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| import numpy as np | |
| import torch | |
| import torchvision.transforms.functional as F | |
| from types import SimpleNamespace | |
| from .extract_features import read_image, resize_image | |
| import cv2 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| confs = { | |
| # Best quality but loads of points. Only use for small scenes | |
| "loftr": { | |
| "output": "matches-loftr", | |
| "model": { | |
| "name": "loftr", | |
| "weights": "outdoor", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": { | |
| "grayscale": True, | |
| "resize_max": 1024, | |
| "dfactor": 8, | |
| "width": 640, | |
| "height": 480, | |
| "force_resize": True, | |
| }, | |
| "max_error": 1, # max error for assigned keypoints (in px) | |
| "cell_size": 1, # size of quantization patch (max 1 kp/patch) | |
| }, | |
| # "loftr_quadtree": { | |
| # "output": "matches-loftr-quadtree", | |
| # "model": { | |
| # "name": "quadtree", | |
| # "weights": "outdoor", | |
| # "max_keypoints": 2000, | |
| # "match_threshold": 0.2, | |
| # }, | |
| # "preprocessing": { | |
| # "grayscale": True, | |
| # "resize_max": 1024, | |
| # "dfactor": 8, | |
| # "width": 640, | |
| # "height": 480, | |
| # "force_resize": True, | |
| # }, | |
| # "max_error": 1, # max error for assigned keypoints (in px) | |
| # "cell_size": 1, # size of quantization patch (max 1 kp/patch) | |
| # }, | |
| "cotr": { | |
| "output": "matches-cotr", | |
| "model": { | |
| "name": "cotr", | |
| "weights": "out/default", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": { | |
| "grayscale": False, | |
| "resize_max": 1024, | |
| "dfactor": 8, | |
| "width": 640, | |
| "height": 480, | |
| "force_resize": True, | |
| }, | |
| "max_error": 1, # max error for assigned keypoints (in px) | |
| "cell_size": 1, # size of quantization patch (max 1 kp/patch) | |
| }, | |
| # Semi-scalable loftr which limits detected keypoints | |
| "loftr_aachen": { | |
| "output": "matches-loftr_aachen", | |
| "model": { | |
| "name": "loftr", | |
| "weights": "outdoor", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": {"grayscale": True, "resize_max": 1024, "dfactor": 8}, | |
| "max_error": 2, # max error for assigned keypoints (in px) | |
| "cell_size": 8, # size of quantization patch (max 1 kp/patch) | |
| }, | |
| # Use for matching superpoint feats with loftr | |
| "loftr_superpoint": { | |
| "output": "matches-loftr_aachen", | |
| "model": { | |
| "name": "loftr", | |
| "weights": "outdoor", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": {"grayscale": True, "resize_max": 1024, "dfactor": 8}, | |
| "max_error": 4, # max error for assigned keypoints (in px) | |
| "cell_size": 4, # size of quantization patch (max 1 kp/patch) | |
| }, | |
| # Use topicfm for matching feats | |
| "topicfm": { | |
| "output": "matches-topicfm", | |
| "model": { | |
| "name": "topicfm", | |
| "weights": "outdoor", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": { | |
| "grayscale": True, | |
| "force_resize": True, | |
| "resize_max": 1024, | |
| "dfactor": 8, | |
| "width": 640, | |
| "height": 480, | |
| }, | |
| }, | |
| # Use aspanformer for matching feats | |
| "aspanformer": { | |
| "output": "matches-aspanformer", | |
| "model": { | |
| "name": "aspanformer", | |
