├── .gitignore ├── 3D ├── laser.max └── laser2.blend ├── LICENSE ├── README.md ├── code ├── experiments │ ├── append.py │ ├── crop-numpy.py │ ├── extrema.py │ ├── global_registration_tutorial.py │ ├── image-difference.py │ ├── interactive-matplotlib.py │ ├── non-blocking-viz.py │ ├── numpy-sort.py │ ├── open3d-with-numpy.py │ ├── scatterplot.py │ ├── test_data │ │ ├── cloud_bin_0.pcd │ │ ├── cloud_bin_1.pcd │ │ └── cloud_bin_2.pcd │ ├── vector-intersection.py │ ├── video.py │ └── weighted-average.py ├── lib │ ├── __init__.py │ ├── image.py │ ├── mesh.py │ ├── pointcloud.py │ ├── registration.py │ ├── transformation.py │ ├── visualization.py │ └── visualization_mpl.py ├── linescanner.py ├── meshing_test.py └── registration_test.py ├── doc ├── calculation.jpg ├── example_input.jpg ├── example_result.jpg ├── laser1a_daylight-difference.jpg ├── laser1a_source.jpg ├── laser1a_striped_720.jpg └── squeeze-problem.jpg ├── export ├── icp.ply ├── laser1_reference.obj ├── laser1a_2048.pcd ├── laser1a_720.pcd └── laser1b_720.pcd ├── images ├── laser1_2048_horizontal.mp4 ├── laser1_2048_horizontal_config.json ├── laser1_RGB_horizontal.jpg ├── laser1_RGB_vertikal.jpg ├── laser1a_2048.mp4 ├── laser1a_2048_config.json ├── laser1a_2048_daylight.mp4 ├── laser1a_2048_daylight_horizontal.mp4 ├── laser1a_720.mp4 ├── laser1a_720_config.json ├── laser1b_720.mp4 ├── laser1b_720_config.json ├── laser2.mp4 ├── laser2_RGB.jpg └── laser2_config.json └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | Thumbs.db 3 | 4 | /tmp/ 5 | /3D/laser_* 6 | -------------------------------------------------------------------------------- /3D/laser.max: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/3D/laser.max -------------------------------------------------------------------------------- /3D/laser2.blend: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/3D/laser2.blend -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LineScanner | work-in-progress 2 | 3 | ### working principle 4 | A stepper motor sweeps a linelaser across the view of a camera. The script tries to detect a laser line 5 | in each row of the camera frame, repeating for each frame during the laser's motion. 6 | It uses frame subtraction to detect subtle changes in brightness and can therefore be used even in bright daylight. 7 | 8 | ![source image](doc/example_input.jpg) 9 | 10 | It then triangulates the 3D position of each point of the laser line using: 11 | * distance between camera <-> laser 12 | * current angle of the laser's stepper motor 13 | * camera intrinic parameters (currently only field-of-view, later also distortion) 14 | 15 | Each point is colored (Vertex Color) either by the point's grayscale-value ( = "Laser-Illuminated") 16 | or by providing a clean image for texturing (e.g. first frame of daylight shooting). 17 | 18 | ![Linescanner](doc/example_result.jpg) 19 | ----------- 20 | 21 | 22 | 23 | LINKS: 24 | Vector Intersection based on [geomalgorithms.com](https://web.archive.org/web/20210428000731/http://geomalgorithms.com/a05-_intersect-1.html) 25 | https://stackoverflow.com/questions/5666222/3d-line-plane-intersection 26 | 27 | indices of max-values in numpy array 28 | https://www.w3resource.com/python-exercises/numpy/python-numpy-exercise-31.php 29 | 30 | weighted average 31 | https://stackoverflow.com/questions/30057046/weighted-mean-in-numpy-python#30057626 32 | 33 | 34 | 35 | 36 | color: 37 | https://github.com/isl-org/Open3D/issues/614 38 | 39 | pcd = PointCloud() 40 | pcd.points = Vector3dVector(np_points) 41 | pcd.colors = Vector3dVector(np_colors) 42 | -------------------------------------------------------------------------------- /code/experiments/append.py: -------------------------------------------------------------------------------- 1 | import open3d as o3d 2 | import numpy as np 3 | 4 | 5 | # open3D visualisation 6 | vis = o3d.visualization.Visualizer() 7 | vis.create_window() 8 | 9 | 10 | pcd = o3d.geometry.PointCloud() 11 | new_pcd = o3d.geometry.PointCloud() 12 | 13 | points = np.random.rand(100, 3) # np.array([[0.0, 10.0, 10.0], [2.0, 5.0, 2.0]], dtype=np.float64) 14 | pcd.points = o3d.utility.Vector3dVector(points) 15 | pcd.colors = o3d.utility.Vector3dVector(points) 16 | vis.add_geometry(pcd) 17 | 18 | for i in range(200): 19 | points = np.random.rand(100, 3) 20 | colors = np.random.rand(100, 3) 21 | 22 | # append pcd to pcd 23 | new_pcd.points = o3d.utility.Vector3dVector(points) 24 | new_pcd.colors = o3d.utility.Vector3dVector(colors) 25 | pcd += new_pcd 26 | 27 | # # append numpy-array to pcd 28 | # o3d.utility.Vector3dVector.extend(pcd.points, points) 29 | 30 | # update 3D-view each line 31 | vis.update_geometry(pcd) 32 | vis.poll_events() 33 | vis.update_renderer() 34 | 35 | vis.destroy_window() 36 | -------------------------------------------------------------------------------- /code/experiments/crop-numpy.py: -------------------------------------------------------------------------------- 1 | 2 | # https://moonbooks.org/Articles/How-to-remove-array-rows-that-contain-only-0-in-python/ 3 | 4 | import numpy as np 5 | im = np.array([ [0,0,0,0,0,0], 6 | [0,0,1,1,1,0], 7 | [0,0,0,0,0,0], 8 | [0,0,1,0,1,0], 9 | [0,0,0,0,0,0]]) 10 | 11 | data = im[~np.all(im == 0, axis=1)] 12 | 13 | print(data) 14 | 15 | 16 | -------------------------------------------------------------------------------- /code/experiments/extrema.py: -------------------------------------------------------------------------------- 1 | ''' 2 | https://stackoverflow.com/questions/4624970/finding-local-maxima-minima-with-numpy-in-a-1d-numpy-array#9667121 3 | https://stackoverflow.com/questions/4624970/finding-local-maxima-minima-with-numpy-in-a-1d-numpy-array 4 | https://stackoverflow.com/questions/53466504/finding-singulars-sets-of-local-maxima-minima-in-a-1d-numpy-array-once-again 5 | ''' 6 | import numpy as np 7 | from scipy.signal import find_peaks # 2 8 | # from scipy.signal import argrelmax, argrelmin # 3 9 | # from scipy.signal import square 10 | 11 | 12 | # DATA 13 | x = np.linspace(0, 10, 100, endpoint=False) 14 | 15 | # 1 interference 16 | data = .2 * np.sin(10 * x) + np.exp(-np.abs(2 - x / 3) ** 2) 17 | 18 | # 2 square function 19 | sig = np.sin(2 * np.pi * x) 20 | # data = square(2 * np.pi * x / 2, duty=(sig + 1)/2) 21 | 22 | 23 | # SMOOTH 24 | def smooth_line(array, blur=5, window='hamming'): 25 | """ 26 | https://scipy-cookbook.readthedocs.io/items/SignalSmooth.html 27 | 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' 28 | """ 29 | s = np.r_[array[blur - 1:0:-1], array, array[-2:-blur - 1:-1]] 30 | if window == 'flat': 31 | w = np.ones(blur, 'd') 32 | else: 33 | w = eval('np.' + window + '(blur)') 34 | array = np.convolve(w / w.sum(), s, mode='valid') 35 | array = array[blur - 1:] 36 | return array 37 | 38 | 39 | data = smooth_line(data) 40 | 41 | 42 | # EXTREMA 43 | 44 | # # 1 detects both plateau flanks 45 | # maxima = (np.diff(np.sign(np.diff(data))) < 0).nonzero()[0] + 1 46 | # minima = (np.diff(np.sign(np.diff(data))) > 0).nonzero()[0] + 1 47 | 48 | # # 2 detects center of plateau 49 | maxima, _ = find_peaks(data, height=0.2, distance=5) 50 | 51 | # # 3 does not detect plateaus! 