From 4b9c610a9682127d22dc3d9844f7e15ea4598128 Mon Sep 17 00:00:00 2001 From: Souriya Trinh Date: Sun, 11 Aug 2024 22:41:49 +0200 Subject: [PATCH] Experimentation with using Blender for automatic camera viewpoints sampling and SIFT extraction for automatic pose computation. --- script/Blender/Keypoints_extraction/README.md | 107 +++++ .../extract_sift_sampling_Suzanne.py | 432 ++++++++++++++++++ .../Keypoints_extraction/img_mosaic.png | Bin 0 -> 15644432 bytes .../Keypoints_extraction/img_result.png | Bin 0 -> 618881 bytes .../Keypoints_extraction/img_result_2.png | Bin 0 -> 625112 bytes .../img_result_compare.png | Bin 0 -> 1082809 bytes .../keypoints_matching_Suzanne.py | 340 ++++++++++++++ .../render_still_image_Suzanne.py | 114 +++++ 8 files changed, 993 insertions(+) create mode 100644 script/Blender/Keypoints_extraction/README.md create mode 100755 script/Blender/Keypoints_extraction/extract_sift_sampling_Suzanne.py create mode 100644 script/Blender/Keypoints_extraction/img_mosaic.png create mode 100644 script/Blender/Keypoints_extraction/img_result.png create mode 100644 script/Blender/Keypoints_extraction/img_result_2.png create mode 100644 script/Blender/Keypoints_extraction/img_result_compare.png create mode 100755 script/Blender/Keypoints_extraction/keypoints_matching_Suzanne.py create mode 100755 script/Blender/Keypoints_extraction/render_still_image_Suzanne.py diff --git a/script/Blender/Keypoints_extraction/README.md b/script/Blender/Keypoints_extraction/README.md new file mode 100644 index 0000000000..f99c95cb91 --- /dev/null +++ b/script/Blender/Keypoints_extraction/README.md @@ -0,0 +1,107 @@ +# Blender keypoints extraction: an experimentation + +Some tests to use Blender to automatically sample camera viewpoints around an object and to extract SIFT descriptors. + +## Install Python OpenCV + +Here, Blender is directly installed from an [archive](https://www.blender.org/download/) for Linux platforms. + +This has the advantage to be able to use the latest version and not depend on the package manager. +To install OpenCV (`opencv-python`), the following [script](https://github.com/luckychris/install_blender_python_modules) (credit to "luckychris") is used: +- launch Blender from the terminal +- switch to the `Scripting` tab +- open and run the script (use `opencv-python` for the module to be installed) +- in the terminal, info about the installation should be displayed + +With this workflow, Python OpenCV is installed locally and directly into the Blender folder. + +## Blender scene + +For these tests, a simple object is used: +- add the Suzanne model (`Add` > `Mesh` > `Monkey`) +- update the camera focal length and the output resolution + +[Triangulate](https://docs.blender.org/manual/en/4.3/modeling/meshes/editing/face/triangulate_faces.html): +- `Edit` mode > `Face` > `Triangulate Faces` (`Ctrl T`) + +For the texturing: +- `Material` > `New` > `Base Color` > `Image Texture` +- here [`Unwrap`](https://docs.blender.org/manual/en/4.3/modeling/meshes/editing/uv.html) is used +- `UV Editing` window > Open the image +- move to center the mesh + +## Run the keypoints extraction script + +Switch to the `Scripting` window, open and run the [`extract_sift_sampling_Suzanne.py`](extract_sift_sampling_Suzanne.py) script. + +The main principles are: +- sample equidistant camera poses around the object +- run SIFT detector and for each feature: + - perform [back-face culling](https://en.wikipedia.org/wiki/Back-face_culling) for visibility test + - compute [ray-triangle intersection](https://docs.blender.org/api/4.3/mathutils.geometry.html#mathutils.geometry.intersect_ray_tri) to get the 3D coordinates and transform to the object frame + - save the list of detected features (SIFT descriptors + 3D