import logging import cv2 import numpy as np from hloc.utils.read_write_model import ( qvec2rotmat, read_cameras_binary, read_cameras_text, read_images_binary, read_images_text, read_model, write_model, ) logger = logging.getLogger(__name__) def scale_sfm_images(full_model, scaled_model, image_dir): """Duplicate the provided model and scale the camera intrinsics so that they match the original image resolution - makes everything easier. """ logger.info("Scaling the COLMAP model to the original image size.") scaled_model.mkdir(exist_ok=True) cameras, images, points3D = read_model(full_model) scaled_cameras = {} for id_, image in images.items(): name = image.name img = cv2.imread(str(image_dir / name)) assert img is not None, image_dir / name h, w = img.shape[:2] cam_id = image.camera_id if cam_id in scaled_cameras: assert scaled_cameras[cam_id].width == w assert scaled_cameras[cam_id].height == h continue camera = cameras[cam_id] assert camera.model == "SIMPLE_RADIAL" sx = w / camera.width sy = h / camera.height assert sx == sy, (sx, sy) scaled_cameras[cam_id] = camera._replace( width=w, height=h, params=camera.params * np.array([sx, sx, sy, 1.0]) ) write_model(scaled_cameras, images, points3D, scaled_model) def create_query_list_with_intrinsics( model, out, list_file=None, ext=".bin", image_dir=None ): """Create a list of query images with intrinsics from the colmap model.""" if ext == ".bin": images = read_images_binary(model / "images.bin") cameras = read_cameras_binary(model / "cameras.bin") else: images = read_images_text(model / "images.txt") cameras = read_cameras_text(model / "cameras.txt") name2id = {image.name: i for i, image in images.items()} if list_file is None: names = list(name2id) else: with open(list_file, "r") as f: names = f.read().rstrip().split("\n") data = [] for name in names: image = images[name2id[name]] camera = cameras[image.camera_id] w, h, params = camera.width, camera.height, camera.params if image_dir is not None: # Check the original image size and rescale the camera intrinsics img = cv2.imread(str(image_dir / name)) assert img is not None, image_dir / name h_orig, w_orig = img.shape[:2] assert camera.model == "SIMPLE_RADIAL" sx = w_orig / w sy = h_orig / h assert sx == sy, (sx, sy) w, h = w_orig, h_orig params = params * np.array([sx, sx, sy, 1.0]) p = [name, camera.model, w, h] + params.tolist() data.append(" ".join(map(str, p))) with open(out, "w") as f: f.write("\n".join(data)) def evaluate(model, results, list_file=None, ext=".bin", only_localized=False): predictions = {} with open(results, "r") as f: for data in f.read().rstrip().split("\n"): data = data.split() name = data[0] q, t = np.split(np.array(data[1:], float), [4]) predictions[name] = (qvec2rotmat(q), t) if ext == ".bin": images = read_images_binary(model / "images.bin") else: images = read_images_text(model / "images.txt") name2id = {image.name: i for i, image in images.items()} if list_file is None: test_names = list(name2id) else: with open(list_file, "r") as f: test_names = f.read().rstrip().split("\n") errors_t = [] errors_R = [] for name in test_names: if name not in predictions: if only_localized: continue e_t = np.inf e_R = 180.0 else: image = images[name2id[name]] R_gt, t_gt = image.qvec2rotmat(), image.tvec R, t = predictions[name] e_t = np.linalg.norm(-R_gt.T @ t_gt + R.T @ t, axis=0) cos = np.clip((np.trace(np.dot(R_gt.T, R)) - 1) / 2, -1.0, 1.0) e_R = np.rad2deg(np.abs(np.arccos(cos))) errors_t.append(e_t) errors_R.append(e_R) errors_t = np.array(errors_t) errors_R = np.array(errors_R) med_t = np.median(errors_t) med_R = np.median(errors_R) out = f"Results for file {results.name}:" out += f"\nMedian errors: {med_t:.3f}m, {med_R:.3f}deg" out += "\nPercentage of test images localized within:" threshs_t = [0.01, 0.02, 0.03, 0.05, 0.25, 0.5, 5.0] threshs_R = [1.0, 2.0, 3.0, 5.0, 2.0, 5.0, 10.0] for th_t, th_R in zip(threshs_t, threshs_R): ratio = np.mean((errors_t < th_t) & (errors_R < th_R)) out += f"\n\t{th_t*100:.0f}cm, {th_R:.0f}deg : {ratio*100:.2f}%" logger.info(out)