Spaces:
Running
Running
| # %BANNER_BEGIN% | |
| # --------------------------------------------------------------------- | |
| # %COPYRIGHT_BEGIN% | |
| # | |
| # Magic Leap, Inc. ("COMPANY") CONFIDENTIAL | |
| # | |
| # Unpublished Copyright (c) 2020 | |
| # Magic Leap, Inc., All Rights Reserved. | |
| # | |
| # NOTICE: All information contained herein is, and remains the property | |
| # of COMPANY. The intellectual and technical concepts contained herein | |
| # are proprietary to COMPANY and may be covered by U.S. and Foreign | |
| # Patents, patents in process, and are protected by trade secret or | |
| # copyright law. Dissemination of this information or reproduction of | |
| # this material is strictly forbidden unless prior written permission is | |
| # obtained from COMPANY. Access to the source code contained herein is | |
| # hereby forbidden to anyone except current COMPANY employees, managers | |
| # or contractors who have executed Confidentiality and Non-disclosure | |
| # agreements explicitly covering such access. | |
| # | |
| # The copyright notice above does not evidence any actual or intended | |
| # publication or disclosure of this source code, which includes | |
| # information that is confidential and/or proprietary, and is a trade | |
| # secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION, | |
| # PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS | |
| # SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS | |
| # STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND | |
| # INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE | |
| # CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS | |
| # TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE, | |
| # USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART. | |
| # | |
| # %COPYRIGHT_END% | |
| # ---------------------------------------------------------------------- | |
| # %AUTHORS_BEGIN% | |
| # | |
| # Originating Authors: Paul-Edouard Sarlin | |
| # | |
| # %AUTHORS_END% | |
| # --------------------------------------------------------------------*/ | |
| # %BANNER_END% | |
| # Adapted by Remi Pautrat, Philipp Lindenberger | |
| import torch | |
| from kornia.color import rgb_to_grayscale | |
| from torch import nn | |
| from .utils import Extractor | |
| def simple_nms(scores, nms_radius: int): | |
| """Fast Non-maximum suppression to remove nearby points""" | |
| assert nms_radius >= 0 | |
| def max_pool(x): | |
| return torch.nn.functional.max_pool2d( | |
| x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius | |
| ) | |
| zeros = torch.zeros_like(scores) | |
| max_mask = scores == max_pool(scores) | |
| for _ in range(2): | |
| supp_mask = max_pool(max_mask.float()) > 0 | |
| supp_scores = torch.where(supp_mask, zeros, scores) | |
| new_max_mask = supp_scores == max_pool(supp_scores) | |
| max_mask = max_mask | (new_max_mask & (~supp_mask)) | |
| return torch.where(max_mask, scores, zeros) | |
| def top_k_keypoints(keypoints, scores, k): | |
| if k >= len(keypoints): | |
| return keypoints, scores | |
| scores, indices = torch.topk(scores, k, dim=0, sorted=True) | |
| return keypoints[indices], scores | |
| def sample_descriptors(keypoints, descriptors, s: int = 8): | |
| """Interpolate descriptors at keypoint locations""" | |
| b, c, h, w = descriptors.shape | |
| keypoints = keypoints - s / 2 + 0.5 | |
| keypoints /= torch.tensor( | |
| [(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)], | |
| ).to( | |
| keypoints | |
| )[None] | |
| keypoints = keypoints * 2 - 1 # normalize to (-1, 1) | |
| args = {"align_corners": True} if torch.__version__ >= "1.3" else {} | |
| descriptors = torch.nn.functional.grid_sample( | |
| descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", **args | |
| ) | |
| descriptors = torch.nn.functional.normalize( | |
| descriptors.reshape(b, c, -1), p=2, dim=1 | |
| ) | |
| return descriptors | |
| class SuperPoint(Extractor): | |
| """SuperPoint Convolutional Detector and Descriptor | |
| SuperPoint: Self-Supervised Interest Point Detection and | |
| Description. Daniel DeTone, Tomasz Malisiewicz, and Andrew | |
| Rabinovich. In CVPRW, 2019. https://arxiv.org/abs/1712.07629 | |
| """ | |
| default_conf = { | |
| "descriptor_dim": 256, | |
