import os import pickle import random import shutil import time import warnings from itertools import combinations from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Tuple, Union import cv2 import gradio as gr import matplotlib.pyplot as plt import numpy as np import poselib import psutil from PIL import Image from hloc import ( DEVICE, extract_features, extractors, logger, match_dense, match_features, matchers, ) from hloc.utils.base_model import dynamic_load from .viz import display_keypoints, display_matches, fig2im, plot_images warnings.simplefilter("ignore") ROOT = Path(__file__).parent.parent # some default values DEFAULT_SETTING_THRESHOLD = 0.1 DEFAULT_SETTING_MAX_FEATURES = 2000 DEFAULT_DEFAULT_KEYPOINT_THRESHOLD = 0.01 DEFAULT_ENABLE_RANSAC = True DEFAULT_RANSAC_METHOD = "CV2_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 class ModelCache: def __init__(self, max_memory_size: int = 8): self.max_memory_size = max_memory_size self.current_memory_size = 0 self.model_dict = {} self.model_timestamps = [] def cache_model(self, model_key, model_loader_func, model_conf): if model_key in self.model_dict: self.model_timestamps.remove(model_key) self.model_timestamps.append(model_key) logger.info(f"Load cached {model_key}") return self.model_dict[model_key] model = self._load_model_from_disk(model_loader_func, model_conf) while self._calculate_model_memory() > self.max_memory_size: if len(self.model_timestamps) == 0: logger.warn( "RAM: {}GB, MAX RAM: {}GB".format( self._calculate_model_memory(), self.max_memory_size ) ) break oldest_model_key = self.model_timestamps.pop(0) self.current_memory_size = self._calculate_model_memory() logger.info(f"Del cached {oldest_model_key}") del self.model_dict[oldest_model_key] self.model_dict[model_key] = model self.model_timestamps.append(model_key) self.print_memory_usage() logger.info(f"Total cached {list(self.model_dict.keys())}") return model def _load_model_from_disk(self, model_loader_func, model_conf): return model_loader_func(model_conf) def _calculate_model_memory(self, verbose=False): host_colocation = int(os.environ.get("HOST_COLOCATION", "1")) vm = psutil.virtual_memory() du = shutil.disk_usage(".") if verbose: logger.info( f"RAM: {vm.used / 1e9:.1f}/{vm.total / host_colocation / 1e9:.1f}GB" ) logger.info( f"DISK: {du.used / 1e9:.1f}/{du.total / host_colocation / 1e9:.1f}GB" ) return vm.used / 1e9 def print_memory_usage(self): self._calculate_model_memory(verbose=True) model_cache = ModelCache() 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(): matcher_zoo_restored[k] = parse_match_config(v) return matcher_zoo_restored def parse_match_config(conf): if conf["dense"]: return { "matcher": match_dense.confs.get(conf["matcher"]), "dense": True, } else: return { "feature": extract_features.confs.get(conf["feature"]), "matcher": match_features.confs.get(conf["matcher"]), "dense": False, } 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", "xfeat(sparse)", "dedode", "loftr", "disk", "RoMa", "d2net", "aspanformer", "topicfm", "superpoint+superglue", "superpoint+lightglue", "superpoint+mnn", "disk", ] def distribute_elements(A, B): new_B = np.array(B, copy=True).flatten() np.random.shuffle(new_B) new_B = np.resize(new_B, len(A)) np.random.shuffle(new_B) return new_B.tolist() # normal examples def gen_images_pairs(count: int = 5): path = str(ROOT / "datasets/sacre_coeur/mapping") 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)) if len(pairs) < count: count = len(pairs) selected = random.sample(range(len(pairs)), count) return [pairs[i] for i in selected] # rotated examples def gen_rot_image_pairs(count: int = 5): path = ROOT / "datasets/sacre_coeur/mapping" path_rot = ROOT / "datasets/sacre_coeur/mapping_rot" rot_list = [45, 180, 90, 225, 270] pairs = [] for file in os.listdir(path): if file.lower().endswith((".jpg", ".jpeg", ".png")): for rot in rot_list: file_rot = "{}_rot{}.jpg".format(Path(file).stem, rot) if (path_rot / file_rot).exists(): pairs.append( [ path / file, path_rot / file_rot, ] ) if len(pairs) < count: count = len(pairs) selected = random.sample(range(len(pairs)), count) return [pairs[i] for i in selected] def gen_scale_image_pairs(count: int = 5): path = ROOT / "datasets/sacre_coeur/mapping" path_scale = ROOT / "datasets/sacre_coeur/mapping_scale" scale_list = [0.3, 0.5] pairs = [] for file in os.listdir(path): if file.lower().endswith((".jpg", ".jpeg", ".png")): for scale in scale_list: file_scale = "{}_scale{}.jpg".format(Path(file).stem, scale) if (path_scale / file_scale).exists(): pairs.append( [ path / file, path_scale / file_scale, ] ) if len(pairs) < count: count = len(pairs) selected = random.sample(range(len(pairs)), count) return [pairs[i] for i in selected] # extramely hard examples def gen_image_pairs_wxbs(count: int = None): prefix = "datasets/wxbs_benchmark/.WxBS/v1.1" wxbs_path = ROOT / prefix pairs = [] for catg in os.listdir(wxbs_path): catg_path = wxbs_path / catg if not catg_path.is_dir(): continue for scene in os.listdir(catg_path): scene_path = catg_path / scene if not scene_path.is_dir(): continue img1_path = scene_path / "01.png" img2_path = scene_path / "02.png" if img1_path.exists() and img2_path.exists(): pairs.append([str(img1_path), str(img2_path)]) return pairs # image pair path pairs = gen_images_pairs() pairs += gen_rot_image_pairs() pairs += gen_scale_image_pairs() pairs += gen_image_pairs_wxbs() 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 = [] dist_examples = distribute_elements(pairs, example_matchers) for pair, mt in zip(pairs, dist_examples): 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 set_null_pred(feature_type: str, pred: dict): if feature_type == "KEYPOINT": pred["mmkeypoints0_orig"] = np.array([]) pred["mmkeypoints1_orig"] = np.array([]) pred["mmconf"] = np.array([]) elif feature_type == "LINE": pred["mline_keypoints0_orig"] = np.array([]) pred["mline_keypoints1_orig"] = np.array([]) pred["H"] = None pred["geom_info"] = {} return pred def _filter_matches_opencv( kp0: np.ndarray, kp1: np.ndarray, method: int = cv2.RANSAC, reproj_threshold: float = 3.0, confidence: float = 0.99, max_iter: int = 2000, geometry_type: str = "Homography", ) -> Tuple[np.ndarray, np.ndarray]: """ Filters matches between two sets of keypoints using OpenCV's findHomography. Args: kp0 (np.ndarray): Array of keypoints from the first image. kp1 (np.ndarray): Array of keypoints from the second image. method (int, optional): RANSAC method. Defaults to "cv2.RANSAC". reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to 3.0. confidence (float, optional): RANSAC confidence. Defaults to 0.99. max_iter (int, optional): RANSAC maximum iterations. Defaults to 2000. geometry_type (str, optional): Type of geometry. Defaults to "Homography". Returns: Tuple[np.ndarray, np.ndarray]: Homography matrix and mask. """ if geometry_type == "Homography": M, mask = cv2.findHomography( kp0, kp1, method=method, ransacReprojThreshold=reproj_threshold, confidence=confidence, maxIters=max_iter, ) elif geometry_type == "Fundamental": M, mask = cv2.findFundamentalMat( kp0, kp1, method=method, ransacReprojThreshold=reproj_threshold, confidence=confidence, maxIters=max_iter, ) mask = np.array(mask.ravel().astype("bool"), dtype="bool") return M, mask def _filter_matches_poselib( kp0: np.ndarray, kp1: np.ndarray, method: int = None, # not used reproj_threshold: float = 3, confidence: float = 0.99, max_iter: int = 2000, geometry_type: str = "Homography", ) -> dict: """ Filters matches between two sets of keypoints using the poselib library. Args: kp0 (np.ndarray): Array of keypoints from the first image. kp1 (np.ndarray): Array of keypoints from the second image. method (str, optional): RANSAC method. Defaults to "RANSAC". reproj_threshold (float, optional): RANSAC reprojection threshold. Defaults to 3. confidence (float, optional): RANSAC confidence. Defaults to 0.99. max_iter (int, optional): RANSAC maximum iterations. Defaults to 2000. geometry_type (str, optional): Type of geometry. Defaults to "Homography". Returns: dict: Information about the homography estimation. """ ransac_options = { "max_iterations": max_iter, # "min_iterations": min_iter, "success_prob": confidence, "max_reproj_error": reproj_threshold, # "progressive_sampling": args.sampler.lower() == 'prosac' } if geometry_type == "Homography": M, info = poselib.estimate_homography(kp0, kp1, ransac_options) elif geometry_type == "Fundamental": M, info = poselib.estimate_fundamental(kp0, kp1, ransac_options) else: