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"""Wrapper for VP estimation with prior gravity using the VP-Estimation-with-Prior-Gravity library.
repo: https://github.com/cvg/VP-Estimation-with-Prior-Gravity
"""
import sys
sys.path.append("third_party/VP-Estimation-with-Prior-Gravity")
sys.path.append("third_party/VP-Estimation-with-Prior-Gravity/src/deeplsd")
import logging
import random
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from vp_estimation_with_prior_gravity.evaluation import get_labels_from_vp, project_vp_to_image
from vp_estimation_with_prior_gravity.features.line_detector import LineDetector
from vp_estimation_with_prior_gravity.solvers import run_hybrid_uncalibrated
from vp_estimation_with_prior_gravity.visualization import plot_images, plot_lines, plot_vp
from siclib.geometry.camera import Pinhole
from siclib.geometry.gravity import Gravity
from siclib.models import BaseModel
from siclib.models.utils.metrics import gravity_error, pitch_error, roll_error, vfov_error
# flake8: noqa
# mypy: ignore-errors
logger = logging.getLogger(__name__)
class VPEstimator(BaseModel):
# Which solvers to us for our hybrid solver:
# 0 - 2lines 200g
# 1 - 2lines 110g
# 2 - 2lines 011g
# 3 - 4lines 211
# 4 - 4lines 220
default_conf = {
"SOLVER_FLAGS": [True, True, True, True, True],
"th_pixels": 3, # RANSAC inlier threshold
"ls_refinement": 2, # 3 uses the gravity in the LS refinement, 2 does not.
"nms": 3, # change to 3 to add a Ceres optimization after the non minimal solver (slower)
"magsac_scoring": True,
"line_type": "deeplsd", # 'lsd' or 'deeplsd'
"min_lines": 5, # only trust images with at least this many lines
"verbose": False,
}
def _init(self, conf):
if conf.SOLVER_FLAGS in [
[True, False, False, False, False],
[False, False, True, False, False],
]:
self.vertical = np.array([random.random() / 1e12, 1, random.random() / 1e12])
self.vertical /= np.linalg.norm(self.vertical)
else:
self.vertical = np.array([0.0, 1, 0.0])
self.line_detector = LineDetector(line_detector=conf.line_type)
self.verbose = conf.verbose
def visualize_lines(self, vp, lines, img, K):
vp_labels = get_labels_from_vp(
lines[:, :, [1, 0]], project_vp_to_image(vp, K), threshold=self.conf.th_pixels
)[0]
plot_images([img, img])
plot_lines([lines, np.empty((0, 2, 2))])
plot_vp([np.empty((0, 2, 2)), lines], [[], vp_labels])
plt.show()
def get_vvp(self, vp, K):
best_idx, best_cossim = 0, -1
for i, point in enumerate(vp):
cossim = np.dot(self.vertical, point) / np.linalg.norm(point)
point = -point * np.dot(self.vertical, point)
try:
gravity = Gravity(np.linalg.inv(K) @ point)
except:
continue
if (
np.abs(cossim) > best_cossim
and gravity.pitch.abs() <= np.pi / 4
and gravity.roll.abs() <= np.pi / 4
):
best_idx, best_cossim = i, np.abs(cossim)
vvp = vp[best_idx]
return -vvp * np.sign(np.dot(self.vertical, vvp))
def _forward(self, data):
device = data["image"].device
images = data["image"].cpu()
estimations = []
for idx, img in enumerate(images.unbind(0)):
if "prior_gravity" in data:
self.vertical = -data["prior_gravity"][idx].vec3d.cpu().numpy()
else:
self.vertical = np.array([0.0, 1, 0.0])
img = img.numpy().transpose(1, 2, 0) * 255
img = img.astype(np.uint8)
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
lines = self.line_detector.detect_lines(gray_img)[:, :, [1, 0]]
if len(lines) < self.conf.min_lines:
logger.warning("Not enough lines detected! Skipping...")
gravity = Gravity.from_rp(np.nan, np.nan)
camera = Pinhole.from_dict(
{"f": np.nan, "height": img.shape[0], "width": img.shape[1]}
)
estimations.append({"camera": camera, "gravity": gravity})
continue
principle_point = np.array([img.shape[1] / 2.0, img.shape[0] / 2.0])
f, vp = run_hybrid_uncalibrated(
lines - principle_point[None, None, :],
self.vertical,
th_pixels=self.conf.th_pixels,
ls_refinement=self.conf.ls_refinement,
nms=self.conf.nms,
magsac_scoring=self.conf.magsac_scoring,
sprt=True,
solver_flags=self.conf.SOLVER_FLAGS,
)
vp[:, 1] *= -1
K = np.array(
[[f, 0.0, principle_point[0]], [0.0, f, principle_point[1]], [0.0, 0.0, 1.0]]
)
if self.verbose:
self.visualize_lines(vp, lines, img, K)
vp_labels = get_labels_from_vp(
lines[:, :, [1, 0]], project_vp_to_image(vp, K), threshold=self.conf.th_pixels
)[0]
out = {"vp": vp, "lines": lines, "K": K, "vp_labels": vp_labels}
vp = project_vp_to_image(vp, K)
vvp = self.get_vvp(vp, K)
vvp = -vvp * np.sign(np.dot(self.vertical, vvp))
try:
K_inv = np.linalg.inv(K)
gravity = Gravity(K_inv @ vvp)
except np.linalg.LinAlgError:
gravity = Gravity.from_rp(np.nan, np.nan)
camera = Pinhole.from_dict({"f": f, "height": img.shape[0], "width": img.shape[1]})
estimations.append({"camera": camera, "gravity": gravity})
if len(estimations) == 0:
return {}
gravity = torch.stack([Gravity(est["gravity"].vec3d) for est in estimations], dim=0)
camera = torch.stack([Pinhole(est["camera"]._data) for est in estimations], dim=0)
return {"camera": camera.float().to(device), "gravity": gravity.float().to(device)} | out
def metrics(self, pred, data):
pred_cam, gt_cam = pred["camera_opt"], data["camera"]
pred_gravity, gt_gravity = pred["gravity_opt"], data["gravity"]
return {
"roll_opt_error": roll_error(pred_gravity, gt_gravity),
"pitch_opt_error": pitch_error(pred_gravity, gt_gravity),
"gravity_opt_error": gravity_error(pred_gravity, gt_gravity),
"vfov_opt_error": vfov_error(pred_cam, gt_cam),
}
def loss(self, pred, data):
return {}, self.metrics(pred, data)
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