52 | # maxima = argrelmax(data) 53 | # minima = argrelmin(data) 54 | 55 | 56 | # VISUALIZE 57 | def plot_row(row, points): 58 | from matplotlib import pyplot as plt 59 | length = len(row) 60 | x = np.linspace(0, length, length, endpoint=False) 61 | print(x) 62 | plt.plot(x, row, "bo", ms=3) 63 | plt.plot(x, row, "b") 64 | plt.plot(x[points], row[points], "rD", ms=4, label="selected") 65 | plt.legend() 66 | plt.show() 67 | 68 | 69 | plot_row(data, maxima) 70 | -------------------------------------------------------------------------------- /code/experiments/global_registration_tutorial.py: -------------------------------------------------------------------------------- 1 | """ 2 | https://www.open3d.org/docs/latest/tutorial/Advanced/global_registration.html 3 | """ 4 | 5 | import open3d as o3d 6 | import numpy as np 7 | import copy 8 | 9 | 10 | def visualize_simple(mesh1, mesh2, transformation, uniform_colors=True): 11 | mesh1_temp = copy.deepcopy(mesh1) 12 | mesh2_temp = copy.deepcopy(mesh2) 13 | 14 | if uniform_colors: 15 | mesh1_temp.paint_uniform_color([1, 0.706, 0]) 16 | mesh2_temp.paint_uniform_color([0, 0.651, 0.929]) 17 | 18 | mesh1_temp.transform(transformation) 19 | o3d.visualization.draw_geometries([mesh1_temp, mesh2_temp], 20 | zoom=0.4559, 21 | front=[0.6452, -0.3036, -0.7011], 22 | lookat=[1.9892, 2.0208, 1.8945], 23 | up=[-0.2779, -0.9482, 0.1556], 24 | mesh_show_back_face=True) 25 | 26 | def estimate_point_normals(pcd, radius=0.1, max_nn=30): 27 | search_param = o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn) 28 | pcd.estimate_normals(search_param=search_param) 29 | return pcd 30 | 31 | def fpfh_from_pointcloud(pcd, radius=0.25, max_nn=100): 32 | ''' Fast Point Feature Histograms (FPFH) descriptor''' 33 | search_param = o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn) 34 | return o3d.pipelines.registration.compute_fpfh_feature(pcd, search_param) 35 | 36 | def preprocess_point_cloud(pcd, voxel_size): 37 | # downsample 38 | pcd_down = pcd.voxel_down_sample(voxel_size) 39 | 40 | # estimate normals 41 | radius_normal = voxel_size * 2 42 | pcd_down = estimate_point_normals(pcd_down, radius=radius_normal, max_nn=30) 43 | 44 | # compute FPFH feature 45 | radius_feature = voxel_size * 5 46 | pcd_fpfh = fpfh_from_pointcloud(pcd_down, radius=radius_feature, max_nn=100) 47 | 48 | return pcd_down, pcd_fpfh 49 | 50 | def prepare_dataset(voxel_size): 51 | source = o3d.io.read_point_cloud("code/experiments/test_data/cloud_bin_0.pcd") 52 | target = o3d.io.read_point_cloud("code/experiments/test_data/cloud_bin_1.pcd") 53 | trans_init = np.asarray([[0.0, 0.0, 1.0, 0.0], [1.0, 0.0, 0.0, 0.0], 54 | [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]]) 55 | source.transform(trans_init) 56 | visualize_simple(source, target, np.identity(4)) 57 | 58 | source_down, source_fpfh = preprocess_point_cloud(source, voxel_size) 59 | target_down, target_fpfh = preprocess_point_cloud(target, voxel_size) 60 | return source, target, source_down, target_down, source_fpfh, target_fpfh 61 | 62 | def execute_global_registration(source_down, target_down, source_fpfh, target_fpfh, voxel_size): 63 | distance_threshold = voxel_size * 1.5 64 | result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching( 65 | source_down, target_down, source_fpfh, target_fpfh, True, distance_threshold, 66 | o3d.pipelines.registration.TransformationEstimationPointToPoint(False), 4, [ 67 | o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9), 68 | o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)], 69 | o3d.pipelines.registration.RANSACConvergenceCriteria(4000000, 0.9)) 70 | 71 | return result 72 | 73 | 74 | voxel_size = 0.05 # means 5cm for this dataset 75 | source, target, source_down, target_down, source_fpfh, target_fpfh = prepare_dataset(voxel_size) 76 | 77 | result_ransac = execute_global_registration(source_down, target_down, 78 | source_fpfh, target_fpfh, 79 | voxel_size) 80 | print(result_ransac) 81 | visualize_simple(source_down, target_down, result_ransac.transformation) 82 | -------------------------------------------------------------------------------- /code/experiments/image-difference.py: -------------------------------------------------------------------------------- 1 | # https://stackoverflow.com/questions/21425992/how-to-subtract-two-images-using-python-opencv2-to-get-the-foreground-object 2 | # https://docs.opencv.org/4.x/d5/d69/tutorial_py_non_local_means.html 3 | 4 | import cv2 5 | import numpy as np 6 | 7 | video_path = "images/laser1a_720.mp4" 8 | cap = cv2.VideoCapture(video_path) 9 | 10 | old_frame = np.zeros((960, 540, 3), np.uint8) 11 | 12 | frame_number = 0 13 | while cap.isOpened(): 14 | ret, frame = cap.read() 15 | if ret: 16 | frame = cv2.resize(frame, (540, 960), interpolation=cv2.INTER_LINEAR) 17 | 18 | img = cv2.subtract(frame, old_frame) 19 | B, G, R = cv2.split(img.astype(np.float64)) 20 | 21 | average = (R + B + G) / 2 22 | average = average.clip(max=255).astype(np.uint8) 23 | 24 | img = cv2.merge([average, average, average]) 25 | cv2.imshow('difference', img) 26 | 27 | old_frame = frame 28 | 29 | if cv2.waitKey(1) & 0xFF == ord('q'): 30 | break 31 | else: 32 | break 33 | cap.release() 34 | cv2.destroyAllWindows() 35 | -------------------------------------------------------------------------------- /code/experiments/interactive-matplotlib.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pylab 3 | 4 | values = np.random.rand(2, 4, 3) 5 | print(values) 6 | 7 | def modify(values): 8 | values = values * 1.1 9 | print("modified", values) 10 | return values 11 | 12 | 13 | class plotter: 14 | def __init__(self, values): 15 | self.values = values 16 | self.fig = pylab.figure() 17 | pylab.gray() 18 | self.ax = self.fig.add_subplot(1, 1, 1, projection="3d") 19 | self.draw() 20 | self.fig.canvas.mpl_connect('key_press_event',self.key) 21 | 22 | def draw(self): 23 | x = self.values[:, 0:1] 24 | y = self.values[:, 1:2] 25 | z = self.values[:, 2:3] 26 | 27 | self.ax.scatter(x, z, y, marker=".", s=1) 28 | pylab.show() 29 | 30 | def key(self, event): 31 | if event.key=='right': 32 | self.values = modify() 33 | elif event.key == 'left': 34 | self.values = modify() 35 | 36 | self.draw() 37 | self.fig.canvas.draw() 38 | 39 | 40 | plot = plotter(values) 41 | plot.draw -------------------------------------------------------------------------------- /code/experiments/non-blocking-viz.py: -------------------------------------------------------------------------------- 1 | # http://www.open3d.org/docs/0.12.0/tutorial/visualization/non_blocking_visualization.html 2 | 3 | # examples/python/visualization/non_blocking_visualization.py 4 | 5 | import open3d as o3d 6 | import numpy as np 7 | import copy 8 | 9 | if __name__ == "__main__": 10 | o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) 11 | source_raw = o3d.io.read_point_cloud("../../open3D/examples/test_data/ICP/cloud_bin_0.pcd") 12 | target_raw = o3d.io.read_point_cloud("../../open3D/examples/test_data/ICP/cloud_bin_1.pcd") 13 | source = source_raw.voxel_down_sample(voxel_size=0.02) 14 | target = target_raw.voxel_down_sample(voxel_size=0.02) 15 | trans = [[0.862, 0.011, -0.507, 0.0], [-0.139, 0.967, -0.215, 0.7], 16 | [0.487, 0.255, 0.835, -1.4], [0.0, 0.0, 0.0, 1.0]] 17 | source.transform(trans) 18 | 19 | flip_transform = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]] 20 | source.transform(flip_transform) 21 | target.transform(flip_transform) 22 | 23 | vis = o3d.visualization.Visualizer() 24 | vis.create_window() 25 | vis.add_geometry(source) 26 | vis.add_geometry(target) 27 | threshold = 0.05 28 | icp_iteration = 100 29 | save_image = False 30 | 31 | for i in