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Merged
MarkYu98 merged 10 commits into from
Jan 9, 2025
Merged

Add formatting with clang-format and ruff #2

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15f70fc
specify clang-format version
B1ueber2y Jan 7, 2025
b6de275
add ruff for python formatting
B1ueber2y Jan 7, 2025
a2c09d1
move to format folder
B1ueber2y Jan 7, 2025
c0611fe
format
B1ueber2y Jan 7, 2025
c8d236b
fix sim102. add E501 linting exception
B1ueber2y Jan 7, 2025
474180e
add formatting ci.
B1ueber2y Jan 7, 2025
1c269e0
change permission
B1ueber2y Jan 7, 2025
cfb6b93
Merge branch 'main' into features/format
MarkYu98 Jan 9, 2025
e2c90c8
optimize format
MarkYu98 Jan 9, 2025
9aa8a62
format C++ changes
MarkYu98 Jan 9, 2025
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44 changes: 44 additions & 0 deletions .github/workflows/format.yml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,44 @@
name: Check code formatting (Ubuntu)

on:
push:
branches:
- main
pull_request:
types: [ assigned, opened, synchronize, reopened ]
release:
types: [ published, edited ]

jobs:
build:
name: ${{ matrix.config.os }} formatting
runs-on: ${{ matrix.config.os }}
strategy:
matrix:
config: [
{
os: ubuntu-22.04,
checkCodeFormat: true,
},
]

env:
COMPILER_CACHE_VERSION: 1
COMPILER_CACHE_DIR: ${{ github.workspace }}/compiler-cache
CCACHE_DIR: ${{ github.workspace }}/compiler-cache/ccache
CCACHE_BASEDIR: ${{ github.workspace }}
CTCACHE_DIR: ${{ github.workspace }}/compiler-cache/ctcache

steps:
- uses: actions/checkout@v4
- name: Check code format
run: |
if [ "${{ matrix.config.checkCodeFormat }}" != "true" ]; then
exit 0
fi
set +x -euo pipefail
python -m pip install ruff==0.6.7 clang-format==19.1.0
./scripts/format/clang_format.sh
./scripts/format/python.sh
git diff --name-only
git diff --exit-code || (echo "Code formatting failed" && exit 1)
78 changes: 50 additions & 28 deletions examples/calibrated.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,13 @@
import json
import os
import numpy as np

import cv2
import numpy as np

import madpose
from madpose.utils import get_depths, compute_pose_error
from madpose.utils import compute_pose_error, get_depths

sample_path = 'examples/image_pairs/0_scannet'
sample_path = "examples/image_pairs/0_scannet"

# Thresholds for reprojection and epipolar errors
reproj_pix_thres = 8.0
Expand All @@ -21,35 +22,35 @@
options.final_least_squares = True
options.threshold_multiplier = 5.0
options.num_lo_steps = 4
options.squared_inlier_thresholds = [reproj_pix_thres ** 2, epipolar_pix_thres ** 2]
options.squared_inlier_thresholds = [reproj_pix_thres**2, epipolar_pix_thres**2]
options.data_type_weights = [1.0, epipolar_weight]
options.random_seed = 0
options.random_seed = 0

est_config = madpose.EstimatorConfig()
est_config.min_depth_constraint = True
est_config.use_shift = True

# Read the image pair
image0 = cv2.imread(os.path.join(sample_path, 'image0.png'))
image1 = cv2.imread(os.path.join(sample_path, 'image1.png'))
image0 = cv2.imread(os.path.join(sample_path, "image0.png"))
image1 = cv2.imread(os.path.join(sample_path, "image1.png"))

# Read info
with open(os.path.join(sample_path, 'info.json'), 'r') as f:
with open(os.path.join(sample_path, "info.json")) as f:
info = json.load(f)

# Load camera intrinsics
K0 = np.array(info['K0'])
K1 = np.array(info['K1'])
K0 = np.array(info["K0"])
K1 = np.array(info["K1"])

# Load pre-computed keypoints (you can also run keypoint detectors of your choice)
matches_0_file = os.path.join(sample_path, info['matches_0_file'])
matches_1_file = os.path.join(sample_path, info['matches_1_file'])
matches_0_file = os.path.join(sample_path, info["matches_0_file"])
matches_1_file = os.path.join(sample_path, info["matches_1_file"])
mkpts0 = np.load(matches_0_file)
mkpts1 = np.load(matches_1_file)