| "weights": "outdoor", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": { | |
| "grayscale": True, | |
| "force_resize": True, | |
| "resize_max": 1024, | |
| "width": 640, | |
| "height": 480, | |
| "dfactor": 8, | |
| }, | |
| }, | |
| "xfeat_dense": { | |
| "output": "matches-xfeat_dense", | |
| "model": { | |
| "name": "xfeat_dense", | |
| "max_keypoints": 8000, | |
| }, | |
| "preprocessing": { | |
| "grayscale": False, | |
| "force_resize": False, | |
| "resize_max": 1024, | |
| "width": 640, | |
| "height": 480, | |
| "dfactor": 8, | |
| }, | |
| }, | |
| "dkm": { | |
| "output": "matches-dkm", | |
| "model": { | |
| "name": "dkm", | |
| "weights": "outdoor", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": { | |
| "grayscale": False, | |
| "force_resize": True, | |
| "resize_max": 1024, | |
| "width": 80, | |
| "height": 60, | |
| "dfactor": 8, | |
| }, | |
| }, | |
| "roma": { | |
| "output": "matches-roma", | |
| "model": { | |
| "name": "roma", | |
| "weights": "outdoor", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": { | |
| "grayscale": False, | |
| "force_resize": True, | |
| "resize_max": 1024, | |
| "width": 320, | |
| "height": 240, | |
| "dfactor": 8, | |
| }, | |
| }, | |
| "dedode_sparse": { | |
| "output": "matches-dedode", | |
| "model": { | |
| "name": "dedode", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| "dense": False, | |
| }, | |
| "preprocessing": { | |
| "grayscale": False, | |
| "force_resize": True, | |
| "resize_max": 1024, | |
| "width": 768, | |
| "height": 768, | |
| "dfactor": 8, | |
| }, | |
| }, | |
| "sold2": { | |
| "output": "matches-sold2", | |
| "model": { | |
| "name": "sold2", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| }, | |
| "preprocessing": { | |
| "grayscale": True, | |
| "force_resize": True, | |
| "resize_max": 1024, | |
| "width": 640, | |
| "height": 480, | |
| "dfactor": 8, | |
| }, | |
| }, | |
| "gluestick": { | |
| "output": "matches-gluestick", | |
| "model": { | |
| "name": "gluestick", | |
| "use_lines": True, | |
| "max_keypoints": 1000, | |
| "max_lines": 300, | |
| "force_num_keypoints": False, | |
| }, | |
| "preprocessing": { | |
| "grayscale": True, | |
| "force_resize": True, | |
| "resize_max": 1024, | |
| "width": 640, | |
| "height": 480, | |
| "dfactor": 8, | |
| }, | |
| }, | |
| } | |
| def scale_keypoints(kpts, scale): | |
| if np.any(scale != 1.0): | |
| kpts *= kpts.new_tensor(scale) | |
| return kpts | |
| def scale_lines(lines, scale): | |
| if np.any(scale != 1.0): | |
| lines *= lines.new_tensor(scale) | |
| return lines | |
| def match(model, path_0, path_1, conf): | |
| default_conf = { | |
| "grayscale": True, | |
| "resize_max": 1024, | |
| "dfactor": 8, | |
| "cache_images": False, | |
| "force_resize": False, | |
| "width": 320, | |
| "height": 240, | |
| } | |
| def preprocess(image: np.ndarray): | |
| image = image.astype(np.float32, copy=False) | |
| size = image.shape[:2][::-1] | |
| scale = np.array([1.0, 1.0]) | |
| if conf.resize_max: | |
| scale = conf.resize_max / max(size) | |
| if scale < 1.0: | |
| size_new = tuple(int(round(x * scale)) for x in size) | |
| image = resize_image(image, size_new, "cv2_area") | |
| scale = np.array(size) / np.array(size_new) | |
| if conf.force_resize: | |
| size = image.shape[:2][::-1] | |
| image = resize_image(image, (conf.width, conf.height), "cv2_area") | |