range(icp_iteration): 32 | reg_p2l = o3d.pipelines.registration.registration_icp( 33 | source, target, threshold, np.identity(4), 34 | o3d.pipelines.registration.TransformationEstimationPointToPlane(), 35 | o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=1)) 36 | source.transform(reg_p2l.transformation) 37 | vis.update_geometry(source) 38 | vis.poll_events() 39 | vis.update_renderer() 40 | if save_image: 41 | vis.capture_screen_image("temp_%04d.jpg" % i) 42 | vis.destroy_window() 43 | -------------------------------------------------------------------------------- /code/experiments/numpy-sort.py: -------------------------------------------------------------------------------- 1 | # https://stackoverflow.com/questions/2828059/sorting-arrays-in-numpy-by-column 2 | 3 | import numpy as np 4 | 5 | 6 | a = np.array([[5,2,3],[4,5,6],[3,6,4]]) 7 | 8 | def sort_numpy_by_column(array, column=0): 9 | return array[array[:,column].argsort()] 10 | 11 | a = sort_numpy_by_column(a, column=2) 12 | print(a) 13 | -------------------------------------------------------------------------------- /code/experiments/open3d-with-numpy.py: -------------------------------------------------------------------------------- 1 | # https://github.com/intel-isl/Open3D/blob/master/examples/Python/Basic/working_with_numpy.py 2 | 3 | import copy 4 | import numpy as np 5 | import open3d as o3d 6 | 7 | if __name__ == "__main__": 8 | 9 | # generate some neat n times 3 matrix using a variant of sync function 10 | x = np.linspace(-3, 3, 5) 11 | mesh_x, mesh_y = np.meshgrid(x, x) 12 | 13 | z = np.sinc((np.power(mesh_x, 2) + np.power(mesh_y, 2))) 14 | z_norm = (z - z.min()) / (z.max() - z.min()) 15 | 16 | xyz = np.zeros((np.size(mesh_x), 3)) 17 | xyz[:, 0] = np.reshape(mesh_x, -1) 18 | xyz[:, 1] = np.reshape(mesh_y, -1) 19 | xyz[:, 2] = np.reshape(z_norm, -1) 20 | print('xyz\n', xyz) 21 | 22 | # Pass xyz to Open3D.o3d.geometry.PointCloud and visualize 23 | pcd = o3d.geometry.PointCloud() 24 | pcd.points = o3d.utility.Vector3dVector(xyz) 25 | o3d.io.write_point_cloud("export/sync.ply", pcd) 26 | 27 | # Load saved point cloud and visualize it 28 | pcd_load = o3d.io.read_point_cloud("export/sync.ply") 29 | o3d.visualization.draw_geometries([pcd_load]) 30 | 31 | # convert Open3D.o3d.geometry.PointCloud to numpy array 32 | xyz_load = np.asarray(pcd_load.points) 33 | print('xyz_load') 34 | print(xyz_load) 35 | 36 | # # save z_norm as an image (change [0,1] range to [0,255] range with uint8 type) 37 | # img = o3d.geometry.Image((z_norm * 255).astype(np.uint8)) 38 | # o3d.io.write_image("3D/export/sync.png", img) 39 | # o3d.visualization.draw_geometries([img]) 40 | 41 | 42 | def save_as_ply(xyz, path): 43 | # Pass xyz to Open3D.o3d.geometry.PointCloud 44 | pcd = o3d.geometry.PointCloud() 45 | pcd.points = o3d.utility.Vector3dVector(xyz) 46 | o3d.io.write_point_cloud(path, pcd) -------------------------------------------------------------------------------- /code/experiments/scatterplot.py: -------------------------------------------------------------------------------- 1 | ''' 2 | tutorial: 3 | https://realpython.com/python-opencv-color-spaces/ 4 | 5 | original bei matlab 6 | https://www.mathworks.com/help/images/image-segmentation-using-the-color-thesholder-app.html 7 | ''' 8 | 9 | import cv2 10 | import numpy as np 11 | from mpl_toolkits.mplot3d import Axes3D 12 | import matplotlib.pyplot as plt 13 | 14 | 15 | 16 | 17 | 18 | points = np.zeros(shape=(10, 4)) 19 | 20 | 21 | x = points[:, 1:2] 22 | y = points[:, 2:3] 23 | z = points[:, 3:4] 24 | 25 | # 3D scatterplot 26 | fig = plt.figure() 27 | axis = fig.add_subplot(1, 1, 1, projection="3d") 28 | axis.scatter(x, y, z, marker=".") 29 | axis.set_xlabel("X") 30 | axis.set_ylabel("Y") 31 | axis.set_zlabel("Z") 32 | plt.show() -------------------------------------------------------------------------------- /code/experiments/test_data/cloud_bin_0.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/code/experiments/test_data/cloud_bin_0.pcd -------------------------------------------------------------------------------- /code/experiments/test_data/cloud_bin_1.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/code/experiments/test_data/cloud_bin_1.pcd -------------------------------------------------------------------------------- /code/experiments/test_data/cloud_bin_2.pcd: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/code/experiments/test_data/cloud_bin_2.pcd -------------------------------------------------------------------------------- /code/experiments/vector-intersection.py: -------------------------------------------------------------------------------- 1 | #Based on http://geomalgorithms.com/a05-_intersect-1.html 2 | #https://stackoverflow.com/questions/5666222/3d-line-plane-intersection 3 | 4 | import numpy as np 5 | import math 6 | 7 | def triangulate(pixel, root, rayPoint, planePoint, planeNormal): 8 | #Pixel vector relative to image root point 9 | rayDirection = np.array([pixel[0]+root[0], root[1]-pixel[1], root[2]]) 10 | # print(rayDirection) 11 | 12 | ndotu = planeNormal.dot(rayDirection) 13 | 14 | # check if parallel or in-plane 15 | almost_zero=1e-6 16 | if abs(ndotu) < almost_zero: 17 | print ("[WARNING] no intersection or line is within plane") 18 | return False 19 | else: 20 | w = rayPoint - planePoint 21 | si = -planeNormal.dot(w) / ndotu 22 | Psi = w + si * rayDirection + planePoint 23 | 24 | if Psi[2] > 0: 25 | print ("3D position: ", Psi) 26 | return Psi 27 | else: 28 | print("[WARNING] intersection behind camera ?!") 29 | return False 30 | 31 | 32 | pixel = (320, 240) 33 | dims = (640, 480) 34 | c = 10 # cm Camera|Laser 35 | beta_degree = -5. # Laser Angle 36 | fov_degree = 60. # Camera horizontal Field of View 37 | 38 | 39 | #ONCE 40 | deg2rad = math.pi/180 41 | fov = fov_degree * deg2rad 42 | lens_length = dims[0]/(2*math.tan(fov/2)) 43 | # print('lens', lens_length) 44 | root = np.array([-dims[0]/2, dims[1]/2, lens_length]) 45 | # print('root', root) 46 | 47 | #Laser position 48 | planePoint = np.array([c, 0, 0]) 49 | #Camera position 50 | rayPoint = np.array([0, 0, 0]) 51 | 52 | 53 | #EACH FRAME 54 | beta = beta_degree * deg2rad 55 | planeNormal = np.array([-1, 0, math.tan(beta)]) #Laser plane normal vector 56 | 57 | 58 | #EACH LINE 59 | point = triangulate(pixel, root, rayPoint, planePoint, planeNormal) 60 | -------------------------------------------------------------------------------- /code/experiments/video.