import numpy as np
from numpy.lib import stride_tricks
import cv2
from matplotlib.colors import hsv_to_rgb
import matplotlib.pyplot as plt
import numpy as np


class PatchMatch(object):
    def __init__(self, a, b, patch_size):
        assert a.shape == b.shape, "Dimensions were unequal for patch-matching input"
        self.A = a
        self.B = b
        self.patch_size = patch_size
        self.nnf = np.zeros((2, self.A.shape[0], self.A.shape[1])).astype(np.int)
        self.nnd = np.zeros((self.A.shape[0], self.A.shape[1]))
        self.initialise_nnf()

    def initialise_nnf(self):
        self.nnf[0] = np.random.randint(self.B.shape[0], size=(self.A.shape[0], self.A.shape[1]))
        self.nnf[1] = np.random.randint(self.B.shape[1], size=(self.A.shape[0], self.A.shape[1]))
        self.nnf = self.nnf.transpose((1, 2, 0))
        for i in range(self.A.shape[0]):
            for j in range(self.A.shape[1]):
                pos = self.nnf[i, j]
                self.nnd[i, j] = self.cal_dist(i, j, pos[0], pos[1])

    def cal_dist(self, ai, aj, bi, bj):
        dx0 = dy0 = self.patch_size // 2
        dx1 = dy1 = self.patch_size // 2 + 1
        dx0 = min(ai, bi, dx0)
        dx1 = min(self.A.shape[0] - ai, self.B.shape[0] - bi, dx1)
        dy0 = min(aj, bj, dy0)
        dy1 = min(self.A.shape[1] - aj, self.B.shape[1] - bj, dy1)
        return np.sum((self.A[ai - dx0:ai + dx1, aj - dy0:aj + dy1] - self.B[bi - dx0:bi + dx1, bj - dy0:bj + dy1]) ** 2) / (dx1 + dx0) / (dy1 + dy0)

    def reconstruct(self):
        ans = np.zeros_like(self.A)
        for i in range(self.A.shape[0]):
            for j in range(self.A.shape[1]):
                pos = self.nnf[i, j]
                ans[i, j] = self.B[pos[0], pos[1]]
        return ans


    def visualize_nnf(self):
        nnf = self.nnf
        nnd = self.nnd

        def angle_between_alt(p1, p2):
            ang1 = np.arctan2(*p1[::-1])
            ang2 = np.arctan2(*p2[::-1])
            return np.rad2deg((ang1 - ang2) % (2 * np.pi))

        def norm_dist(arr):
            return (arr) / (arr.max())

        img = np.zeros((nnf.shape[0], nnf.shape[1], 3), dtype=np.uint8)
        for i in range(1, nnf.shape[0]):
            for j in range(1, nnf.shape[1]):
                angle = angle_between_alt([j, i], [nnf[i, j][0], nnf[i, j][1]])
                img[i, j, :] = np.array([angle, nnd[i, j], 250])
        img = hsv_to_rgb(norm_dist(img / 255))
        plt.imshow(img)
        plt.show()

    def propagate(self):
        compare_value = -1
        for i in range(self.A.shape[0]):
            for j in range(self.A.shape[1]):
                x, y = self.nnf[i, j]
                bestx, besty, bestd = x, y, self.nnd[i, j]

                compare_value *= -1

                if (i + compare_value >= 0 and compare_value == -1) or (i + compare_value < self.A.shape[0] and compare_value == 1):
                    rx, ry = self.nnf[i + compare_value, j][0], self.nnf[i + compare_value, j][1]
                    if rx < self.B.shape[0]:
                        val = self.cal_dist(i, j, rx, ry)
                        if val < bestd:
                            bestx, besty, bestd = rx, ry, val

                if (j + compare_value >= 0 and compare_value == -1) or (j + compare_value < self.A.shape[1] and compare_value == 1):
                    rx, ry = self.nnf[i, j + compare_value][0], self.nnf[i, j + compare_value][1]
                    if ry < self.B.shape[1]:
                        val = self.cal_dist(i, j, rx, ry)
                        if val < bestd:
                            bestx, besty, bestd = rx, ry, val

                rand_d = min(self.B.shape[0] // 2, self.B.shape[1] // 2)
                while rand_d > 0:
                    try:
                        xmin = max(bestx - rand_d, 0)
                        xmax = min(bestx + rand_d, self.B.shape[0])
                        ymin = max(besty - rand_d, 0)
                        ymax = min(besty + rand_d, self.B.shape[1])
                        # print(xmin, xmax)
                        rx = np.random.randint(xmin, xmax)
                        ry = np.random.randint(ymin, ymax)
                        val = self.cal_dist(i, j, rx, ry)
                        if val < bestd:
                            bestx, besty, bestd = rx, ry, val
                    except:
                        print(rand_d)
                        print(xmin, xmax)
                        print(ymin, ymax)
                        print(bestx, besty)
                        print(self.B.shape)
                    rand_d = rand_d // 2

                self.nnf[i, j] = [bestx, besty]
                self.nnd[i, j] = bestd

        print("Done")

