.7.2.tar.gz (23.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page