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | 3 | def canny(gray): 4 | # https://www.geeksforgeeks.org/find-and-draw-contours-using-opencv-python/ 5 | # Find Canny edges 6 | edged = gray.copy() 7 | edged = cv2.Canny(gray, 30, 200) 8 | contours, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 9 | return edged, contours, hierarchy 10 | 11 | 12 | videopath = "images/laser1a_720.mp4" 13 | 14 | 15 | camera = cv2.VideoCapture(videopath) 16 | 17 | while True: 18 | # grab the current frame 19 | (grabbed, frame) = camera.read() 20 | if grabbed is False: 21 | break 22 | # cv2.imshow("Frame", frame) 23 | 24 | # Grayscale 25 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 26 | 27 | edged, contours, hierarchy = canny(gray) 28 | 29 | cv2.imshow('Canny Edges After Contouring', edged) 30 | 31 | key = cv2.waitKey(1) & 0xFF 32 | if key == ord("q"): 33 | break 34 | 35 | camera.release() 36 | cv2.destroyAllWindows() 37 | -------------------------------------------------------------------------------- /code/experiments/weighted-average.py: -------------------------------------------------------------------------------- 1 | # https://stackoverflow.com/questions/30057046/weighted-mean-in-numpy-python#30057626 2 | 3 | import numpy as np 4 | 5 | values = np.array([1,2,3,4,5, 417, 418, 419, 420, 421, 422, 423, 424]) 6 | intensities = np.array([1,1,1,1,1, 1, 1, 20, 50, 60, 80, 90, 255]) 7 | 8 | # normalized brightness as weights 9 | weights = intensities/255 10 | print("weights", weights) 11 | 12 | weighted_average = np.sum(values * weights) / np.sum(weights) 13 | print("weighted average", weighted_average) 14 | 15 | average = np.sum(values) / len(values) 16 | print("average", average) -------------------------------------------------------------------------------- /code/lib/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/code/lib/__init__.py -------------------------------------------------------------------------------- /code/lib/image.py: -------------------------------------------------------------------------------- 1 | """ 2 | rotate_bound forked from jrosebr1:imutils (MIT license) 3 | https://github.com/PyImageSearch/imutils/blob/master/imutils/convenience.py 4 | """ 5 | 6 | import numpy as np 7 | import cv2 8 | from scipy.signal import find_peaks 9 | from matplotlib import pyplot as plt 10 | 11 | 12 | def find_laser(img, channel=2, threshold=180, preview_on_black=False, texture=None, desaturate=False): 13 | 14 | def find_line_maxima(row, threshold=180, distance=5, multi=False): 15 | if multi is False: 16 | max_indices = np.nonzero(row > threshold) 17 | if len(max_indices[0]) == 0: 18 | return 0 19 | else: 20 | # initialize array for pixel intensities 21 | intensities = np.zeros(len(max_indices[0])) 22 | # once for every element in max_indices 23 | for n in range(len(max_indices[0])): 24 | intensity = row[max_indices[0][n]] 25 | intensities[n] = intensity 26 | # WEIGHTED AVERAGE 27 | weights = intensities/255 28 | max_index = np.sum(max_indices * weights) / np.sum(weights) 29 | return max_index 30 | 31 | # find multiple Maxima 32 | else: 33 | row = smooth_line(row, blur=3) 34 | maxima, _ = find_peaks(row, height=threshold, distance=distance) 35 | if len(maxima) == 0: 36 | return 0 37 | else: 38 | return maxima 39 | 40 | def smooth_line(array, blur=5, window='hamming'): 41 | """ 42 | https://scipy-cookbook.readthedocs.io/items/SignalSmooth.html 43 | 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' 44 | """ 45 | s = np.r_[array[blur - 1:0:-1], array, array[-2:-blur - 1:-1]] 46 | if window == 'flat': 47 | w = np.ones(blur, 'd') 48 | else: 49 | w = eval('np.' + window + '(blur)') 50 | array = np.convolve(w / w.sum(), s, mode='valid') 51 | array = array[blur - 1:] 52 | return array 53 | 54 | def draw_point(image, pos, color=(255, 0, 0)): 55 | cv2.line(image, (int(pos[0]), int(pos[1])), (int(pos[0]+2), int(pos[1])), color, 1) 56 | return image 57 | 58 | def plot_row(row, points): 59 | length = len(row) 60 | x = np.linspace(0, length, length, endpoint=False) 61 | plt.plot(x, row, "bo", ms=3) 62 | plt.plot(x, row, "b") 63 | plt.plot(x[points], row[points], "rD", ms=4, label="selected") 64 | plt.legend() 65 | plt.show() 66 | 67 | # extract single color channel for maxima search 68 | img_channel = img[:, :, channel] 69 | 70 | # if texture is not numpy, use image 71 | if not isinstance(texture, np.ndarray): 72 | texture = img 73 | # if texture is grayscale, convert to RGB 74 | elif texture.shape[2] != 3: 75 | texture = cv2.cvtColor(texture, cv2.COLOR_GRAY2BGR) 76 | 77 | if desaturate: 78 | texture = cv2.cvtColor(cv2.cvtColor(texture, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) 79 | 80 | if preview_on_black: 81 | preview_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8) 82 | else: 83 | preview_img = cv2.cvtColor(img_channel, cv2.COLOR_GRAY2BGR) 84 | 85 | # # initialize array of 2D (+ later 3D) coordinates + RGB values for this frame 86 | pointlist = np.zeros(shape=(img_channel.shape[0], 8)) 87 | 88 | # vertical laser line -> work through all rows and save x and RGB 89 | for y in range(img.shape[0]): 90 | # crop to current row 91 | row = img_channel[y:y + 1, :][0] 92 | # search for brightness maximum, else return -1 93 | x = find_line_maxima(row, threshold=threshold) 94 | 95 | # TODO: now there could be multiple maxima 96 | if isinstance(x, np.ndarray): 97 | x = x[0] 98 | 99 | if x < 0.5: # if nothing found, skip line 100 | continue 101 | 102 | # plot_row(row, x) 103 | 104 | # screenspace coordinates (2D) at [0:2] 105 | pointlist[y][0] = x 106 | pointlist[y][1] = y 107 | # RGB values at [5:8] (reversed opencv order) 108 | pointlist[y][5] = texture[int(y), int(x)][2] 109 | pointlist[y][6] = texture[int(y), int(x)][1] 110 | pointlist[y][7] = texture[int(y), int(x)][0] 111 | 112 | # draw current point into preview image 113 | preview_img = draw_point(preview_img, (x, y)) 114 | return pointlist, preview_img 115 | 116 | 117 | def subtract_images(current_frame, previous_frame, return_RGB=True): 118 | img = cv2.subtract(current_frame, previous_frame).astype(np.float64) 119 | average = np.mean(img, axis=2).clip(max=255).astype(np.uint8) 120 | 121 | return cv2.merge([average, average, average]) if return_RGB else average 122 | 123 | 124 | def rotate_bound(image, angle): 125 | # grab the dimensions of the image and then determine the center 126 | (h, w) = image.shape[:2] 127 | (cX, cY) = (w / 2, h / 2) 128 | 129 | # grab the rotation matrix (applying the negative of the angle to rotate clockwise), 130 | # then grab the sine and cosine (i.e., the rotation components of the matrix) 131 | M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) 132 | cos = np.abs(M[0, 0]) 133 | sin = np.abs(M[0, 1]) 134 | 135 | # compute the new bounding dimensions of the image 136 | nW = int((h * sin) + (w * cos)) 137 | nH = int((h * cos) + (w * sin)) 138 | 139 | # adjust the rotation matrix to take into account translation 140 | M[0, 2] += (nW / 2) - cX 141 | M[1, 2] += (nH / 2) - cY 142 | 143 | # perform the actual rotation and return the image 144 | return cv2.warpAffine(image, M, (nW, nH)) 145 | -------------------------------------------------------------------------------- /code/lib/mesh.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import open3d as o3d 3 | import os 4 | 5 | 6 | def estimate_mesh_normals(mesh): 7 | return mesh.compute_vertex_normals() 8 | 9 | def mesh_optimize(mesh, count=1000000): 10 | mesh = mesh.simplify_quadric_decimation(count) 11 | mesh.remove_degenerate_triangles() 12 | mesh.remove_duplicated_triangles() 13 | mesh.remove_duplicated_vertices() 14 | mesh.remove_non_manifold_edges() 15 | return mesh 16 | 17 | def mesh_from_alpha_shape(pcd, alpha=0.03): 18 | return o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pcd, alpha) 19 | 20 | def mesh_from_ball_pivoting(pcd): 21 | '''https://towardsdatascience.com/5-step-guide-to-generate-3d-meshes-from-point-clouds-with-python-36bad397d8ba''' 22 | distances = pcd.compute_nearest_neighbor_distance() 23 | avg_dist = np.mean(distances) 24 | radius = avg_dist * 2 25 | ball_radii = o3d.utility.DoubleVector([radius, radius * 2]) 26 | mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_ball_pivoting(pcd, ball_radii) 27 | return mesh 28 | 29 | def mesh_from_poisson(pcd, depth=9, normal_plane=100): 30 | pcd.estimate_normals() 31 | pcd.orient_normals_consistent_tangent_plane(normal_plane) 32 | mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=depth) 33 | return mesh, densities 34 | 35 | def remove_low_density_vertices(mesh, densities, quantile=0.01): 36 | vertices_to_remove = densities < np.quantile(densities, quantile) 37 | return mesh.remove_vertices_by_mask(vertices_to_remove) 38 | 39 | 40 | if __name__ == "__main__": 41 | path0 = "code/experiments/test_data/cloud_bin_0.pcd" 42 | pcd = o3d.io.read_point_cloud(path0) 43 | 44 | mesh, densities = mesh_from_poisson(pcd, depth=10, normal_plane=100) 45 | o3d.visualization.draw_geometries([mesh], mesh_show_back_face=False) 46 | 47 | remove_low_density_vertices(mesh, densities, quantile=0.01) 48 | o3d.visualization.draw_geometries([mesh], mesh_show_back_face=True) 49 | -------------------------------------------------------------------------------- /code/lib/pointcloud.