| "nms_radius": 4, | |
| "max_num_keypoints": None, | |
| "detection_threshold": 0.0005, | |
| "remove_borders": 4, | |
| } | |
| preprocess_conf = { | |
| "resize": 1024, | |
| } | |
| required_data_keys = ["image"] | |
| def __init__(self, **conf): | |
| super().__init__(**conf) # Update with default configuration. | |
| self.relu = nn.ReLU(inplace=True) | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| c1, c2, c3, c4, c5 = 64, 64, 128, 128, 256 | |
| self.conv1a = nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1) | |
| self.conv1b = nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1) | |
| self.conv2a = nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1) | |
| self.conv2b = nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1) | |
| self.conv3a = nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1) | |
| self.conv3b = nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1) | |
| self.conv4a = nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1) | |
| self.conv4b = nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1) | |
| self.convPa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) | |
| self.convPb = nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0) | |
| self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) | |
| self.convDb = nn.Conv2d( | |
| c5, self.conf.descriptor_dim, kernel_size=1, stride=1, padding=0 | |
| ) | |
| url = "https://github.com/cvg/LightGlue/releases/download/v0.1_arxiv/superpoint_v1.pth" # noqa | |
| self.load_state_dict(torch.hub.load_state_dict_from_url(url)) | |
| if self.conf.max_num_keypoints is not None and self.conf.max_num_keypoints <= 0: | |
| raise ValueError("max_num_keypoints must be positive or None") | |
| def forward(self, data: dict) -> dict: | |
| """Compute keypoints, scores, descriptors for image""" | |
| for key in self.required_data_keys: | |
| assert key in data, f"Missing key {key} in data" | |
| image = data["image"] | |
| if image.shape[1] == 3: | |
| image = rgb_to_grayscale(image) | |
| # Shared Encoder | |
| x = self.relu(self.conv1a(image)) | |
| x = self.relu(self.conv1b(x)) | |
| x = self.pool(x) | |
| x = self.relu(self.conv2a(x)) | |
| x = self.relu(self.conv2b(x)) | |
| x = self.pool(x) | |
| x = self.relu(self.conv3a(x)) | |
| x = self.relu(self.conv3b(x)) | |
| x = self.pool(x) | |
| x = self.relu(self.conv4a(x)) | |
| x = self.relu(self.conv4b(x)) | |
| # Compute the dense keypoint scores | |
| cPa = self.relu(self.convPa(x)) | |
| scores = self.convPb(cPa) | |
| scores = torch.nn.functional.softmax(scores, 1)[:, :-1] | |
| b, _, h, w = scores.shape | |
| scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8) | |
| scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h * 8, w * 8) | |
| scores = simple_nms(scores, self.conf.nms_radius) | |
| # Discard keypoints near the image borders | |
| if self.conf.remove_borders: | |
| pad = self.conf.remove_borders | |
| scores[:, :pad] = -1 | |
| scores[:, :, :pad] = -1 | |
| scores[:, -pad:] = -1 | |
| scores[:, :, -pad:] = -1 | |
| # Extract keypoints | |
| best_kp = torch.where(scores > self.conf.detection_threshold) | |
| scores = scores[best_kp] | |
| # Separate into batches | |
| keypoints = [ | |
| torch.stack(best_kp[1:3], dim=-1)[best_kp[0] == i] for i in range(b) | |
| ] | |
| scores = [scores[best_kp[0] == i] for i in range(b)] | |
| # Keep the k keypoints with highest score | |
| if self.conf.max_num_keypoints is not None: | |
| keypoints, scores = list( | |
| zip( | |
| *[ | |
| top_k_keypoints(k, s, self.conf.max_num_keypoints) | |
| for k, s in zip(keypoints, scores) | |
| ] | |
| ) | |
| ) | |
| # Convert (h, w) to (x, y) | |
| keypoints = [torch.flip(k, [1]).float() for k in keypoints] | |
| # Compute the dense descriptors | |
| cDa = self.relu(self.convDa(x)) | |
| descriptors = self.convDb(cDa) | |
| descriptors = torch.nn.functional.normalize(descriptors, p=2, dim=1) | |
| # Extract descriptors | |
| descriptors = [ | |
| sample_descriptors(k[None], d[None], 8)[0] | |
| for k, d in zip(keypoints, descriptors) | |
| ] | |
| return { | |
| "keypoints": torch.stack(keypoints, 0), | |
| "keypoint_scores": torch.stack(scores, 0), | |
| "descriptors": torch.stack(descriptors, 0).transpose(-1, -2).contiguous(), | |
| } | |