From 318537603353a6b498c64ac4310304f9788fac78 Mon Sep 17 00:00:00 2001 From: AndReGeist Date: Mon, 11 Mar 2024 09:24:08 +0100 Subject: [PATCH 1/4] Added lipschitz_constants.py and cleaned up script --- visu/lipschitz_constants.py | 139 ++++++++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) create mode 100644 visu/lipschitz_constants.py diff --git a/visu/lipschitz_constants.py b/visu/lipschitz_constants.py new file mode 100644 index 0000000..182b5e1 --- /dev/null +++ b/visu/lipschitz_constants.py @@ -0,0 +1,139 @@ +import os +import numpy as np +from scipy.spatial.transform import Rotation +import jax +import jax.numpy as jnp +import matplotlib.pyplot as plt +from matplotlib.ticker import FormatStrFormatter +import seaborn as sns +from hitchhiking_rotations import HITCHHIKING_ROOT_DIR + +# SETTINGS +# plot_type = 'Paper' +plot_type = "Appendix" +N = int(4e6) # int(2e5) # number of random rotations +N_pairs = int(4e5) # int(2e4 + +# USE SCIPY TO COMPUTE DISTANCES +# Approach: Randomly sample SO(3) matrices then compute representation vectors +my_rot = Rotation.random(N) # generate N random rotations +my_rot = my_rot[np.random.choice(N, 2 * N_pairs, replace=False)] # generate N_pairs pairs of rotations +rot1, rot2 = my_rot[N_pairs:], my_rot[:N_pairs] +dist_so3 = np.linalg.norm(rot1.as_matrix() - rot2.as_matrix(), axis=(1, 2), ord="fro") +l = rot1.as_matrix().shape[0] + + +def eucl_norm(mat1, mat2): + return jnp.linalg.norm(mat1 - mat2) + + +# Rotation representations +mat1 = jnp.array(rot1.as_matrix().reshape(l, -1)) +mat2 = jnp.array(rot2.as_matrix().reshape(l, -1)) +euler1 = jnp.array(rot1.as_euler("xyz", degrees=False)) +euler2 = jnp.array(rot2.as_euler("xyz", degrees=False)) +quat1 = rot1.as_quat(canonical=False) +quat2 = rot2.as_quat(canonical=False) +exp1 = jnp.array(rot1.as_rotvec()) +exp2 = jnp.array(rot2.as_rotvec()) +mrp1 = jnp.array(rot1.as_mrp()) +mrp2 = jnp.array(rot2.as_mrp()) + +# Distances +dist_so3_L2 = jax.vmap(eucl_norm)(mat1, mat2) +dist_euler = jax.vmap(eucl_norm)(euler1, euler2) +dist_quat = jax.vmap(eucl_norm)(quat1, quat2) +dist_exp = jax.vmap(eucl_norm)(exp1, exp2) +dist_mrp = jax.vmap(eucl_norm)(mrp1, mrp2) + +size = 9 +linewidth = 3.0 +max_so3 = np.max(dist_so3) +s2 = 2 * np.sqrt(2) +max_exp_coord = 2 * np.pi +max_mrp = 2 +max_euler_angle = 2 * np.pi + +sns.set_style("whitegrid") +plt.rcParams["figure.figsize"] = [8, 8] +plt.rcParams.update({"font.size": 18}) + +colors = [ + (0.368, 0.507, 0.71), + (0.881, 0.611, 0.142), + (0.923, 0.386, 0.209), + (0.56, 0.692, 0.195), + (0.528, 0.471, 0.701), + (0.772, 0.432, 0.102), + (0.572, 0.586, 0.0), +] + +if plot_type == "Paper": + dist_list = [dist_so3_L2, dist_euler, dist_quat] + limit_list = [None, max_euler_angle, 2] + text_list = [ + "Rotation\nmatrix\nas vector\nin " + r"$\mathbb{R}^9$", + "Euler\nangles", + "Quaternions", + "Modified Rodrigues\nparameters", + ] + ticklist = [[0, max_so3], [0, 2 * np.pi], [0, 2]] + ticklabels = [[r"$0$", r"$2\sqrt{2}$"], [r"$0$", r"$2\pi$"], [r"$0$", r"$2$"]] + slope = [1, np.pi / s2, np.sqrt(2) / s2] + linecolors = [colors[2], colors[2], colors[2]] + markercolors = [colors[0], colors[0], colors[0]] + + fig, axs = plt.subplots(ncols=len(dist_list), nrows=1, tight_layout=True) + fig.set_figheight(5) + fig.set_figwidth(9) + +if plot_type == "Appendix": + dist_list = [dist_so3_L2, dist_euler, dist_exp, dist_quat, dist_mrp] + limit_list = [None, max_euler_angle, max_exp_coord, 2, max_mrp] + text_list = [ + "Rotation matrix\nas vector in " + r"$\mathbb{R}^9$", + "Euler\nangles", + "Exponential\ncoordinates", + "Quaternions", + "Modified Rodrigues\nparameters", + ] + ticklist = [[0, max_so3], [0, 2 * np.pi], [0, 2 * np.pi], [0, 2], [0, 2]] + ticklabels = [ + [r"$0$", r"$2\sqrt{2}$"], + [r"$0$", r"$2\pi$"], + [r"$0$", r"$2\pi$"], + [r"$0$", r"$2$"], + [r"$0$", r"$2$"], + ] + slope = [1, np.pi / s2, np.pi / s2, np.sqrt(2) / s2, np.tan(np.pi / 4) / s2] + linecolors = [colors[2], colors[2], colors[2], colors[2], colors[2]] + markercolors = [colors[0], colors[0], colors[0], colors[0], colors[0]] + + fig, axs = plt.subplots(ncols=len(dist_list), nrows=1, tight_layout=True) + fig.set_figheight(6) + fig.set_figwidth(18) + +for i in range(len(dist_list)): + axs[i].margins(x=0) + axs[i].plot(dist_so3, dist_list[i], ".", alpha=0.01, markersize=size, color=markercolors[i]) + axs[i].plot([0, max_so3], [0, max_so3 * slope[i]], "k--", linewidth=linewidth) + if limit_list[i] is not None: + axs[i].plot( + [0, max_so3], [limit_list[i], limit_list[i]], "--", color=linecolors[i], alpha=0.8, linewidth=linewidth + ) + + if plot_type == "Paper" and i == 0: + axs[i].text(0.05, 0.68, text_list[i], transform=axs[i].transAxes) + else: + axs[i].text(0.05, 0.76, text_list[i], transform=axs[i].transAxes) + + axs[i].yaxis.set_major_formatter(FormatStrFormatter("%.0f")) + axs[i].set_yticks(ticklist[i]) + axs[i].set_yticklabels(ticklabels[i]) + axs[i].set_xlabel(r"$\|R_1 - R_2\|_{\mathrm{F}}$") + +axs[0].set_ylabel(r"$\|r_1-r_2\|_2$") +plt.subplots_adjust(wspace=0.0, hspace=0.0) +out_p = os.path.join(HITCHHIKING_ROOT_DIR, "results", f"lipschitz_constants_{plot_type}.png") +plt.savefig(out_p, bbox_inches="tight", dpi=200) +plt.show() From efa5ac583580033f80d04c040a887ac649c649cf Mon Sep 17 00:00:00 2001 From: AndReGeist Date: Mon, 11 Mar 2024 09:42:34 +0100 Subject: [PATCH 2/4] Added loss_gradients.py and cleaned up code --- .gitignore | 2 + visu/loss_gradients.py | 149 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 151 insertions(+) create mode 100644 visu/loss_gradients.py diff --git a/.gitignore b/.gitignore index c270bf0..b5e1f7d 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,8 @@ ModelNet40 ModelNet40.zip *.npy pcd_results* +*.png +*.pdf # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/visu/loss_gradients.py b/visu/loss_gradients.py new file mode 100644 index 0000000..02eecea --- /dev/null +++ b/visu/loss_gradients.py @@ -0,0 +1,149 @@ +import os +import numpy as np +import jax +import jax.numpy as jnp +import matplotlib.pyplot as plt +import matplotlib.colors +import seaborn as sns +from hitchhiking_rotations import HITCHHIKING_ROOT_DIR + +xmin, xmax, ymin, ymax = -1.5, 1.5, -1.5, 1.5 +num_points_x, num_points_y = 24, 24 # 20, 20 # You can adjust these numbers +x_values = np.linspace(xmin, xmax, num_points_x) +y_values = np.linspace(ymin, ymax, num_points_y) +x_mesh, y_mesh = np.meshgrid(x_values, y_values) +vec = jnp.column_stack((x_mesh.flatten(), y_mesh.flatten())) + + +################################ +# DEFINE LOSS FUNCTIONS +################################ +def norm(x): + return (x / jnp.max(jnp.array([jnp.linalg.norm(x), 