import glob
import os
from datetime import datetime
from pathlib import Path
from typing import Tuple

import cv2
import numpy as np
from sklearn import cluster, metrics, svm, neural_network
from sklearn.externals import joblib
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

from util import RANK, POSITION, imwrite, PIECE, COLOR, Squares, OUR_PIECES

here: Path = Path(__file__).parent


def generate_centers(number_of_clusters, sift: cv2.xfeatures2d_SIFT):
    features = None
    for piece in OUR_PIECES:
        for color in COLOR:
            for filename in glob.glob(f"training_images/{piece}/{color}_square/*.png"):
                image = cv2.imread(filename)
                #image = selective_search(image, use_fast=True)
                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                kp, desc = sift.detectAndCompute(gray, None)
                print(f"{piece}, {color}, {filename}")

                if features is None:
                    features = np.array(desc)
                else:
                    print(f"{piece}, {color}, {filename}")
                    features = np.vstack((features, desc))

    features = np.array(features)
    k_means = cluster.KMeans(number_of_clusters)
    k_means.fit(features)
    return k_means.cluster_centers_


def generate_bag_of_words(image, centers, sift: cv2.xfeatures2d_SIFT):
    num_centers = centers.shape[0]
    histogram = np.zeros((1, num_centers))

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    kp, desc = sift.detectAndCompute(gray_image, None)

    if not kp:
        return histogram

    distances = metrics.pairwise.pairwise_distances(desc, centers)
    best_centers = np.argmin(distances, axis=1)

    for i in best_centers:  # TODO: Could do this way faster in one line with numpy somehow
        histogram[0, i] += + 1

    return histogram / np.sum(histogram)


def do_pre_processing() -> None:
    sift = cv2.xfeatures2d.SIFT_create()
    centers = generate_centers(8, sift)

    np.save("training_data/centers", centers)

    for piece in OUR_PIECES:
        for color in COLOR:
            for filename in glob.glob(f"training_images/{piece}/{color}_square/*.png"):
                image = cv2.imread(filename)
                #image = selective_search(image, image_name=filename, use_fast=True)
                bow_features = generate_bag_of_words(image, centers, sift)
                np.save(f"training_data/{piece}/{color}_square/{os.path.basename(filename)}", bow_features)


def load_training_data(piece: PIECE, color: COLOR) -> Tuple[np.array, np.array]:
    X = []
    Y = []
    for p in OUR_PIECES:
        for filename in glob.glob(f"training_data/{piece}/{color}_square/*.npy"):
            data = np.load(filename)
            X.append(data[0])
            Y.append(p == piece)
    return np.array(X), np.array(Y)


def train_empty_or_piece_hist() -> None:
    for square_color in COLOR:
        X = []
        Y = []
        for piece in OUR_PIECES + (PIECE.EMPTY,):
            print(f"training training_images/{piece}/{square_color}_square/*.png")
            for filename in glob.glob(f"training_images/{piece}/{square_color}_square/*.png"):
                img = cv2.imread(filename)
                y, x = np.histogram(img.ravel(), bins=32, range=[0, 256])
                X.append(y)
                Y.append(piece == PIECE.EMPTY)

        classifier = make_pipeline(StandardScaler(),
                                   svm.SVC(C=10.0, gamma=0.01, probability=True))
        classifier.fit(np.array(X), np.array(Y))
        joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")


def train_pieces_svm() -> None:
    for piece in OUR_PIECES:
        for color in COLOR:
            total_weights = len(glob.glob(f"training_images/{piece}/{color}_square/*.png"))

    for piece in OUR_PIECES:
        for color in COLOR:
            current_weight = len(glob.glob(f"training_images/{piece}/{color}_square/*.png"))
            print(f"Training for piece: {piece}")
            X, Y = load_training_data(piece, color)
            classifier = svm.SVC(C=10, gamma=0.01, class_weight={0: 15, 1: 0.8}, probability=True)
            classifier.fit(X, Y)
            joblib.dump(classifier, f"classifiers/classifier_{piece}/{color}.pkl")


def train_pieces_svm_canny() -> None:
    for square_color in COLOR:
        X = []
        Y = []
        for piece in OUR_PIECES + (PIECE.EMPTY,):
            for filename in glob.glob(f"training_images/{piece}/{square_color}_square/*.png"):
                img = cv2.imread(filename)
                y, x = np.histogram(img.ravel(), bins=32, range=[0, 256])
                X.append(y)
                Y.append(piece == PIECE.EMPTY)

        classifier = make_pipeline(StandardScaler(),
                                   svm.SVC(C=10.0, gamma=0.01, probability=True))
        classifier.fit(np.array(X), np.array(Y))
        joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")


def find_keypoints(camera_image: np.ndarray, baseline: np.ndarray, debug=False) -> Tuple[np.ndarray, np.ndarray]:
    """
    Find keypoints in raw camera image of board.

