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from scene.cameras import Camera |
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import numpy as np |
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from utils.graphics_utils import fov2focal |
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from PIL import Image |
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import cv2 |
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WARNED = False |
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def loadCam(args, id, cam_info, resolution_scale, is_nerf_synthetic, is_test_dataset): |
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image = Image.open(cam_info.image_path) |
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if cam_info.depth_path != "": |
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try: |
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if is_nerf_synthetic: |
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invdepthmap = cv2.imread(cam_info.depth_path, -1).astype(np.float32) / 512 |
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else: |
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invdepthmap = cv2.imread(cam_info.depth_path, -1).astype(np.float32) / float(2**16) |
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except FileNotFoundError: |
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print(f"Error: The depth file at path '{cam_info.depth_path}' was not found.") |
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raise |
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except IOError: |
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print(f"Error: Unable to open the image file '{cam_info.depth_path}'. It may be corrupted or an unsupported format.") |
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raise |
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except Exception as e: |
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print(f"An unexpected error occurred when trying to read depth at {cam_info.depth_path}: {e}") |
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raise |
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else: |
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invdepthmap = None |
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orig_w, orig_h = image.size |
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if args.resolution in [1, 2, 4, 8]: |
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resolution = round(orig_w/(resolution_scale * args.resolution)), round(orig_h/(resolution_scale * args.resolution)) |
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else: |
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if args.resolution == -1: |
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if orig_w > 1600: |
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global WARNED |
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if not WARNED: |
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print("[ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K.\n " |
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"If this is not desired, please explicitly specify '--resolution/-r' as 1") |
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WARNED = True |
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global_down = orig_w / 1600 |
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else: |
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global_down = 1 |
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else: |
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global_down = orig_w / args.resolution |
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scale = float(global_down) * float(resolution_scale) |
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resolution = (int(orig_w / scale), int(orig_h / scale)) |
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return Camera(resolution, colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T, |
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FoVx=cam_info.FovX, FoVy=cam_info.FovY, depth_params=cam_info.depth_params, |
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image=image, invdepthmap=invdepthmap, |
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image_name=cam_info.image_name, uid=id, data_device=args.data_device, |
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train_test_exp=args.train_test_exp, is_test_dataset=is_test_dataset, is_test_view=cam_info.is_test) |
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def cameraList_from_camInfos(cam_infos, resolution_scale, args, is_nerf_synthetic, is_test_dataset): |
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camera_list = [] |
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for id, c in enumerate(cam_infos): |
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camera_list.append(loadCam(args, id, c, resolution_scale, is_nerf_synthetic, is_test_dataset)) |
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return camera_list |
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def camera_to_JSON(id, camera : Camera): |
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Rt = np.zeros((4, 4)) |
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Rt[:3, :3] = camera.R.transpose() |
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Rt[:3, 3] = camera.T |
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Rt[3, 3] = 1.0 |
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W2C = np.linalg.inv(Rt) |
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pos = W2C[:3, 3] |
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rot = W2C[:3, :3] |
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serializable_array_2d = [x.tolist() for x in rot] |
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camera_entry = { |
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'id' : id, |
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'img_name' : camera.image_name, |
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'width' : camera.width, |
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'height' : camera.height, |
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'position': pos.tolist(), |
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'rotation': serializable_array_2d, |
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'fy' : fov2focal(camera.FovY, camera.height), |
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'fx' : fov2focal(camera.FovX, camera.width) |
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} |
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return camera_entry |
