def __init__(self, java_object=None):
self.left = pyboof.CameraPinhole()
self.right = pyboof.CameraPinhole()
self.right_to_left = pyboof.Se3_F64()
if java_object is not None:
self.set_from_boof(java_object)
def test_save_load(self):
calib = pb.CameraPinhole()
calib.set_image_shape(200, 300)
calib.set_matrix(100, 101, 102, 103, 104)
calib.set_distortion([0.1, 0.2], 103, 104)
calib.save("junk")
calib = pb.CameraPinhole()
calib.load("junk")
os.remove("junk")
self.check_expected_values(calib)
def test_setters(self):
calib = pb.CameraPinhole()
calib.set_image_shape(200, 300)
calib.set_matrix(100, 101, 102, 103, 104)
calib.set_distortion([0.1, 0.2], 103, 104)
self.check_expected_values(calib)
#!/usr/bin/env python3
import pyboof as pb
import numpy as np
import os
data_path = "../data/example/fiducial/image/examples/"
# Load the camera parameters
intrinsic = pb.CameraPinhole()
intrinsic.load(os.path.join(data_path, "intrinsic.yaml"))
configFiducial = pb.ConfigFiducialImage()
configThreshold = pb.ConfigThreshold.create_local(pb.ThresholdType.LOCAL_MEAN,
10)
print("Configuring detector")
detector = pb.FactoryFiducial(np.uint8).square_image(configFiducial,
configThreshold)
detector.set_intrinsic(intrinsic)
detector.add_pattern(pb.load_single_band(
data_path + "../patterns/pentarose.png", np.uint8),
side_length=4.0)
detector.add_pattern(pb.load_single_band(data_path + "../patterns/yu.png",
np.uint8),
side_length=4.0)
print("Detecting image")
detector.detect(
pb.load_single_band(os.path.join(data_path, "image00.jpg"), np.uint8))
#!/usr/bin/env python3
import pyboof as pb
import numpy as np
import os
import cv2
import transforms3d.euler # pip install transforms3d
pb.init_memmap(5)
data_path = "../data/example/fisheye/theta/"
model_pinhole = pb.CameraPinhole()
model_pinhole.set_image_shape(450, 450)
model_pinhole.set_matrix(250, 250, 0, 300, 300)
model_fisheye = pb.CameraUniversalOmni()
model_fisheye.load(os.path.join(data_path, "front.yaml"))
transform = pb.NarrowToWideFovPtoP(narrow_model=model_pinhole,
wide_model=model_fisheye)
image_fisheye = pb.load_planar(os.path.join(data_path, "front_hike.jpg"),
np.uint8)
image_pinhole = image_fisheye.createNew(model_pinhole.width,
model_pinhole.height)
image_distorter = transform.create_image_distort(
pb.ImageType(image_fisheye.getImageType()))
image_distorter.apply(image_fisheye, image_pinhole)
#!/usr/bin/env python3
import numpy as np
import pyboof as pb
from pyboof.swing import visualize_matches
# Enable use of memory mapped files for MUCH faster conversion between some python and boofcv data types
pb.init_memmap(5)
# Load two images and camera calibration
image0 = pb.load_single_band("../data/example/stereo/mono_wall_01.jpg",
np.uint8)
image1 = pb.load_single_band("../data/example/stereo/mono_wall_02.jpg",
np.uint8)
intrinsic = pb.CameraPinhole().load(
"../data/example/calibration/mono/Sony_DSC-HX5V_Chess/intrinsic.yaml")
# Set up the SURF fast hessian feature detector. Reduce the number of features it will detect by putting a limit
# on how close two features can be and the maximum number at each scale
config_fh = pb.ConfigFastHessian()
config_fh.extractRadius = 4
config_fh.maxFeaturesPerScale = 300
# Create the detector and use default for everything else
feature_detector = pb.FactoryDetectDescribe(
np.uint8).createSurf(config_detect=config_fh)
# Detect features in the first image
locs0, desc0 = feature_detector.detect(image0)
locs1, desc1 = feature_detector.detect(image1)
