# python
from datetime import datetime
from functools import partial
from pathlib import Path
import warnings

# from pypi
from PIL import Image, ImageFile
from tabulate import tabulate
from torchvision import datasets
import matplotlib
warnings.filterwarnings("ignore", category=matplotlib.cbook.mplDeprecation)
import cv2
import face_recognition
import matplotlib.image as matplotlib_image
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import seaborn
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optimizer
import torchvision.models as models
import torchvision.transforms as transforms

pyplot = plt
numpy = np

get_ipython().run_line_magic('matplotlib', 'inline')
get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")
seaborn.set(style="whitegrid",
            rc={"axes.grid": False,
                "font.family": ["sans-serif"],
                "font.sans-serif": ["Open Sans", "Latin Modern Sans", "Lato"],
                "figure.figsize": (8, 6)},
            font_scale=1)

INCEPTION_IMAGE_SIZE = 299
SCRATCH_IMAGE_SIZE = INCEPTION_IMAGE_SIZE
VGG_IMAGE_SIZE = 224

MEANS = [0.485, 0.456, 0.406]
DEVIATIONS = [0.229, 0.224, 0.225]
DOG_LOWER, DOG_UPPER = 150, 269

ROOT_PATH = Path("~/data/datasets/dog-breed-classification/").expanduser()
HUMAN_PATH = ROOT_PATH.joinpath("lfw")
DOG_PATH = ROOT_PATH.joinpath("dogImages")
MODEL_PATH = Path("~/models/dog-breed-classification").expanduser()

assert HUMAN_PATH.is_dir()
assert DOG_PATH.is_dir()
assert MODEL_PATH.is_dir()

MODELS = []

use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print("Using {}".format(device))

ImageFile.LOAD_TRUNCATED_IMAGES = True

human_files = np.array(list(HUMAN_PATH.glob("*/*")))
dog_files = np.array(list(DOG_PATH.glob("*/*/*")))

assert len(human_files) > 0
assert len(dog_files) > 0

# print number of images in each dataset
print('There are {:,} total human images.'.format(len(human_files)))
print('There are {:,} total dog images.'.format(len(dog_files)))

import cv2
import warnings
import matplotlib
warnings.filterwarnings("ignore", category=matplotlib.cbook.mplDeprecation)
import matplotlib.pyplot as plt

# extract pre-trained face detector
haar_path = ROOT_PATH.joinpath('haarcascades/haarcascade_frontalface_alt.xml')
assert haar_path.is_file()
face_cascade = cv2.CascadeClassifier(str(haar_path))

# load color (BGR) image
img = cv2.imread(str(human_files[0]))
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()

def face_detector(img_path):
    """"returns True if face is detected in image stored at img_path"""
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

from tqdm import tqdm

human_files_short = human_files[:100]
dog_files_short = dog_files[:100]

#-#-# Do NOT modify the code above this line. #-#-#

set([" ".join(filename.name.split("_")[:-1]) for filename in dog_files_short])

def species_scorer(predictor: callable,
                   true_species: list,
                   false_species: list,
                   labels: list) -> list:
    """Emit a score-table for the predictor

    Args:
     predictor: callable that returns True if it detects the expected species
     true_species: list of images that should be matched by predictor
     false_species: list of images that shouldn't be matched by predictor
     labels: column labels for the table

    Returns:
     false-positive indices
    """
    misses = [predictor(str(image)) for image in false_species]
    false_positives = sum(misses)
    true_positives = sum([predictor(str(image)) for image in true_species])
    false_negatives = len(true_species) - true_positives
    others = len(false_species)
    expected = len(true_species)
    values = ("{:.2f}%".format(100 * true_positives/expected),
            "{:.2f}%".format(100 * false_positives/others),
              "{:.2f}".format((2 * true_positives)/(2 * true_positives
                                                    + false_positives
                                                    + false_negatives)))
    table = zip(labels, values)
    print(tabulate(table, tablefmt="github", headers=["Metric", "Value"]))
    return misses

face_scorer = partial(species_scorer,
                      true_species=human_files_short,
                      false_species=dog_files_short,
                      labels=("First 100 images in `human_files` detected with a face",
                              "First 100 images in `dog_files` detected with a face",
                              "F1"))

open_cv_false_positives = face_scorer(face_detector)

def detect_faces(image_path: str) -> numpy.ndarray:
    """Finds the locations of faces
    
