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) }, # 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 @torch.no_grad() 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