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import open3d as o3d 3 | import copy 4 | 5 | 6 | def set_verbosity(): 7 | o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Error) # .Debug 8 | 9 | def sample_poisson_disk(pcd, count=1000000): 10 | return pcd.sample_points_poisson_disk(count) 11 | 12 | def estimate_point_normals(pcd, radius=0.1, max_nn=30): 13 | search_param = o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn) 14 | pcd.estimate_normals(search_param=search_param) 15 | return pcd 16 | 17 | def export_pointcloud(pcd, savepath, type="pcd", write_ascii=True, compressed=True): 18 | if type == "pcd" or type == "ply": 19 | if write_ascii: 20 | compressed = False 21 | o3d.io.write_point_cloud(savepath+"."+type, pcd, write_ascii=write_ascii, compressed=compressed), 22 | 23 | elif type == "csv": 24 | if not isinstance(pcd, np.ndarray): 25 | array = np.asarray(pcd.points) 26 | np.savetxt(savepath+"."+type, array, delimiter=",") 27 | 28 | def fpfh_from_pointcloud(pcd, radius=5, max_nn=100): 29 | ''' Fast Point Feature Histograms (FPFH) descriptor''' 30 | search_param = o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn) 31 | return o3d.pipelines.registration.compute_fpfh_feature(pcd, search_param) 32 | 33 | def preprocess_point_cloud(pcd, voxel_size=0): 34 | # downsample 35 | if voxel_size > 0: 36 | pcd_down = pcd.voxel_down_sample(voxel_size) 37 | else: 38 | pcd_down = copy.deepcopy(pcd) 39 | 40 | # estimate normals 41 | pcd_down = estimate_point_normals(pcd_down, radius=voxel_size*2, max_nn=30) 42 | 43 | # compute FPFH feature 44 | pcd_fpfh = fpfh_from_pointcloud(pcd_down, radius=voxel_size*5, max_nn=100) 45 | 46 | return pcd_down, pcd_fpfh 47 | 48 | 49 | if __name__ == "__main__": 50 | from visualization import visualize 51 | from mesh import mesh_from_ball_pivoting, mesh_optimize 52 | 53 | pcd = o3d.io.read_point_cloud("export/laser1a_720.pcd") 54 | pcd_down = preprocess_point_cloud(pcd, voxel_size=0) # if voxel_size > 0: return pcd_down, pcd_fpfh 55 | 56 | mesh = mesh_from_ball_pivoting(pcd) 57 | mesh = mesh_optimize(mesh, count=1000000) 58 | visualize([mesh]) 59 | -------------------------------------------------------------------------------- /code/lib/registration.py: -------------------------------------------------------------------------------- 1 | import open3d as o3d 2 | import numpy as np 3 | import copy 4 | 5 | 6 | def global_registration(source_down, target_down, source_fpfh, target_fpfh, distance_threshold, use_fast=False, max_iteration=4000000, confidence=0.9): 7 | if use_fast: 8 | result = o3d.pipelines.registration.registration_fgr_based_on_feature_matching( 9 | source_down, target_down, source_fpfh, target_fpfh, 10 | o3d.pipelines.registration.FastGlobalRegistrationOption( 11 | maximum_correspondence_distance=distance_threshold)) 12 | 13 | else: # RANSAC 14 | result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching( 15 | source_down, target_down, source_fpfh, target_fpfh, True, distance_threshold, 16 | o3d.pipelines.registration.TransformationEstimationPointToPoint(False), 4, [ 17 | o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9), 18 | o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)], 19 | o3d.pipelines.registration.RANSACConvergenceCriteria(max_iteration, confidence)) 20 | 21 | return result 22 | 23 | def ICP_registration(source, target, distance_threshold, transformation, use_p2l=True, p2p_max_iteration=200): 24 | if use_p2l: # point-to-plane ICP 25 | icp_method = o3d.pipelines.registration.TransformationEstimationPointToPlane() 26 | 27 | return o3d.pipelines.registration.registration_icp( 28 | source, target, distance_threshold, transformation, icp_method) 29 | 30 | else: # point-to-point ICP 31 | icp_method = o3d.pipelines.registration.TransformationEstimationPointToPoint() 32 | convergence_criteria = o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=p2p_max_iteration) 33 | 34 | return o3d.pipelines.registration.registration_icp( 35 | source, target, distance_threshold, transformation, icp_method, convergence_criteria) 36 | 37 | def evaluate_registration(source, target, threshold, transformation=None): 38 | source_temp = copy.deepcopy(source) 39 | 40 | if transformation is None: 41 | transformation = np.identity(4) 42 | source_temp.transform(transformation) 43 | 44 | return o3d.pipelines.registration.evaluate_registration(source_temp, target, threshold, transformation) 45 | -------------------------------------------------------------------------------- /code/lib/transformation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import copy 3 | from scipy.spatial.transform import Rotation as R 4 | 5 | 6 | def get_transform_vectors(transform_M): 7 | # Extract translation (top-right 3x1 sub-matrix) 8 | translation = transform_M[:3, 3] 9 | 10 | # Extract rotation (top-left 3x3 sub-matrix), make a copy to avoid read only error 11 | rotation_M = np.array(transform_M[:3, :3]) 12 | # Convert rotation matrix to Euler angles 13 | r = R.from_matrix(rotation_M) 14 | euler_angles = r.as_euler('xyz', degrees=True) 15 | 16 | return translation, euler_angles 17 | 18 | def transform(pcd, transformation=None, translate=None, euler_rotate_deg=None, pivot=(0,0,0)): 19 | pcd_temp = copy.deepcopy(pcd) 20 | 21 | if transformation is not None: 22 | pcd_temp.transform(transformation) 23 | 24 | if translate is not None: 25 | pcd_temp.translate(translate) 26 | 27 | if euler_rotate_deg is not None: 28 | euler_rotate_rad = np.deg2rad(euler_rotate_deg) 29 | rotation_matrix = pcd_temp.get_rotation_matrix_from_xyz(euler_rotate_rad) 30 | pcd_temp.rotate(rotation_matrix, center=pivot) 31 | 32 | return pcd_temp 33 | -------------------------------------------------------------------------------- /code/lib/visualization.py: -------------------------------------------------------------------------------- 1 | import open3d as o3d 2 | import copy 3 | 4 | try: 5 | from lib.transformation import transform 6 | except: 7 | from transformation import transform 8 | 9 | 10 | def init_visualizer(width=1920, 11 | height=1080, 12 | left=0, 13 | point_size=1.5, 14 | unlit=False, 15 | backface=True): 16 | vis = o3d.visualization.Visualizer() 17 | vis.create_window(width=width, height=height, left=left) 18 | 19 | render_option = vis.get_render_option() 20 | render_option.point_size = point_size 21 | render_option.light_on = False if unlit else True 22 | render_option.mesh_show_back_face = backface 23 | 24 | # TODO: view_control not working 25 | # view_control = vis.get_view_control() 26 | # view_control.set_zoom(0.4559) # 0.2 