import json

import cv2
import numpy as np
import torch
from skimage.transform import resize, rotate
from torchvision.transforms import Normalize

from .constants import IMG_NORM_MEAN, IMG_NORM_STD, IMG_RES


def get_transform(center, scale, res, rot=0):
    """Generate transformation matrix."""
    h = 200 * scale
    t = np.zeros((3, 3))
    t[0, 0] = float(res[1]) / h
    t[1, 1] = float(res[0]) / h
    t[0, 2] = res[1] * (-float(center[0]) / h + 0.5)
    t[1, 2] = res[0] * (-float(center[1]) / h + 0.5)
    t[2, 2] = 1
    if not rot == 0:
        rot = -rot  # To match direction of rotation from cropping
        rot_mat = np.zeros((3, 3))
        rot_rad = rot * np.pi / 180
        sn, cs = np.sin(rot_rad), np.cos(rot_rad)
        rot_mat[0, :2] = [cs, -sn]
        rot_mat[1, :2] = [sn, cs]
        rot_mat[2, 2] = 1
        # Need to rotate around center
        t_mat = np.eye(3)
        t_mat[0, 2] = -res[1] / 2
        t_mat[1, 2] = -res[0] / 2
        t_inv = t_mat.copy()
        t_inv[:2, 2] *= -1
        t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t)))

    return t


def transform(pt, center, scale, res, invert=0, rot=0):
    """Transform pixel location to different reference."""
    t = get_transform(center, scale, res, rot=rot)
    if invert:
        t = np.linalg.inv(t)
    new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.0]).T
    new_pt = np.dot(t, new_pt)

    return new_pt[:2].astype(int) + 1


def crop(img, center, scale, res, rot=0):
    """Crop image according to the supplied bounding box."""
    # Upper left point
    ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1
    # Bottom right point
    br = np.array(transform([res[0] + 1, res[1] + 1], center, scale, res, invert=1)) - 1

    # Padding so that when rotated proper amount of context is included
    pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2)
    if not rot == 0:
        ul -= pad
        br += pad

    new_shape = [br[1] - ul[1], br[0] - ul[0]]
    if len(img.shape) > 2:
        new_shape += [img.shape[2]]
    new_img = np.zeros(new_shape)

    # Range to fill new array
    new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0]
    new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1]
    # Range to sample from original image
    old_x = max(0, ul[0]), min(len(img[0]), br[0])
    old_y = max(0, ul[1]), min(len(img), br[1])
    new_img[new_y[0] : new_y[1], new_x[0] : new_x[1]] = img[
        old_y[0] : old_y[1], old_x[0] : old_x[1]
    ]

    if not rot == 0:
        # Remove padding
        new_img = rotate(new_img, rot)
        new_img = new_img[pad:-pad, pad:-pad]

    new_img = resize(new_img, res)

    return new_img


def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2):
    """Get center and scale for bounding box from openpose detections."""
    with open(openpose_file, "r") as f:
        keypoints = json.load(f)["people"][0]["pose_keypoints_2d"]
    keypoints = np.reshape(np.array(keypoints), (-1, 3))
    valid = keypoints[:, -1] > detection_thresh
    valid_keypoints = keypoints[valid][:, :-1]
    center = valid_keypoints.mean(axis=0)
    bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max()
    # adjust bounding box tightness
    scale = bbox_size / 200.0
    scale *= rescale

    return center, scale


def bbox_from_json(bbox_file):
    """Get center and scale of bounding box from bounding box annotations.
    The expected format is [top_left(x), top_left(y), width, height].
    """
    with open(bbox_file, "r") as f:
        bbox = np.array(json.load(f)["bbox"]).astype(np.float32)
    ul_corner = bbox[:2]
    center = ul_corner + 0.5 * bbox[2:]
    width = max(bbox[2], bbox[3])
    scale = width / 200.0
    # make sure the bounding box is rectangular
    return center, scale


def process_image(img_file, bbox_file=None, openpose_file=None, input_res=IMG_RES):
    """Read image, do preprocessing and possibly crop it according to the bounding box.
    If there are bounding box annotations, use them to crop the image.
    If no bounding box is specified but openpose detections are available, use them to get the bounding box.
    """
    img_file = str(img_file)
    normalize_img = Normalize(mean=IMG_NORM_MEAN, std=IMG_NORM_STD)
    img = cv2.imread(img_file)[
        :, :, ::-1
    ].copy()  # PyTorch does not support negative stride at the moment
    if bbox_file is None and openpose_file is None:
        # Assume that the person is centerered in the image
        height = img.shape[0]
        width = img.shape[1]
        center = np.array([width // 2, height // 2])
        scale = max(height, width) / 200
    else:
        if bbox_file is not None:
            center, scale = bbox_from_json(bbox_file)
        elif openpose_file is not None:
            center, scale = bbox_from_openpose(openpose_file)

    img = crop(img, center, scale, (input_res, input_res))
    img = img.astype(np.float32) / 255.0
    img = torch.from_numpy(img).permute(2, 0, 1)
    norm_img = normalize_img(img.clone())

    return img, norm_img