1e-8]))).squeeze() + + +def dot(x, y): + # return jnp.clip(jnp.dot(x, y), -1.0, 1.0) + return jnp.dot(x, y) + + +def cosine_distance(x, y): + x, y = norm(x), norm(y) + return (1 - dot(x, y)).squeeze() + + +def cos_similarity(x, y): + x, y = norm(x), norm(y) + return jnp.dot(x, y).squeeze() + + +def l2_loss(x, y): + diff = jnp.subtract(x.squeeze(), y.squeeze()) + return jnp.sqrt(dot(diff, diff)).squeeze() + + +def l2n_loss(x, y): + x, y = norm(x), norm(y) + diff = jnp.subtract(x, y) + return jnp.sqrt(dot(diff, diff)).squeeze() + + +def ang_distance(x, y): + x, y = norm(x), norm(y) + return jnp.arccos(dot(x, y)).squeeze() + + +def ang_distance_dp(x, y): + x, y = norm(x), norm(y) + d1 = ang_distance(x, y) + d2 = ang_distance(-x, y) + return jnp.min(jnp.array([d1, d2])).squeeze() + + +################################ +# COMPUTE LOSS GRADIENTS +################################ +distances = [] +gradients = [] +gradient_lengths = [] +ground_truth = jnp.array([[1], [0]]) +for distfunc in [cosine_distance, l2n_loss, l2_loss, ang_distance, ang_distance_dp]: + distfunc_vmap = jax.vmap(distfunc, in_axes=(0, None))(vec, ground_truth) + distfunc_grad = jax.grad(distfunc, argnums=0) + distfunc_gradvmap = jax.vmap(distfunc_grad, in_axes=(0, None))(vec, ground_truth) + distfunc_gradlength = jnp.linalg.norm(distfunc_gradvmap, axis=1) + + distances.append(distfunc_vmap) + gradients.append(distfunc_gradvmap) + gradient_lengths.append(distfunc_gradlength) + + +################################ +# PLOT +################################ +sns.set_style("whitegrid") +plt.rcParams["figure.figsize"] = [8, 8] +plt.rcParams.update({"font.size": 18}) + +colors = [ + (0.368, 0.507, 0.71), + (0.881, 0.611, 0.142), + (0.923, 0.386, 0.209), + (0.56, 0.692, 0.195), + (0.528, 0.471, 0.701), + (0.772, 0.432, 0.102), + (0.572, 0.586, 0.0), +] + +labels = [ + r"$d_{\mathrm{cd}}=1-\frac{y \cdot z}{\|y\|\|z\|}$", + r"$d_{\mathrm{E,n}}=\sqrt{\left(\frac{y}{\|y\|}-\frac{z}{\|z\|}\right)\cdot \left(\frac{y}{\|y\|}-\frac{z}{\|z\|}\right)}$", + r"$d_{\mathrm{E}}=\sqrt{(y-z)\cdot(y-z)}$", + r"$d_{\mathrm{ang}}=\mathrm{arccos}\left( \frac{y \cdot z}{\|y\|\|z\|}\right)$", + r"$\mathrm{min}\left(d_{\mathrm{ang}}(y,z), d_{\mathrm{ang}}(-y,z)\right)$", +] + +scales = [5.0, 5.0, 5.0, 5.0] + +fmin = float(jnp.min(jnp.array(gradient_lengths))) +fmax = float(jnp.max(jnp.array(gradient_lengths))) +norm = matplotlib.colors.Normalize(vmin=0.0, vmax=1.2, clip=False) +cmap = matplotlib.colormaps["coolwarm_r"] # rainbow + +fig, ax = plt.subplots(1, len(gradients), sharey=True, figsize=(32, 6), gridspec_kw={"wspace": 0.1, "hspace": 0.1}) + +for i, axis in enumerate(ax): + circle = plt.Circle((0, 0), 1, color="k", fill=False, linestyle="--", linewidth=2.0) + axis.add_patch(circle) + axis.set_aspect("equal") + axis.set_xlim(-1.5, 1.5) + axis.set_ylim(-1.5, 1.5) + + scaled_gradients = jnp.divide(gradients[i], jnp.linalg.norm(gradients[i], axis=1, keepdims=True)) + quiver_plot = axis.quiver( + vec[:, 0], + vec[:, 1], + -1 * scaled_gradients[:, 0], + -1 * scaled_gradients[:, 1], + gradient_lengths[i], + cmap=cmap, + norm=norm, + units="width", + pivot="mid", + scale=30.0, + headwidth=3, + width=0.01, + ) + axis.plot(ground_truth[0], ground_truth[1], "o", color="k", markersize=10, alpha=0.7) + axis.annotate(r"$z$", (ground_truth[0] + 