    Args:
     image_path: path to the image
        
    Returns:
     array of bounding box coordinates for the face(s)
    """
    image = face_recognition.load_image_file(str(image_path))
    return face_recognition.face_locations(image)

def add_bounding_boxes(image_path: str,
                       axe: matplotlib.axes.Axes) -> None:
    """Adds patches to the current matplotlib figure
    
    Args:
     image_path: path to the image file
     axe: axes to add the rectangle to
    """
    for (top, right, bottom, left) in detect_faces(image_path):
        width = right - left
        height = top - bottom
        rectangle = matplotlib.patches.Rectangle((top, right), width, height,
                                      fill=False)
        axe.add_patch(rectangle)
    return    

figure, axe = pyplot.subplots()
human = human_files[0]
name = " ".join(human.name.split("_")[:-1])
image = matplotlib.image.imread(human)
figure.suptitle("dlib Face Recognition Bounding-Box ({})".format(name),
                weight='bold')
add_bounding_boxes(human, axe)
axe.tick_params(dict(axis="both",
                     which="both",
                     bottom=False,
                     top=False))
axe.get_xaxis().set_ticks([])
axe.get_yaxis().set_ticks([])
        
plot = axe.imshow(image)

def has_face(image_path: str) -> bool:
    """Checks if there is at least one face in the image

    Args:
     image_path: path to the image file

    Returns:
     True if there's at least one face in the image
    """
    return len(detect_faces(image_path)) > 0

dlib_false_positives = face_scorer(has_face)

import torch
import torchvision.models as models

# define VGG16 model
VGG16 = models.vgg16(pretrained=True)

# move model to GPU if CUDA is available
if use_cuda:
    VGG16 = VGG16.cuda()
    MODELS.append(VGG16)

vgg_transform = transforms.Compose([transforms.Resize(255),
                                    transforms.CenterCrop(VGG_IMAGE_SIZE),
                                    transforms.ToTensor(),
                                    transforms.Normalize(MEANS,
                                                         DEVIATIONS)])

def model_predict(image_path: str, model: nn.Module,
                  transform: transforms.Compose) -> int:
    """Predicts the class of item in image

    Args:
     image_path: path to the image to check
     model: model to make the prediction
     transform: callable to convert the image to a tensor

    Returns:
     index corresponding to the model's prediction
    """
    image = Image.open(str(image_path))
    image = transform(image).unsqueeze(0).to(device)
    output = model(image)
    probabilities = torch.exp(output)
    _, top_class = probabilities.topk(1, dim=1)
    return top_class.item()    

VGG16_predict = partial(model_predict, model=VGG16, transform=vgg_transform)

def dog_detector(img_path: str, predictor: callable=VGG16_predict) -> bool:
    """Predicts if the image is a dog

    Args:
     img_path: path to image file
     predictor: callable that maps the image to an ID
    
    Returns:
     is-dog: True if the image contains a dog
    """
    return DOG_LOWER < predictor(img_path) < DOG_UPPER

dog_scorer = partial(species_scorer,
                     true_species=dog_files_short,
                     false_species=human_files_short,
                     labels=("Images in `dog_files_short` with a detected dog",
                             "Images in `human_files_short with a detected dog", "F1"))

false_dogs = dog_scorer(dog_detector)

inception = models.inception_v3(pretrained=True)
inception.to(device)
MODELS.append(inception)
inception.eval()
pass # this is to prevent the output from dumping into the notebook