raise NotImplementedError return M, np.array(info["inliers"]) def proc_ransac_matches( mkpts0: np.ndarray, mkpts1: np.ndarray, ransac_method: str = DEFAULT_RANSAC_METHOD, ransac_reproj_threshold: float = 3.0, ransac_confidence: float = 0.99, ransac_max_iter: int = 2000, geometry_type: str = "Homography", ): if ransac_method.startswith("CV2"): logger.info( f"ransac_method: {ransac_method}, geometry_type: {geometry_type}" ) return _filter_matches_opencv( mkpts0, mkpts1, ransac_zoo[ransac_method], ransac_reproj_threshold, ransac_confidence, ransac_max_iter, geometry_type, ) elif ransac_method.startswith("POSELIB"): logger.info( f"ransac_method: {ransac_method}, geometry_type: {geometry_type}" ) return _filter_matches_poselib( mkpts0, mkpts1, None, ransac_reproj_threshold, ransac_confidence, ransac_max_iter, geometry_type, ) else: raise NotImplementedError 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, ransac_estimator: str = None, ): """ 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 "mkeypoints0_orig" in pred.keys() and "mkeypoints1_orig" in pred.keys(): mkpts0 = pred["mkeypoints0_orig"] mkpts1 = pred["mkeypoints1_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 set_null_pred(feature_type, pred) if mkpts0 is None or mkpts0 is None: return set_null_pred(feature_type, pred) if ransac_method not in ransac_zoo.keys(): ransac_method = DEFAULT_RANSAC_METHOD if len(mkpts0) < DEFAULT_MIN_NUM_MATCHES: return set_null_pred(feature_type, pred) geom_info = compute_geometry( pred, ransac_method=ransac_method, ransac_reproj_threshold=ransac_reproj_threshold, ransac_confidence=ransac_confidence, ransac_max_iter=ransac_max_iter, ) if "Homography" in geom_info.keys(): mask = geom_info["mask_h"] if feature_type == "KEYPOINT": pred["mmkeypoints0_orig"] = mkpts0[mask] pred["mmkeypoints1_orig"] = mkpts1[mask] pred["mmconf"] = pred["mconf"][mask] elif feature_type == "LINE": pred["mline_keypoints0_orig"] = mkpts0[mask] pred["mline_keypoints1_orig"] = mkpts1[mask] pred["H"] = np.array(geom_info["Homography"]) else: set_null_pred(feature_type, pred) # do not show mask geom_info.pop("mask_h", None) geom_info.pop("mask_f", None) pred["geom_info"] = geom_info return pred def compute_geometry( 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 "mkeypoints0_orig" in pred.keys() and "mkeypoints1_orig" in pred.keys(): mkpts0 = pred["mkeypoints0_orig"] mkpts1 = pred["mkeypoints1_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 {} geo_info: Dict[str, List[float]] = {} F, mask_f = proc_ransac_matches( mkpts0, mkpts1, ransac_method, ransac_reproj_threshold, ransac_confidence, ransac_max_iter, geometry_type="Fundamental", ) if F is not None: geo_info["Fundamental"] = F.tolist() geo_info["mask_f"] = mask_f H, mask_h = proc_ransac_matches( mkpts1, mkpts0, ransac_method, ransac_reproj_threshold, ransac_confidence, ransac_max_iter, geometry_type="Homography", ) h0, w0, _ = pred["image0_orig"].shape if H is not None: geo_info["Homography"] = H.tolist() geo_info["mask_h"] = mask_h try: _, H1, H2 = cv2.stereoRectifyUncalibrated( mkpts0.reshape(-1, 2), mkpts1.reshape(-1, 2), F, imgSize=(w0, h0), ) geo_info["H1"] = H1.tolist() geo_info["H2"] = H2.tolist() except cv2.error as e: logger.error( f"StereoRectifyUncalibrated failed, skip! error: {e}" ) 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. """ h0, w0, _ = img0.shape h1, w1, _ = img1.shape if geo_info is not None and len(geo_info) != 0: rectified_image0 = img0 rectified_image1 = None if "Homography" not in geo_info: logger.warning(f"{geom_type} not exist, maybe too less matches") return None, None H = np.array(geo_info["Homography"]) title: List[str] = [] if geom_type == "Homography": rectified_image1 = cv2.warpPerspective(img1, H, (w0, h0)) title = ["Image 0", "Image 1 - warped"] elif geom_type == "Fundamental": if geom_type not in geo_info: logger.warning(f"{geom_type} not exist, maybe too less matches") return None, None else: H1, H2 = np.array(geo_info["H1"]), np.array(geo_info["H2"]) rectified_image0 = cv2.warpPerspective(img0, H1, (w0, h0)) rectified_image1 = cv2.warpPerspective(img1, H2, (w1, h1)) 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, ) return fig2im(fig), rectified_image1 else: return None, None def