# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

"""

Model validation metrics

"""

import math

import warnings

from pathlib import Path

import matplotlib.pyplot as plt

import numpy as np

import torch

def fitness(x):

    # Model fitness as a weighted combination of metrics

    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]

    return (x[:, :4] * w).sum(1)

def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):

    """ Compute the average precision, given the recall and precision curves.

    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.

    # Arguments

        tp:  True positives (nparray, nx1 or nx10).

        conf:  Objectness value from 0-1 (nparray).

        pred_cls:  Predicted object classes (nparray).

        target_cls:  True object classes (nparray).

        plot:  Plot precision-recall curve at mAP@0.5

        save_dir:  Plot save directory

    # Returns

        The average precision as computed in py-faster-rcnn.

    """

    # Sort by objectness

    i = np.argsort(-conf)

    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]

    # Find unique classes

    unique_classes, nt = np.unique(target_cls, return_counts=True)

    nc = unique_classes.shape[0]  # number of classes, number of detections

    # Create Precision-Recall curve and compute AP for each class

    px, py = np.linspace(0, 1, 1000), []  # for plotting

    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))

    for ci, c in enumerate(unique_classes):

        i = pred_cls == c

        n_l = nt[ci]  # number of labels

        n_p = i.sum()  # number of predictions

        if n_p == 0 or n_l == 0:

            continue

        else:

            # Accumulate FPs and TPs

            fpc = (1 - tp[i]).cumsum(0)

            tpc = tp[i].cumsum(0)

            # Recall

            recall = tpc / (n_l + eps)  # recall curve

            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases

            # Precision

            precision = tpc / (tpc + fpc)  # precision curve

            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score

            # AP from recall-precision curve

            for j in range(tp.shape[1]):

                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])

                if plot and j == 0:

                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5

    # Compute F1 (harmonic mean of precision and recall)

    f1 = 2 * p * r / (p + r + eps)

    names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data

    names = {i: v for i, v in enumerate(names)}  # to dict

    if plot:

        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)

        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')

        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')

        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')

    i = f1.mean(0).argmax()  # max F1 index

    p, r, f1 = p[:, i], r[:, i], f1[:, i]

    tp = (r * nt).round()  # true positives

    fp = (tp / (p + eps) - tp).round()  # false positives

    return tp, fp, p, r, f1, ap, unique_classes.astype('int32')

def compute_ap(recall, precision):

    """ Compute the average precision, given the recall and precision curves

    # Arguments

        recall:    The recall curve (list)

        precision: The precision curve (list)

    # Returns

        Average precision, precision curve, recall curve

    """

    # Append sentinel values to beginning and end

    mrec = np.concatenate(([0.0], recall, [1.0]))

    mpre = np.concatenate(([1.0], precision, [0.0]))

    # Compute the precision envelope

    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))

    # Integrate area under curve

    method = 'interp'  # methods: 'continuous', 'interp'

    if method == 'interp':

        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)

        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate

    else:  # 'continuous'

        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes

        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve

    return ap, mpre, mrec

class ConfusionMatrix:

    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix

    def __init__(self, nc, conf=0.25, iou_thres=0.45):

        self.matrix = np.zeros((nc + 1, nc + 1))

        self.nc = nc  # number of classes

        self.conf = conf

        self.iou_thres = iou_thres

    def process_batch(self, detections, labels):

        """

        Return intersection-over-union (Jaccard index) of boxes.

        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.

        Arguments:

            detections (Array[N, 6]), x1, y1, x2, y2, conf, class

            labels (Array[M, 5]), class, x1, y1, x2, y2

        Returns:

            None, updates confusion matrix accordingly

        """

        detections = detections[detections[:, 4] > self.conf]

        gt_classes = labels[:, 0].int()

        detection_classes = detections[:, 5].int()

        iou = box_iou(labels[:, 1:], detections[:, :4])

        x = torch.where(iou > self.iou_thres)

        if x[0].shape[0]:

            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()

            if x[0].shape[0] > 1:

                matches = matches[matches[:, 2].argsort()[::-1]]

                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]

                matches = matches[matches[:, 2].argsort()[::-1]]