inception_transforms = transforms.Compose([transforms.Resize(INCEPTION_IMAGE_SIZE),
                                           transforms.CenterCrop(INCEPTION_IMAGE_SIZE),
                                           transforms.ToTensor(),
                                           transforms.Normalize(MEANS,
                                                                DEVIATIONS)])

inception_predicts = partial(model_predict, model=inception, transform=inception_transforms)

inception_dog_detector = partial(dog_detector, predictor=inception_predicts)

dlib_false_dogs = dog_scorer(inception_dog_detector)

train_transform = transforms.Compose([
    transforms.RandomRotation(30),
    transforms.RandomResizedCrop(SCRATCH_IMAGE_SIZE),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(MEANS,
                         DEVIATIONS)])

test_transform = transforms.Compose([transforms.Resize(350),
                                     transforms.CenterCrop(SCRATCH_IMAGE_SIZE),
                                     transforms.ToTensor(),
                                     transforms.Normalize(MEANS,
                                                          DEVIATIONS)])

dog_training_path = DOG_PATH.joinpath("train")
dog_validation_path = DOG_PATH.joinpath("valid")
dog_testing_path = DOG_PATH.joinpath("test")

training = datasets.ImageFolder(root=str(dog_training_path),
                                transform=train_transform)
validation = datasets.ImageFolder(root=str(dog_validation_path),
                                  transform=test_transform)
testing = datasets.ImageFolder(root=str(dog_testing_path),
                               transform=test_transform)

BATCH_SIZE = 32
WORKERS = 0

train_batches = torch.utils.data.DataLoader(training, batch_size=BATCH_SIZE,
                                            shuffle=True, num_workers=WORKERS)
validation_batches = torch.utils.data.DataLoader(
    validation, batch_size=BATCH_SIZE, shuffle=True, num_workers=WORKERS)
test_batches = torch.utils.data.DataLoader(
    testing, batch_size=BATCH_SIZE, shuffle=True, num_workers=WORKERS)

loaders_scratch = dict(train=train_batches,
                       validation=validation_batches,
                       test=test_batches)

BREEDS = len(training.classes)
print("There are {} breeds.".format(BREEDS))

LAYER_ONE_IN = 3
LAYER_ONE_OUT = 16
LAYER_TWO_OUT = LAYER_ONE_OUT * 2
LAYER_THREE_OUT = LAYER_TWO_OUT * 2
FLATTEN_TO = (SCRATCH_IMAGE_SIZE//8)**2 * LAYER_THREE_OUT
FULLY_CONNECTED_OUT = int(str(FLATTEN_TO)[:3])//100 * 100
KERNEL = 3
PADDING = 1

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(LAYER_ONE_IN, LAYER_ONE_OUT,
                               KERNEL, padding=PADDING)
        self.conv2 = nn.Conv2d(LAYER_ONE_OUT, LAYER_TWO_OUT,
                               KERNEL, padding=PADDING)
        self.conv3 = nn.Conv2d(LAYER_TWO_OUT, LAYER_THREE_OUT,
                               KERNEL, padding=PADDING)
        # max pooling layer
        self.pool = nn.MaxPool2d(2, 2)
        # linear layer
        self.fc1 = nn.Linear(FLATTEN_TO, FULLY_CONNECTED_OUT)
        self.fc2 = nn.Linear(FULLY_CONNECTED_OUT, BREEDS)
        # dropout layer
        self.dropout = nn.Dropout(0.25)
        return
    
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))

        x = x.view(-1, FLATTEN_TO)
        x = self.dropout(x)

        x = self.dropout(F.relu(self.fc1(x)))
        return self.fc2(x)
#-#-# You so NOT have to modify the code below this line. #-#-#

# instantiate the CNN
model_scratch = Net()

# move tensors to GPU if CUDA is available
if use_cuda:
    model_scratch.cuda()
    MODELS.append(model_scratch)

import torch.optim as optimizer

criterion_scratch = nn.CrossEntropyLoss()
optimizer_scratch = optimizer.SGD(model_scratch.parameters(),
                                  lr=0.001,
                                  momentum=0.9)

def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path,
          print_function: callable=print,
          is_inception: bool=False):
    """Trains the model