27 | # view_control.set_front([0.6452, -0.3036, -0.7011]) # (0.0, 0.0, 0.01) 28 | # view_control.set_lookat([1.9892, 2.0208, 1.8945]) # (0.0, 0.0, -1.0) 29 | # view_control.set_up([-0.2779, -0.9482, 0.1556]) # (0.0, 1.0, 0.0) 30 | return vis 31 | 32 | def update_visualizer(vis, object): 33 | # Update 3D-view each line 34 | vis.update_geometry(object) 35 | vis.poll_events() 36 | vis.update_renderer() 37 | 38 | def visualize(object_list, 39 | transformation=None, 40 | width=1800, 41 | height=1000, 42 | left=0, 43 | point_size=1.5, 44 | uniform_colors=False, 45 | unlit=False): 46 | vis = init_visualizer(width=width, height=height, left=left, point_size=point_size, unlit=unlit) 47 | 48 | object_list = copy.deepcopy(object_list) 49 | 50 | if transformation is not None: 51 | object_list[0] = transform(object_list[0], transformation=transformation) 52 | 53 | if uniform_colors: 54 | object_list = copy.deepcopy(object_list) 55 | object_list[0].paint_uniform_color([1, 0.706, 0]) 56 | object_list[1].paint_uniform_color([0, 0.651, 0.929]) 57 | 58 | # Add the geometry to the visualization window 59 | for object in object_list: 60 | vis.add_geometry(object) 61 | 62 | vis.run() 63 | vis.destroy_window() 64 | 65 | def visualize_simple(mesh1, mesh2, transformation, uniform_colors=True): 66 | mesh1_temp = copy.deepcopy(mesh1) 67 | mesh2_temp = copy.deepcopy(mesh2) 68 | 69 | if uniform_colors: 70 | mesh1_temp.paint_uniform_color([1, 0.706, 0]) 71 | mesh2_temp.paint_uniform_color([0, 0.651, 0.929]) 72 | 73 | mesh1_temp.transform(transformation) 74 | o3d.visualization.draw_geometries([mesh1_temp, mesh2_temp], 75 | zoom=0.4559, 76 | front=[0.6452, -0.3036, -0.7011], 77 | lookat=[1.9892, 2.0208, 1.8945], 78 | up=[-0.2779, -0.9482, 0.1556], 79 | mesh_show_back_face=True) 80 | -------------------------------------------------------------------------------- /code/lib/visualization_mpl.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | # from mpl_toolkits.mplot3d import Axes3D 4 | 5 | 6 | def scatterplot(pcd): 7 | array = np.asarray(pcd.points) 8 | 9 | fig = plt.figure() 10 | axis = fig.add_subplot(1, 1, 1, projection="3d") 11 | 12 | # matplotlib is Z-up, I am Y-up 13 | axis.set_xlabel("X") 14 | axis.set_ylabel("Z") 15 | axis.set_zlabel("Y") 16 | 17 | x = array[:, 0:1] 18 | y = array[:, 1:2] 19 | z = array[:, 2:3] 20 | 21 | axis.scatter(x, z, y, marker=".", s=1) 22 | 23 | limit = max(y) 24 | axis.set_xlim3d(-limit/2, limit/2) 25 | axis.set_ylim3d(-limit, 0) 26 | axis.set_zlim3d(0, limit) 27 | 28 | plt.show() 29 | -------------------------------------------------------------------------------- /code/linescanner.py: -------------------------------------------------------------------------------- 1 | """ 2 | ░░ ░░ ░░░ ░░ ░░░░░░░ ░░░░░░░ ░░░░░░ ░░░░░ ░░░ ░░ ░░░ ░░ ░░░░░░░ ░░░░░░ 3 | ▒▒ ▒▒ ▒▒▒▒ ▒▒ ▒▒ ▒▒ ▒▒ ▒▒ ▒▒ ▒▒▒▒ ▒▒ ▒▒▒▒ ▒▒ ▒▒ ▒▒ ▒▒ 4 | ▒▒ ▒▒ ▒▒ ▒▒ ▒▒ ▒▒▒▒▒ ▒▒▒▒▒▒▒ ▒▒ ▒▒▒▒▒▒▒ ▒▒ ▒▒ ▒▒ ▒▒ ▒▒ ▒▒ ▒▒▒▒▒ ▒▒▒▒▒▒ 5 | ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ ▓▓ 6 | ███████ ██ ██ ████ ███████ ███████ ██████ ██ ██ ██ ████ ██ ████ ███████ ██ ██ 7 | """ 8 | import numpy as np 9 | import cv2 10 | import math 11 | import open3d as o3d 12 | import json 13 | 14 | from lib.pointcloud import set_verbosity, estimate_point_normals, export_pointcloud 15 | from lib.visualization import init_visualizer, update_visualizer, visualize 16 | from lib.image import find_laser, subtract_images # , rotate_bound 17 | 18 | class LineScanner: 19 | def __init__(self, config_path): 20 | 21 | # Load settings from JSON file 22 | with open(config_path, 'r') as f: 23 | configs = json.load(f) 24 | 25 | self.video_path = configs['video_path'] 26 | self.export_path = configs['export_path'] 27 | self.export_type = configs['export_type'] 28 | self.cam_pos = np.array(configs['cam_pos']) 29 | self.laser_pos = np.array(configs['laser_pos']) 30 | self.hfov = configs['horizontal_fov'] 31 | # self.sweep_direction = configs['sweep_direction'] # TODO: unused 32 | 33 | self.sweep_step = configs['sweep_step'] 34 | self.laser_thres = configs['laser_thres'] 35 | 36 | self.shrink_x = configs['shrink_x'] 37 | self.shrink_y = configs['shrink_y'] 38 | self.shrink_preview = configs['shrink_preview'] 39 | self.window_size = configs['window_size'] 40 | self.verbose = configs['verbose'] 41 | 42 | self.sweep_angle = configs['sweep_startangle'] 43 | self.frame_index = 0 44 | self.cap = cv2.VideoCapture(self.video_path) # load frame 0 to get dimensions, 45 | self.iterate_frame() # then increment sweep_angle and frame_index 46 | 47 | ret, frame = self.cap.read() 48 | source_h, source_w = frame.shape[:2] 49 | self.input_dims = (source_w, source_h) 50 | self.preview_width = int(source_w / self.shrink_preview) 51 | self.preview_height = int(source_h / self.shrink_preview) 52 | self.width = int(source_w / self.shrink_x) 53 | self.height = int(source_h / self.shrink_y) 54 | self.dims = (self.width, self.height) 55 | self.preview_dims = (self.preview_width, self.preview_height) 56 | self.zero_value = configs['zero_value'] 57 | self.KDTree_radius = configs['KDTree_radius'] 58 | self.KDTree_max_nn = configs['KDTree_max_nn'] 59 | 60 | # load image texture 61 | texpath = configs['texture_path'] 62 | self.texture = cv2.resize(cv2.imread(texpath, 1), self.dims, interpolation=cv2.INTER_LINEAR) if texpath != "" else None 63 | self.desaturate = configs['desaturate'] 64 | 65 | # reduce vertical resolution 66 | self.vertical_stretch = (source_w / self.width) / (source_h / self.height) 67 | 68 | self.fov_rad = math.radians(self.hfov) 69 | self.lens_length = self.dims[0] / (2 * math.tan(self.fov_rad / 2)) 70 | 71 | # project imageplane into 3d space 72 | self.topleft_corner = np.array([-self.dims[0] / 2, self.dims[1] / 2, self.lens_length]) 73 | 74 | # init interactive 3D-Viewer 75 | self.vis = init_visualizer(width=self.window_size[0], height=self.window_size[1]) 76 | 77 | self.pointcloud = o3d.geometry.PointCloud() 78 | self.pointcloud_frame = o3d.geometry.PointCloud() 79 | 80 | ss = 50 # scenesize TODO: scale seems to be sensitive; will require scale-to-fit and wider clipping plane 81 | self.pointcloud.points = o3d.utility.Vector3dVector(np.array([[-ss, -ss, -ss], [ss, ss, ss]])) 82 | self.pointcloud.colors = o3d.utility.Vector3dVector(np.array([[1, 0, 0], [1, 0, 0]])) 83 | self.vis.add_geometry(self.pointcloud) 84 | 85 | def triangulate(self, pixel, plane_normal): 86 | # Pixel vector relative to image topleft_corner point 87 | rayDirection = np.array([pixel[0] + self.topleft_corner[0], self.topleft_corner[1] - pixel[1], self.topleft_corner[2]]) 88 | 89 | dotProduct = plane_normal.dot(rayDirection) 90 | 91 | # check if parallel or in-plane 92 | if abs(dotProduct) < self.zero_value: 93 | print("[WARNING] no intersection at line", pixel[1]) 94 | return np.array([0, 0, 0]) 95 | else: 96 | w = self.cam_pos - self.laser_pos 97 | si = -plane_normal.dot(w) / dotProduct 98 | intersection = w + si * rayDirection + self.laser_pos 99 | 100 | if intersection[2] > 0: 101 | return intersection 102 | # print("[WARNING] intersection behind camera") 103 | return np.array([0, 0, 0]) 104 | 105 | # def sort_numpy_by_column(array, column=0): 106 | # return array[array[:, column].argsort()] 107 | 108 | def scan(self): 109 | previous_frame = self.init_framebuffer() 110 | 111 | # MAIN LOOP 112 | while True: 113 | # calculate current normal-vector of laserplane 114 | plane_normal = self.calculate_plane_normal() 115 | if self.verbose: 116 | print(f"frame {self.frame_index} | laser-angle {self.sweep_angle}°") 117 | 118 | difference_map, frame = self.read_and_resize_frame(previous_frame) 119 | if difference_map is None: 120 | break 121 | 122 | pointlist = self.process_frame(difference_map, frame, plane_normal) 123 | self.append_points(pointlist) 124 | 125 | # update preview for each frame 126 | update_visualizer(self.vis, self.pointcloud) 127 | 128 | self.iterate_frame() 129 | previous_frame = frame 130 | 131 | if cv2.waitKey(1) & 0xFF == ord('q'): 132 | break 133 | 134 | self.vis.destroy_window() 135 | cv2.destroyAllWindows() 136 | 137 | # calculate point normals 138 | self.pointcloud = estimate_point_normals(self.pointcloud, radius=self.KDTree_radius, max_nn=self.KDTree_max_nn) 139 | 140 | # export PCD, PLY or CSV model 141 | export_pointcloud(self.pointcloud, self.export_path, type=self.export_type, write_ascii=True) 142 | print("export successful.") 