# Load pre-computed depth maps (you can also run Monocular Depth models of your choice)
depth_0_file = os.path.join(sample_path, info['depth_0_file'])
depth_1_file = os.path.join(sample_path, info['depth_1_file'])
depth_0_file = os.path.join(sample_path, info["depth_0_file"])
depth_1_file = os.path.join(sample_path, info["depth_1_file"])
depth_map0 = np.load(depth_0_file)
depth_map1 = np.load(depth_1_file)

Expand All @@ -59,18 +60,23 @@

# Run hybrid estimation
pose, stats = madpose.HybridEstimatePoseScaleOffset(
mkpts0, mkpts1,
depth0, depth1,
[depth_map0.min(), depth_map1.min()],
K0, K1, options, est_config
)
mkpts0,
mkpts1,
depth0,
depth1,
[depth_map0.min(), depth_map1.min()],
K0,
K1,
options,
est_config,
)
# rotation and translation of the estimated pose
R_est, t_est = pose.R(), pose.t()
# scale and offsets of the affine corrected depth maps
s_est, o0_est, o1_est = pose.scale, pose.offset0, pose.offset1

# Load the GT Pose
T_0to1 = np.array(info['T_0to1'])
T_0to1 = np.array(info["T_0to1"])

# Compute the pose error
err_t, err_R = compute_pose_error(T_0to1, R_est, t_est)
Expand All @@ -81,18 +87,34 @@
print(f"Estimated scale, offset0, offset1: {s_est:.4f}, {o0_est:.4f}, {o1_est:.4f}")

# Run point-based estimation using PoseLib
import poselib

ransac_opt_dict = {'max_epipolar_error': epipolar_pix_thres, 'min_iterations': 1000, 'max_iterations': 10000}
cam0_dict = {'model': 'PINHOLE', 'width': image0.shape[1], 'height': image0.shape[0], 'params': [K0[0, 0], K0[1, 1], K0[0, 2], K0[1, 2]]}
cam1_dict = {'model': 'PINHOLE', 'width': image1.shape[1], 'height': image1.shape[0], 'params': [K1[0, 0], K1[1, 1], K1[0, 2], K1[1, 2]]}

pose_ponly, stats = poselib.estimate_relative_pose(mkpts0, mkpts1, cam0_dict, cam1_dict, ransac_opt_dict)
import poselib # noqa: E402

ransac_opt_dict = {
"max_epipolar_error": epipolar_pix_thres,
"min_iterations": 1000,
"max_iterations": 10000,
}
cam0_dict = {
"model": "PINHOLE",
"width": image0.shape[1],
"height": image0.shape[0],
"params": [K0[0, 0], K0[1, 1], K0[0, 2], K0[1, 2]],
}
cam1_dict = {
"model": "PINHOLE",
"width": image1.shape[1],
"height": image1.shape[0],
"params": [K1[0, 0], K1[1, 1], K1[0, 2], K1[1, 2]],
}

pose_ponly, stats = poselib.estimate_relative_pose(
mkpts0, mkpts1, cam0_dict, cam1_dict, ransac_opt_dict
)
R_ponly, t_ponly = pose_ponly.R, pose_ponly.t

# Compute the pose error
err_t, err_R = compute_pose_error(T_0to1, R_ponly, t_ponly)

print("--- Point-based Estimation Results ---")
print(f"Rotation Error: {err_R:.4f} degrees")
print(f"Translation Error: {err_t:.4f} degrees")
print(f"Translation Error: {err_t:.4f} degrees")
56 changes: 33 additions & 23 deletions examples/shared_focal.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,13 @@
import json
import os
import numpy as np

import cv2
import numpy as np

import madpose
from madpose.utils import get_depths, compute_pose_error
from madpose.utils import compute_pose_error, get_depths

sample_path = 'examples/image_pairs/1_eth3d'
sample_path = "examples/image_pairs/1_eth3d"