    Args:
     n_epochs: the number of times to repeat training
     loaders: dict of data batch-loaders
     model: the model to train
     optimizer: the gradient descent object
     criterion: The object to calculate the loss
     use_cuda: boolean to decide whether to move the data to the GPU
     save_path: path to file to save best model to
     print_function: something to pass output to
     is_inception: if True, expect a tuple of tensors as the model output
    """
    # initialize tracker for minimum validation loss
    valid_loss_min = np.Inf
    
    # check the keys are right so you don't waste an entire epoch to find out
    training_batches = loaders["train"]
    validation_batches = loaders["validation"]
    started = datetime.now()
    print_function("Training Started: {}".format(started))
    for epoch in range(1, n_epochs+1):
        # initialize variables to monitor training and validation loss
        epoch_started = datetime.now()
        train_loss = 0.0
        valid_loss = 0.0
        
        ###################
        # train the model #
        ###################
        model.train()
        for data, target in training_batches:
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            optimizer.zero_grad()
            if is_inception:
                output, _ = model(data)
            else:
                output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            train_loss += loss.item() * data.size(0)
        train_loss /= len(training_batches.dataset)

        ######################    
        # validate the model #
        ######################
        model.eval()
        for data, target in validation_batches:
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            output = model(data)
            loss = criterion(output, target)
            valid_loss += loss.item() * data.size(0)
        valid_loss /= len(validation_batches.dataset)
        print_function('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}\tElapsed: {}'.format(
            epoch,                     
            train_loss,
            valid_loss,
            datetime.now() - epoch_started,
            ))
        
        if valid_loss < valid_loss_min:
            print_function(
                ("Validation loss decreased ({:.6f} --> {:.6f}). "
                 "Saving model ...").format(
                     valid_loss_min,
                     valid_loss))
            torch.save(model.state_dict(), save_path)
            valid_loss_min = valid_loss
    ended = datetime.now()
    print_function("Training Ended: {}".format(ended))
    print_function("Total Training Time: {}".format(ended - started))            
    return model

class Tee:
    """Save the input to a file and print it

    Args:
     log_name: name to give the log    
     directory_path: path to the directory for the file
    """
    def __init__(self, log_name: str, 
                 directory_name: str="../../../logs/dog-breed-classifier") -> None:
        self.directory_name = directory_name
        self.log_name = log_name
        self._path = None
        self._log = None
        return

    @property
    def path(self) -> Path:
        """path to the log-file"""
        if self._path is None:
            self._path = Path(self.directory_name).expanduser()
            assert self._path.is_dir()
            self._path = self._path.joinpath(self.log_name)
        return self._path

    @property
    def log(self):
        """File object to write log to"""
        if self._log is None:
            self._log = self.path.open("w", buffering=1)
        return self._log

    def __call__(self, line: str) -> None:
        """Writes to the file and stdout

        Args:
         line: text to emit
        """
        self.log.write("{}\n".format(line))
        print(line)
        return

scratch_path = MODEL_PATH.joinpath("model_scratch.pt")
scratch_log = Tee(log_name="scratch_train.log")

EPOCHS = 100

model_scratch = train(EPOCHS, loaders_scratch, model_scratch, optimizer_scratch, 
                      criterion_scratch, use_cuda, scratch_path, print_function=scratch_log)

model_scratch.load_state_dict(torch.load(scratch_path))

def test(loaders, model, criterion, use_cuda, print_function=print):

