# https://github.com/Tangshitao/MVDiffusion
import os
import sys
import cv2
import numpy as np

class Equirectangular:
    def __init__(self, img_name, text2light=False):
        if isinstance(img_name, str):
            self._img = cv2.imread(img_name, cv2.IMREAD_COLOR)
        else:
            self._img = img_name
        if text2light:
            self._img = np.roll(self._img, -60, axis=0)
        
        [self._height, self._width, _] = self._img.shape
    

    def GetPerspective(self, FOV, THETA, PHI, height, width):
        #
        # THETA is left/right angle, PHI is up/down angle, both in degree
        #

        equ_h = self._height
        equ_w = self._width
        equ_cx = (equ_w - 1) / 2.0
        equ_cy = (equ_h - 1) / 2.0

        wFOV = FOV
        hFOV = float(height) / width * wFOV

        w_len = np.tan(np.radians(wFOV / 2.0))
        h_len = np.tan(np.radians(hFOV / 2.0))


        x_map = np.ones([height, width], np.float32)
        y_map = np.tile(np.linspace(-w_len, w_len,width), [height,1])
        z_map = -np.tile(np.linspace(-h_len, h_len,height), [width,1]).T

        D = np.sqrt(x_map**2 + y_map**2 + z_map**2)
        xyz = np.stack((x_map,y_map,z_map),axis=2)/np.repeat(D[:, :, np.newaxis], 3, axis=2)
        
        y_axis = np.array([0.0, 1.0, 0.0], np.float32)
        z_axis = np.array([0.0, 0.0, 1.0], np.float32)
        [R1, _] = cv2.Rodrigues(z_axis * np.radians(THETA))
        [R2, _] = cv2.Rodrigues(np.dot(R1, y_axis) * np.radians(-PHI))

        xyz = xyz.reshape([height * width, 3]).T
        xyz = np.dot(R1, xyz)
        xyz = np.dot(R2, xyz).T
        lat = np.arcsin(xyz[:, 2])
        lon = np.arctan2(xyz[:, 1] , xyz[:, 0])

        lon = lon.reshape([height, width]) / np.pi * 180
        lat = -lat.reshape([height, width]) / np.pi * 180

        lon = lon / 180 * equ_cx + equ_cx
        lat = lat / 90  * equ_cy + equ_cy

        
            
        persp = cv2.remap(self._img, lon.astype(np.float32), lat.astype(np.float32), cv2.INTER_CUBIC, borderMode=cv2.BORDER_WRAP)
        return persp
        






