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| import os | |
| import random | |
| import numpy as np | |
| import torch | |
| from itertools import combinations | |
| import cv2 | |
| import gradio as gr | |
| from hloc import matchers, extractors | |
| 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, plot_color_line_matches | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| DEFAULT_SETTING_THRESHOLD = 0.1 | |
| DEFAULT_SETTING_MAX_FEATURES = 4096 | |
| 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" | |
| def get_model(match_conf): | |
| Model = dynamic_load(matchers, match_conf["model"]["name"]) | |
| model = Model(match_conf["model"]).eval().to(device) | |
| return model | |
| def get_feature_model(conf): | |
| Model = dynamic_load(extractors, conf["model"]["name"]) | |
| model = Model(conf["model"]).eval().to(device) | |
| return model | |
| def gen_examples(): | |
| import datetime | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'gen_examples start') | |
| random.seed(1) | |
| example_matchers = [ | |
| "gim", | |
| "gim", | |
| "gim", | |
| "gim", | |
| ] | |
| 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 = "datasets/sacre_coeur/mapping" | |
| pairs = gen_images_pairs(path, len(example_matchers)) | |
| gim_pairs = [('datasets/gim/0a.png', 'datasets/gim/0b.png'), | |
| ('datasets/gim/1a.png', 'datasets/gim/1b.png'), | |
| ('datasets/gim/2a.png', 'datasets/gim/2b.png'), | |
| ('datasets/gim/3a.png', 'datasets/gim/3b.png')] | |
| pairs = gim_pairs | |
| 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(gim_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, | |
| ] | |
| ) | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'gen_examples end') | |
| return input_lists | |
| def filter_matches( | |
| pred, | |
| ransac_method=DEFAULT_RANSAC_METHOD, | |
| ransac_reproj_threshold=DEFAULT_RANSAC_REPROJ_THRESHOLD, | |
| ransac_confidence=DEFAULT_RANSAC_CONFIDENCE, | |
| ransac_max_iter=DEFAULT_RANSAC_MAX_ITER, | |
| ): | |
| mkpts0 = None | |
| mkpts1 = None | |
| feature_type = 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, | |
| ransac_method=DEFAULT_RANSAC_METHOD, | |
| ransac_reproj_threshold=DEFAULT_RANSAC_REPROJ_THRESHOLD, | |
| ransac_confidence=DEFAULT_RANSAC_CONFIDENCE, | |
| ransac_max_iter=DEFAULT_RANSAC_MAX_ITER, | |
| ) -> dict: | |
| mkpts0 = None | |
| mkpts1 = None | |
| if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys(): | |
| mkpts0 = pred["keypoints0_orig"] | |
| mkpts1 = pred["keypoints1_orig"] | |
| if ( | |
| "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 = {} | |
| 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() | |
| _, 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() | |
| return geo_info | |
| else: | |
| return {} | |
| def wrap_images(img0, img1, geo_info, geom_type): | |
| h1, w1, _ = img0.shape | |
| h2, w2, _ = img1.shape | |
| result_matrix = 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 = [] | |
| 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, input_image1, matches_info, choice): | |
| 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"] | |
| wrapped_images = 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 display_matches(pred: dict, titles=[], dpi=300): | |
| img0 = pred["image0_orig"] | |
| img1 = pred["image1_orig"] | |
| num_inliers = 0 | |
| if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys(): | |
| mkpts0 = pred["keypoints0_orig"] | |
| mkpts1 = pred["keypoints1_orig"] | |
| num_inliers = len(mkpts0) | |
| if "mconf" in pred.keys(): | |
| mconf = pred["mconf"] | |
| else: | |
| mconf = np.ones(len(mkpts0)) | |
| fig_mkpts = draw_matches( | |
| mkpts0, | |
| mkpts1, | |
| img0, | |
| img1, | |
| mconf, | |
| dpi=dpi, | |
| titles=titles, | |
| ) | |
| fig = fig_mkpts | |
| if "line0_orig" in pred.keys() and "line1_orig" in pred.keys(): | |
| # lines | |
| mtlines0 = pred["line0_orig"] | |
| mtlines1 = pred["line1_orig"] | |
| num_inliers = len(mtlines0) | |
| fig_lines = plot_images( | |
| [img0.squeeze(), img1.squeeze()], | |
| ["Image 0 - matched lines", "Image 1 - matched lines"], | |
| dpi=300, | |
| ) | |
| fig_lines = plot_color_line_matches([mtlines0, mtlines1], lw=2) | |
| fig_lines = fig2im(fig_lines) | |
| # keypoints | |
| mkpts0 = pred["line_keypoints0_orig"] | |
| mkpts1 = pred["line_keypoints1_orig"] | |
| if mkpts0 is not None and mkpts1 is not None: | |
| num_inliers = len(mkpts0) | |
| if "mconf" in pred.keys(): | |
