pyslam/test/cv/test_feature_manager.py at master · luigifreda/pyslam · GitHub

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#!/usr/bin/env -S python3 -O
import sys
import numpy as np
import cv2
from matplotlib import pyplot as plt


from pyslam.config import Config

from pyslam.viz.mplot_figure import MPlotFigure
from pyslam.local_features.feature_manager import feature_manager_factory
from pyslam.local_features.feature_types import (
    FeatureDetectorTypes,
    FeatureDescriptorTypes,
    FeatureInfo,
)
from pyslam.utilities.features import ssc_nms

from collections import defaultdict, Counter

from pyslam.local_features.feature_manager_configs import FeatureManagerConfigs
from pyslam.local_features.feature_tracker_configs import FeatureTrackerConfigs

from pyslam.utilities.timer import TimerFps

from pyslam.local_features.feature_keynet import KeyNetDescFeature2D


# ==================================================================================================
# N.B.: here we test feature manager detectAndCompute()
# ==================================================================================================


timer = TimerFps()

# img = cv2.imread('../data/kitti06-12.png',cv2.IMREAD_COLOR)
# img = cv2.imread('../data/kitti06-435.png',cv2.IMREAD_COLOR)
img = cv2.imread("../data/kitti06-12-color.png", cv2.IMREAD_COLOR)
# img = cv2.imread('../data/mars1.png')

num_features = 2000


# select your tracker configuration (see the file feature_tracker_configs.py)
# FeatureTrackerConfigs: SHI_TOMASI_ORB, FAST_ORB, ORB, ORB2, ORB2_FREAK, ORB2_BEBLID, BRISK, AKAZE, FAST_FREAK, SIFT, ROOT_SIFT, SURF, KEYNET, SUPERPOINT, CONTEXTDESC, LIGHTGLUE, XFEAT, XFEAT_XFEAT
feature_tracker_config = FeatureTrackerConfigs.ROOT_SIFT
feature_tracker_config["num_features"] = num_features

feature_manager_config = FeatureManagerConfigs.extract_from(feature_tracker_config)
print("feature_manager_config: ", feature_manager_config)
feature_manager = feature_manager_factory(**feature_manager_config)

des = None

img_old = img.copy()

# loop for measuring time performance
N = 1
for i in range(N):
    timer.start()

    # just detect keypoints
    # kps = feature_manager.detect(img)

    # detect keypoints and compute descriptors
    kps, des = feature_manager.detectAndCompute(img)

    timer.refresh()

# sizes = np.array([x.size for x in kps], dtype=np.float32)

print("#kps: ", len(kps))
des = np.array(des)
if des is not None:
    print("des shape: ", des.shape)

# print('octaves: ', [p.octave for p in kps])
# count points for each octave
kps_octaves = [k.octave for k in kps]
kps_octaves = Counter(kps_octaves)
print("kps levels-histogram: \n", kps_octaves.most_common())

imgDraw = cv2.drawKeypoints(
    img, kps, None, color=(0, 255, 0), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
)

fig = MPlotFigure(imgDraw[:, :, [2, 1, 0]], title="features")
MPlotFigure.show()