Metadata-Version: 2.1
Name: tf-semantic-segmentation
Version: 0.2.2
Summary: Implementation of various semantic segmentation models in tensorflow & keras including popular datasets
Home-page: https://github.com/baudcode/tf-semantic-segmentation
Author: Malte Koch
Author-email: malte-koch@gmx.net
Maintainer: Malte Koch
Maintainer-email: malte-koch@gmx.net
License: MIT
Description: # TF Semantic Segmentation
        
        [![Build Status](https://travis-ci.org/baudcode/tf-semantic-segmentation.svg?branch=master)](https://travis-ci.org/baudcode/tf-semantic-segmentation)
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        [![latest tag](https://img.shields.io/github/v/tag/baudcode/tf-semantic-segmentation)]()
        
        ## Features
        
        - Datasets
        
          - Ade20k
          - Camvid
          - Cityscapes
          - MappingChallenge
          - MotsChallenge
          - Coco
          - PascalVoc2012
          - Taco
          - Shapes (randomly creating triangles, rectangles and circles)
          - Toy (Overlaying TinyImageNet with MNIST)
          - ISIC2018
          - CVC-ClinicDB
        
        - Distributed Training on Multiple GPUs
        - Hyper Parameter Optimization using WandB
        - WandB Integration
        - Easily create TFRecord from Directory
        - Tensorboard visualizations
        
        - Models:
        
          - Unet
          - Erfnet
          - MultiResUnet
        
        - Losses:
        
          - Catagorical Crossentropy
          - Binary Crossentropy
          - Crossentropy + SSIM
          - Dice
          - Crossentropy + Dice
          - Tversky
          - Focal
          - Focal + Tversky
        
        - Activations:
        
          - mish
          - swish
          - relu6
        
        - Optimizers:
        
          - Ranger
          - RAdam
        
        - Normalization
        
          - Instance Norm
          - Batch Norm
        
        - On the fly Augmentations
        
          - flip left/right
          - flip up/down
          - rot 180
          - color
        
        ## Requirements
        
        ```shell
        sudo apt-get install libsm6 libxext6 libxrender-dev libyaml-dev libpython3-dev
        ```
        
        #### Tensorflow (2.x) & Tensorflow Addons (optional)
        
        ```shell
        pip install tensorflow-gpu==2.1.0 --upgrade
        pip install tensorflow-addons==0.7.0 --upgrade
        ```
        
        ## Training
        
        ### Hint: To see train/test/val images you have to start tensorboard like this
        
        ```bash
        tensorboard --logdir=logs/ --reload_multifile=true
        ```
        
        ### On inbuild datasets (generator)
        
        ```bash
        python -m tf_semantic_segmentation.bin.train -ds 'tacobinary' -bs 8 -e 100 \
            -logdir 'logs/taco-binary-test' -o 'adam' -lr 5e-3 --size 256,256 \
            -l 'binary_crossentropy' -fa 'sigmoid' \
            --train_on_generator --gpus='0' \
            --tensorboard_train_images --tensorboard_val_images
        ```
        
        ### Using a fixed record path
        
        ```bash
        python -m tf_semantic_segmentation.bin.train --record_dir=records/cityscapes-512x256-rgb/ \
            -bs 4 -e 100 -logdir 'logs/cityscapes-bs8-e100-512x256' -o 'adam' -lr 1e-4 -l 'categorical_crossentropy' \
            -fa 'softmax' -bufsize 50 --metrics='iou_score,f1_score' -m 'erfnet' --gpus='0' -a 'mish' \
            --tensorboard_train_images --tensorboard_val_images
        ```
        
        ### Multi GPU training
        
        ```bash
        python -m tf_semantic_segmentation.bin.train --record_dir=records/cityscapes-512x256-rgb/ \
            -bs 4 -e 100 -logdir 'logs/cityscapes-bs8-e100-512x256' -o 'adam' -lr 1e-4 -l 'categorical_crossentropy' \
            -fa 'softmax' -bufsize 50 --metrics='iou_score,f1_score' -m 'erfnet' --gpus='0,1,2,3' -a 'mish'
        ```
        
        ## Using Code
        
        ```python
        from tf_semantic_segmentation.bin.train import train_test_model, get_args
        
        # get the default args
        args = get_args({})
        
        # change some parameters
        # !rm -r logs/
        args.model = 'erfnet'
        # args['color_mode'] = 0
        args.batch_size = 8
        args.size = [128, 128] # resize input dataset to this size
        args.epochs = 10
        args.learning_rate = 1e-4
        args.optimizer = 'adam' # ['adam', 'radam', 'ranger']
        args.loss = 'dice'
        args.logdir = 'logs'
        args.record_dir = "datasets/shapes/records"
        args.final_activation = 'softmax'
        
        # train and test
        results, model = train_test_model(args)
        ```
        
        ## Models
        
        - Erfnet
        - Unet
        
        ```python
        from tf_semantic_segmentation import models
        
        # print all available models
        print(list(modes.models_by_name.keys()))
        
        # returns a model without the final activation function
        # because the activation function depends on the loss function
        model = models.get_model_by_name('erfnet', {"input_shape": (128, 128, 3), "num_classes": 5})
        
