import argparse
import copy
import math

import cv2
import numpy as np
import torch

# private package
from .lib import utility


class GetCropMatrix():
    """
    from_shape -> transform_matrix
    """

    def __init__(self, image_size, target_face_scale, align_corners=False):
        self.image_size = image_size
        self.target_face_scale = target_face_scale
        self.align_corners = align_corners

    def _compose_rotate_and_scale(self, angle, scale, shift_xy, from_center,
                                  to_center):
        cosv = math.cos(angle)
        sinv = math.sin(angle)

        fx, fy = from_center
        tx, ty = to_center

        acos = scale * cosv
        asin = scale * sinv

        a0 = acos
        a1 = -asin
        a2 = tx - acos * fx + asin * fy + shift_xy[0]

        b0 = asin
        b1 = acos
        b2 = ty - asin * fx - acos * fy + shift_xy[1]

        rot_scale_m = np.array([[a0, a1, a2], [b0, b1, b2], [0.0, 0.0, 1.0]],
                               np.float32)
        return rot_scale_m

    def process(self, scale, center_w, center_h):
        if self.align_corners:
            to_w, to_h = self.image_size - 1, self.image_size - 1
        else:
            to_w, to_h = self.image_size, self.image_size

        rot_mu = 0
        scale_mu = self.image_size / (scale * self.target_face_scale * 200.0)
        shift_xy_mu = (0, 0)
        matrix = self._compose_rotate_and_scale(
            rot_mu,
            scale_mu,
            shift_xy_mu,
            from_center=[center_w, center_h],
            to_center=[to_w / 2.0, to_h / 2.0])
        return matrix


class TransformPerspective():
    """
    image, matrix3x3 -> transformed_image
    """

    def __init__(self, image_size):
        self.image_size = image_size

    def process(self, image, matrix):
        return cv2.warpPerspective(
            image,
            matrix,
            dsize=(self.image_size, self.image_size),
            flags=cv2.INTER_LINEAR,
            borderValue=0)


class TransformPoints2D():
    """
    points (nx2), matrix (3x3) -> points (nx2)
    """

    def process(self, srcPoints, matrix):
        # nx3
        desPoints = np.concatenate(
            [srcPoints, np.ones_like(srcPoints[:, [0]])], axis=1)
        desPoints = desPoints @ np.transpose(matrix)  # nx3
        desPoints = desPoints[:, :2] / desPoints[:, [2, 2]]
        return desPoints.astype(srcPoints.dtype)


class Alignment:

    def __init__(self, args, model_path, dl_framework, device_ids):
        self.input_size = 256
        self.target_face_scale = 1.0
        self.dl_framework = dl_framework

        # model
        if self.dl_framework == 'pytorch':
            # conf
            self.config = utility.get_config(args)
            self.config.device_id = device_ids[0]

            # set environment
            utility.set_environment(self.config)

            net = utility.get_net(self.config)
            if device_ids == [-1]:
                checkpoint = torch.load(model_path, map_location='cpu')
            else:
                checkpoint = torch.load(model_path)
            net.load_state_dict(checkpoint['net'])

            if self.config.device_id == -1:
                net = net.cpu()
            else:
                net = net.to(self.config.device_id)

            net.eval()
            self.alignment = net
        else:
            assert False

        self.getCropMatrix = GetCropMatrix(
            image_size=self.input_size,
            target_face_scale=self.target_face_scale,
            align_corners=True)
        self.transformPerspective = TransformPerspective(
            image_size=self.input_size)
        self.transformPoints2D = TransformPoints2D()

    def norm_points(self, points, align_corners=False):
        if align_corners:
            # [0, SIZE-1] -> [-1, +1]
            return points / torch.tensor([
                self.input_size - 1, self.input_size - 1
            ]).to(points).view(1, 1, 2) * 2 - 1
        else:
            # [-0.5, SIZE-0.5] -> [-1, +1]
            return (points * 2 + 1) / torch.tensor([
                self.input_size, self.input_size
            ]).to(points).view(1, 1, 2) - 1

    def denorm_points(self, points, align_corners=False):
        if align_corners:
            # [-1, +1] -> [0, SIZE-1]
            return (points + 1) / 2 * torch.tensor([
                self.input_size - 1, self.input_size - 1
            ]).to(points).view(1, 1, 2)
        else:
            # [-1, +1] -> [-0.5, SIZE-0.5]
            return ((points + 1) * torch.tensor(  # noqa
                [self.input_size, self.input_size]).to(points).view(1, 1,
                                                                    2)  # noqa
                    - 1) / 2  # noqa

    def preprocess(self, image, scale, center_w, center_h):
        matrix = self.getCropMatrix.process(scale, center_w, center_h)
        input_tensor = self.transformPerspective.process(image, matrix)
        input_tensor = input_tensor[np.newaxis, :]

        input_tensor = torch.from_numpy(input_tensor)
        input_tensor = input_tensor.float().permute(0, 3, 1, 2)
        input_tensor = input_tensor / 255.0 * 2.0 - 1.0

        if self.config.device_id == -1:
            input_tensor = input_tensor.cpu()
        else:
            input_tensor = input_tensor.to(self.config.device_id)

        return input_tensor, matrix

    def postprocess(self, srcPoints, coeff):
        # dstPoints = self.transformPoints2D.process(srcPoints, coeff)
        # matrix^(-1) * src = dst
        # src = matrix * dst
        dstPoints = np.zeros(srcPoints.shape, dtype=np.float32)
        for i in range(srcPoints.shape[0]):
            dstPoints[i][0] = coeff[0][0] * srcPoints[i][0] + coeff[0][
                1] * srcPoints[i][1] + coeff[0][2]
            dstPoints[i][1] = coeff[1][0] * srcPoints[i][0] + coeff[1][
                1] * srcPoints[i][1] + coeff[1][2]
        return dstPoints

    def analyze(self, image, scale, center_w, center_h):
        input_tensor, matrix = self.preprocess(image, scale, center_w,
                                               center_h)

        if self.dl_framework == 'pytorch':
            with torch.no_grad():
                output = self.alignment(input_tensor)
            landmarks = output[-1][0]
        else:
            assert False

        landmarks = self.denorm_points(landmarks)
        landmarks = landmarks.data.cpu().numpy()[0]
        landmarks = self.postprocess(landmarks, np.linalg.inv(matrix))

        return landmarks