# Thresholds for reprojection and epipolar errors
reproj_pix_thres = 8.0
Expand All @@ -21,35 +22,35 @@
options.final_least_squares = True
options.threshold_multiplier = 5.0
options.num_lo_steps = 4
options.squared_inlier_thresholds = [reproj_pix_thres ** 2, epipolar_pix_thres ** 2]
options.squared_inlier_thresholds = [reproj_pix_thres**2, epipolar_pix_thres**2]
options.data_type_weights = [1.0, epipolar_weight]
options.random_seed = 0
options.random_seed = 0

est_config = madpose.EstimatorConfig()
est_config.min_depth_constraint = True
est_config.use_shift = True

# Read the image pair
image0 = cv2.imread(os.path.join(sample_path, 'image0.png'))
image1 = cv2.imread(os.path.join(sample_path, 'image1.png'))
image0 = cv2.imread(os.path.join(sample_path, "image0.png"))
image1 = cv2.imread(os.path.join(sample_path, "image1.png"))

# Read info
with open(os.path.join(sample_path, 'info.json'), 'r') as f:
with open(os.path.join(sample_path, "info.json")) as f:
info = json.load(f)

# Load camera intrinsics
K0 = np.array(info['K0'])
K1 = np.array(info['K1'])
K0 = np.array(info["K0"])
K1 = np.array(info["K1"])

# Load pre-computed keypoints (you can also run keypoint detectors of your choice)
matches_0_file = os.path.join(sample_path, info['matches_0_file'])
matches_1_file = os.path.join(sample_path, info['matches_1_file'])
matches_0_file = os.path.join(sample_path, info["matches_0_file"])
matches_1_file = os.path.join(sample_path, info["matches_1_file"])
mkpts0 = np.load(matches_0_file)
mkpts1 = np.load(matches_1_file)

# Load pre-computed depth maps (you can also run Monocular Depth models of your choice)
depth_0_file = os.path.join(sample_path, info['depth_0_file'])
depth_1_file = os.path.join(sample_path, info['depth_1_file'])
depth_0_file = os.path.join(sample_path, info["depth_0_file"])
depth_1_file = os.path.join(sample_path, info["depth_1_file"])
depth_map0 = np.load(depth_0_file)
depth_map1 = np.load(depth_1_file)

Expand All @@ -64,11 +65,16 @@

# Run hybrid estimation
pose, stats = madpose.HybridEstimatePoseScaleOffsetSharedFocal(
mkpts0, mkpts1,
depth0, depth1,
[depth_map0.min(), depth_map1.min()],
pp0, pp1, options, est_config
)
mkpts0,
mkpts1,
depth0,
depth1,
[depth_map0.min(), depth_map1.min()],
pp0,
pp1,
options,
est_config,
)
# rotation and translation of the estimated pose
R_est, t_est = pose.R(), pose.t()
# scale and offsets of the affine corrected depth maps
Expand All @@ -77,7 +83,7 @@
f_est = pose.focal

# Load the GT Pose
T_0to1 = np.array(info['T_0to1'])
T_0to1 = np.array(info["T_0to1"])

# Compute the pose error
err_t, err_R = compute_pose_error(T_0to1, R_est, t_est)
Expand All @@ -93,9 +99,13 @@
print(f"Estimated scale, offset0, offset1: {s_est:.4f}, {o0_est:.4f}, {o1_est:.4f}")

# Run point-based estimation using PoseLib
import poselib
import poselib # noqa: E402

ransac_opt_dict = {'max_epipolar_error': epipolar_pix_thres, 'min_iterations': 1000, 'max_iterations': 10000}
ransac_opt_dict = {
"max_epipolar_error": epipolar_pix_thres,
"min_iterations": 1000,
"max_iterations": 10000,
}
img_pair, stats = poselib.estimate_shared_focal_relative_pose(mkpts0, mkpts1, pp, ransac_opt_dict)
shared_focal_pose = img_pair.pose
R_ponly, t_ponly = shared_focal_pose.R, shared_focal_pose.t
Expand All @@ -110,4 +120,4 @@
print("--- Point-based Estimation Results ---")
print(f"Rotation Error: {err_R:.4f} degrees")
print(f"Translation Error: {err_t:.4f} degrees")
print(f"Focal Error: {(err_f * 100):.2f}%")
print(f"Focal Error: {(err_f * 100):.2f}%")
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