# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
this code is based on https://github.com/open-mmlab/mmpose
"""

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


def get_affine_mat_kernel(h, w, s, inv=False):
    if w < h:
        w_ = s
        h_ = int(np.ceil((s / w * h) / 64.) * 64)
        scale_w = w
        scale_h = h_ / w_ * w

    else:
        h_ = s
        w_ = int(np.ceil((s / h * w) / 64.) * 64)
        scale_h = h
        scale_w = w_ / h_ * h

    center = np.array([np.round(w / 2.), np.round(h / 2.)])

    size_resized = (w_, h_)
    trans = get_affine_transform(
        center, np.array([scale_w, scale_h]), 0, size_resized, inv=inv)

    return trans, size_resized


def get_affine_transform(center,
                         input_size,
                         rot,
                         output_size,
                         shift=(0., 0.),
                         inv=False):
    """Get the affine transform matrix, given the center/scale/rot/output_size.

    Args:
        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        input_size (np.ndarray[2, ]): Size of input feature (width, height).
        rot (float): Rotation angle (degree).
        output_size (np.ndarray[2, ]): Size of the destination heatmaps.
        shift (0-100%): Shift translation ratio wrt the width/height.
            Default (0., 0.).
        inv (bool): Option to inverse the affine transform direction.
            (inv=False: src->dst or inv=True: dst->src)

    Returns:
        np.ndarray: The transform matrix.
    """
    assert len(center) == 2
    assert len(output_size) == 2
    assert len(shift) == 2

    if not isinstance(input_size, (np.ndarray, list)):
        input_size = np.array([input_size, input_size], dtype=np.float32)
    scale_tmp = input_size

    shift = np.array(shift)
    src_w = scale_tmp[0]
    dst_w = output_size[0]
    dst_h = output_size[1]

    rot_rad = np.pi * rot / 180
    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
    dst_dir = np.array([0., dst_w * -0.5])

    src = np.zeros((3, 2), dtype=np.float32)

    src[0, :] = center + scale_tmp * shift
    src[1, :] = center + src_dir + scale_tmp * shift
    src[2, :] = _get_3rd_point(src[0, :], src[1, :])

    dst = np.zeros((3, 2), dtype=np.float32)
    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])

    if inv:
        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
    else:
        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))

    return trans


def get_warp_matrix(theta, size_input, size_dst, size_target):
    """This code is based on
        https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py

        Calculate the transformation matrix under the constraint of unbiased.
    Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
    Data Processing for Human Pose Estimation (CVPR 2020).

    Args:
        theta (float): Rotation angle in degrees.
        size_input (np.ndarray): Size of input image [w, h].
        size_dst (np.ndarray): Size of output image [w, h].
        size_target (np.ndarray): Size of ROI in input plane [w, h].

    Returns:
        matrix (np.ndarray): A matrix for transformation.
    """
    theta = np.deg2rad(theta)
    matrix = np.zeros((2, 3), dtype=np.float32)
    scale_x = size_dst[0] / size_target[0]
    scale_y = size_dst[1] / size_target[1]
    matrix[0, 0] = np.cos(theta) * scale_x
    matrix[0, 1] = -np.sin(theta) * scale_x
    matrix[0, 2] = scale_x * (
        -0.5 * size_input[0] * np.cos(theta) + 0.5 * size_input[1] *
        np.sin(theta) + 0.5 * size_target[0])
    matrix[1, 0] = np.sin(theta) * scale_y
    matrix[1, 1] = np.cos(theta) * scale_y
    matrix[1, 2] = scale_y * (
        -0.5 * size_input[0] * np.sin(theta) - 0.5 * size_input[1] *
        np.cos(theta) + 0.5 * size_target[1])
    return matrix


def _get_3rd_point(a, b):
    """To calculate the affine matrix, three pairs of points are required. This
    function is used to get the 3rd point, given 2D points a & b.

    The 3rd point is defined by rotating vector `a - b` by 90 degrees
    anticlockwise, using b as the rotation center.