generate_warp_images( input_image0: np.ndarray, input_image1: np.ndarray, matches_info: Dict[str, Any], choice: str, ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]: """ 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 warpped images. """ if ( matches_info is None or len(matches_info) < 1 or "geom_info" not in matches_info.keys() ): return None, None geom_info = matches_info["geom_info"] warped_image = None if choice != "No": wrapped_image_pair, warped_image = wrap_images( input_image0, input_image1, geom_info, choice ) return wrapped_image_pair, warped_image else: return None, None def send_to_match(state_cache: Dict[str, Any]): """ Send the state cache to the match function. Args: state_cache (Dict[str, Any]): Current state of the app. Returns: None """ if state_cache: return ( state_cache["image0_orig"], state_cache["wrapped_image"], ) else: return None, None def run_ransac( state_cache: Dict[str, Any], choice_geometry_type: 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, ) -> Tuple[Optional[np.ndarray], Optional[Dict[str, int]]]: """ Run RANSAC matches and return the output images and the number of matches. Args: state_cache (Dict[str, Any]): Current state of the app, including the matches. ransac_method (str, optional): RANSAC method. Defaults to DEFAULT_RANSAC_METHOD. ransac_reproj_threshold (int, 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: Tuple[Optional[np.ndarray], Optional[Dict[str, int]]]: Tuple containing the output images and the number of matches. """ if not state_cache: logger.info("Run Match first before Rerun RANSAC") gr.Warning("Run Match first before Rerun RANSAC") return None, None t1 = time.time() logger.info( f"Run RANSAC matches using: {ransac_method} with threshold: {ransac_reproj_threshold}" ) logger.info( f"Run RANSAC matches using: {ransac_confidence} with iter: {ransac_max_iter}" ) # if enable_ransac: filter_matches( state_cache, ransac_method=ransac_method, ransac_reproj_threshold=ransac_reproj_threshold, ransac_confidence=ransac_confidence, ransac_max_iter=ransac_max_iter, ) logger.info(f"RANSAC matches done using: {time.time()-t1:.3f}s") t1 = time.time() # plot images with ransac matches titles = [ "Image 0 - Ransac matched keypoints", "Image 1 - Ransac matched keypoints", ] output_matches_ransac, num_matches_ransac = display_matches( state_cache, titles=titles, tag="KPTS_RANSAC" ) logger.info(f"Display matches done using: {time.time()-t1:.3f}s") t1 = time.time() # compute warp images output_wrapped, warped_image = generate_warp_images( state_cache["image0_orig"], state_cache["image1_orig"], state_cache, choice_geometry_type, ) plt.close("all") num_matches_raw = state_cache["num_matches_raw"] state_cache["wrapped_image"] = warped_image # tmp_state_cache = tempfile.NamedTemporaryFile(suffix='.pkl', delete=False) tmp_state_cache = "output.pkl" with open(tmp_state_cache, "wb") as f: pickle.dump(state_cache, f) logger.info("Dump results done!") return ( output_matches_ransac, { "num_matches_raw": num_matches_raw, "num_matches_ransac": num_matches_ransac, }, output_wrapped, tmp_state_cache, ) 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_geometry_type: str = DEFAULT_SETTING_GEOMETRY, matcher_zoo: Dict[str, Any] = None, force_resize: bool = False, image_width: int = 640, image_height: int = 480, use_cached_model: bool = False, ) -> 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_geometry_type (str, optional): setting of geometry estimation. matcher_zoo (Dict[str, Any], optional): matcher zoo. Defaults to None. force_resize (bool, optional): force resize. Defaults to False. image_width (int, optional): image width. Defaults to 640. image_height (int, optional): image height. Defaults to 480. use_cached_model (bool, optional): use cached model. Defaults to False. 