                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]

        else:

            matches = np.zeros((0, 3))

        n = matches.shape[0] > 0

        m0, m1, _ = matches.transpose().astype(np.int16)

        for i, gc in enumerate(gt_classes):

            j = m0 == i

            if n and sum(j) == 1:

                self.matrix[detection_classes[m1[j]], gc] += 1  # correct

            else:

                self.matrix[self.nc, gc] += 1  # background FP

        if n:

            for i, dc in enumerate(detection_classes):

                if not any(m1 == i):

                    self.matrix[dc, self.nc] += 1  # background FN

    def matrix(self):

        return self.matrix

    def tp_fp(self):

        tp = self.matrix.diagonal()  # true positives

        fp = self.matrix.sum(1) - tp  # false positives

        # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)

        return tp[:-1], fp[:-1]  # remove background class

    def plot(self, normalize=True, save_dir='', names=()):

        try:

            import seaborn as sn

            array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-6) if normalize else 1)  # normalize columns

            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)

            fig = plt.figure(figsize=(12, 9), tight_layout=True)

            sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size

            labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels

            with warnings.catch_warnings():

                warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered

                sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,

                           xticklabels=names + ['background FP'] if labels else "auto",

                           yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))

            fig.axes[0].set_xlabel('True')

            fig.axes[0].set_ylabel('Predicted')

            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)

            plt.close()

        except Exception as e:

            print(f'WARNING: ConfusionMatrix plot failure: {e}')

    def print(self):

        for i in range(self.nc + 1):

            print(' '.join(map(str, self.matrix[i])))

def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):

    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4

    box2 = box2.T

    # Get the coordinates of bounding boxes

    if x1y1x2y2:  # x1, y1, x2, y2 = box1

        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]

        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]

    else:  # transform from xywh to xyxy

        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2

        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2

        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2

        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2

    # Intersection area

    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \

            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)

    # Union Area

    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps

    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps

    union = w1 * h1 + w2 * h2 - inter + eps

    iou = inter / union

    if GIoU or DIoU or CIoU:

        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width

        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height

        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1

            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared

            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +

                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared

            if DIoU:

                return iou - rho2 / c2  # DIoU

            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47

                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)

                with torch.no_grad():

                    alpha = v / (v - iou + (1 + eps))

                return iou - (rho2 / c2 + v * alpha)  # CIoU

        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf

            c_area = cw * ch + eps  # convex area

            return iou - (c_area - union) / c_area  # GIoU

    else:

        return iou  # IoU

def box_iou(box1, box2):

    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py

    """

    Return intersection-over-union (Jaccard index) of boxes.

    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.

    Arguments:

        box1 (Tensor[N, 4])

        box2 (Tensor[M, 4])

    Returns:

        iou (Tensor[N, M]): the NxM matrix containing the pairwise

            IoU values for every element in boxes1 and boxes2

    """

    def box_area(box):

        # box = 4xn

        return (box[2] - box[0]) * (box[3] - box[1])

    area1 = box_area(box1.T)

    area2 = box_area(box2.T)

    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)

    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)

    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)

def bbox_ioa(box1, box2, eps=1E-7):

    """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2

    box1:       np.array of shape(4)

    box2:       np.array of shape(nx4)

    returns:    np.array of shape(n)

    """

    box2 = box2.transpose()

    # Get the coordinates of bounding boxes

    b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]

    b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]

    # Intersection area

    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \

                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)

    # box2 area

    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps

    # Intersection over box2 area

    return inter_area / box2_area

def wh_iou(wh1, wh2):

    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2

    wh1 = wh1[:, None]  # [N,1,2]

    wh2 = wh2[None]  # [1,M,2]

    inter = torch.min(wh1, wh2).prod(2)  # [N,M]

    return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)

# Plots ----------------------------------------------------------------------------------------------------------------

def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):

    # Precision-recall curve

    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)

    py = np.stack(py, axis=1)

    if 0 < len(names) < 21:  # display per-class legend if < 21 classes

        for i, y in enumerate(py.T):

            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)

    else:

        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)

    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())

    ax.set_xlabel('Recall')

    ax.set_ylabel('Precision')

    ax.set_xlim(0, 1)

    ax.set_ylim(0, 1)

    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")

    fig.savefig(Path(save_dir), dpi=250)

    plt.close()

def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):

    # Metric-confidence curve

    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)

    if 0 < len(names) < 21:  # display per-class legend if < 21 classes

        for i, y in enumerate(py):

            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)

    else:

        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)

    y = py.mean(0)

    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')

    ax.set_xlabel(xlabel)

    ax.set_ylabel(ylabel)

    ax.set_xlim(0, 1)

    ax.set_ylim(0, 1)

    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")

    fig.savefig(Path(save_dir), dpi=250)

    plt.close()