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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 | |
| try: | |
| import pycolmap | |
| except ImportError: | |
| pycolmap = None | |
| from .utils import Extractor | |
| 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(Extractor): | |
| default_conf = { | |
| "rootsift": True, | |
| "nms_radius": 0, # None to disable filtering entirely. | |
| "max_num_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, | |
| } | |
| preprocess_conf = { | |
| "resize": 1024, | |
| } | |
| required_data_keys = ["image"] | |
| def __init__(self, **conf): | |
| super().__init__(**conf) # Update with default configuration. | |
| 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_num_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_num_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)}}}." | |
| ) | |
| 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["keypoint_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("keypoint_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_num_keypoints | |
| if num_points is not None and len(pred["keypoints"]) > num_points: | |
| indices = torch.topk(pred["keypoint_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"]) | |
| return pred | |