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import cv2
import logging
import numpy as np
from hloc.utils.read_write_model import (
read_cameras_binary,
read_images_binary,
read_model,
write_model,
qvec2rotmat,
read_images_text,
read_cameras_text,
)
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)
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