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| import numpy as np | |
| def deg2rad(deg): | |
| return deg*np.pi/180 | |
| def inv_RT(RT): | |
| # RT_h = np.concatenate([RT, np.array([[0,0,0,1]])], axis=0) | |
| RT_inv = np.linalg.inv(RT) | |
| return RT_inv[:3, :] | |
| def camNormal2worldNormal(rot_c2w, camNormal): | |
| H,W,_ = camNormal.shape | |
| normal_img = np.matmul(rot_c2w[None, :, :], camNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3]) | |
| return normal_img | |
| def worldNormal2camNormal(rot_w2c, normal_map_world): | |
| H,W,_ = normal_map_world.shape | |
| # normal_img = np.matmul(rot_w2c[None, :, :], worldNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3]) | |
| # faster version | |
| # Reshape the normal map into a 2D array where each row represents a normal vector | |
| normal_map_flat = normal_map_world.reshape(-1, 3) | |
| # Transform the normal vectors using the transformation matrix | |
| normal_map_camera_flat = np.dot(normal_map_flat, rot_w2c.T) | |
| # Reshape the transformed normal map back to its original shape | |
| normal_map_camera = normal_map_camera_flat.reshape(normal_map_world.shape) | |
| return normal_map_camera | |
| def trans_normal(normal, RT_w2c, RT_w2c_target): | |
| # normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal) | |
| # normal_target_cam = worldNormal2camNormal(RT_w2c_target[:3,:3], normal_world) | |
| relative_RT = np.matmul(RT_w2c_target[:3,:3], np.linalg.inv(RT_w2c[:3,:3])) | |
| return worldNormal2camNormal(relative_RT[:3,:3], normal) | |
| def trans_normal_complex(normal, RT_w2c, RT_w2c_rela_to_cond): | |
| # camview -> world -> condview | |
| normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal) | |
| # debug_normal_world = normal2img(normal_world) | |
| # relative_RT = np.matmul(RT_w2c_rela_to_cond[:3,:3], np.linalg.inv(RT_w2c[:3,:3])) | |
| normal_target_cam = worldNormal2camNormal(RT_w2c_rela_to_cond[:3,:3], normal_world) | |
| # normal_condview = normal2img(normal_target_cam) | |
| return normal_target_cam | |
| def img2normal(img): | |
| return (img/255.)*2-1 | |
| def normal2img(normal): | |
| return np.uint8((normal*0.5+0.5)*255) | |
| def norm_normalize(normal, dim=-1): | |
| normal = normal/(np.linalg.norm(normal, axis=dim, keepdims=True)+1e-6) | |
| return normal | |
| def plot_grid_images(images, row, col, path=None): | |
| import cv2 | |
| """ | |
| Args: | |
| images: np.array [B, H, W, 3] | |
| row: | |
| col: | |
| save_path: | |
| Returns: | |
| """ | |
| images = images.detach().cpu().numpy() | |
| assert row * col == images.shape[0] | |
| images = np.vstack([np.hstack(images[r * col:(r + 1) * col]) for r in range(row)]) | |
| if path: | |
| cv2.imwrite(path, images[:,:,::-1] * 255) | |
| return images |