        # call models directly
        model = models.erfnet(input_shape=(128, 128), num_classes=5)
        ```
        
        ## Use your own dataset
        
        - Accepted file types are: jpg(jpeg) and png
        
        If you already have a train/test/val split then use the following data structure:
        
        ```text
        dataset/
            labels.txt
            test/
                images/
                masks/
            train/
                images/
                masks/
            val/
                images/
                masks/
        ```
        
        or use
        
        ```text
        dataset/
            labels.txt
            images/
            masks/
        ```
        
        The labels.txt should contain a list of labels separated by newline [/n]. For instance it looks like this:
        
        ```text
        background
        car
        pedestrian
        ```
        
        - To create a tfrecord using the original image size and color use the script like this:
        
        ```shell
        INPUT_DIR = ...
        tf-semantic-segmentation-tfrecord-writer -dir $INPUT_DIR -r $INPUT_DIR/records
        ```
        
        There are the following addition arguments:
        
        - -s [--size] '$width,$height' (f.e. "512,512")
        - -rm [--resize_method] ('resize', 'resize_with_pad', 'resize_with_crop_or_pad)
        - cm [--color_mode] (0=RGB, 1=GRAY, 2=NONE (default))
        
        ## Datasets
        
        ```python
        from tf_semantic_sementation.datasets import get_dataset by name, datasets_by_name, DataType, get_cache_dir
        
        # print availiable dataset names
        print(list(datasets_by_name.keys()))
        
        # get the binary (waste or not) dataset
        data_dir = '/hdd/data/'
        name = 'tacobinary'
        cache_dir = get_cache_dir(data_dir, name.lower())
        ds = get_dataset_by_name(name, cache_dir)
        
        # print labels and classes
        print(ds.labels)
        print(ds.num_classes)
        
        # print number of training examples
        print(ds.num_examples(DataType.TRAIN))
        
        # or simply print the summary
        ds.summary()
        ```
        
        ## TFRecords
        
        #### This library simplicifies the process of creating a tfrecord dataset for faster training.
        
        Write tfrecords:
        
        ```python
        from tf_semantic_segmentation.datasets import TFWriter
        ds = ...
        writer = TFWriter(record_dir)
        writer.write(ds)
        writer.validate(ds)
        ```
        
        or use simple with this script (will be save with size 128 x 128 (width x height)):
        
        ```bash
        tf-semantic-segmentation-tfrecord-writer -d 'toy' -c /hdd/datasets/ -s '128,128'
        ```
        
        ## Docker
        
        ```shell
        docker build -t tf_semantic_segmentation -f docker/Dockerfile ./
        ```
        
        ## Prediction
        
        ```shell
        pip install matplotlib
        ```
        
        #### Using Code
        
        ```python
        from tensorflow.keras.models import load_model
        import numpy as np
        from tf_semantic_segmentation.processing import dataset
        from tf_semantic_segmentation.visualizations import show, masks
        
        
        model = load_model('logs/model-best.h5', compile=False)
        
        # model parameters
        size = tuple(model.input.shape[1:3])
        depth = model.input.shape[-1]
        color_mode = dataset.ColorMode.GRAY if depth == 1 else dataset.ColorMode.RGB
        
        # define an image
        image = np.zeros((256, 256, 3), np.uint8)
        
        # preprocessing
        image = image.astype(np.float32) / 255.
        image, _ = dataset.resize_and_change_color(image, None, size, color_mode, resize_method='resize')
        
        image_batch = np.expand_dims(image, axis=0)
        
        # predict (returns probabilities)
        p = model.predict(image_batch)
        
        # draw segmentation map
        num_classes = p.shape[-1] if p.shape[-1] > 1 else 2
        predictions_rgb = masks.get_colored_segmentation_mask(p, num_classes, images=image_batch, binary_threshold=0.5)
        
        # show images using matplotlib
        show.show_images([predictions_rgb[0], image_batch[0]])
        ```
        
        #### Using scripts
        
        - On image
        
        ```shell
        python -m tf_semantic_segmentation.evaluation.predict -m model-best.h5  -i image.png
        ```
        
        - On TFRecord (data type 'val' is default)
        
        ```shell
        python -m tf_semantic_segmentation.evaluation.predict -m model-best.h5 -r records/camvid/
        ```
        
        - On TFRecord (with export to directory)
        
        ```shell
        python -m tf_semantic_segmentation.evaluation.predict -m model-best.h5 -r records/cubbinary/ -o out/ -rm 'resize_with_pad'
        ```
        
        - On Video
        
        ```shell
        python -m tf_semantic_segmentation.evaluation.predict -m model-best.h5 -v video.mp4
        ```
        
        - On Video (with export to out/p-video.mp4)
        
        ```shell
        python -m tf_semantic_segmentation.evaluation.predict -m model-best.h5 -v video.mp4 -o out/
        ```
        
Keywords: keras,tensorflow,tf_semantic_segmentation,semantic,segmentation,ade20k,coco,pascalvoc,cityscapes
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Operating System :: POSIX :: Linux
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