    # monitor test loss and accuracy
    test_loss = 0.
    correct = 0.
    total = 0.

    model.eval()
    for batch_idx, (data, target) in enumerate(loaders['test']):
        # move to GPU
        if use_cuda:
            data, target = data.cuda(), target.cuda()
        # forward pass: compute predicted outputs by passing inputs to the model
        output = model(data)
        # calculate the loss
        loss = criterion(output, target)
        # update average test loss 
        test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
        # convert output probabilities to predicted class
        pred = output.data.max(1, keepdim=True)[1]
        # compare predictions to true label
        correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
        total += data.size(0)
            
    print_function('Test Loss: {:.6f}\n'.format(test_loss))

    print_function('\nTest Accuracy: %2d%% (%2d/%2d)' % (
        100. * correct / total, correct, total))

scratch_test_log = Tee("scratch_test.log")

# call test function    
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda, print_function=scratch_test_log)

loaders_transfer = loaders_scratch

model_transfer = models.inception_v3(pretrained=True)
for parameter in model_transfer.parameters():
    parameter.requires_grad = False
classifier_inputs = model_transfer.fc.in_features
model_transfer.fc = nn.Linear(in_features=classifier_inputs,
                              out_features=BREEDS,
                              bias=True)
model_transfer.to(device)
MODELS.append(model_transfer)

criterion_transfer = nn.CrossEntropyLoss()
optimizer_transfer = optimizer.SGD(
    model_transfer.parameters(),
    lr=0.001,
    momentum=0.9)

transfer_model_path = MODEL_PATH.joinpath("model_transfer.pt")

transfer_log = Tee(log_name="transfer_train.log")

EPOCHS = 100

# train the model
model_transfer = train(EPOCHS,
                       loaders=loaders_transfer,
                       model=model_transfer,
                       optimizer=optimizer_transfer,
                       criterion=criterion_transfer,
                       use_cuda=use_cuda,
                       save_path=transfer_model_path,
                       print_function=transfer_log,
                       is_inception=True)

with transfer_log.path.open() as reader:
    for line in reader:
        print(line.rstrip())

# load the model that got the best validation accuracy (uncomment the line below)
model_transfer.load_state_dict(torch.load(transfer_model_path))

transfer_test_log = Tee("transfer_test.log")

test(loaders_transfer, model_transfer, criterion_transfer, use_cuda, print_function=transfer_test_log)

class_names = [item[4:].replace("_", " ") for item in training.classes]

def predict_breed_transfer(img_path: str) -> str:
    """Predicts the dog-breed of what's in the image

    Args:
     img_path: path to the image to search

    Returns:
     the name of the dog-breed
    """
    # load the image
    image = Image.open(image_path)

    # convert the image to a tensor
    tensor = test_transform(image)

    # add a batch number
    tensor = tensor.unsqueeze_(0)

    # put on the GPU or CPU
    tensor = tensor.to(device)

    # make it a variable
    x = torch.autograd.Variable(tensor)

    # make the prediction
    output = model(x)
    return class_names[output.data.cpu().numpy().argmax()]

class Transformer:
    """Builds the image transformers

    Args:
     means: list of means for each channel
     deviations: list of standard deviations for each channel
     image_size: size to crop the image to
    """
    def __init__(self,
                 means: list=MEANS,
                 deviations: list=DEVIATIONS,
                 image_size: int=INCEPTION_IMAGE_SIZE) -> None:
        self.means = means
        self.deviations = deviations
        self.image_size = image_size
        self._training = None
        self._testing = None
        return

    @property
    def training(self) -> transforms.Compose:
        """The image transformers for the training"""
        if self._training is None:
            self._training = transforms.Compose([
                transforms.RandomRotation(30),
                transforms.RandomResizedCrop(self.image_size),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize(self.means,
                                     self.deviations)])
        return self._training

