import random

import cv2
import numpy as np
import torch
import torch.nn.functional as F

from utils import torch_utils

# Set printoptions
torch.set_printoptions(linewidth=1320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5


def init_seeds(seed=0):
    random.seed(seed)
    np.random.seed(seed)
    torch_utils.init_seeds(seed=seed)


def load_classes(path):
    """
    Loads class labels at 'path'
    """
    fp = open(path, 'r')
    names = fp.read().split('\n')
    return list(filter(None, names))  # filter removes empty strings (such as last line)


def model_info(model):  # Plots a line-by-line description of a PyTorch model
    n_p = sum(x.numel() for x in model.parameters())  # number parameters
    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
    print('\n%5s %50s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
    for i, (name, p) in enumerate(model.named_parameters()):
        name = name.replace('module_list.', '')
        print('%5g %50s %9s %12g %20s %12.3g %12.3g' % (
            i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
    print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))


def class_weights():  # frequency of each class in coco train2014
    weights = 1 / torch.FloatTensor(
        [187437, 4955, 30920, 6033, 3838, 4332, 3160, 7051, 7677, 9167, 1316, 1372, 833, 6757, 7355, 3302, 3776, 4671,
         6769, 5706, 3908, 903, 3686, 3596, 6200, 7920, 8779, 4505, 4272, 1862, 4698, 1962, 4403, 6659, 2402, 2689,
         4012, 4175, 3411, 17048, 5637, 14553, 3923, 5539, 4289, 10084, 7018, 4314, 3099, 4638, 4939, 5543, 2038, 4004,
         5053, 4578, 27292, 4113, 5931, 2905, 11174, 2873, 4036, 3415, 1517, 4122, 1980, 4464, 1190, 2302, 156, 3933,
         1877, 17630, 4337, 4624, 1075, 3468, 135, 1380])
    weights /= weights.sum()
    return weights


def plot_one_box(x, img, color=None, label=None, line_thickness=None):  # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1  # line thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        cv2.rectangle(img, c1, c2, color, -1)  # filled
        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)


def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.03)
    elif classname.find('BatchNorm2d') != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.03)
        torch.nn.init.constant_(m.bias.data, 0.0)


def xyxy2xywh(x):
    # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
    y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
    y[:, 0] = (x[:, 0] + x[:, 2]) / 2
    y[:, 1] = (x[:, 1] + x[:, 3]) / 2
    y[:, 2] = x[:, 2] - x[:, 0]
    y[:, 3] = x[:, 3] - x[:, 1]
    return y


def xywh2xyxy(x):
    # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
    y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
    y[:, 0] = (x[:, 0] - x[:, 2] / 2)
    y[:, 1] = (x[:, 1] - x[:, 3] / 2)
    y[:, 2] = (x[:, 0] + x[:, 2] / 2)
    y[:, 3] = (x[:, 1] + x[:, 3] / 2)
    return y


def scale_coords(img_size, coords, img0_shape):
    # Rescale x1, y1, x2, y2 from 416 to image size
    gain = float(img_size) / max(img0_shape)  # gain  = old / new
    pad_x = (img_size - img0_shape[1] * gain) / 2  # width padding
    pad_y = (img_size - img0_shape[0] * gain) / 2  # height padding
    coords[:, [0, 2]] -= pad_x
    coords[:, [1, 3]] -= pad_y
    coords[:, :4] /= gain
    coords[:, :4] = torch.round(torch.clamp(coords[:, :4], min=0))
    return coords


def ap_per_class(tp, conf, pred_cls, target_cls):
    """ Compute the average precision, given the recall and precision curves.
    Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
    # Arguments
        tp:    True positives (list).
        conf:  Objectness value from 0-1 (list).
        pred_cls: Predicted object classes (list).
        target_cls: True object classes (list).
    # Returns
        The average precision as computed in py-faster-rcnn.
    """

