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)

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)
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)

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()

from tf_semantic_segmentation.datasets import TFWriter
ds = ...
writer = TFWriter(record_dir)
writer.write(ds)
writer.validate(ds)

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]])

from tf_semantic_segmentation.serving import predict, predict_on_batch, ensamble_prediction, get_models_from_directory
from tf_semantic_segmentation.processing.dataset import resize_and_change_color

image = np.zeros((128, 128, 3))
image_size = (256, 256)
color_mode = 0  # 0=RGB, 1=GRAY
resize_method = 'resize'
scale_mask = False # only scale mask when model output is scaled using sigmoid activation
num_classes = 3

# preprocess image
image = image.astype(np.float32) / 255.
image, _ = resize_and_change_color(image, None, image_size, color_mode, resize_method='resize')

# prediction on 1 image
p = predict(image.numpy(), host='localhost', port=8501, input_name='input_1', model_name='0')

#############################################################################################################
# if the image size should not match, the color mode does not match or the model_name does not match
# you'll most likely get a `400 Client Error: Bad Request for url: http://localhost:8501/v1/models/0:predict`
# hint: if you only started 1 model try using model_name 'default'
#############################################################################################################

# prediction on batch (for faster prediction of multiple images)
p = predict_on_batch([image], host='localhost', port=8501, input_name='input_1', model_name='0')

# ensamble prediction (average the predictions of multiple models)

# either specify models like this:
models = [
    {
        "name": "0",
        "path": "/home/user/models/mymodel/saved_model/",
        "version": 0, # optional
        "input_name": "input_1"
    },
    {
        "name": "1",
        "path": "/home/user/models/mymodel2/saved_model/",
        "input_name": "input_1"
    }
]


# or load from models in directory (models/) that contain the name 'unet'
models = get_models_from_directory('models/', contains='unet')

# returns the ensamble and all predictions made
ensamble, predictions = ensamble_prediction(models, image.numpy(), host='localhost', port=8501)