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| import os | |
| import sys | |
| import cv2 | |
| import numpy as np | |
| class Perspective: | |
| def __init__(self, img_name , FOV, THETA, PHI ): | |
| if isinstance(img_name, str): | |
| self._img = cv2.imread(img_name, cv2.IMREAD_COLOR) | |
| else: | |
| self._img = img_name | |
| [self._height, self._width, _] = self._img.shape | |
| self.wFOV = FOV | |
| self.THETA = THETA | |
| self.PHI = PHI | |
| self.hFOV = float(self._height) / self._width * FOV | |
| self.w_len = np.tan(np.radians(self.wFOV / 2.0)) | |
| self.h_len = np.tan(np.radians(self.hFOV / 2.0)) | |
| def GetEquirec(self,height,width): | |
| # | |
| # THETA is left/right angle, PHI is up/down angle, both in degree | |
| # | |
| x,y = np.meshgrid(np.linspace(-180, 180,width),np.linspace(90,-90,height)) | |
| x_map = np.cos(np.radians(x)) * np.cos(np.radians(y)) | |
| y_map = np.sin(np.radians(x)) * np.cos(np.radians(y)) | |
| z_map = np.sin(np.radians(y)) | |
| xyz = np.stack((x_map,y_map,z_map),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(self.THETA)) | |
| [R2, _] = cv2.Rodrigues(np.dot(R1, y_axis) * np.radians(-self.PHI)) | |
| R1 = np.linalg.inv(R1) | |
| R2 = np.linalg.inv(R2) | |
| xyz = xyz.reshape([height * width, 3]).T | |
| xyz = np.dot(R2, xyz) | |
| xyz = np.dot(R1, xyz).T | |
| xyz = xyz.reshape([height , width, 3]) | |
| inverse_mask = np.where(xyz[:,:,0]>0,1,0) | |
| xyz[:,:] = xyz[:,:]/np.repeat(xyz[:,:,0][:, :, np.newaxis], 3, axis=2) | |
| lon_map = np.where((-self.w_len<xyz[:,:,1])&(xyz[:,:,1]<self.w_len)&(-self.h_len<xyz[:,:,2]) | |
| &(xyz[:,:,2]<self.h_len),(xyz[:,:,1]+self.w_len)/2/self.w_len*self._width,0) | |
| lat_map = np.where((-self.w_len<xyz[:,:,1])&(xyz[:,:,1]<self.w_len)&(-self.h_len<xyz[:,:,2]) | |
| &(xyz[:,:,2]<self.h_len),(-xyz[:,:,2]+self.h_len)/2/self.h_len*self._height,0) | |
| mask = np.where((-self.w_len<xyz[:,:,1])&(xyz[:,:,1]<self.w_len)&(-self.h_len<xyz[:,:,2]) | |
| &(xyz[:,:,2]<self.h_len),1,0) | |
| persp = cv2.remap(self._img, lon_map.astype(np.float32), lat_map.astype(np.float32), cv2.INTER_CUBIC, borderMode=cv2.BORDER_WRAP) | |
| mask = mask * inverse_mask | |
| mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2) | |
| persp = persp * mask | |
| return persp , mask | |