| size_new = (conf.width, conf.height) | |
| scale = np.array(size) / np.array(size_new) | |
| if conf.grayscale: | |
| assert image.ndim == 2, image.shape | |
| image = image[None] | |
| else: | |
| image = image.transpose((2, 0, 1)) # HxWxC to CxHxW | |
| image = torch.from_numpy(image / 255.0).float() | |
| # assure that the size is divisible by dfactor | |
| size_new = tuple( | |
| map( | |
| lambda x: int(x // conf.dfactor * conf.dfactor), | |
| image.shape[-2:], | |
| ) | |
| ) | |
| image = F.resize(image, size=size_new, antialias=True) | |
| scale = np.array(size) / np.array(size_new)[::-1] | |
| return image, scale | |
| conf = SimpleNamespace(**{**default_conf, **conf}) | |
| image0 = read_image(path_0, conf.grayscale) | |
| image1 = read_image(path_1, conf.grayscale) | |
| image0, scale0 = preprocess(image0) | |
| image1, scale1 = preprocess(image1) | |
| image0 = image0.to(device)[None] | |
| image1 = image1.to(device)[None] | |
| pred = model({"image0": image0, "image1": image1}) | |
| # Rescale keypoints and move to cpu | |
| kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"] | |
| kpts0 = scale_keypoints(kpts0 + 0.5, scale0) - 0.5 | |
| kpts1 = scale_keypoints(kpts1 + 0.5, scale1) - 0.5 | |
| ret = { | |
| "image0": image0.squeeze().cpu().numpy(), | |
| "image1": image1.squeeze().cpu().numpy(), | |
| "keypoints0": kpts0.cpu().numpy(), | |
| "keypoints1": kpts1.cpu().numpy(), | |
| } | |
| if "mconf" in pred.keys(): | |
| ret["mconf"] = pred["mconf"].cpu().numpy() | |
| return ret | |
| def match_images(model, image_0, image_1, conf, device="cpu"): | |
| default_conf = { | |
| "grayscale": True, | |
| "resize_max": 1024, | |
| "dfactor": 8, | |
| "cache_images": False, | |
| "force_resize": False, | |
| "width": 320, | |
| "height": 240, | |
| } | |
| def preprocess(image: np.ndarray): | |
| image = image.astype(np.float32, copy=False) | |
| size = image.shape[:2][::-1] | |
| scale = np.array([1.0, 1.0]) | |
| if conf.resize_max: | |
| scale = conf.resize_max / max(size) | |
| if scale < 1.0: | |
| size_new = tuple(int(round(x * scale)) for x in size) | |
| image = resize_image(image, size_new, "cv2_area") | |
| scale = np.array(size) / np.array(size_new) | |
| if conf.force_resize: | |
| size = image.shape[:2][::-1] | |
| image = resize_image(image, (conf.width, conf.height), "cv2_area") | |
| size_new = (conf.width, conf.height) | |
| scale = np.array(size) / np.array(size_new) | |
| if conf.grayscale: | |
| assert image.ndim == 2, image.shape | |
| image = image[None] | |
| else: | |
| image = image.transpose((2, 0, 1)) # HxWxC to CxHxW | |
| image = torch.from_numpy(image / 255.0).float() | |
| # assure that the size is divisible by dfactor | |
| size_new = tuple( | |
| map( | |
| lambda x: int(x // conf.dfactor * conf.dfactor), | |
| image.shape[-2:], | |
| ) | |
| ) | |
| image = F.resize(image, size=size_new) | |
| scale = np.array(size) / np.array(size_new)[::-1] | |
| return image, scale | |
| conf = SimpleNamespace(**{**default_conf, **conf}) | |
| if len(image_0.shape) == 3 and conf.grayscale: | |
| image0 = cv2.cvtColor(image_0, cv2.COLOR_RGB2GRAY) | |
| else: | |
| image0 = image_0 | |
| if len(image_0.shape) == 3 and conf.grayscale: | |
| image1 = cv2.cvtColor(image_1, cv2.COLOR_RGB2GRAY) | |
| else: | |
