common/utils.py

import os
import random
import numpy as np
import torch
import cv2
import gradio as gr
from pathlib import Path
from itertools import combinations
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, plot_color_line_matches

device = "cuda" if torch.cuda.is_available() else "cpu"

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]

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():
    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 = Path(__file__).parent.parent / "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,
    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()
            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, 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,
):
    # 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",
    ]
    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,
    )

    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 = {
    # 'dedode-sparse': {
    #     'config': match_dense.confs['dedode_sparse'],
    #     'dense': True  # dense mode, we need 2 images
    # },
    "roma": {"config": match_dense.confs["roma"], "dense": True},
    "loftr": {"config": match_dense.confs["loftr"], "dense": True},
    "topicfm": {"config": match_dense.confs["topicfm"], "dense": True},
    "aspanformer": {"config": match_dense.confs["aspanformer"], "dense": True},
    "dedode": {
        "config": match_features.confs["Dual-Softmax"],
        "config_feature": extract_features.confs["dedode"],
        "dense": False,
    },
    "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,
    },
    "disk": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["disk"],
        "dense": False,
    },
    "disk+dualsoftmax": {
        "config": match_features.confs["Dual-Softmax"],
        "config_feature": extract_features.confs["disk"],
        "dense": False,
    },
    "superpoint+dualsoftmax": {
        "config": match_features.confs["Dual-Softmax"],
        "config_feature": extract_features.confs["superpoint_max"],
        "dense": False,
    },
    "disk+lightglue": {
        "config": match_features.confs["disk-lightglue"],
        "config_feature": extract_features.confs["disk"],
        "dense": False,
    },
    "superpoint+mnn": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["superpoint_max"],
        "dense": False,
    },
    "sift+sgmnet": {
        "config": match_features.confs["sgmnet"],
        "config_feature": extract_features.confs["sift"],
        "dense": False,
    },
    "sosnet": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["sosnet"],
        "dense": False,
    },
    "hardnet": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["hardnet"],
        "dense": False,
    },
    "d2net": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["d2net-ss"],
        "dense": False,
    },
    "rord": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["rord"],
        "dense": False,
    },
    # "d2net-ms": {
    #     "config": match_features.confs["NN-mutual"],
    #     "config_feature": extract_features.confs["d2net-ms"],
    #     "dense": False,
    # },
    "alike": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["alike"],
        "dense": False,
    },
    "lanet": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["lanet"],
        "dense": False,
    },
    "r2d2": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["r2d2"],
        "dense": False,
    },
    "darkfeat": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["darkfeat"],
        "dense": False,
    },
    "sift": {
        "config": match_features.confs["NN-mutual"],
        "config_feature": extract_features.confs["sift"],
        "dense": False,
    },
    "gluestick": {"config": match_dense.confs["gluestick"], "dense": True},
    "sold2": {"config": match_dense.confs["sold2"], "dense": True},
    # "DKMv3": {"config": match_dense.confs["dkm"], "dense": True},
}