import os import cv2 import torch import random import numpy as np import gradio as gr from pathlib import Path from itertools import combinations from typing import Callable, Dict, Any, Optional, Tuple, List, Union from hloc import matchers, extractors, logger from hloc.utils.base_model import dynamic_load from hloc import match_dense, match_features, extract_features from hloc.utils.viz import add_text, plot_keypoints from .viz import ( draw_matches, fig2im, plot_images, display_matches, plot_color_line_matches, ) import time import matplotlib.pyplot as plt device = "cuda" if torch.cuda.is_available() else "cpu" ROOT = Path(__file__).parent.parent DEFAULT_SETTING_THRESHOLD = 0.1 DEFAULT_SETTING_MAX_FEATURES = 2000 DEFAULT_DEFAULT_KEYPOINT_THRESHOLD = 0.01 DEFAULT_ENABLE_RANSAC = True DEFAULT_RANSAC_METHOD = "USAC_MAGSAC" DEFAULT_RANSAC_REPROJ_THRESHOLD = 8 DEFAULT_RANSAC_CONFIDENCE = 0.999 DEFAULT_RANSAC_MAX_ITER = 10000 DEFAULT_MIN_NUM_MATCHES = 4 DEFAULT_MATCHING_THRESHOLD = 0.2 DEFAULT_SETTING_GEOMETRY = "Homography" GRADIO_VERSION = gr.__version__.split(".")[0] MATCHER_ZOO = None def load_config(config_name: str) -> Dict[str, Any]: """ Load a YAML configuration file. Args: config_name: The path to the YAML configuration file. Returns: The configuration dictionary, with string keys and arbitrary values. """ import yaml with open(config_name, "r") as stream: try: config: Dict[str, Any] = yaml.safe_load(stream) except yaml.YAMLError as exc: logger.error(exc) return config def get_matcher_zoo( matcher_zoo: Dict[str, Dict[str, Union[str, bool]]] ) -> Dict[str, Dict[str, Union[Callable, bool]]]: """ Restore matcher configurations from a dictionary. Args: matcher_zoo: A dictionary with the matcher configurations, where the configuration is a dictionary as loaded from a YAML file. Returns: A dictionary with the matcher configurations, where the configuration is a function or a function instead of a string. """ matcher_zoo_restored = {} for k, v in matcher_zoo.items(): dense = v["dense"] if dense: matcher_zoo_restored[k] = { "matcher": match_dense.confs.get(v["matcher"]), "dense": dense, } else: matcher_zoo_restored[k] = { "feature": extract_features.confs.get(v["feature"]), "matcher": match_features.confs.get(v["matcher"]), "dense": dense, } return matcher_zoo_restored def get_model(match_conf: Dict[str, Any]): """ Load a matcher model from the provided configuration. Args: match_conf: A dictionary containing the model configuration. Returns: A matcher model instance. """ Model = dynamic_load(matchers, match_conf["model"]["name"]) model = Model(match_conf["model"]).eval().to(device) return model def get_feature_model(conf: Dict[str, Dict[str, Any]]): """ Load a feature extraction model from the provided configuration. Args: conf: A dictionary containing the model configuration. Returns: A feature extraction model instance. """ Model = dynamic_load(extractors, conf["model"]["name"]) model = Model(conf["model"]).eval().to(device) return model def gen_examples(): random.seed(1) example_matchers = [ "disk+lightglue", "loftr", "disk", "d2net", "topicfm", "superpoint+superglue", "disk+dualsoftmax", "roma", ] def gen_images_pairs(path: str, count: int = 5): imgs_list = [ os.path.join(path, file) for file in os.listdir(path) if file.lower().endswith((".jpg", ".jpeg", ".png")) ] pairs = list(combinations(imgs_list, 2)) selected = random.sample(range(len(pairs)), count) return [pairs[i] for i in selected] # image pair path path = ROOT / "datasets/sacre_coeur/mapping" pairs = gen_images_pairs(str(path), len(example_matchers)) match_setting_threshold = DEFAULT_SETTING_THRESHOLD match_setting_max_features = DEFAULT_SETTING_MAX_FEATURES detect_keypoints_threshold = DEFAULT_DEFAULT_KEYPOINT_THRESHOLD ransac_method = DEFAULT_RANSAC_METHOD ransac_reproj_threshold = DEFAULT_RANSAC_REPROJ_THRESHOLD ransac_confidence = DEFAULT_RANSAC_CONFIDENCE ransac_max_iter = DEFAULT_RANSAC_MAX_ITER input_lists = [] for pair, mt in