object coordinates) into a `.npz` file format + +## Render a test image + +Move the camera, the light at the desired positions and use the [`render_still_image_Suzanne.py`](render_still_image_Suzanne.py) script to conveniently render the image and save the camera pose. + +## Keypoints matching and pose computation + +Use the [`keypoints_matching_Suzanne.py`](keypoints_matching_Suzanne.py) script. + +## Results + +### Keypoints matching + +A brief overview of the keypoints matching process can be seen: + +![keypoints matching mosaic](img_mosaic.png) + +Matching lines are those from the keypoints matching process, RANSAC results are not displayed. + +### Pose computation + +Running the script should give result similar to: + +```bash +rvec=[[ 1.83592335 -0.06509804 -0.60995061]] +rvec_gt=[[ 1.83833405 -0.06378919 -0.60991766]] + +c_T_o_est: +[[ 0.86373993 0.25108214 -0.43693374 -0.4630725 ] + [-0.33764126 -0.35531447 -0.8716364 -0.11690215] + [-0.37410121 0.90039402 -0.2221236 5.08996118] + [ 0. 0. 0. 1. ]] +c_T_o_gt: +[[ 0.86392033 0.25132278 -0.4364382 -0.46306577] + [-0.33618754 -0.35746148 -0.87132031 -0.11758688] + [-0.37499252 0.89947647 -0.22432667 5.09212732] + [ 0. 0. 0. 1. ]] + +``` + +Result with a rough simplified model should give: + +![pose computation](img_result.png) + +### Illumination change test + +Same camera viewpoint but with a different illumination condition: + +```bash +rvec=[[ 2.3786033 -0.60075198 -0.0076497 ]] +rvec_gt=[[ 1.83833405 -0.06378919 -0.60991766]] + +c_T_o_est: +[[ 0.89370737 -0.41880363 -0.16090572 -1.16669042] + [-0.42276497 -0.66606072 -0.61451842 -0.50213294] + [ 0.15018957 0.61722495 -0.77231888 5.45509487] + [ 0. 0. 0. 1. ]] +c_T_o_gt: +[[ 0.86392033 0.25132278 -0.4364382 -0.46306577] + [-0.33618754 -0.35746148 -0.87132031 -0.11758688] + [-0.37499252 0.89947647 -0.22432667 5.09212732] + [ 0. 0. 0. 1. ]] +``` + +![Illumination chage](img_result_compare.png) diff --git a/script/Blender/Keypoints_extraction/extract_sift_sampling_Suzanne.py b/script/Blender/Keypoints_extraction/extract_sift_sampling_Suzanne.py new file mode 100755 index 0000000000..4f67ab41cc --- /dev/null +++ b/script/Blender/Keypoints_extraction/extract_sift_sampling_Suzanne.py @@ -0,0 +1,432 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Copyright 2024 ViSP contributor +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import bpy +import os +import cv2 as cv +import numpy as np +from mathutils import geometry, Vector + +# https://www.rojtberg.net/1601/from-blender-to-opencv-camera-and-back/ +def get_calibration_matrix_K_from_blender(camera_name): + # get the relevant data + cam = bpy.data.objects[camera_name].data + scene = bpy.context.scene + # assume image is not scaled + assert scene.render.resolution_percentage == 100 + # assume angles describe the horizontal field of view + assert cam.sensor_fit != 'VERTICAL' + + f_in_mm = cam.lens + sensor_width_in_mm = cam.sensor_width + + w = scene.render.resolution_x + h = scene.render.resolution_y + + pixel_aspect = scene.render.pixel_aspect_y / scene.render.pixel_aspect_x + + f_x = f_in_mm / sensor_width_in_mm * w + f_y = f_x * pixel_aspect + + # yes, shift_x is inverted. WTF blender? + c_x = w * (0.5 - cam.shift_x) + # and shift_y is still a percentage of width.. + c_y = h * 0.5 + w * cam.shift_y + + K = np.array([ + [f_x, 0, c_x], + [0, f_y, c_y], + [0, 0, 1] + ]) + + return K + +def inv_transfo(w_T_o): + R = w_T_o[:3,:3] + t = w_T_o[:3,3] + + o_T_w = np.eye(4) + o_T_w[:3,:3] = R.T + o_T_w[:3,3] = -R.T @ t + + return o_T_w + +def get_camera_pose(cameraName, objectName): + M = np.eye(4) + M[1][1] = -1 + M[2][2] = -1 + + cam = bpy.data.objects[cameraName] + object_pose = bpy.data.objects[objectName].matrix_world + + # Normalize orientation