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def centroid_embedding(outfit_embedding_list):
    number_of_outfits = outfit_embedding_list.shape[0]
    length_of_embedding = outfit_embedding_list.shape[1]
    centroid = []
    for i in range(length_of_embedding):
        centroid.append(np.sum(outfit_embedding_list[:, i])/number_of_outfits)
    return centroid

def generate_image_mix(list_1, list_2, number_of_items=12, ratio=0.5):
    list_1_num_items = int(number_of_items * ratio)

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def centroid_embedding(outfit_embedding_list):
    number_of_outfits = outfit_embedding_list.shape[0]
    length_of_embedding = outfit_embedding_list.shape[1]
    centroid = []
    for i in range(length_of_embedding):
        centroid.append(np.sum(outfit_embedding_list[:, i])/number_of_outfits)
    return centroid

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# for images similar to centroid 
def get_similar_images_annoy_centroid(annoy_tree, vector_value, number_of_items=12):
    start = time.time()
    similar_img_ids = annoy_tree.get_nns_by_vector(vector_value, number_of_items+1)
    end = time.time()
    print(f'{(end - start) * 1000} ms')
    # ignore first item as it is always target image
    return data_df_ouput.iloc[similar_img_ids[1:]] 

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train_image_list = ImageList.from_df(df=data_df, path=root_path, cols='images').split_by_idxs(
    (data_df[data_df['dataset']=='train'].index),
    (data_df[data_df['dataset']=='val'].index)).label_from_df(cols='category')

data = train_image_list.transform(get_transforms(), size=224).databunch(bs=128).normalize(imagenet_stats)
data.add_test(ImageList.from_df(df=data_df[data_df['dataset'] == 'test'], path=root_path, cols='images'))
data.show_batch(rows=3, figsize=(8,8))

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# Using Spotify's Annoy
def get_similar_images_annoy(annoy_tree, img_index, number_of_items=12):
    start = time.time()
    img_id, img_label  = data_df_ouput.iloc[img_index, [0, 1]]
    similar_img_ids = annoy_tree.get_nns_by_item(img_index, number_of_items+1)
    end = time.time()
    print(f'{(end - start) * 1000} ms')
    # ignore first item as it is always target image
    return img_id, img_label, data_df_ouput.iloc[similar_img_ids[1:]] 

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# load the trained model
def load_learner(data, pretrained_model, model_metrics, model_path):
    learner = cnn_learner(data, pretrained_model, metrics=model_metrics)
    learner.model = torch.nn.DataParallel(learner.model)
    learner = learner.load(model_path)
    return learner

pretrained_model = models.resnet18
model_metrics = [accuracy, partial(top_k_accuracy, k=1), partial(top_k_accuracy, k=5)]
model_path = "/content/gdrive/My Drive/resnet18-fashion"

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def train_model(data, pretrained_model, model_metrics):
    learner = cnn_learner(data, pretrained_model, metrics=model_metrics)
    learner.model = torch.nn.DataParallel(learner.model)
    learner.lr_find()
    learner.recorder.plot(suggestion=True)
    return learner

pretrained_model = models.resnet18
model_metrics = [accuracy, partial(top_k_accuracy, k=1), partial(top_k_accuracy, k=5)]
learner = train_model(data, pretrained_model, model_metrics)

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