143 | 144 | # display static scene 145 | visualize([self.pointcloud], width=self.window_size[0], height=self.window_size[1], left=1000) 146 | 147 | def init_framebuffer(self): 148 | return np.zeros((self.dims[1], self.dims[0], 3), np.uint8) 149 | 150 | def calculate_plane_normal(self): 151 | laser_angle_rad = math.radians(self.sweep_angle) # beta 152 | return np.array([-1, 0, math.tan(laser_angle_rad)]) 153 | 154 | def read_and_resize_frame(self, previous_frame): 155 | (grabbed, frame) = self.cap.read() 156 | if grabbed is False: # check if file is finished 157 | return None, previous_frame 158 | frame = cv2.resize(frame, self.dims, interpolation=cv2.INTER_LINEAR) 159 | difference_map = subtract_images(frame, previous_frame, return_RGB=True) 160 | return difference_map, frame 161 | 162 | def process_frame(self, difference_map, frame, plane_normal): 163 | # use texture if available 164 | tex = frame if self.texture is None else self.texture 165 | 166 | # search frame for laserline, returns ndarray and preview image. 167 | # format: ndarray[height, 8]->[[x_2d,y_2d,x,y,z,r,g,b]..] with y_2d as index 168 | pointlist, preview_img = find_laser(difference_map, channel=2, threshold=self.laser_thres, texture=tex, desaturate=self.desaturate) 169 | preview_img = cv2.resize(preview_img, self.preview_dims, interpolation=cv2.INTER_NEAREST) 170 | cv2.imshow('preview', preview_img) 171 | 172 | # run through each row to triangulate a 3D point 173 | for y, values in enumerate(pointlist): 174 | x = values[0] 175 | if x < 0.5: # skip lines without matches 176 | continue 177 | 178 | point3d = self.triangulate((x, y), plane_normal) 179 | 180 | # add 3D coordinates to pointlist 181 | pointlist[y][2] = point3d[0] 182 | pointlist[y][3] = point3d[1] / self.vertical_stretch 183 | pointlist[y][4] = point3d[2] * -1 184 | 185 | # remove empty rows 186 | pointlist = pointlist[~np.all(pointlist == 0, axis=1)] 187 | return pointlist 188 | 189 | def append_points(self, pointlist): 190 | def to_vector3d(data): 191 | return o3d.utility.Vector3dVector(data) 192 | 193 | # Update points and colors 194 | self.pointcloud_frame.points = to_vector3d(pointlist[:, 2:5]) 195 | self.pointcloud_frame.colors = to_vector3d(pointlist[:, 5:8] / 255) 196 | self.pointcloud += self.pointcloud_frame 197 | 198 | def iterate_frame(self): 199 | self.frame_index += 1 200 | self.sweep_angle += self.sweep_step 201 | 202 | 203 | if __name__ == '__main__': 204 | set_verbosity() 205 | 206 | # config = 'images/laser1a_2048_config.json' 207 | # config = 'images/laser1a_720_config.json' 208 | # config = 'images/laser1b_720_config.json' 209 | config = 'images/laser2_config.json' 210 | 211 | linescanner = LineScanner(config) 212 | linescanner.scan() 213 | -------------------------------------------------------------------------------- /code/meshing_test.py: -------------------------------------------------------------------------------- 1 | """ 2 | http://www.open3d.org/docs/latest/tutorial/Advanced/surface_reconstruction.html 3 | 4 | normals: http://www.open3d.org/docs/release/python_api/open3d.geometry.PointCloud.html 5 | """ 6 | 7 | import open3d as o3d 8 | from lib.mesh import mesh_from_alpha_shape, estimate_mesh_normals 9 | from lib.pointcloud import sample_poisson_disk 10 | 11 | 12 | bunny = o3d.data.BunnyMesh() 13 | mesh = o3d.io.read_triangle_mesh(bunny.path) 14 | estimate_mesh_normals(mesh) 15 | 16 | pcd = sample_poisson_disk(mesh, count=1000) 17 | o3d.visualization.draw_geometries([mesh, pcd]) 18 | 19 | mesh = mesh_from_alpha_shape(pcd) 20 | estimate_mesh_normals(mesh) 21 | o3d.visualization.draw_geometries([mesh, pcd], mesh_show_back_face=True) 22 | -------------------------------------------------------------------------------- /code/registration_test.py: -------------------------------------------------------------------------------- 1 | """ 2 | http://www.open3d.org/docs/release/tutorial/visualization/non_blocking_visualization.html 3 | http://www.open3d.org/docs/latest/tutorial/Basic/transformation.html 4 | 5 | https://www.open3d.org/docs/latest/tutorial/Advanced/global_registration.html 6 | https://www.open3d.org/docs/latest/tutorial/Basic/icp_registration.html 7 | """ 8 | 9 | import open3d as o3d 10 | import os 11 | import time 12 | 13 | from lib.transformation import get_transform_vectors, transform 14 | from lib.pointcloud import set_verbosity, preprocess_point_cloud, export_pointcloud 15 | from lib.registration import global_registration, ICP_registration 16 | from lib.visualization import visualize # visualize_simple 17 | 18 | 19 | # # LINESCANNER GROUND-TRUTH 20 | # groundtruth_translation = (50, 0, 100) 21 | # groundtruth_euler = (0.0, 20.0, 0) 22 | # # groundtruth_source = transform(source, translate=translate, euler_rotate_deg=rotate) 23 | 24 | set_verbosity() 25 | 26 | voxel_size = 0.05 # meter units # 3 # cm units 27 | gr_max_iteration = 1000000 28 | gr_confidence = 0.9 29 | 30 | icp_threshold = voxel_size * 0.4 31 | p2p_max_iteration = 200 32 | 33 | verbose = False 34 | 35 | basedir = "code/experiments/test_data" # "export" 36 | path0 = os.path.join(basedir, "cloud_bin_0.pcd") # "laser1a_720.pcd" 37 | path1 = os.path.join(basedir, "cloud_bin_1.pcd") # "laser1b_720.pcd" 38 | path2 = os.path.join(basedir, "cloud_bin_2.pcd") 39 | 40 | # TODO: 0 <> 2 not working with P2L 41 | source = o3d.io.read_point_cloud(path2) 42 | target = o3d.io.read_point_cloud(path0) 43 | 44 | # downsample, compute normals, and compute FPFH feature 45 | source_down, source_fpfh = preprocess_point_cloud(source, voxel_size) 46 | target_down, target_fpfh = preprocess_point_cloud(target, voxel_size) 47 | 48 | visualize([source, target], uniform_colors=True) 49 | visualize([source_down, target_down], uniform_colors=True) 50 | # visualize_simple(source, target, np.identity(4)) 51 | # visualize_simple(source_down, target_down, np.identity(4)) 52 | 53 | 54 | 55 | ######################################## 56 | # GLOBAL REGISTRATION 57 | ######################################## 58 | # FAST 59 | 60 | # start = time.time() 61 | # distance_threshold = voxel_size * 0.5 62 | # reg_fast = global_registration(source_down, target_down, source_fpfh, target_fpfh, distance_threshold, 63 | # use_fast=True, max_iteration=gr_max_iteration, confidence=gr_confidence) 64 | 65 | # print(f"FAST global registration took {time.time() - start:.3f} sec.") 66 | # # print(reg_fast) 67 | 68 | # visualize([source_down, target_down], transformation=reg_fast.transformation, uniform_colors=True) 