    @property
    def testing(self) -> transforms.Compose:
        """Image transforms for the testing"""
        if self._testing is None:
            self._testing = transforms.Compose(
                [transforms.Resize(self.image_size),
                 transforms.CenterCrop(INCEPTION_IMAGE_SIZE),
                 transforms.ToTensor(),
                 transforms.Normalize(self.means,
                                      self.deviations)])
        return self._testing

class DogDetector:
    """Detects dogs

    Args:
     model_definition: definition for the model
     device: where to run the model (CPU or CUDA)
     image_size: what to resize the file to (depends on the model-definition)
     means: mean for each channel
     deviations: standard deviation for each channel
     dog_lower_bound: index below where dogs start
     dog_upper_bound: index above where dogs end
    """
    def __init__(self,
                 model_definition: nn.Module=models.inception_v3,
                 image_size: int=INCEPTION_IMAGE_SIZE,
                 means: list=MEANS,
                 deviations: list=DEVIATIONS,
                 dog_lower_bound: int=DOG_LOWER,
                 dog_upper_bound: int=DOG_UPPER,
                 device: torch.device=None) -> None:
        self.model_definition = model_definition
        self.image_size = image_size
        self.means = means
        self.deviations = deviations
        self.dog_lower_bound = dog_lower_bound
        self.dog_upper_bound = dog_upper_bound
        self._device = device
        self._model = None
        self._transformer = None
        return

    @property
    def device(self) -> torch.device:
        """The device to add the model to"""
        if self._device is None:
            self._device = torch.device("cuda"
                                        if torch.cuda.is_available()
                                        else "cpu")
        return self._device

    @property
    def model(self) -> nn.Module:
        """Build the model"""
        if self._model is None:
            self._model = self.model_definition(pretrained=True)
            self._model.to(self.device)
            self._model.eval()
        return self._model

    @property
    def transformer(self) -> Transformer:
        """The transformer for the image data"""
        if self._transformer is None:
            self._transformer = Transformer()
        return self._transformer

    def __call__(self, image_path: str) -> bool:
        """Checks if there is a dog in the image"""
        image = Image.open(str(image_path))
        image = self.transformer.testing(image).unsqueeze(0).to(self.device)
        output = self.model(image)
        probabilities = torch.exp(output)
        _, top_class = probabilities.topk(1, dim=1)
        return self.dog_lower_bound < top_class.item() < self.dog_upper_bound

class SpeciesDetector:
    """Detect dogs and humans

    Args:
     device: where to put the dog-detecting model
    """
    def __init__(self, device: torch.device=None) -> None:
        self.device = device
        self._dog_detector = None
        return

    @property
    def dog_detector(self) -> DogDetector:
        """Neural Network dog-detector"""
        if self._dog_detector is None:
            self._dog_detector = DogDetector(device=self.device)
        return self._dog_detector

    def is_human(self, image_path: str) -> bool:
        """Checks if the image is a human
        
        Args:
         image_path: path to the image

        Returns:
         True if there is a human face in the image
        """
        image = face_recognition.load_image_file(str(image_path))
        faces = face_recognition.face_locations(image)
        return len(faces) > 0

    def is_dog(self, image_path: str) -> bool:        
        """Checks if there is a dog in the image"""
        return self.dog_detector(image_path)

class DogPaths:
    """holds the paths to the dog images"""
    def __init__(self) -> None:
        self._main = None
        self._training = None
        self._testing = None
        self._validation = None
        return

    @property
    def main(self) -> Path:
        """The path to the main folder"""
        if self._main is None:
            self._main = DOG_PATH
        return self._main

    @property
    def training(self) -> Path:
        """Path to the training images"""
        if self._training is None:
            self._training = DOG_PATH.joinpath("train")
        return self._training

    @property
    def validation(self) -> Path:
        """Path to the validation images"""
        if self._validation is None:
            self._validation = DOG_PATH.joinpath("valid")
        return self._validation

    @property
    def testing(self) -> Path:
        """Path to the testing images"""
        if self._testing is None:
            self._testing = DOG_PATH.joinpath("test")
        return self._testing

class Inception:
    """Sets up the model, criterion, and optimizer for the transfer learning

    Args:
     classes: number of outputs for the final layer
     device: processor to use
     model_path: path to a saved model
     learning_rate: learning rate for the optimizer
     momentum: momentum for the optimizer
    """
    def __init__(self, classes: int,
                 device: torch.device=None,
                 model_path: str=None,
                 learning_rate: float=0.001, momentum: float=0.9) -> None:
        self.classes = classes
        self.model_path = model_path
        self.learning_rate = learning_rate
        self.momentum = momentum
        self._device = device
        self._model = None
        self._classifier_inputs = None
        self._criterion = None
        self._optimizer = None
        return