with respect to the scale + object_pose_normalized_blender = object_pose.copy() + object_orientation_normalized_blender = object_pose_normalized_blender.to_3x3().normalized() + for i in range(3): + for j in range(3): + object_pose_normalized_blender[i][j] = object_orientation_normalized_blender[i][j] + + w_T_o = np.array(object_pose_normalized_blender) + print(f"object_pose_normalized:\n{object_pose_normalized_blender}") + + w_T_c = np.array(cam.matrix_world) + print(f"w_T_c:\n{w_T_c}") + + c_T_w = np.array(cam.matrix_world.inverted()) + print(f"c_T_w:\n{c_T_w}") + + c_T_o = M @ c_T_w @ w_T_o + print(f"c_T_o:\n{c_T_o}") + + return c_T_o + +def get_object_vertices(objectName): + # https://blender.stackexchange.com/questions/3637/get-indices-of-vertices-of-triangulated-faces-in-python/3657#3657 + obj = bpy.data.objects[objectName] + mesh = obj.data + + model_faces = [] + model_normals = [] + for face in mesh.polygons: + face_vert = [] + for i in range(len(face.vertices)): + vert = mesh.vertices[face.vertices[i]] + vert_xyz = vert.co.xyz + face_vert.append(vert_xyz) + model_normals.append(vert.normal) + + model_faces.append(face_vert) + + return [model_faces, model_normals] + +def vec_augment(pt_3d): + return np.array([pt_3d[0], pt_3d[1], pt_3d[2], 1]) + +def convert_pt(pt_3d, w_T_o): + o_pt = vec_augment(pt_3d) + w_pt = w_T_o @ o_pt + + return w_pt[:,3] + +def compute_ray(K, im_pt): + fx = K[0,0] + fy = K[1,1] + cx = K[0,2] + cy = K[1,2] + + x = (im_pt[0] - cx) / fx + y = (im_pt[1] - cy) / fy + return np.array([x, y, 1]) + +def is_face_visible(ray_, vertex0_, normal_): + # back-face culling + # https://en.wikipedia.org/wiki/Back-face_culling + ray = np.array(ray_) + vertex0 = np.array(vertex0_) + normal = np.array(normal_) + eps = 1e-9 + return np.dot((vertex0 - ray), normal) > eps + +def ray_vertex_intersection(ray, vertex, c_T_o): + c_vert0 = c_T_o @ vec_augment(vertex[0]) + c_vert1 = c_T_o @ vec_augment(vertex[1]) + c_vert2 = c_T_o @ vec_augment(vertex[2]) + + # https://docs.blender.org/api/4.3/mathutils.geometry.html#mathutils.geometry.intersect_ray_tri + clip = True + intersect = geometry.intersect_ray_tri(c_vert0, c_vert1, c_vert2, ray, [0,0,0], clip) + return intersect + +def getNED(lon_, lat_, r, in_radian=False): + """ + Get the homogeneous transformation matrix corresponding to the local tangent plane transformation at the specified + longitude/latitude and radius coordinates, using the NED and ECEF conventions and a perfect sphere. + See also: + - https://en.wikipedia.org/wiki/Earth-centered,_Earth-fixed_coordinate_system + - https://en.wikipedia.org/wiki/Local_tangent_plane_coordinates + + Parameters + ---------- + lon_: float + The longitude coordinate. + lat_: float + The latitude coordinate. + r: float + The sphere radius. + in_radian: boolean + If true coordinates are in radian, otherwise in degree. + + Returns: + ------- + numpy matrix + The homogeneous matrix allowing converting a 3D point expressed in the NED frame to the ECEF frame. + """ + if not in_radian: + # lambda + lon = np.radians(lon_) + # phi + lat = np.radians(lat_) + else: + lon = lon_ + lat = lat_ + + Tdata = [ [-np.sin(lat)*np.cos(lon), -np.sin(lon), -np.cos(lat)*np.cos(lon), r*np.cos(lon)*np.cos(lat)], \ + [-np.sin(lat)*np.sin(lon), np.cos(lon), -np.cos(lat)*np.sin(lon), r*np.sin(lon)*np.cos(lat)], \ + [ np.cos(lat), 0, -np.sin(lat), r*np.sin(lat)], \ + [ 0, 0, 0, 1] \ + ] + T = np.matrix(Tdata) + + return T + +def getENU(lon_, lat_, r, in_radian=False): + """ + Get the homogeneous transformation matrix corresponding to the local tangent plane transformation at the specified + longitude/latitude and radius coordinates, using the ENU and ECEF conventions and a perfect sphere. + See also: + - https://en.wikipedia.org/wiki/Earth-centered,_Earth-fixed_coordinate_system + - https://en.wikipedia.org/wiki/Local_tangent_plane_coordinates + + Parameters + ---------- + lon_: float + The longitude coordinate. + lat_: float + The latitude coordinate. + r: float + The sphere radius. + in_radian: boolean + If true coordinates are in radian, otherwise in degree. + + Returns: + ------- + numpy matrix + The homogeneous matrix allowing converting a 3D point expressed in the ENU frame to the ECEF frame. + """ + if not in_radian: + # lambda + lon = np.radians(lon_) + # phi + lat = np.radians(lat_) + else: + lon = lon_ + lat = lat_ + + Tdata = [ [-np.sin(lon), -np.sin(lat)*np.cos(lon), np.cos(lat)*np.cos(lon), r*np.cos(lon)*np.cos(lat)], \ + [ np.cos(lon), -np.sin(lat)*np.sin(lon), np.cos(lat)*np.sin(lon), r*np.sin(lon)*np.cos(lat)], \ + [ 0, np.cos(lat), np.sin(lat), r*np.sin(lat)], \ + [ 0, 0, 0, 1] \ + ] + T = np.matrix(Tdata) + + return T + +def regular_on_sphere_points(num, full_sphere=False): + """ + Generate equidistributed points on the surface of a sphere. + From: + - "How to generate equidistributed points on the surface of a sphere", Markus Deserno + - https://www.cmu.edu/biolphys/deserno/pdf/sphere_equi.pdf + - https://gist.github.com/dinob0t/9597525 + + Parameters + ---------- + num: int + The desired number of points on the surface of a sphere. + + Returns: + ------- + list + The list of equidistributed points on the surface of a sphere in the lon-lat coordinates. + + Note: + ------- + This method does not return exactly the specified number of points. + """ + r = 1 + points = [] + # Break out if zero points + if num == 0: + return points + + a = 4.0 * np.pi*(r**2.0 / num) + d = np.sqrt(a) + m_theta = int(round(np.pi / d)) + d_theta = np.pi / m_theta + d_phi = a / d_theta + pi_2 = np.pi/2 + + if full_sphere: + m_upper_bound = m_theta + else: + m_upper_bound = int(m_theta/2) + + for m in range(m_upper_bound): + theta = np.pi * (m + 0.5) / m_theta + m_phi = int(round(2.0 * np.pi * np.sin(theta) / d_phi)) + + for n in range(m_phi): + phi = 2.0 * np.pi * n / m_phi + lon = phi + lat = pi_2-theta + points.append([lon,lat]) + + return points + +def look_at(ecef_T_cv, point): + # https://blender.stackexchange.com/questions/5210/pointing-the-camera-in-a-particular-direction-programmatically/5220#5220 + # https://docs.blender.org/api/current/mathutils.html#mathutils.Vector + direction = Vector((point[0], point[1], point[2])) - Vector((ecef_T_cv[0,3], ecef_T_cv[1,3], ecef_T_cv[2,3])) + # point the cameras '-Z' and use its 'Y' as up + rot_quat = direction.to_track_quat('-Z', 'Y') + + ecef_T_cv_look_at = np.eye(4) + # https://docs.blender.org/api/current/mathutils.html#mathutils.Quaternion.to_matrix + ecef_T_cv_look_at[:3,:3] = rot_quat.to_matrix() + ecef_T_cv_look_at[:3,3] = ecef_T_cv[:3,3].ravel() + + return ecef_T_cv_look_at + +def set_camera_pose(obj_camera, pose): + # gl2cv = Matrix().to_4x4() + # gl2cv[1][1] = -1 + # gl2cv[2][2] = -1 + # obj_camera.matrix_world = (gl2cv * Matrix(pose)).inverted() + + # cv_T_gl = np.eye(4) + # cv_T_gl[1,1] = -1 + # cv_T_gl[1,2] = -1 + + # w_T_cv = pose + # w_T_gl = w_T_cv @ cv_T_gl + # # obj_camera.matrix_world = w_T_gl.T + # obj_camera.matrix_world = w_T_gl + + # Column-major? + obj_camera.matrix_world = pose.T + +if __name__ == "__main__": + output_dir = "/tmp" + output_file_pattern_string = "blender_render_%04d.png" + + K = get_calibration_matrix_K_from_blender("Camera") + print(f"Camera Matrix:\n", K) + + data_dict = dict() + data_dict["K"] = K + + debug_print = False + + npoints = 60 + full_sphere = False + regular_surf_points = regular_on_sphere_points(npoints, full_sphere) + print(f"regular_surf_points={len(regular_surf_points)}\n{regular_surf_points}") + + # Transformation from