    # lists/pytorch to numpy
    tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(pred_cls), np.array(target_cls)

    # Sort by objectness
    i = np.argsort(-conf)
    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]

    # Find unique classes
    unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0))

    # Create Precision-Recall curve and compute AP for each class
    ap, p, r = [], [], []
    for c in unique_classes:
        i = pred_cls == c
        n_gt = sum(target_cls == c)  # Number of ground truth objects
        n_p = sum(i)  # Number of predicted objects

        if (n_p == 0) and (n_gt == 0):
            continue
        elif (n_p == 0) or (n_gt == 0):
            ap.append(0)
            r.append(0)
            p.append(0)
        else:
            # Accumulate FPs and TPs
            fpc = np.cumsum(1 - tp[i])
            tpc = np.cumsum(tp[i])

            # Recall
            recall_curve = tpc / (n_gt + 1e-16)
            r.append(tpc[-1] / (n_gt + 1e-16))

            # Precision
            precision_curve = tpc / (tpc + fpc)
            p.append(tpc[-1] / (tpc[-1] + fpc[-1]))

            # AP from recall-precision curve
            ap.append(compute_ap(recall_curve, precision_curve))

    return np.array(ap), unique_classes.astype('int32'), np.array(r), np.array(p)


def compute_ap(recall, precision):
    """ Compute the average precision, given the recall and precision curves.
    Code originally from https://github.com/rbgirshick/py-faster-rcnn.
    # Arguments
        recall:    The recall curve (list).
        precision: The precision curve (list).
    # Returns
        The average precision as computed in py-faster-rcnn.
    """
    # correct AP calculation
    # first append sentinel values at the end

    mrec = np.concatenate(([0.], recall, [1.]))
    mpre = np.concatenate(([0.], precision, [0.]))

    # compute the precision envelope
    for i in range(mpre.size - 1, 0, -1):
        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

    # to calculate area under PR curve, look for points
    # where X axis (recall) changes value
    i = np.where(mrec[1:] != mrec[:-1])[0]

    # and sum (\Delta recall) * prec
    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap


def bbox_iou(box1, box2, x1y1x2y2=True):
    """
    Returns the IoU of two bounding boxes
    """
    if x1y1x2y2:
        # 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]
    else:
        # Transform from center and width to exact coordinates
        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

    # get the coordinates of the intersection rectangle
    inter_rect_x1 = torch.max(b1_x1, b2_x1)
    inter_rect_y1 = torch.max(b1_y1, b2_y1)
    inter_rect_x2 = torch.min(b1_x2, b2_x2)
    inter_rect_y2 = torch.min(b1_y2, b2_y2)
    # Intersection area
    inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0)
    # Union Area
    b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
    b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)

    return inter_area / (b1_area + b2_area - inter_area + 1e-16)


def build_targets(target, anchor_wh, nA, nC, nG):
    """
    returns nT, nCorrect, tx, ty, tw, th, tconf, tcls
    """
    nB = len(target)  # number of images in batch
    nT = [len(x) for x in target]  # torch.argmin(target[:, :, 4], 1)  # targets per image
    tx = torch.zeros(nB, nA, nG, nG)  # batch size (4), number of anchors (3), number of grid points (13)
    ty = torch.zeros(nB, nA, nG, nG)
    tw = torch.zeros(nB, nA, nG, nG)
    th = torch.zeros(nB, nA, nG, nG)
    tconf = torch.ByteTensor(nB, nA, nG, nG).fill_(0)
    tcls = torch.ByteTensor(nB, nA, nG, nG, nC).fill_(0)  # nC = number of classes

    for b in range(nB):
        nTb = nT[b]  # number of targets
        if nTb == 0:
            continue
        t = target[b]

        # Convert to position relative to box
        gx, gy, gw, gh = t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG

        # Get grid box indices and prevent overflows (i.e. 13.01 on 13 anchors)
        gi = torch.clamp(gx.long(), min=0, max=nG - 1)
        gj = torch.clamp(gy.long(), min=0, max=nG - 1)

        # iou of targets-anchors (using wh only)
        box1 = t[:, 3:5] * nG
        box2 = anchor_wh.unsqueeze(1)
        inter_area = torch.min(box1, box2).prod(2)
        iou = inter_area / (gw * gh + box2.prod(2) - inter_area + 1e-16)