| mconf = pred["mconf"] | |
| else: | |
| mconf = np.ones(len(mkpts0)) | |
| fig_mkpts = draw_matches(mkpts0, mkpts1, img0, img1, mconf, dpi=300) | |
| fig_lines = cv2.resize( | |
| fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0]) | |
| ) | |
| fig = np.concatenate([fig_mkpts, fig_lines], axis=0) | |
| else: | |
| fig = fig_lines | |
| return fig, num_inliers | |
| def run_matching( | |
| image0, | |
| image1, | |
| match_threshold, | |
| extract_max_keypoints, | |
| keypoint_threshold, | |
| key, | |
| ransac_method=DEFAULT_RANSAC_METHOD, | |
| ransac_reproj_threshold=DEFAULT_RANSAC_REPROJ_THRESHOLD, | |
| ransac_confidence=DEFAULT_RANSAC_CONFIDENCE, | |
| ransac_max_iter=DEFAULT_RANSAC_MAX_ITER, | |
| choice_estimate_geom=DEFAULT_SETTING_GEOMETRY, | |
| ): | |
| import datetime | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'run_matching start') | |
| # 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["config"] | |
| # update match config | |
| match_conf["model"]["match_threshold"] = match_threshold | |
| match_conf["model"]["max_keypoints"] = extract_max_keypoints | |
| 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["config_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", | |
| ] | |
| import datetime | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot_images start') | |
| output_keypoints = plot_images([image0, image1], titles=titles, dpi=300) | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot_images end') | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot_keypoints start') | |
| 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) | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot_keypoints end') | |
| # plot images with raw matches | |
| titles = [ | |
| "Image 0 - Raw matched keypoints", | |
| "Image 1 - Raw matched keypoints", | |
| ] | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot images with raw matches start') | |
| output_matches_raw, num_matches_raw = display_matches(pred, titles=titles) | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot images with raw matches end') | |
| # if enable_ransac: | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'filter_matches start') | |
| filter_matches( | |
| pred, | |
| ransac_method=ransac_method, | |
| ransac_reproj_threshold=ransac_reproj_threshold, | |
| ransac_confidence=ransac_confidence, | |
| ransac_max_iter=ransac_max_iter, | |
| ) | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'filter_matches end') | |
| # plot images with ransac matches | |
| titles = [ | |
| "Image 0 - Ransac matched keypoints", | |
| "Image 1 - Ransac matched keypoints", | |
| ] | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot images with raw matches start') | |
| output_matches_ransac, num_matches_ransac = display_matches( | |
| pred, titles=titles | |
| ) | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot images with raw matches end') | |
| # plot wrapped images | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot wrapped images start') | |
| geom_info = compute_geom(pred) | |
| output_wrapped, _ = change_estimate_geom( | |
| pred["image0_orig"], | |
| pred["image1_orig"], | |
| {"geom_info": geom_info}, | |
| choice_estimate_geom, | |
| ) | |
| print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'plot wrapped images end') | |
| del pred | |
| 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, | |
| } | |
| # Matchers collections | |
| matcher_zoo = { | |
| "gim": {"config": match_dense.confs["gim"], "dense": True}, | |
| "loftr": {"config": match_dense.confs["loftr"], "dense": True}, | |
| "superpoint+superglue": { | |
| "config": match_features.confs["superglue"], | |
| "config_feature": extract_features.confs["superpoint_max"], | |
| "dense": False, | |
| }, | |
| "superpoint+lightglue": { | |
| "config": match_features.confs["superpoint-lightglue"], | |
| "config_feature": extract_features.confs["superpoint_max"], | |
| "dense": False, | |
| }, | |
| "d2net": { | |
| "config": match_features.confs["NN-mutual"], | |
| "config_feature": extract_features.confs["d2net-ss"], | |
| "dense": False, | |
| }, | |
| "hardnet": { | |
| "config": match_features.confs["NN-mutual"], | |
| "config_feature": extract_features.confs["hardnet"], | |
| "dense": False, | |
| }, | |
| "sift": { | |
| "config": match_features.confs["NN-mutual"], | |
| "config_feature": extract_features.confs["sift"], | |
| "dense": False, | |
| }, | |
| } | |