CV frame to NED frame + ned_T_cv = np.eye(4) + ned_T_cv[0,0] = 0 + ned_T_cv[0,1] = -1 + ned_T_cv[1,0] = 1 + ned_T_cv[1,1] = 0 + print(f"ned_T_cv:\n{ned_T_cv}") + + radius = 5.0 + camera_name = "Camera" + object_name = "Suzanne" + camera = bpy.data.objects[camera_name] + for keyframe, point in enumerate(regular_surf_points): + print() + print(f"{keyframe+1}/{len(regular_surf_points)}") + lon = point[0] + lat = point[1] + + in_radian = True + ecef_T_ned = getNED(lon, lat, radius, in_radian) + ecef_T_cv = ecef_T_ned @ ned_T_cv + print(f"ecef_T_cv:\n{ecef_T_cv}") + + ecef_T_cv_look_at = look_at(ecef_T_cv, np.zeros((3,1))) + print(f"ecef_T_cv_look_at:\n{ecef_T_cv_look_at}") + set_camera_pose(camera, ecef_T_cv_look_at) + + output_filepath = os.path.join(output_dir, (output_file_pattern_string % keyframe)) + bpy.context.scene.render.filepath = output_filepath + bpy.ops.render.render(write_still = True) + + img = cv.imread(output_filepath) + print(f"img: {img.shape}") + + sift_detector = cv.SIFT.create() + keypoints, descriptors = sift_detector.detectAndCompute(img, None) + if keypoints is not None and descriptors is not None: + print(f"keypoints: {len(keypoints)} ; type={type(keypoints)}") + print(f"descriptors: {len(descriptors)} ; type={type(descriptors)} ; shape={descriptors.shape}") + if keypoints is None: + keypoints = [] + if descriptors is None: + descriptors = [] + + c_T_o = get_camera_pose(camera_name, object_name) + + obj_faces_vert, obj_faces_normal = get_object_vertices(object_name) + print(f"obj_faces_vert={len(obj_faces_vert)}") + + if debug_print: + for face_id, face in enumerate(obj_faces_vert): + print(f"\nFace {face_id}") + print(f" normal: {obj_faces_normal[face_id]}") + for vertex_id, vertex in enumerate(face): + print(f" vertex {vertex_id}: {face}") + + pointcloud = [] + image_pts = [] + object_pts = [] + descriptors_pcl = [] + img_results = np.copy(img) + for idx, kpt in enumerate(keypoints): + kpt_pt_normalized = compute_ray(K, kpt.pt) + # print(f"kpt: {kpt_pt_normalized}") + + kpt_pt = np.array(kpt.pt, dtype=np.int32) + cv.drawMarker(img_results, kpt_pt, (0,0,255),cv.MARKER_CROSS, 6, 1) + + for face_id, face in enumerate(obj_faces_vert): + is_visible = is_face_visible(kpt_pt_normalized, face[0], obj_faces_normal[face_id]) + if is_visible: + intersect_pt = ray_vertex_intersection(kpt_pt_normalized, face, c_T_o) + if intersect_pt is not None: + if debug_print: + print(f"Face {face_id}, is visible={is_visible}, intersect_pt={intersect_pt}") + + pointcloud.append([intersect_pt[0], intersect_pt[1], intersect_pt[2]]) + image_pts.append(kpt_pt) + c_object_pt = vec_augment([intersect_pt[0], intersect_pt[1], intersect_pt[2]]) + o_object_pt = inv_transfo(c_T_o) @ c_object_pt + object_pts.append(o_object_pt[:3]) + descriptors_pcl.append(descriptors[idx]) + + cv.imwrite("/tmp/blender_render_{:04d}_results.png".format(keyframe), img_results) + + data_dict["image_{:04d}".format(keyframe)] = img + data_dict["pointcloud_{:04d}".format(keyframe)] = pointcloud + data_dict["image_pts_{:04d}".format(keyframe)] = image_pts + data_dict["object_pts_{:04d}".format(keyframe)] = object_pts + data_dict["descriptors_pcl_{:04d}".format(keyframe)] = descriptors_pcl + data_dict["descriptors_{:04d}".format(keyframe)] = descriptors + data_dict["c_T_o_{:04d}".format(keyframe)] = c_T_o + + data_dict["nb_data"] = len(regular_surf_points) + np.savez("/tmp/blender_render_keypoints_sampling.npz", **data_dict) diff --git a/script/Blender/Keypoints_extraction/img_mosaic.png b/script/Blender/Keypoints_extraction/img_mosaic.png new file mode 100644 index 0000000000000000000000000000000000000000..69891147e917a42a2be1155383a3134f69a62cf1 GIT binary patch literal 15644432 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