| | import cv2 |
| | import numpy as np |
| |
|
| | |
| | |
| |
|
| |
|
| | def get_perspective(img, FOV, THETA, PHI, height, width): |
| | |
| | |
| | |
| | [orig_width, orig_height, _] = img.shape |
| | equ_h = orig_height |
| | equ_w = orig_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( |
| | img, |
| | lon.astype(np.float32), |
| | lat.astype(np.float32), |
| | cv2.INTER_CUBIC, |
| | borderMode=cv2.BORDER_WRAP, |
| | ) |
| | return persp |
| |
|