    Args:
        a (np.ndarray): point(x,y)
        b (np.ndarray): point(x,y)

    Returns:
        np.ndarray: The 3rd point.
    """
    assert len(
        a) == 2, 'input of _get_3rd_point should be point with length of 2'
    assert len(
        b) == 2, 'input of _get_3rd_point should be point with length of 2'
    direction = a - b
    third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)

    return third_pt


def rotate_point(pt, angle_rad):
    """Rotate a point by an angle.

    Args:
        pt (list[float]): 2 dimensional point to be rotated
        angle_rad (float): rotation angle by radian

    Returns:
        list[float]: Rotated point.
    """
    assert len(pt) == 2
    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
    new_x = pt[0] * cs - pt[1] * sn
    new_y = pt[0] * sn + pt[1] * cs
    rotated_pt = [new_x, new_y]

    return rotated_pt


def transpred(kpts, h, w, s):
    trans, _ = get_affine_mat_kernel(h, w, s, inv=True)

    return warp_affine_joints(kpts[..., :2].copy(), trans)


def warp_affine_joints(joints, mat):
    """Apply affine transformation defined by the transform matrix on the
    joints.

    Args:
        joints (np.ndarray[..., 2]): Origin coordinate of joints.
        mat (np.ndarray[3, 2]): The affine matrix.

    Returns:
        matrix (np.ndarray[..., 2]): Result coordinate of joints.
    """
    joints = np.array(joints)
    shape = joints.shape
    joints = joints.reshape(-1, 2)
    return np.dot(np.concatenate(
        (joints, joints[:, 0:1] * 0 + 1), axis=1),
                  mat.T).reshape(shape)


def affine_transform(pt, t):
    new_pt = np.array([pt[0], pt[1], 1.]).T
    new_pt = np.dot(t, new_pt)
    return new_pt[:2]


def transform_preds(coords, center, scale, output_size):
    target_coords = np.zeros(coords.shape)
    trans = get_affine_transform(center, scale * 200, 0, output_size, inv=1)
    for p in range(coords.shape[0]):
        target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
    return target_coords


def oks_iou(g, d, a_g, a_d, sigmas=None, in_vis_thre=None):
    if not isinstance(sigmas, np.ndarray):
        sigmas = np.array([
            .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07,
            .87, .87, .89, .89
        ]) / 10.0
    vars = (sigmas * 2)**2
    xg = g[0::3]
    yg = g[1::3]
    vg = g[2::3]
    ious = np.zeros((d.shape[0]))
    for n_d in range(0, d.shape[0]):
        xd = d[n_d, 0::3]
        yd = d[n_d, 1::3]
        vd = d[n_d, 2::3]
        dx = xd - xg
        dy = yd - yg
        e = (dx**2 + dy**2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2
        if in_vis_thre is not None:
            ind = list(vg > in_vis_thre) and list(vd > in_vis_thre)
            e = e[ind]
        ious[n_d] = np.sum(np.exp(-e)) / e.shape[0] if e.shape[0] != 0 else 0.0
    return ious


def oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
    """greedily select boxes with high confidence and overlap with current maximum <= thresh
    rule out overlap >= thresh

    Args:
        kpts_db (list): The predicted keypoints within the image
        thresh (float): The threshold to select the boxes
        sigmas (np.array): The variance to calculate the oks iou
            Default: None
        in_vis_thre (float): The threshold to select the high confidence boxes
            Default: None

    Return:
        keep (list): indexes to keep
    """

    if len(kpts_db) == 0:
        return []

    scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))])
    kpts = np.array(
        [kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))])
    areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))])

    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)

        oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]],
                          sigmas, in_vis_thre)

        inds = np.where(oks_ovr <= thresh)[0]
        order = order[inds + 1]

    return keep


def rescore(overlap, scores, thresh, type='gaussian'):
    assert overlap.shape[0] == scores.shape[0]
    if type == 'linear':
        inds = np.where(overlap >= thresh)[0]
        scores[inds] = scores[inds] * (1 - overlap[inds])
    else:
        scores = scores * np.exp(-overlap**2 / thresh)

    return scores


def soft_oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
    """greedily select boxes with high confidence and overlap with current maximum <= thresh
    rule out overlap >= thresh