    :return: (src points, dest points)
    """
    camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
    baseline_gray = cv2.cvtColor(baseline, cv2.COLOR_BGR2GRAY)

    sift = cv2.xfeatures2d.SIFT_create()
    camera_image_keypoints = sift.detect(camera_image_gray, None)
    baseline_keypoints = sift.detect(baseline_gray, None)

    camera_image_keypoints, des = sift.compute(camera_image_gray, camera_image_keypoints)
    baseline_keypoints, des2 = sift.compute(baseline_gray, baseline_keypoints)

    # FLANN parameters
    FLANN_INDEX_KDTREE = 0
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=8)
    search_params = dict(checks=100)  # or pass empty dictionary

    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des, des2, k=2)

    # Need to draw only good matches, so create a mask
    matchesMask = [[0, 0] for _ in range(len(matches))]

    # Ratio test as per Lowe's paper
    good_matches = []
    for i, (m, n) in enumerate(matches):
        if m.distance < 0.55 * n.distance:
            matchesMask[i] = [1, 0]
            good_matches.append([m, n])

    if debug:
        # Save keypoints
        keypoints_image = camera_image.copy()
        cv2.drawKeypoints(camera_image, keypoints=camera_image_keypoints, outImage=keypoints_image)
        cv2.imwrite("keypoints.png", keypoints_image)
        # Save matches
        matches_image = cv2.drawMatchesKnn(
            camera_image,
            camera_image_keypoints,
            baseline,
            baseline_keypoints,
            matches,
            None,
            matchColor=(0, 255, 0),
            singlePointColor=(255, 0, 0),
            matchesMask=matchesMask,
            flags=0
        )
        cv2.imwrite("matches.png", matches_image)

    # Extract location of good matches
    src_points = np.zeros((len(good_matches), 2), dtype=np.float32)
    dst_points = np.zeros((len(good_matches), 2), dtype=np.float32)

    for i, (m, n) in enumerate(good_matches):
        src_points[i, :] = camera_image_keypoints[m.queryIdx].pt
        dst_points[i, :] = baseline_keypoints[m.trainIdx].pt

    return src_points, dst_points


def find_homography(camera_image: np.ndarray,
                    baseline: np.ndarray = cv2.imread(str(here.joinpath("new_baseline_board.png"))),
                    debug=False) -> np.ndarray:
    src_points, dst_points = find_keypoints(camera_image, baseline, debug=debug)
    h, mask = cv2.findHomography(src_points, dst_points, cv2.RANSAC)

    return h


def warp_board(camera_image: np.ndarray, homography: np.ndarray = None, debug=False) -> np.ndarray:
    baseline = cv2.imread(str(here.joinpath("new_baseline_board.png")))
    if homography is None:
        homography = find_homography(camera_image, baseline, debug=debug)

    height, width, channels = baseline.shape
    return cv2.warpPerspective(camera_image, homography, (width, height))


def get_square(warped_board: np.ndarray, position: POSITION) -> np.ndarray:
    width, _, _ = warped_board.shape  # board is square anyway
    side = int(width * 0.045)
    size = width - 2 * side
    square_size = size // 8
    padding = 2

    x1 = side + (square_size * (position.file - 1))
    x2 = x1 + square_size + padding
    y1 = max(0, side + (square_size * (8 - position.rank)) - padding)  # 8 - rank because chessboard is from 8 to 1

    y2 = min(width, y1 + square_size + padding)

    square = warped_board[y1:y2, x1:x2]
    return square


def get_squares(warped_board: np.ndarray) -> Squares:
    # cv2.imwrite(f"warped_square_{square}.png", square)
    return {position: get_square(warped_board, position)
            for position in POSITION}


def save_empty_fields(warped_board: np.ndarray, skip_rank: RANK = None) -> None:
    for position in POSITION:
        if position.rank == skip_rank:
            continue
        square = get_square(warped_board, position)
        imwrite(f"training_images/empty/{position.color}_square/training_{position}_{datetime.utcnow().timestamp()}.png", square)


def load_data_nn(spec_piece, color):
    X = None
    Y = None
    for piece in OUR_PIECES:
        piece_class = int(spec_piece == piece)

        for filename in glob.glob(f"training_images/{piece}/{color}_square/*.png"):
            image = cv2.imread(filename)
            data = np.reshape(image, (1, np.product(image.shape)))
            if X is None:
                if piece_class == 1:
                    for _ in range(10):
                        X = np.array(data)
                        Y = np.array([piece_class])
                else:
                    for _ in range(5):
                        X = np.array(data)
                        Y = np.array([piece_class])
            else:
                if piece_class == 1:
                    for _ in range(10):
                        X = np.vstack((X, data))
                        Y = np.vstack((Y, [piece_class]))
                else:
                    for _ in range(5):
                        X = np.vstack((X, data))
                        Y = np.vstack((Y, [piece_class]))
    return X, Y


def train_nn():
    for piece in OUR_PIECES:
        for color in COLOR:
            X, Y = load_data_nn(piece, color)

            classifier = neural_network.MLPClassifier(hidden_layer_sizes=256)

            classifier.fit(X, Y)

            joblib.dump(classifier, f"classifiers/neural_net_{piece}/{color}.pkl")


if __name__ == '__main__':
    #train_nn()
    train_empty_or_piece_hist()