Metadata-Version: 2.1
Name: imagepreprocessing
Version: 1.4.0
Summary: image preprocessing
Home-page: https://github.com/cccaaannn/imagepreprocessing
Author: Can Kurt
Author-email: can.kurt.aa@gmail.com
License: UNKNOWN
Description: ## imagepreprocessing
        
        - **Creates train ready data for image classification tasks for keras in a single line**
        - **Makes multiple image prediction process easier with using keras model from both array and directory**
        
        - **Creates required files for darknet-yolo including cfg file with default parameters and class count calculations in a single line**
        - **Predicts and saves multiple images on a directory with using darknet**
        - **Annotation tool for yolo**
        - **Auto annotation by given random points for yolo**
        - **Draw bounding boxes of the images from annotation files that formatted for yolo**
        
        - **Plots confusion matrix**
        
        
        ## Install
        ```sh
        pip install imagepreprocessing
        ```
        
        ## Create training data for keras
        ```python
        from  imagepreprocessing.keras_functions import create_training_data_keras
        source_path = "datasets/deep_learning/food-101/only3"
        save_path = "food10class1000sampleeach"
        create_training_data_keras(source_path, save_path, image_size = 299, validation_split=0.1, percent_to_use=0.1, grayscale = True)
        ```
        
        ## Make prediction from directory with a keras model and plot confusion matrix
        ```python
        from  imagepreprocessing.keras_functions import make_prediction_from_directory_keras
        from  imagepreprocessing.utilities import create_confusion_matrix
        
        images_path = "deep_learning/test_images/food2"
        model_path = "deep_learning/saved_models/alexnet.h5"
        
        predictions = make_prediction_from_directory_keras(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)
        ```
        
        
        ## Make prediction and create the confusion matrix
        ```python
        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 = "deep_learning/test_images/food2"
        save_path = "food"
        model_path = "deep_learning/saved_models/alexnet.h5"
        
        # Create training data split the data
        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)
        
        # 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_path, print_output=False)
        
        # create confusion matrix
        create_confusion_matrix(predictions, test_y, class_names=class_names, one_hot=True)
        ```
        
        
        ## Make multi input model prediction and create the confusion matrix
        ```python
        from imagepreprocessing.keras_functions import create_training_data_kera
        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 = "/content/trainingSet"
        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, "models/model.h5",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)
        
        ```
        
        
        ## Create required files for training on darknet-yolo and auto annotate images by center
        ```python
        from imagepreprocessing.darknet_functions import create_training_data_yolo, auto_annotation_by_random_points
        import os
        
        main_dir = "30_class/train_30_class"
        
        # auto annotating all images by their center points (x,y,w,h)
        # (it is only possible to make classification this way for detection you have to annotate images by hand)
        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)
        ```
        
        ## Annotation tool
        ```python
        from imagepreprocessing.darknet_functions import yolo_annotation_tool
        yolo_annotation_tool("test_stuff/images", "test_stuff/obj.names")
        ```
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