    @property
    def device(self) -> torch.device:
        """Processor to use (cpu or cuda)"""
        if self._device is None:
            self._device = torch.device(
                "cuda" if torch.cuda.is_available() else "cpu")
        return self._device

    @property
    def model(self) -> models.inception_v3:
        """The inception model"""
        if self._model is None:
            self._model = models.inception_v3(pretrained=True)
            for parameter in self._model.parameters():
                parameter.requires_grad = False
            classifier_inputs = self._model.fc.in_features
            self._model.fc = nn.Linear(in_features=classifier_inputs,
                                       out_features=self.classes,
                                       bias=True)
            self._model.to(self.device)
            if self.model_path:
                self._model.load_state_dict(torch.load(self.model_path))
        return self._model

    @property
    def criterion(self) -> nn.CrossEntropyLoss:
        """The loss callable"""
        if self._criterion is None:
            self._criterion = nn.CrossEntropyLoss()
        return self._criterion

    @property
    def optimizer(self) -> optimizer.SGD:
        """The Gradient Descent object"""
        if self._optimizer is None:
            self._optimizer = optimizer.SGD(
                self.model.parameters(),
                lr=self.learning_rate,
                momentum=self.momentum)
        return self._optimizer

class DataSets:
    """Builds the data-sets

    Args:
     paths: object with the paths to the data-sets
    """
    def __init__(self, paths: DogPaths=None, transformer: Transformer=None) -> None:
        self._paths = paths
        self._transformer = transformer
        self._training = None
        self._validation = None
        self._testing = None
        return

    @property
    def paths(self) -> DogPaths:
        """Object with the paths to the image files"""
        if self._paths is None:
            self._paths = DogPaths()
        return self._paths

    @property
    def transformer(self) -> Transformer:
        """Object with the image transforms"""
        if self._transformer is None:
            self._transformer = Transformer()
        return self._transformer

    @property
    def training(self) -> datasets.ImageFolder:
        """The training data set"""
        if self._training is None:
            self._training = datasets.ImageFolder(
                root=self.paths.training,
                transform=self.transformer.training)
        return self._training

    @property
    def validation(self) -> datasets.ImageFolder:
        """The validation dataset"""
        if self._validation is None:
            self._validation = datasets.ImageFolder(
                root=self.paths.validation,
                transform=self.transformer.testing)
        return self._validation

    @property
    def testing(self) -> datasets.ImageFolder:
        """The test set"""
        if self._testing is None:
            self._testing = datasets.ImageFolder(
                root=self.paths.testing,
                transform=self.transformer.testing)
        return self._testing

class DogPredictor:
    """Makes dog-breed predictions
    
    Args:
     model_path: path to the model's state-dict
     device: processor to run the model on
     data_sets: a DataSets object
     inception: an Inception object
    """
    def __init__(self, model_path: str=None,
                 device: torch.device=None,
                 data_sets: DataSets=None,
                 inception: Inception=None) -> None:
        self.model_path = model_path
        self.device = device
        self._data_sets = data_sets
        self._inception = inception
        self._breeds = None
        return

    @property
    def data_sets(self) -> DataSets:
        if self._data_sets is None:
            self._data_sets = DataSets()
        return self._data_sets

    @property
    def inception(self) -> Inception:
        """An Inception object"""
        if self._inception is None:
            self._inception = Inception(
                classes=len(self.data_sets.training.classes),
                model_path=self.model_path,
                device=self.device)
            self._inception.model.eval()
        return self._inception

    @property
    def breeds(self) -> list:
        """A list of dog-breeds"""
        if self._breeds is None:
            self._breeds = [name[4:].replace("_", " ")
                            for name in self.data_sets.training.classes]
        return self._breeds

    def predict_index(self, image_path:str) -> int:
        """Predicts the index of the breed of the dog in the image

