360DegreeTo2DImage.py

"""
Reference
# https://github.com/fuenwang/PanoramaUtility
# https://github.com/fuenwang/Equirec2Perspec
# https://github.com/fuenwang/PanoramaUtility
# https://github.com/timy90022/Perspective-and-Equirectangular
"""

import os
import sys
import cv2
import matplotlib.pyplot as plt
import numpy as np

def xyz2lonlat(xyz):
    atan2 = np.arctan2
    asin = np.arcsin

    norm = np.linalg.norm(xyz, axis=-1, keepdims=True)
    xyz_norm = xyz / norm
    x = xyz_norm[..., 0:1]
    y = xyz_norm[..., 1:2]
    z = xyz_norm[..., 2:]

    lon = atan2(x, z)
    lat = asin(y)
    lst = [lon, lat]

    out = np.concatenate(lst, axis=-1)
    return out

def lonlat2XY(lonlat, shape):
    X = (lonlat[..., 0:1] / (2 * np.pi) + 0.5) * (shape[1] - 1)
    Y = (lonlat[..., 1:] / (np.pi) + 0.5) * (shape[0] - 1)
    lst = [X, Y]
    out = np.concatenate(lst, axis=-1)

    return out 

class Equirectangular:
    def __init__(self, img_name):
        self._img = cv2.imread(img_name)[:,:,::-1 ]
        [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
        #
        f = 0.5 * width * 1 / np.tan(0.5 * FOV / 180.0 * np.pi)
        cx = (width - 1) / 2.0
        cy = (height - 1) / 2.0
        K = np.array([
                [f, 0, cx],
                [0, f, cy],
                [0, 0,  1],
            ], np.float32)
        K_inv = np.linalg.inv(K)
        
        x = np.arange(width)
        y = np.arange(height)
        x, y = np.meshgrid(x, y)
        z = np.ones_like(x)
        xyz = np.concatenate([x[..., None], y[..., None], z[..., None]], axis=-1)
        xyz = xyz @ K_inv.T

        y_axis = np.array([0.0, 1.0, 0.0], np.float32)
        x_axis = np.array([1.0, 0.0, 0.0], np.float32)
        R1, _ = cv2.Rodrigues(y_axis * np.radians(THETA))
        R2, _ = cv2.Rodrigues(np.dot(R1, x_axis) * np.radians(PHI))
        R = R2 @ R1
        xyz = xyz @ R.T
        lonlat = xyz2lonlat(xyz) 
        XY = lonlat2XY(lonlat, shape=self._img.shape).astype(np.float32)
        persp = cv2.remap(self._img, XY[..., 0], XY[..., 1], cv2.INTER_CUBIC, borderMode=cv2.BORDER_WRAP)

        return persp
        
# an image with 360 degree view
equ = Equirectangular('/home/jupyter/working/base-image.JPG')

# # THETA is left/right angle, PHI is up/down angle, both in degree

# FOV unit is degree / zoom degree
# theta is z-axis angle(right direction is positive, left direction is negative)
# phi is y-axis angle(up direction positive, down direction negative)
# height and width is output image dimension 

all_patches = []

for phi_i in [0, 30, -50]:
    for i in [0, 30, 90, 130, 180, 230, 280, 330]:
        img = equ.GetPerspective(
            FOV    = 100, 
            THETA  = i, 
            PHI    = phi_i, # 0 (plain shot), 30 (upper shot), -50 (down shot)
            height = 720, 
            width  = 1080
        )

        plt.figure(figsize=(10, 10))
        plt.imshow(img)
        plt.title(i)
        plt.savefig(f'{phi}{i}.png)
        plt.show()

        all_patches.append(img)

    
all_patches = np.concatenate([all_patches], axis=0)
all_patches.shape

plt.figure(figsize=(25,25))
for i in range(all_patches.shape[0]):
    plt.subplot(6, 4, i + 1)
    plt.imshow(all_patches[i])
    plt.axis("off")
plt.tight_layout()
plt.savefig('a.png', dpi=100)
plt.show()