        # Select best iou_pred and anchor
        iou_best, a = iou.max(0)  # best anchor [0-2] for each target

        # Select best unique target-anchor combinations
        if nTb > 1:
            iou_order = torch.argsort(-iou_best)  # best to worst

            # Unique anchor selection
            u = torch.cat((gi, gj, a), 0).view((3, -1))
            _, first_unique = np.unique(u[:, iou_order], axis=1, return_index=True)  # first unique indices
            # _, first_unique = torch.unique(u[:, iou_order], dim=1, return_inverse=True)  # different than numpy?

            i = iou_order[first_unique]
            # best anchor must share significant commonality (iou) with target
            i = i[iou_best[i] > 0.10]
            if len(i) == 0:
                continue

            a, gj, gi, t = a[i], gj[i], gi[i], t[i]
            if len(t.shape) == 1:
                t = t.view(1, 5)
        else:
            if iou_best < 0.10:
                continue

        tc, gx, gy, gw, gh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG

        # Coordinates
        tx[b, a, gj, gi] = gx - gi.float()
        ty[b, a, gj, gi] = gy - gj.float()

        # Width and height (yolo method)
        tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0])
        th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1])

        # Width and height (power method)
        # tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2
        # th[b, a, gj, gi] = torch.sqrt(gh / anchor_wh[a, 1]) / 2

        # One-hot encoding of label
        tcls[b, a, gj, gi, tc] = 1
        tconf[b, a, gj, gi] = 1

    return tx, ty, tw, th, tconf, tcls


def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
    """
    Removes detections with lower object confidence score than 'conf_thres'
    Non-Maximum Suppression to further filter detections.
    Returns detections with shape:
        (x1, y1, x2, y2, object_conf, class_score, class_pred)
    """

    output = [None for _ in range(len(prediction))]
    for image_i, pred in enumerate(prediction):
        # Filter out confidence scores below threshold
        # Get score and class with highest confidence

        # cross-class NMS (experimental)
        cross_class_nms = False
        if cross_class_nms:
            a = pred.clone()
            _, indices = torch.sort(-a[:, 4], 0)  # sort best to worst
            a = a[indices]
            radius = 30  # area to search for cross-class ious
            for i in range(len(a)):
                if i >= len(a) - 1:
                    break

                close = (torch.abs(a[i, 0] - a[i + 1:, 0]) < radius) & (torch.abs(a[i, 1] - a[i + 1:, 1]) < radius)
                close = close.nonzero()

                if len(close) > 0:
                    close = close + i + 1
                    iou = bbox_iou(a[i:i + 1, :4], a[close.squeeze(), :4].reshape(-1, 4), x1y1x2y2=False)
                    bad = close[iou > nms_thres]

                    if len(bad) > 0:
                        mask = torch.ones(len(a)).type(torch.ByteTensor)
                        mask[bad] = 0
                        a = a[mask]
            pred = a

        # Experiment: Prior class size rejection
        # x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
        # a = w * h  # area
        # ar = w / (h + 1e-16)  # aspect ratio
        # n = len(w)
        # log_w, log_h, log_a, log_ar = torch.log(w), torch.log(h), torch.log(a), torch.log(ar)
        # shape_likelihood = np.zeros((n, 60), dtype=np.float32)
        # x = np.concatenate((log_w.reshape(-1, 1), log_h.reshape(-1, 1)), 1)
        # from scipy.stats import multivariate_normal
        # for c in range(60):
        # shape_likelihood[:, c] =
        #   multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])

        class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1)

        v = ((pred[:, 4] > conf_thres) & (class_prob > .3))  # TODO examine arbitrary 0.3 thres here
        v = v.nonzero().squeeze()
        if len(v.shape) == 0:
            v = v.unsqueeze(0)

        pred = pred[v]
        class_prob = class_prob[v]
        class_pred = class_pred[v]