zip(pairs, example_matchers): input_lists.append( [ pair[0], pair[1], match_setting_threshold, match_setting_max_features, detect_keypoints_threshold, mt, # enable_ransac, ransac_method, ransac_reproj_threshold, ransac_confidence, ransac_max_iter, ] ) return input_lists def filter_matches( pred: Dict[str, Any], ransac_method: str = DEFAULT_RANSAC_METHOD, ransac_reproj_threshold: float = DEFAULT_RANSAC_REPROJ_THRESHOLD, ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE, ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER, ) -> Dict[str, Any]: """ Filter matches using RANSAC. If keypoints are available, filter by keypoints. If lines are available, filter by lines. If both keypoints and lines are available, filter by keypoints. Args: pred (Dict[str, Any]): dict of matches, including original keypoints. ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD. ransac_reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD. ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE. ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER. Returns: Dict[str, Any]: filtered matches. """ mkpts0: Optional[np.ndarray] = None mkpts1: Optional[np.ndarray] = None feature_type: Optional[str] = None if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys(): mkpts0 = pred["keypoints0_orig"] mkpts1 = pred["keypoints1_orig"] feature_type = "KEYPOINT" elif ( "line_keypoints0_orig" in pred.keys() and "line_keypoints1_orig" in pred.keys() ): mkpts0 = pred["line_keypoints0_orig"] mkpts1 = pred["line_keypoints1_orig"] feature_type = "LINE" else: return pred if mkpts0 is None or mkpts0 is None: return pred if ransac_method not in ransac_zoo.keys(): ransac_method = DEFAULT_RANSAC_METHOD if len(mkpts0) < DEFAULT_MIN_NUM_MATCHES: return pred H, mask = cv2.findHomography( mkpts0, mkpts1, method=ransac_zoo[ransac_method], ransacReprojThreshold=ransac_reproj_threshold, confidence=ransac_confidence, maxIters=ransac_max_iter, ) mask = np.array(mask.ravel().astype("bool"), dtype="bool") if H is not None: if feature_type == "KEYPOINT": pred["keypoints0_orig"] = mkpts0[mask] pred["keypoints1_orig"] = mkpts1[mask] pred["mconf"] = pred["mconf"][mask] elif feature_type == "LINE": pred["line_keypoints0_orig"] = mkpts0[mask] pred["line_keypoints1_orig"] = mkpts1[mask] return pred def compute_geom( pred: Dict[str, Any], ransac_method: str = DEFAULT_RANSAC_METHOD, ransac_reproj_threshold: float = DEFAULT_RANSAC_REPROJ_THRESHOLD, ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE, ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER, ) -> Dict[str, List[float]]: """ Compute geometric information of matches, including Fundamental matrix, Homography matrix, and rectification matrices (if available). Args: pred (Dict[str, Any]): dict of matches, including original keypoints. ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD. ransac_reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to DEFAULT_RANSAC_REPROJ_THRESHOLD. ransac_confidence (float, optional): RANSAC confidence. Defaults to DEFAULT_RANSAC_CONFIDENCE. ransac_max_iter (int, optional): RANSAC maximum iterations. Defaults to DEFAULT_RANSAC_MAX_ITER. Returns: Dict[str, List[float]]: geometric information in form of a dict. """ mkpts0: Optional[np.ndarray] = None mkpts1: Optional[np.ndarray] = None if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys(): mkpts0 = pred["keypoints0_orig"] mkpts1 = pred["keypoints1_orig"] elif ( "line_keypoints0_orig" in pred.keys() and "line_keypoints1_orig" in pred.keys() ): mkpts0 = pred["line_keypoints0_orig"] mkpts1 = pred["line_keypoints1_orig"] if mkpts0 is not None and mkpts1 is not None: if len(mkpts0) < 2 * DEFAULT_MIN_NUM_MATCHES: return {} h1, w1, _ = pred["image0_orig"].shape geo_info: Dict[str, List[float]] = {} F, inliers = cv2.findFundamentalMat( mkpts0, mkpts1, method=ransac_zoo[ransac_method], ransacReprojThreshold=ransac_reproj_threshold, confidence=ransac_confidence, maxIters=ransac_max_iter, ) if F is not None: geo_info["Fundamental"] = F.tolist() H, _ = cv2.findHomography( mkpts1, mkpts0, method=ransac_zoo[ransac_method], ransacReprojThreshold=ransac_reproj_threshold, confidence=ransac_confidence, maxIters=ransac_max_iter, ) if H is not None: geo_info["Homography"] = H.tolist() try: _, H1, H2 = cv2.stereoRectifyUncalibrated( mkpts0.reshape(-1, 2), mkpts1.reshape(-1, 2), F, imgSize=(w1, h1), ) geo_info["H1"] = H1.tolist() geo_info["H2"] = H2.tolist() except cv2.error as e: logger.error(f"{e}, skip") return geo_info else: return {} def wrap_images( img0: np.ndarray, img1: np.ndarray, geo_info: Optional[Dict[str, List[float]]], geom_type: str, ) -> Tuple[Optional[str], Optional[Dict[str, List[float]]]]: """ Wraps the images based on the geometric transformation used to align them. Args: img0: numpy array representing the first image. img1: numpy array representing the second image. geo_info: dictionary containing the geometric transformation information. geom_type: type of geometric transformation used to align the images. Returns: A tuple containing a base64 encoded image string and a dictionary with the transformation matrix. """ h1, w1, _ = img0.shape h2, w2, _ = img1.shape result_matrix: Optional[np.ndarray] = None if geo_info is not None and len(geo_info) != 0: rectified_image0 = img0 rectified_image1 = None H = np.array(geo_info["Homography"]) F = np.array(geo_info["Fundamental"]) title: List[str] = [] if geom_type == "Homography": rectified_image1 = cv2.warpPerspective( img1, H, (img0.shape[1], img0.shape[0]) ) result_matrix = H title = ["Image 0", "Image 1 - warped"] elif geom_type == "Fundamental": H1, H2 = np.array(geo_info["H1"]), np.array(geo_info["H2"]) rectified_image0 = cv2.warpPerspective(img0, H1, (w1, h1)) rectified_image1 = cv2.warpPerspective(img1, H2, (w2, h2)) result_matrix = F title = ["Image 0 - warped", "Image 1 - warped"] else: print("Error: Unknown geometry type") fig = plot_images( [rectified_image0.squeeze(), rectified_image1.squeeze()], title, dpi=300, ) dictionary = { "row1": result_matrix[0].tolist(), "row2": result_matrix[1].tolist(), "row3": result_matrix[2].tolist(), } return fig2im(fig), dictionary else: return None, None def change_estimate_geom( input_image0: np.ndarray, input_image1: np.ndarray, matches_info: Dict[str, Any], choice: str, ) -> Tuple[Optional[np.ndarray], Optional[Dict[str, Any]]]: """ Changes the estimate of the geometric transformation used to align the images. Args: input_image0: First input image. input_image1: Second input image. matches_info: Dictionary containing information about the matches. choice: Type of geometric transformation to use ('Homography' or 'Fundamental') or 'No' to disable. Returns: A tuple containing the updated images and the updated matches info. """ if ( matches_info is None or len(matches_info) < 1 or "geom_info" not in matches_info.keys() ): return None, None geom_info: Dict[str, Any] = matches_info["geom_info"] wrapped_images: Optional[np.ndarray] = None if choice != "No": wrapped_images, _ = wrap_images( input_image0, input_image1, geom_info, choice ) return wrapped_images, matches_info else: return None, None def run_matching( image0: np.ndarray, image1: np.ndarray, match_threshold: float, extract_max_keypoints: int, keypoint_threshold: float, key: str, ransac_method: str = DEFAULT_RANSAC_METHOD, ransac_reproj_threshold: int = DEFAULT_RANSAC_REPROJ_THRESHOLD, ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE, ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER, choice_estimate_geom: str = DEFAULT_SETTING_GEOMETRY, matcher_zoo: Dict[str, Any] = None, ) -> Tuple[ np.ndarray, np.ndarray, np.ndarray, Dict[str, int], Dict[str, Dict[str, Any]], Dict[str, Dict[str, float]], np.ndarray, ]: """Match two images using the given parameters. Args: image0 (np.ndarray): RGB image 0. image1 (np.ndarray): RGB image 1. match_threshold (float): match threshold. extract_max_keypoints (int): number of keypoints to extract. keypoint_threshold (float): keypoint threshold. key (str): key of the model to use. ransac_method (str, optional): RANSAC method to use. ransac_reproj_threshold (int, optional): RANSAC reprojection threshold. ransac_confidence (float, optional): RANSAC confidence level. ransac_max_iter (int, optional): RANSAC maximum number of iterations. choice_estimate_geom (str, optional): setting of geometry estimation. Returns: tuple: - output_keypoints (np.ndarray): image with keypoints. - output_matches_raw (np.ndarray): image with raw matches. - output_matches_ransac (np.ndarray): image with RANSAC matches. - num_matches (Dict[str, int]): number of raw and RANSAC matches. - configs (Dict[str, Dict[str, Any]]): match and feature extraction configs. - geom_info (Dict[str, Dict[str, float]]): geometry information. - output_wrapped (np.ndarray): wrapped images. """ # image0 and image1 is RGB mode if image0 is None or image1 is None: raise gr.Error("Error: No images found! Please upload two images.") # init output output_keypoints = None output_matches_raw = None output_matches_ransac = None model = matcher_zoo[key] match_conf = model["matcher"] # update match config match_conf["model"]["match_threshold"] = match_threshold match_conf["model"]["max_keypoints"] = extract_max_keypoints t1 = time.time() matcher = get_model(match_conf) if model["dense"]: pred = match_dense.match_images( matcher, image0, image1, match_conf["preprocessing"], device=device ) del matcher extract_conf = None else: extract_conf = model["feature"] # update extract config extract_conf["model"]["max_keypoints"] = extract_max_keypoints extract_conf["model"]["keypoint_threshold"] = keypoint_threshold extractor = get_feature_model(extract_conf) pred0 = extract_features.extract( extractor, image0, extract_conf["preprocessing"] ) pred1 = extract_features.extract( extractor, image1, extract_conf["preprocessing"] ) pred = match_features.match_images(matcher, pred0, pred1) del extractor # plot images with keypoints titles = [ "Image 0 - Keypoints", "Image 1 - Keypoints", ] output_keypoints = plot_images([image0, image1], titles=titles, dpi=300) if "keypoints0" in pred.keys() and "keypoints1" in pred.keys(): plot_keypoints([pred["keypoints0"], pred["keypoints1"]]) text = ( f"# keypoints0: {len(pred['keypoints0'])} \n" + f"# keypoints1: {len(pred['keypoints1'])}" ) add_text(0, text, fs=15) output_keypoints = fig2im(output_keypoints) # plot images with raw matches titles = [ "Image 0 - Raw matched keypoints", "Image 1 - Raw matched keypoints", ] output_matches_raw, num_matches_raw = display_matches(pred, titles=titles) # if enable_ransac: filter_matches( pred, ransac_method=ransac_method, ransac_reproj_threshold=ransac_reproj_threshold, ransac_confidence=ransac_confidence, ransac_max_iter=ransac_max_iter, ) # plot images with ransac matches titles = [ "Image 0 - Ransac matched keypoints", "Image 1 - Ransac matched keypoints", ] output_matches_ransac, num_matches_ransac = display_matches( pred, titles=titles ) # plot wrapped images geom_info = compute_geom(pred) output_wrapped, _ = change_estimate_geom( pred["image0_orig"], pred["image1_orig"], {"geom_info": geom_info}, choice_estimate_geom, ) plt.close("all") del pred logger.info(f"TOTAL time: {time.time()-t1:.3f}s") return ( output_keypoints, output_matches_raw, output_matches_ransac, { "number raw matches": num_matches_raw, "number ransac matches": num_matches_ransac, }, { "match_conf": match_conf, "extractor_conf": extract_conf, }, { "geom_info": geom_info, }, output_wrapped, ) # @ref: https://docs.opencv.org/4.x/d0/d74/md__build_4_x-contrib_docs-lin64_opencv_doc_tutorials_calib3d_usac.html # AND: https://opencv.org/blog/2021/06/09/evaluating-opencvs-new-ransacs ransac_zoo = { "RANSAC": cv2.RANSAC, "USAC_MAGSAC": cv2.USAC_MAGSAC, "USAC_DEFAULT": cv2.USAC_DEFAULT, "USAC_FM_8PTS": cv2.USAC_FM_8PTS, "USAC_PROSAC": cv2.USAC_PROSAC, "USAC_FAST": cv2.USAC_FAST, "USAC_ACCURATE": cv2.USAC_ACCURATE, "USAC_PARALLEL": cv2.USAC_PARALLEL, }