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: logger.error( "Error: No images found! Please upload two images or select an example." ) raise gr.Error( "Error: No images found! Please upload two images or select an example." ) # init output output_keypoints = None output_matches_raw = None output_matches_ransac = None # super slow! if "roma" in key.lower() and DEVICE == "cpu": gr.Info( f"Success! Please be patient and allow for about 2-3 minutes." f" Due to CPU inference, {key} is quiet slow." ) t0 = time.time() 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 cache_key = "{}_{}".format(key, match_conf["model"]["name"]) if use_cached_model: # because of the model cache, we need to update the config matcher = model_cache.cache_model(cache_key, get_model, match_conf) matcher.conf["max_keypoints"] = extract_max_keypoints matcher.conf["match_threshold"] = match_threshold logger.info(f"Loaded cached model {cache_key}") else: matcher = get_model(match_conf) logger.info(f"Loading model using: {time.time()-t0:.3f}s") t1 = time.time() if model["dense"]: match_conf["preprocessing"]["force_resize"] = force_resize if force_resize: match_conf["preprocessing"]["height"] = image_height match_conf["preprocessing"]["width"] = image_width logger.info(f"Force resize to {image_width}x{image_height}") 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 cache_key = "{}_{}".format(key, extract_conf["model"]["name"]) if use_cached_model: extractor = model_cache.cache_model( cache_key, get_feature_model, extract_conf ) # because of the model cache, we need to update the config extractor.conf["max_keypoints"] = extract_max_keypoints extractor.conf["keypoint_threshold"] = keypoint_threshold logger.info(f"Loaded cached model {cache_key}") else: extractor = get_feature_model(extract_conf) extract_conf["preprocessing"]["force_resize"] = force_resize if force_resize: extract_conf["preprocessing"]["height"] = image_height extract_conf["preprocessing"]["width"] = image_width logger.info(f"Force resize to {image_width}x{image_height}") 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 # gr.Info( # f"Matching images done using: {time.time()-t1:.3f}s", # ) logger.info(f"Matching images done using: {time.time()-t1:.3f}s") t1 = time.time() # plot images with keypoints titles = [ "Image 0 - Keypoints", "Image 1 - Keypoints", ] output_keypoints = display_keypoints(pred, titles=titles) # 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, ) # gr.Info(f"RANSAC matches done using: {time.time()-t1:.3f}s") logger.info(f"RANSAC matches done using: {time.time()-t1:.3f}s") t1 = time.time() # 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, tag="KPTS_RANSAC" ) # gr.Info(f"Display matches done using: {time.time()-t1:.3f}s") logger.info(f"Display matches done using: {time.time()-t1:.3f}s") t1 = time.time() # plot wrapped images output_wrapped, warped_image = generate_warp_images( pred["image0_orig"], pred["image1_orig"], pred, choice_geometry_type, ) plt.close("all") # gr.Info(f"In summary, total time: {time.time()-t0:.3f}s") logger.info(f"TOTAL time: {time.time()-t0:.3f}s") state_cache = pred state_cache["num_matches_raw"] = num_matches_raw state_cache["num_matches_ransac"] = num_matches_ransac state_cache["wrapped_image"] = warped_image # tmp_state_cache = tempfile.NamedTemporaryFile(suffix='.pkl', delete=False) tmp_state_cache = "output.pkl" with open(tmp_state_cache, "wb") as f: pickle.dump(state_cache, f) logger.info("Dump results done!") return ( output_keypoints, output_matches_raw, output_matches_ransac, { "num_raw_matches": num_matches_raw, "num_ransac_matches": num_matches_ransac, }, { "match_conf": match_conf, "extractor_conf": extract_conf, }, { "geom_info": pred.get("geom_info", {}), }, output_wrapped, state_cache, tmp_state_cache, ) # @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 = { "POSELIB": "LO-RANSAC", "CV2_RANSAC": cv2.RANSAC, "CV2_USAC_MAGSAC": cv2.USAC_MAGSAC, "CV2_USAC_DEFAULT": cv2.USAC_DEFAULT, "CV2_USAC_FM_8PTS": cv2.USAC_FM_8PTS, "CV2_USAC_PROSAC": cv2.USAC_PROSAC, "CV2_USAC_FAST": cv2.USAC_FAST, "CV2_USAC_ACCURATE": cv2.USAC_ACCURATE, "CV2_USAC_PARALLEL": cv2.USAC_PARALLEL, } def rotate_image(input_path, degrees, output_path): img = Image.open(input_path) img_rotated = img.rotate(-degrees) img_rotated.save(output_path) def scale_image(input_path, scale_factor, output_path): img = Image.open(input_path) width, height = img.size new_width = int(width * scale_factor) new_height = int(height * scale_factor) new_img = Image.new("RGB", (width, height), (0, 0, 0)) img_resized = img.resize((new_width, new_height)) position = ((width - new_width) // 2, (height - new_height) // 2) new_img.paste(img_resized, position) new_img.save(output_path)