        # If none are remaining => process next image
        nP = pred.shape[0]
        if not nP:
            continue

        # From (center x, center y, width, height) to (x1, y1, x2, y2)
        pred[:, :4] = xywh2xyxy(pred[:, :4])

        # Detections ordered as (x1, y1, x2, y2, obj_conf, class_prob, class_pred)
        detections = torch.cat((pred[:, :5], class_prob.float().unsqueeze(1), class_pred.float().unsqueeze(1)), 1)
        # Iterate through all predicted classes
        unique_labels = detections[:, -1].cpu().unique()
        if prediction.is_cuda:
            unique_labels = unique_labels.cuda(prediction.device)

        nms_style = 'MERGE'  # 'OR' (default), 'AND', 'MERGE' (experimental)
        for c in unique_labels:
            # Get the detections with class c
            dc = detections[detections[:, -1] == c]
            # Sort the detections by maximum object confidence
            _, conf_sort_index = torch.sort(dc[:, 4], descending=True)
            dc = dc[conf_sort_index]

            # Non-maximum suppression
            det_max = []
            if nms_style == 'OR':  # default
                while dc.shape[0]:
                    det_max.append(dc[:1])  # save highest conf detection
                    if len(dc) == 1:  # Stop if we're at the last detection
                        break
                    iou = bbox_iou(det_max[-1], dc[1:])  # iou with other boxes
                    dc = dc[1:][iou < nms_thres]  # remove ious > threshold

                # Image      Total          P          R        mAP
                #  5000       5000      0.627      0.593      0.584

            elif nms_style == 'AND':  # requires overlap, single boxes erased
                while len(dc) > 1:
                    iou = bbox_iou(dc[:1], dc[1:])  # iou with other boxes
                    if iou.max() > 0.5:
                        det_max.append(dc[:1])
                    dc = dc[1:][iou < nms_thres]  # remove ious > threshold

            elif nms_style == 'MERGE':  # weighted mixture box
                while len(dc) > 0:
                    iou = bbox_iou(dc[:1], dc[0:])  # iou with other boxes
                    i = iou > .6

                    weights = dc[i, 4:5] * dc[i, 5:6]
                    dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
                    det_max.append(dc[:1])
                    dc = dc[iou < .6]

                # Image      Total          P          R        mAP
                #  4964       5000      0.632      0.597      0.588  # normal
                #  4964       5000      0.632      0.597      0.588  # squared
                #  4964       5000      0.631      0.597      0.588  # sqrt

            if len(det_max) > 0:
                det_max = torch.cat(det_max)
                # Add max detections to outputs
                output[image_i] = det_max if output[image_i] is None else torch.cat((output[image_i], det_max))

    return output


def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
    # Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
    import torch
    a = torch.load(filename, map_location='cpu')
    a['optimizer'] = []
    torch.save(a, filename.replace('.pt', '_lite.pt'))


def coco_class_count(path='../coco/labels/train2014/'):
    # histogram of occurrences per class
    import glob

    nC = 80  # number classes
    x = np.zeros(nC, dtype='int32')
    files = sorted(glob.glob('%s/*.*' % path))
    for i, file in enumerate(files):
        labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
        x += np.bincount(labels[:, 0].astype('int32'), minlength=nC)
        print(i, len(files))


def plot_results():
    # Plot YOLO training results file 'results.txt'
    import glob
    import matplotlib.pyplot as plt
    import numpy as np
    # import os; os.system('rm -rf results.txt && wget https://storage.googleapis.com/ultralytics/results_v1_0.txt')

    plt.figure(figsize=(16, 8))
    s = ['X', 'Y', 'Width', 'Height', 'Confidence', 'Classification', 'Total Loss', 'mAP', 'Recall', 'Precision']
    files = sorted(glob.glob('results*.txt'))
    for f in files:
        results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 11, 12, 13]).T  # column 13 is mAP
        n = results.shape[1]
        for i in range(10):
            plt.subplot(2, 5, i + 1)
            plt.plot(range(1, n), results[i, 1:], marker='.', label=f)
            plt.title(s[i])
            if i == 0:
                plt.legend()