import numpy as np
from numpy.lib import stride_tricks
import cv2
from matplotlib.colors import hsv_to_rgb
import matplotlib.pyplot as plt
import numpy as np


class PatchMatch(object):
    def __init__(self, a, b, patch_size):
        assert a.shape == b.shape, "Dimensions were unequal for patch-matching input"
        self.A = a
        self.B = b
        self.patch_size = patch_size
        self.nnf = np.zeros((2, self.A.shape[0], self.A.shape[1])).astype(np.int)
        self.nnd = np.zeros((self.A.shape[0], self.A.shape[1]))
        self.initialise_nnf()

    def initialise_nnf(self):
        self.nnf[0] = np.random.randint(self.B.shape[0], size=(self.A.shape[0], self.A.shape[1]))
        self.nnf[1] = np.random.randint(self.B.shape[1], size=(self.A.shape[0], self.A.shape[1]))
        self.nnf = self.nnf.transpose((1, 2, 0))
        for i in range(self.A.shape[0]):
            for j in range(self.A.shape[1]):
                pos = self.nnf[i, j]
                self.nnd[i, j] = self.cal_dist(i, j, pos[0], pos[1])

    def cal_dist(self, ai, aj, bi, bj):
        dx0 = dy0 = self.patch_size // 2
        dx1 = dy1 = self.patch_size // 2 + 1
        dx0 = min(ai, bi, dx0)
        dx1 = min(self.A.shape[0] - ai, self.B.shape[0] - bi, dx1)
        dy0 = min(aj, bj, dy0)
        dy1 = min(self.A.shape[1] - aj, self.B.shape[1] - bj, dy1)
        return np.sum((self.A[ai - dx0:ai + dx1, aj - dy0:aj + dy1] - self.B[bi - dx0:bi + dx1, bj - dy0:bj + dy1]) ** 2) / (dx1 + dx0) / (dy1 + dy0)

    def reconstruct(self):
        ans = np.zeros_like(self.A)
        for i in range(self.A.shape[0]):
            for j in range(self.A.shape[1]):
                pos = self.nnf[i, j]
                ans[i, j] = self.B[pos[0], pos[1]]
        return ans


    def visualize_nnf(self):
        nnf = self.nnf
        nnd = self.nnd

        def angle_between_alt(p1, p2):
            ang1 = np.arctan2(*p1[::-1])
            ang2 = np.arctan2(*p2[::-1])
            return np.rad2deg((ang1 - ang2) % (2 * np.pi))

        def norm_dist(arr):
            return (arr) / (arr.max())

        img = np.zeros((nnf.shape[0], nnf.shape[1], 3), dtype=np.uint8)
        for i in range(1, nnf.shape[0]):
            for j in range(1, nnf.shape[1]):
                angle = angle_between_alt([j, i], [nnf[i, j][0], nnf[i, j][1]])
                img[i, j, :] = np.array([angle, nnd[i, j], 250])
        img = hsv_to_rgb(norm_dist(img / 255))
        plt.imshow(img)
        plt.show()

    def propagate(self):
        compare_value = -1
        for i in range(self.A.shape[0]):
            for j in range(self.A.shape[1]):
                x, y = self.nnf[i, j]
                bestx, besty, bestd = x, y, self.nnd[i, j]

                compare_value *= -1

                if (i + compare_value >= 0 and compare_value == -1) or (i + compare_value < self.A.shape[0] and compare_value == 1):
                    rx, ry = self.nnf[i + compare_value, j][0], self.nnf[i + compare_value, j][1]
                    if rx < self.B.shape[0]:
                        val = self.cal_dist(i, j, rx, ry)
                        if val < bestd:
                            bestx, besty, bestd = rx, ry, val

                if (j + compare_value >= 0 and compare_value == -1) or (j + compare_value < self.A.shape[1] and compare_value == 1):
                    rx, ry = self.nnf[i, j + compare_value][0], self.nnf[i, j + compare_value][1]
                    if ry < self.B.shape[1]:
                        val = self.cal_dist(i, j, rx, ry)
                        if val < bestd:
                            bestx, besty, bestd = rx, ry, val

                rand_d = min(self.B.shape[0] // 2, self.B.shape[1] // 2)
                while rand_d > 0:
                    try:
                        xmin = max(bestx - rand_d, 0)
                        xmax = min(bestx + rand_d, self.B.shape[0])
                        ymin = max(besty - rand_d, 0)
                        ymax = min(besty + rand_d, self.B.shape[1])
                        # print(xmin, xmax)
                        rx = np.random.randint(xmin, xmax)
                        ry = np.random.randint(ymin, ymax)
                        val = self.cal_dist(i, j, rx, ry)
                        if val < bestd:
                            bestx, besty, bestd = rx, ry, val
                    except:
                        print(rand_d)
                        print(xmin, xmax)
                        print(ymin, ymax)
                        print(bestx, besty)
                        print(self.B.shape)
                    rand_d = rand_d // 2

                self.nnf[i, j] = [bestx, besty]
                self.nnd[i, j] = bestd

        print("Done")

# print(xmin, xmax)