        Args:
         image_path: path to the image
        Returns:
         index in the breeds list for the image
        """
        model = self.inception.model        
        image = Image.open(image_path)
        tensor = self.data_sets.transformer.testing(image)
        # add a batch number
        tensor = tensor.unsqueeze_(0)
        tensor = tensor.to(self.inception.device)
        x = torch.autograd.Variable(tensor)
        output = model(x)
        return output.data.cpu().numpy().argmax()

    def __call__(self, image_path) -> str:
        """Predicts the breed of the dog in the image

        Args:
         image_path: path to the image
        Returns:
         name of the breed
        """
        return self.breeds[self.predict_index(image_path)]

class DogBreedClassifier:
    """Tries To predict the dog-breed for an image

    Args:
     model_path: path to the inception-model
    """
    def __init__(self, model_path: str) -> None:
        self.model_path = model_path
        self._breed_predictor = None
        self._species_detector = None
        return

    @property
    def breed_predictor(self) -> DogPredictor:
        """Predictor of dog-breeds"""
        if self._breed_predictor is None:
            self._breed_predictor = DogPredictor(model_path=self.model_path)
        return self._breed_predictor

    @property
    def species_detector(self) -> SpeciesDetector:
        """Detector of humans and dogs"""
        if self._species_detector is None:
            self._species_detector = SpeciesDetector(
                device=self.breed_predictor.inception.device)
        return self._species_detector

    def render(self, image_path: str, species: str, breed: str) -> None:
        """Renders the image

        Args:
         image_path: path to the image to render
         species: identified species
         breed: identified breed
        """
        name = " ".join(image_path.name.split(".")[0].split("_")).title()
        figure, axe = pyplot.subplots()
        figure.suptitle("{} ({})".format(species, name), weight="bold")
        axe.set_xlabel("Looks like a {}.".format(breed))
        image = Image.open(image_path)
        axe.tick_params(dict(axis="both",
                             which="both",
                             bottom=False,
                             top=False))
        axe.get_xaxis().set_ticks([])
        axe.get_yaxis().set_ticks([])
        axe_image = axe.imshow(image)
        return

    def __call__(self, image_path:str) -> None:
        """detects the dog-breed and displays the image

        Args:
         image_path: path to the image
        """
        image_path = Path(image_path)
        is_dog = self.species_detector.is_dog(image_path)
        is_human = self.species_detector.is_human(image_path)

        if not is_dog and not is_human:
            species = "Error: Neither Human nor Dog"
            breed = "?"
        else:
            breed = self.breed_predictor(image_path)

        if is_dog and is_human:
            species = "Human-Dog Hybrid"
        elif is_dog:
            species = "Dog"
        elif is_human:
            species = "Human"
        self.render(image_path, species, breed)
        return

for model in MODELS:
    model.cpu()
classifier = DogBreedClassifier(model_path=transfer_model_path)

def run_app(img_path):
    """Runs the dog breed classifier

    Args:
     img_path: path to the image to classify
    """
    classifier(img_path)
    return

def first_prediction(source: list, start:int=0, count: int=1) -> int:
    """Gets the index of the first True prediction

    Args:
     source: list of True/False predictions
     start: index to start the search from
     count: number of indices to find

    Returns:
     indices of first True predictions found
    """
    indices = []
    found = 0
    for index, prediction in enumerate(source[start:]):
        if prediction:
            print("{}: {}".format(start + index, prediction))
            indices.append(index)
            found += 1
            if found == count:
                break
    return indices

human_dog = first_prediction(dlib_false_positives)

hot_dog = "hot_dog.jpg"
rabbit = "rabbit.jpg"
test_images = [dog_files_short[human_dog[0]], hot_dog, rabbit]

dogs = numpy.random.choice(dog_files, 3)
humans = numpy.random.choice(human_files, 3)