69 | # # visualize_simple(source_down, target_down, reg_fast.transformation) 70 | 71 | 72 | # # print(evaluate_registration(source, target, icp_threshold, transform=reg_ransac.transformation)) 73 | 74 | # visualize([source, target], transformation=reg_fast.transformation, uniform_colors=True) 75 | # # visualize_simple(source, target, reg_fast.transformation) 76 | 77 | 78 | # ######################################## 79 | # RANSAC 80 | 81 | start = time.time() 82 | distance_threshold = voxel_size * 1.5 83 | reg_ransac = global_registration(source_down, target_down, source_fpfh, target_fpfh, distance_threshold, 84 | use_fast=False, max_iteration=gr_max_iteration, confidence=gr_confidence) 85 | 86 | print(f"\nRANSAC global registration took {time.time() - start:.3f} sec.") 87 | # print(reg_ransac) 88 | 89 | ransac_translation, ransac_euler = get_transform_vectors(reg_ransac.transformation) 90 | print(f"[RANSAC] translate:\t{ransac_translation})") 91 | print(f"[RANSAC] rotate:\t{ransac_euler})") 92 | 93 | visualize([source, target], transformation=reg_ransac.transformation, uniform_colors=True) 94 | # visualize_simple(source_down, target_down, reg_ransac.transformation) 95 | 96 | 97 | 98 | ######################################## 99 | # ICP REGISTRATION 100 | ######################################## 101 | # # P2P 102 | 103 | # start = time.time() 104 | # reg_p2p = ICP_registration(source, target, icp_threshold, 105 | # reg_ransac.transformation, use_p2l=False, p2p_max_iteration=p2p_max_iteration) 106 | 107 | # print(f"P2P ICP took {time.time() - start:.3f} sec.") 108 | # # print(reg_p2p) 109 | 110 | # visualize([source, target], transformation=reg_p2p.transformation, uniform_colors=True) 111 | # # visualize_simple(source, target, reg_p2p.transformation) 112 | 113 | 114 | # ######################################## 115 | # P2L 116 | 117 | start = time.time() 118 | reg_p2l = ICP_registration(source, target, icp_threshold, 119 | reg_ransac.transformation, use_p2l=True) 120 | 121 | print(f"\nP2L ICP took {time.time() - start:.3f} sec.") 122 | # print(reg_p2l) 123 | 124 | icp_translation, icp_euler = get_transform_vectors(reg_p2l.transformation) 125 | print(f"[P2L ICP] translate:\t{icp_translation}") 126 | print(f"[P2L ICP] rotate:\t{icp_euler}") 127 | 128 | visualize([source, target], transformation=reg_p2l.transformation, uniform_colors=True) 129 | # visualize_simple(source, target, reg_p2l.transformation) 130 | 131 | 132 | 133 | ######################################## 134 | # EXPORT 135 | ######################################## 136 | 137 | export_pointcloud(source + target, "export/icp", type="ply") 138 | -------------------------------------------------------------------------------- /doc/calculation.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/doc/calculation.jpg -------------------------------------------------------------------------------- /doc/example_input.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/doc/example_input.jpg 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-------------------------------------------------------------------------------- /images/laser1_2048_horizontal_config.json: -------------------------------------------------------------------------------- 1 | { 2 | "video_path": "images/laser1_2048_horizontal.mp4", 3 | "texture_path": "images/laser1_RGB_horizontal.jpg", 4 | "desaturate": false, 5 | 6 | "export_path": "export/laser1_2048", 7 | "export_type" : "pcd", 8 | 9 | "cam_pos": [0, 0, 0], 10 | "laser_pos" : [6.5, 0, 0], 11 | "horizontal_fov" : 46.73, 12 | 13 | "sweep_direction" : "up", 14 | "sweep_startangle" : -15.0, 15 | "sweep_step": 0.5, 16 | 17 | "laser_thres": 100, 18 | 19 | "shrink_x": 1, 20 | "shrink_y": 3, 21 | "shrink_preview": 3, 22 | "window_size" : [800, 800], 23 | "verbose": false, 24 | "zero_value" : 1e-6, 25 | 26 | "KDTree_radius" : 5, 27 | "KDTree_max_nn" : 30 28 | } 29 | -------------------------------------------------------------------------------- /images/laser1_RGB_horizontal.jpg: 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-------------------------------------------------------------------------------- 1 | { 2 | "video_path": "images/laser1a_2048.mp4", 3 | "texture_path": "images/laser1_RGB_vertikal.jpg", 4 | "desaturate": false, 5 | 6 | "export_path": "export/laser1a_2048", 7 | "export_type" : "pcd", 8 | 9 | "cam_pos": [0, 0, 0], 10 | "laser_pos" : [10, 0, 0], 11 | "horizontal_fov" : 48, 12 | 13 | "sweep_direction" : "right", 14 | "sweep_startangle" : -28.0, 15 | "sweep_step": 0.17578, 16 | 17 | "laser_thres": 100, 18 | 19 | "shrink_x": 1, 20 | "shrink_y": 3, 21 | "shrink_preview": 3, 22 | "window_size" : [800, 800], 23 | "verbose": false, 24 | "zero_value" : 1e-6, 25 | 26 | "KDTree_radius" : 5, 27 | "KDTree_max_nn" : 30 28 | } 29 | -------------------------------------------------------------------------------- /images/laser1a_2048_daylight.mp4: -------------------------------------------------------------------------------- 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"video_path": "images/laser1a_720.mp4", 3 | "texture_path": "images/laser1_RGB_vertikal.jpg", 4 | "desaturate": false, 5 | 6 | "export_path": "export/laser1a_720", 7 | "export_type" : "pcd", 8 | 9 | "cam_pos": [0, 0, 0], 10 | "laser_pos" : [10, 0, 0], 11 | "horizontal_fov" : 48, 12 | 13 | "sweep_direction" : "right", 14 | "sweep_startangle" : -28.0, 15 | "sweep_step": 0.5, 16 | 17 | "laser_thres": 100, 18 | 19 | "shrink_x": 1, 20 | "shrink_y": 3, 21 | "shrink_preview": 3, 22 | "window_size" : [800, 800], 23 | "verbose": false, 24 | "zero_value" : 1e-6, 25 | 26 | "KDTree_radius" : 5, 27 | "KDTree_max_nn" : 30 28 | } 29 | -------------------------------------------------------------------------------- /images/laser1b_720.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaserBorg/LineScanner/97b9af350d1e634c98f7dd0f27aed92378467cb3/images/laser1b_720.mp4 -------------------------------------------------------------------------------- /images/laser1b_720_config.json: -------------------------------------------------------------------------------- 1 | { 2 | "video_path": "images/laser1b_720.mp4", 3 | "texture_path": "", 4 | "desaturate": true, 5 | 6 | "export_path": "export/laser1b_720", 7 | "export_type" : "pcd", 8 | 9 | "cam_pos": [0, 0, 0], 10 | "laser_pos" : [10, 0, 0], 11 | "horizontal_fov" : 48, 12 | 13 | "sweep_direction" : "right", 14 | "sweep_startangle" : -28.0, 15 | "sweep_step": 0.5, 16 | 17 | "laser_thres": 100, 18 | 19 | "shrink_x": 1, 20 | "shrink_y": 3, 21 | "shrink_preview": 3, 22 | "window_size" : [800, 800], 23 | "verbose": false, 24 | "zero_value" : 1e-6, 25 | 26 | "KDTree_radius" : 5, 27 | "KDTree_max_nn" : 30 28 | } 29 | -------------------------------------------------------------------------------- /images/laser2.mp4: -------------------------------------------------------------------------------- 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"laser_thres": 100, 18 | 19 | "shrink_x": 1, 20 | "shrink_y": 3, 21 | "shrink_preview": 3, 22 | "window_size" : [800, 800], 23 | "verbose": true, 24 | "zero_value" : 1e-6, 25 | 26 | "KDTree_radius" : 5, 27 | "KDTree_max_nn" : 30 28 | } 29 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | open3d>=0.18.0 2 | opencv-python 3 | numpy 4 | matplotlib 5 | scipy 6 | --------------------------------------------------------------------------------