# Auto-anchor utils

import numpy as np

import torch

import yaml

from scipy.cluster.vq import kmeans

from tqdm import tqdm

from utils.general import colorstr

def check_anchor_order(m):

    # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary

    a = m.anchor_grid.prod(-1).view(-1)  # anchor area

    da = a[-1] - a[0]  # delta a

    ds = m.stride[-1] - m.stride[0]  # delta s

    if da.sign() != ds.sign():  # same order

        print('Reversing anchor order')

        m.anchors[:] = m.anchors.flip(0)

        m.anchor_grid[:] = m.anchor_grid.flip(0)

def check_anchors(dataset, model, thr=4.0, imgsz=640):

    # Check anchor fit to data, recompute if necessary

    prefix = colorstr('autoanchor: ')

    print(f'\n{prefix}Analyzing anchors... ', end='')

    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()

    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)

    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale

    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh

    def metric(k):  # compute metric

        r = wh[:, None] / k[None]

        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric

        best = x.max(1)[0]  # best_x

        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold

        bpr = (best > 1. / thr).float().mean()  # best possible recall

        return bpr, aat

    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors

    bpr, aat = metric(anchors)

    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')

    if bpr < 0.98:  # threshold to recompute

        print('. Attempting to improve anchors, please wait...')

        na = m.anchor_grid.numel() // 2  # number of anchors

        try:

            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)

        except Exception as e:

            print(f'{prefix}ERROR: {e}')

        new_bpr = metric(anchors)[0]

        if new_bpr > bpr:  # replace anchors

            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)

            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference

            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss

            check_anchor_order(m)

            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')

        else:

            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')

    print('')  # newline

def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):

    """ Creates kmeans-evolved anchors from training dataset

        Arguments:

            path: path to dataset *.yaml, or a loaded dataset

            n: number of anchors

            img_size: image size used for training

            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0

            gen: generations to evolve anchors using genetic algorithm

            verbose: print all results

        Return:

            k: kmeans evolved anchors

        Usage:

            from utils.autoanchor import *; _ = kmean_anchors()

    """

    thr = 1. / thr

    prefix = colorstr('autoanchor: ')

    def metric(k, wh):  # compute metrics

        r = wh[:, None] / k[None]

        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric

        # x = wh_iou(wh, torch.tensor(k))  # iou metric

        return x, x.max(1)[0]  # x, best_x

    def anchor_fitness(k):  # mutation fitness

        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)

        return (best * (best > thr).float()).mean()  # fitness

    def print_results(k):

        k = k[np.argsort(k.prod(1))]  # sort small to large

        x, best = metric(k, wh0)

        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr

        print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')

        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '

              f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')

        for i, x in enumerate(k):

            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg

        return k

    if isinstance(path, str):  # *.yaml file

        with open(path) as f:

            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict

        from utils.datasets import LoadImagesAndLabels

        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)

    else:

        dataset = path  # dataset

    # Get label wh

    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)

    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh

    # Filter

    i = (wh0 < 3.0).any(1).sum()

    if i:

        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')

    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels

    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1

    # Kmeans calculation

    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')

    s = wh.std(0)  # sigmas for whitening

    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance

    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')

    k *= s

    wh = torch.tensor(wh, dtype=torch.float32)  # filtered

    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered

    k = print_results(k)

    # Plot

    # k, d = [None] * 20, [None] * 20

    # for i in tqdm(range(1, 21)):

    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance

    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)

    # ax = ax.ravel()

    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')

    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh

    # ax[0].hist(wh[wh[:, 0]<100, 0],400)

    # ax[1].hist(wh[wh[:, 1]<100, 1],400)

    # fig.savefig('wh.png', dpi=200)

    # Evolve

    npr = np.random

    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma

    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar

    for _ in pbar:

        v = np.ones(sh)

        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)

            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)

        kg = (k.copy() * v).clip(min=2.0)

        fg = anchor_fitness(kg)

        if fg > f:

            f, k = fg, kg.copy()

            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'

            if verbose:

                print_results(k)

    return print_results(k)