| image1 = image_1 | |
| # comment following lines, image is always RGB mode | |
| # if not conf.grayscale and len(image0.shape) == 3: | |
| # image0 = image0[:, :, ::-1] # BGR to RGB | |
| # if not conf.grayscale and len(image1.shape) == 3: | |
| # image1 = image1[:, :, ::-1] # BGR to RGB | |
| image0, scale0 = preprocess(image0) | |
| image1, scale1 = preprocess(image1) | |
| image0 = image0.to(device)[None] | |
| image1 = image1.to(device)[None] | |
| pred = model({"image0": image0, "image1": image1}) | |
| s0 = np.array(image_0.shape[:2][::-1]) / np.array(image0.shape[-2:][::-1]) | |
| s1 = np.array(image_1.shape[:2][::-1]) / np.array(image1.shape[-2:][::-1]) | |
| # Rescale keypoints and move to cpu | |
| if "keypoints0" in pred.keys() and "keypoints1" in pred.keys(): | |
| kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"] | |
| kpts0_origin = scale_keypoints(kpts0 + 0.5, s0) - 0.5 | |
| kpts1_origin = scale_keypoints(kpts1 + 0.5, s1) - 0.5 | |
| ret = { | |
| "image0": image0.squeeze().cpu().numpy(), | |
| "image1": image1.squeeze().cpu().numpy(), | |
| "image0_orig": image_0, | |
| "image1_orig": image_1, | |
| "keypoints0": kpts0_origin.cpu().numpy(), | |
| "keypoints1": kpts1_origin.cpu().numpy(), | |
| "keypoints0_orig": kpts0_origin.cpu().numpy(), | |
| "keypoints1_orig": kpts1_origin.cpu().numpy(), | |
| "original_size0": np.array(image_0.shape[:2][::-1]), | |
| "original_size1": np.array(image_1.shape[:2][::-1]), | |
| "new_size0": np.array(image0.shape[-2:][::-1]), | |
| "new_size1": np.array(image1.shape[-2:][::-1]), | |
| "scale0": s0, | |
| "scale1": s1, | |
| } | |
| if "mconf" in pred.keys(): | |
| ret["mconf"] = pred["mconf"].cpu().numpy() | |
| elif "scores" in pred.keys(): # adapting loftr | |
| ret["mconf"] = pred["scores"].cpu().numpy() | |
| else: | |
| ret["mconf"] = np.ones_like(kpts0.cpu().numpy()[:, 0]) | |
| if "lines0" in pred.keys() and "lines1" in pred.keys(): | |
| if "keypoints0" in pred.keys() and "keypoints1" in pred.keys(): | |
| kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"] | |
| kpts0_origin = scale_keypoints(kpts0 + 0.5, s0) - 0.5 | |
| kpts1_origin = scale_keypoints(kpts1 + 0.5, s1) - 0.5 | |
| kpts0_origin = kpts0_origin.cpu().numpy() | |
| kpts1_origin = kpts1_origin.cpu().numpy() | |
| else: | |
| kpts0_origin, kpts1_origin = ( | |
| None, | |
| None, | |
| ) # np.zeros([0]), np.zeros([0]) | |
| lines0, lines1 = pred["lines0"], pred["lines1"] | |
| lines0_raw, lines1_raw = pred["raw_lines0"], pred["raw_lines1"] | |
| lines0_raw = torch.from_numpy(lines0_raw.copy()) | |
| lines1_raw = torch.from_numpy(lines1_raw.copy()) | |
| lines0_raw = scale_lines(lines0_raw + 0.5, s0) - 0.5 | |
| lines1_raw = scale_lines(lines1_raw + 0.5, s1) - 0.5 | |
| lines0 = torch.from_numpy(lines0.copy()) | |
| lines1 = torch.from_numpy(lines1.copy()) | |
| lines0 = scale_lines(lines0 + 0.5, s0) - 0.5 | |
| lines1 = scale_lines(lines1 + 0.5, s1) - 0.5 | |
| ret = { | |
| "image0_orig": image_0, | |
| "image1_orig": image_1, | |
| "line0": lines0_raw.cpu().numpy(), | |
| "line1": lines1_raw.cpu().numpy(), | |
| "line0_orig": lines0.cpu().numpy(), | |
| "line1_orig": lines1.cpu().numpy(), | |
| "line_keypoints0_orig": kpts0_origin, | |
| "line_keypoints1_orig": kpts1_origin, | |
| } | |
| del pred | |
| torch.cuda.empty_cache() | |
| return ret | |