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import warnings
import cv2
import numpy as np
import torch
from kornia.color import rgb_to_grayscale
from packaging import version
from omegaconf import OmegaConf
try:
import pycolmap
except ImportError:
pycolmap = None
from hloc import logger
from ..utils.base_model import BaseModel
def filter_dog_point(
points, scales, angles, image_shape, nms_radius, scores=None
):
h, w = image_shape
ij = np.round(points - 0.5).astype(int).T[::-1]
# Remove duplicate points (identical coordinates).
# Pick highest scale or score
s = scales if scores is None else scores
buffer = np.zeros((h, w))
np.maximum.at(buffer, tuple(ij), s)
keep = np.where(buffer[tuple(ij)] == s)[0]
# Pick lowest angle (arbitrary).
ij = ij[:, keep]
buffer[:] = np.inf
o_abs = np.abs(angles[keep])
np.minimum.at(buffer, tuple(ij), o_abs)
mask = buffer[tuple(ij)] == o_abs
ij = ij[:, mask]
keep = keep[mask]
if nms_radius > 0:
# Apply NMS on the remaining points
buffer[:] = 0
buffer[tuple(ij)] = s[keep] # scores or scale
local_max = torch.nn.functional.max_pool2d(
torch.from_numpy(buffer).unsqueeze(0),
kernel_size=nms_radius * 2 + 1,
stride=1,
padding=nms_radius,
).squeeze(0)
is_local_max = buffer == local_max.numpy()
keep = keep[is_local_max[tuple(ij)]]
return keep
def sift_to_rootsift(x: torch.Tensor, eps=1e-6) -> torch.Tensor:
x = torch.nn.functional.normalize(x, p=1, dim=-1, eps=eps)
x.clip_(min=eps).sqrt_()
return torch.nn.functional.normalize(x, p=2, dim=-1, eps=eps)
def run_opencv_sift(features: cv2.Feature2D, image: np.ndarray) -> np.ndarray:
"""
Detect keypoints using OpenCV Detector.
Optionally, perform description.
Args:
features: OpenCV based keypoints detector and descriptor
image: Grayscale image of uint8 data type
Returns:
keypoints: 1D array of detected cv2.KeyPoint
scores: 1D array of responses
descriptors: 1D array of descriptors
"""
detections, descriptors = features.detectAndCompute(image, None)
points = np.array([k.pt for k in detections], dtype=np.float32)
scores = np.array([k.response for k in detections], dtype=np.float32)
scales = np.array([k.size for k in detections], dtype=np.float32)
angles = np.deg2rad(
np.array([k.angle for k in detections], dtype=np.float32)
)
return points, scores, scales, angles, descriptors
class SIFT(BaseModel):
default_conf = {
"rootsift": True,
"nms_radius": 0, # None to disable filtering entirely.
"max_keypoints": 4096,
"backend": "opencv", # in {opencv, pycolmap, pycolmap_cpu, pycolmap_cuda}
"detection_threshold": 0.0066667, # from COLMAP
"edge_threshold": 10,
"first_octave": -1, # only used by pycolmap, the default of COLMAP
"num_octaves": 4,
}
required_data_keys = ["image"]
def _init(self, conf):
self.conf = OmegaConf.create(self.conf)
backend = self.conf.backend
if backend.startswith("pycolmap"):
if pycolmap is None:
raise ImportError(
"Cannot find module pycolmap: install it with pip"
"or use backend=opencv."
)
options = {
"peak_threshold": self.conf.detection_threshold,
"edge_threshold": self.conf.edge_threshold,
"first_octave": self.conf.first_octave,
"num_octaves": self.conf.num_octaves,
"normalization": pycolmap.Normalization.L2, # L1_ROOT is buggy.
}
device = (
"auto"
if backend == "pycolmap"
else backend.replace("pycolmap_", "")
)
if (
backend == "pycolmap_cpu" or not pycolmap.has_cuda
) and pycolmap.__version__ < "0.5.0":
warnings.warn(
"The pycolmap CPU SIFT is buggy in version < 0.5.0, "
"consider upgrading pycolmap or use the CUDA version.",
stacklevel=1,
)
else:
options["max_num_features"] = self.conf.max_keypoints
self.sift = pycolmap.Sift(options=options, device=device)
elif backend == "opencv":
self.sift = cv2.SIFT_create(
contrastThreshold=self.conf.detection_threshold,
nfeatures=self.conf.max_keypoints,
edgeThreshold=self.conf.edge_threshold,
nOctaveLayers=self.conf.num_octaves,
)
else:
backends = {"opencv", "pycolmap", "pycolmap_cpu", "pycolmap_cuda"}
raise ValueError(
f"Unknown backend: {backend} not in "
f"{{{','.join(backends)}}}."
)
logger.info(f"Load SIFT model done.")
def extract_single_image(self, image: torch.Tensor):
image_np = image.cpu().numpy().squeeze(0)
if self.conf.backend.startswith("pycolmap"):
if version.parse(pycolmap.__version__) >= version.parse("0.5.0"):
detections, descriptors = self.sift.extract(image_np)
scores = None # Scores are not exposed by COLMAP anymore.
else:
detections, scores, descriptors = self.sift.extract(image_np)
keypoints = detections[:, :2] # Keep only (x, y).
scales, angles = detections[:, -2:].T
if scores is not None and (
self.conf.backend == "pycolmap_cpu" or not pycolmap.has_cuda
):
# Set the scores as a combination of abs. response and scale.
scores = np.abs(scores) * scales
elif self.conf.backend == "opencv":
# TODO: Check if opencv keypoints are already in corner convention
keypoints, scores, scales, angles, descriptors = run_opencv_sift(
self.sift, (image_np * 255.0).astype(np.uint8)
)
pred = {
"keypoints": keypoints,
"scales": scales,
"oris": angles,
"descriptors": descriptors,
}
if scores is not None:
pred["scores"] = scores
# sometimes pycolmap returns points outside the image. We remove them
if self.conf.backend.startswith("pycolmap"):
is_inside = (
pred["keypoints"] + 0.5 < np.array([image_np.shape[-2:][::-1]])
).all(-1)
pred = {k: v[is_inside] for k, v in pred.items()}
if self.conf.nms_radius is not None:
keep = filter_dog_point(
pred["keypoints"],
pred["scales"],
pred["oris"],
image_np.shape,
self.conf.nms_radius,
scores=pred.get("scores"),
)
pred = {k: v[keep] for k, v in pred.items()}
pred = {k: torch.from_numpy(v) for k, v in pred.items()}
if scores is not None:
# Keep the k keypoints with highest score
num_points = self.conf.max_keypoints
if num_points is not None and len(pred["keypoints"]) > num_points:
indices = torch.topk(pred["scores"], num_points).indices
pred = {k: v[indices] for k, v in pred.items()}
return pred
def _forward(self, data: dict) -> dict:
image = data["image"]
if image.shape[1] == 3:
image = rgb_to_grayscale(image)
device = image.device
image = image.cpu()
pred = []
for k in range(len(image)):
img = image[k]
if "image_size" in data.keys():
# avoid extracting points in padded areas
w, h = data["image_size"][k]
img = img[:, :h, :w]
p = self.extract_single_image(img)
pred.append(p)
pred = {
k: torch.stack([p[k] for p in pred], 0).to(device) for k in pred[0]
}
if self.conf.rootsift:
pred["descriptors"] = sift_to_rootsift(pred["descriptors"])
pred["descriptors"] = pred["descriptors"].permute(0, 2, 1)
pred["keypoint_scores"] = pred["scores"].clone()
return pred
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