    Args:
        kpts_db (list): The predicted keypoints within the image
        thresh (float): The threshold to select the boxes
        sigmas (np.array): The variance to calculate the oks iou
            Default: None
        in_vis_thre (float): The threshold to select the high confidence boxes
            Default: None

    Return:
        keep (list): indexes to keep
    """

    if len(kpts_db) == 0:
        return []

    scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))])
    kpts = np.array(
        [kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))])
    areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))])

    order = scores.argsort()[::-1]
    scores = scores[order]

    # max_dets = order.size
    max_dets = 20
    keep = np.zeros(max_dets, dtype=np.intp)
    keep_cnt = 0
    while order.size > 0 and keep_cnt < max_dets:
        i = order[0]

        oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]],
                          sigmas, in_vis_thre)

        order = order[1:]
        scores = rescore(oks_ovr, scores[1:], thresh)

        tmp = scores.argsort()[::-1]
        order = order[tmp]
        scores = scores[tmp]

        keep[keep_cnt] = i
        keep_cnt += 1

    keep = keep[:keep_cnt]

    return keep


def resize(input,
           size=None,
           scale_factor=None,
           mode='nearest',
           align_corners=None,
           warning=True):
    if warning:
        if size is not None and align_corners:
            input_h, input_w = tuple(int(x) for x in input.shape[2:])
            output_h, output_w = tuple(int(x) for x in size)
            if output_h > input_h or output_w > output_h:
                if ((output_h > 1 and output_w > 1 and input_h > 1 and
                     input_w > 1) and (output_h - 1) % (input_h - 1) and
                    (output_w - 1) % (input_w - 1)):
                    warnings.warn(
                        f'When align_corners={align_corners}, '
                        'the output would more aligned if '
                        f'input size {(input_h, input_w)} is `x+1` and '
                        f'out size {(output_h, output_w)} is `nx+1`')

    return F.interpolate(input, size, scale_factor, mode, align_corners)


def flip_back(output_flipped, flip_pairs, target_type='GaussianHeatmap'):
    """Flip the flipped heatmaps back to the original form.
    Note:
        - batch_size: N
        - num_keypoints: K
        - heatmap height: H
        - heatmap width: W
    Args:
        output_flipped (np.ndarray[N, K, H, W]): The output heatmaps obtained
            from the flipped images.
        flip_pairs (list[tuple()): Pairs of keypoints which are mirrored
            (for example, left ear -- right ear).
        target_type (str): GaussianHeatmap or CombinedTarget
    Returns:
        np.ndarray: heatmaps that flipped back to the original image
    """
    assert len(output_flipped.shape) == 4, \
        'output_flipped should be [batch_size, num_keypoints, height, width]'
    shape_ori = output_flipped.shape
    channels = 1
    if target_type.lower() == 'CombinedTarget'.lower():
        channels = 3
        output_flipped[:, 1::3, ...] = -output_flipped[:, 1::3, ...]
    output_flipped = output_flipped.reshape((shape_ori[0], -1, channels,
                                             shape_ori[2], shape_ori[3]))
    output_flipped_back = output_flipped.clone()

    # Swap left-right parts
    for left, right in flip_pairs:
        output_flipped_back[:, left, ...] = output_flipped[:, right, ...]
        output_flipped_back[:, right, ...] = output_flipped[:, left, ...]
    output_flipped_back = output_flipped_back.reshape(shape_ori)
    # Flip horizontally
    output_flipped_back = output_flipped_back[..., ::-1]
    return output_flipped_back


def _calc_distances(preds, targets, mask, normalize):
    """Calculate the normalized distances between preds and target.

