| for idx, frame in enumerate(frames): | |
| cam_name = os.path.join(path, frame["file_path"] + extension) | |
| # NeRF 'transform_matrix' is a camera-to-world transform | |
| c2w = np.array(frame["transform_matrix"]) | |
| # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) | |
| c2w[:3, 1:3] *= -1 | |
| # get the world-to-camera transform and set R, T | |
| w2c = np.linalg.inv(c2w) | |
| R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code | |
| T = w2c[:3, 3] | |
| image_path = os.path.join(path, cam_name) | |
| image_name = Path(cam_name).stem | |
| image = Image.open(image_path) | |
| im_data = np.array(image.convert("RGBA")) | |
| bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0]) | |
| norm_data = im_data / 255.0 | |
| arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4]) | |
| image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB") | |
| fovy = focal2fov(fov2focal(fovx, image.size[0]), image.size[1]) | |
| FovY = fovy | |
| FovX = fovx | |
| cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image, | |
| image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1])) | |
| return cam_infos | |
| def readNerfSyntheticInfo(path, white_background, eval, extension=".png"): | |
| print("Reading Training Transforms") | |
| train_cam_infos = readCamerasFromTransforms(path, "transforms_train.json", white_background, extension) | |
| print("Reading Test Transforms") | |
| test_cam_infos = readCamerasFromTransforms(path, "transforms_test.json", white_background, extension) | |
| if not eval: | |
| train_cam_infos.extend(test_cam_info |