    Note:
        batch_size: N
        num_keypoints: K
        dimension of keypoints: D (normally, D=2 or D=3)

    Args:
        preds (np.ndarray[N, K, D]): Predicted keypoint location.
        targets (np.ndarray[N, K, D]): Groundtruth keypoint location.
        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
            joints, and True for visible. Invisible joints will be ignored for
            accuracy calculation.
        normalize (np.ndarray[N, D]): Typical value is heatmap_size

    Returns:
        np.ndarray[K, N]: The normalized distances. \
            If target keypoints are missing, the distance is -1.
    """
    N, K, _ = preds.shape
    # set mask=0 when normalize==0
    _mask = mask.copy()
    _mask[np.where((normalize == 0).sum(1))[0], :] = False
    distances = np.full((N, K), -1, dtype=np.float32)
    # handle invalid values
    normalize[np.where(normalize <= 0)] = 1e6
    distances[_mask] = np.linalg.norm(
        ((preds - targets) / normalize[:, None, :])[_mask], axis=-1)
    return distances.T


def _distance_acc(distances, thr=0.5):
    """Return the percentage below the distance threshold, while ignoring
    distances values with -1.

    Note:
        batch_size: N
    Args:
        distances (np.ndarray[N, ]): The normalized distances.
        thr (float): Threshold of the distances.

    Returns:
        float: Percentage of distances below the threshold. \
            If all target keypoints are missing, return -1.
    """
    distance_valid = distances != -1
    num_distance_valid = distance_valid.sum()
    if num_distance_valid > 0:
        return (distances[distance_valid] < thr).sum() / num_distance_valid
    return -1


def keypoint_pck_accuracy(pred, gt, mask, thr, normalize):
    """Calculate the pose accuracy of PCK for each individual keypoint and the
    averaged accuracy across all keypoints for coordinates.

    Note:
        PCK metric measures accuracy of the localization of the body joints.
        The distances between predicted positions and the ground-truth ones
        are typically normalized by the bounding box size.
        The threshold (thr) of the normalized distance is commonly set
        as 0.05, 0.1 or 0.2 etc.

        - batch_size: N
        - num_keypoints: K

    Args:
        pred (np.ndarray[N, K, 2]): Predicted keypoint location.
        gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
            joints, and True for visible. Invisible joints will be ignored for
            accuracy calculation.
        thr (float): Threshold of PCK calculation.
        normalize (np.ndarray[N, 2]): Normalization factor for H&W.

    Returns:
        tuple: A tuple containing keypoint accuracy.

        - acc (np.ndarray[K]): Accuracy of each keypoint.
        - avg_acc (float): Averaged accuracy across all keypoints.
        - cnt (int): Number of valid keypoints.
    """
    distances = _calc_distances(pred, gt, mask, normalize)

    acc = np.array([_distance_acc(d, thr) for d in distances])
    valid_acc = acc[acc >= 0]
    cnt = len(valid_acc)
    avg_acc = valid_acc.mean() if cnt > 0 else 0
    return acc, avg_acc, cnt


def keypoint_auc(pred, gt, mask, normalize, num_step=20):
    """Calculate the pose accuracy of PCK for each individual keypoint and the
    averaged accuracy across all keypoints for coordinates.

    Note:
        - batch_size: N
        - num_keypoints: K

    Args:
        pred (np.ndarray[N, K, 2]): Predicted keypoint location.
        gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
            joints, and True for visible. Invisible joints will be ignored for
            accuracy calculation.
        normalize (float): Normalization factor.

    Returns:
        float: Area under curve.
    """
    nor = np.tile(np.array([[normalize, normalize]]), (pred.shape[0], 1))
    x = [1.0 * i / num_step for i in range(num_step)]
    y = []
    for thr in x:
        _, avg_acc, _ = keypoint_pck_accuracy(pred, gt, mask, thr, nor)
        y.append(avg_acc)

    auc = 0
    for i in range(num_step):
        auc += 1.0 / num_step * y[i]
    return auc


def keypoint_epe(pred, gt, mask):
    """Calculate the end-point error.

    Note:
        - batch_size: N
        - num_keypoints: K

    Args:
        pred (np.ndarray[N, K, 2]): Predicted keypoint location.
        gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
        mask (np.ndarray[N, K]): Visibility of the target. False for invisible
            joints, and True for visible. Invisible joints will be ignored for
            accuracy calculation.

    Returns:
        float: Average end-point error.
    """

    normalize = np.ones((pred.shape[0], pred.shape[2]), dtype=np.float32)
    distances = _calc_distances(pred, gt, mask, normalize)
    distance_valid = distances[distances != -1]
    return distance_valid.sum() / max(1, len(distance_valid))