0.05, ground_truth[1] + 0.1)) + axis.title.set_text(labels[i]) + axis.set_xlabel(r"$y_1$") + +ax[0].set_ylabel(r"$y_2$") + +cbar = fig.colorbar(quiver_plot, ax=ax, format="%.1f", ticks=[0, 0.5, 1.0], extend="max") +cbar.set_label(r"gradient length $\|\nabla_{y}d(y,z)\|$") +fig.suptitle(r"Negative gradients $-\nabla_{y}d(y,z)$ with z=[1,0]", fontsize=20) +out_p = os.path.join(HITCHHIKING_ROOT_DIR, "results", f"loss_gradients.pdf") +plt.savefig(out_p, bbox_inches="tight") +plt.show() From 60d60adeaf41929ad1224887b29eb769c39028e4 Mon Sep 17 00:00:00 2001 From: Mikel Zhobro Date: Mon, 11 Mar 2024 11:56:32 +0100 Subject: [PATCH 3/4] add mocup vis scripts --- visu/figure_16.py | 529 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 529 insertions(+) create mode 100644 visu/figure_16.py diff --git a/visu/figure_16.py b/visu/figure_16.py new file mode 100644 index 0000000..877b462 --- /dev/null +++ b/visu/figure_16.py @@ -0,0 +1,529 @@ +import os +from tqdm import tqdm +from path import Path +import numpy as np +from mpl_toolkits.mplot3d import Axes3D +import matplotlib.pyplot as plt +import pandas as pd +import random +from scipy.spatial.transform import Rotation +import seaborn as sns + +from hitchhiking_rotations import HITCHHIKING_ROOT_DIR + + + +#################################################### +######### DEFINE AMC_PARSER FUNCTIONALITY ######### +#################################################### +import numpy as np +import matplotlib.pyplot as plt +from transforms3d.euler import euler2mat +from mpl_toolkits.mplot3d import Axes3D + + +class Joint: + def __init__(self, name, direction, length, axis, dof, limits): + """ + Definition of basic joint. The joint also contains the information of the + bone between it's parent joint and itself. Refer + [here](https://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/ASF-AMC.html) + for detailed description for asf files. + + Parameter + --------- + name: Name of the joint defined in the asf file. There should always be one + root joint. String. + + direction: Default direction of the joint(bone). The motions are all defined + based on this default pose. + + length: Length of the bone. + + axis: Axis of rotation for the bone. + + dof: Degree of freedom. Specifies the number of motion channels and in what + order they appear in the AMC file. + + limits: Limits on each of the channels in the dof specification + + """ + self.name = name + self.length = length + self.limits = np.zeros([3, 2]) + for lm, nm in zip(limits, dof): + if nm == "rx": + self.limits[0] = lm + elif nm == "ry": + self.limits[1] = lm + else: + self.limits[2] = lm + self.parent = None + self.children = [] + + # -- spatial transformation information -- + self.direction = np.reshape(direction, [3, 1]) # direction from parent to the + axis = np.deg2rad(axis) + self.C = euler2mat(*axis) # rotation matrix from local to parent pRl + self.Cinv = np.linalg.inv(self.C) + self.coordinate = None # position in world frame + self.matrix = None # rotation in world frame + self.rot_in_parent = None # rotation in parent frame + + def set_motion(self, motion): + if self.name == "root": + ## Want to set the root to be at the origin + # self.coordinate = np.reshape(np.array(motion['root'][:3]), [3, 1]) + # rotation = np.deg2rad(motion['root'][3:]) + self.coordinate = np.zeros([3, 1]) + rotation = np.zeros(3) + + self.rot_in_parent = self.C.dot(euler2mat(*rotation)).dot(self.Cinv) + self.matrix = self.rot_in_parent + else: + idx = 0 + rotation = np.zeros(3) + for axis, lm in enumerate(self.limits): + if not np.array_equal(lm, np.zeros(2)): + rotation[axis] = motion[self.name][idx] + idx += 1 + rotation = np.deg2rad(rotation) # rotation in local frame R^l + # R^p = pRl * R^l * lRp: (right to left) rot_back_to_parent <- rotate_in_local <- rot_from_parent_to_local + self.rot_in_parent = self.C.dot(euler2mat(*rotation)).dot(self.Cinv) # R^p + self.matrix = self.parent.matrix.dot(self.rot_in_parent) + self.coordinate = self.parent.coordinate + self.length * self.matrix.dot( + self.direction + ) + + for child in self.children: + child.set_motion(motion) + + def draw(self): + joints = self.to_dict() + fig = plt.figure() + ax = Axes3D(fig, auto_add_to_figure=False) + fig.add_axes(ax) + + ax.set_xlim3d(-50, 10) + ax.set_ylim3d(-20, 40) + ax.set_zlim3d(-20, 40) + + xs, ys, zs = [], [], [] + for joint in joints.values(): + xs.append(joint.coordinate[0, 0]) + ys.append(joint.coordinate[1, 0]) + zs.append(joint.coordinate[2, 0]) + plt.plot(zs, xs, ys, "b.") + + for joint in joints.values(): + child = joint + if child.parent is not None: + parent = child.parent + xs = [child.coordinate[0, 0], parent.coordinate[0, 0]] + ys = [child.coordinate[1, 0], parent.coordinate[1, 0]] + zs = [child.coordinate[2, 0], parent.coordinate[2, 0]] + plt.plot(zs, xs, ys, "r") + plt.show() + + def to_dict(self): + ret = {self.name: self} + for child in self.children: + ret.update(child.to_dict()) + return ret + + def pretty_print(self): + print("===================================") + print("joint: %s" % self.name) + print("direction:") + print(self.direction) + print("limits:", self.limits) + print("parent:", self.parent) + print("children:", self.children) + +def read_line(stream, idx): + if idx >= len(stream): + return None, idx + line = stream[idx].strip().split() + idx += 1 + return line, idx + +def parse_asf(file_path): + """read joint data only""" + with open(file_path) as f: + content = f.read().splitlines() + + for idx, line in enumerate(content): + # meta infomation is ignored + if line == ":bonedata": + content = content[idx + 1 :] + break + + # read joints + joints = {"root": Joint("root", np.zeros(3), 0, np.zeros(3), [], [])} + idx = 0 + while True: + # the order of each section is hard-coded + + line, idx = read_line(content, idx) + + if line[0] == ":hierarchy": + break + + assert line[0] == "begin" + + line, idx = read_line(content, idx) + assert line[0] == "id" + + line, idx = read_line(content, idx) + assert line[0] == "name" + name = line[1] + + line, idx = read_line(content, idx) + assert line[0] == "direction" + direction = np.array([float(axis) for axis in line[1:]]) + + # skip length + line, idx = read_line(content, idx) + assert line[0] == "length" + length = float(line[1]) + + line, idx = read_line(content, idx) + assert line[0] == "axis" + assert line[4] == "XYZ" + + axis = np.array([float(axis) for axis in line[1:-1]]) + + dof = [] + limits = [] + + line, idx = read_line(content, idx) + if line[0] == "dof": + dof = line[1:] + for i in range(len(dof)): + line, idx = read_line(content, idx) + if i == 0: + assert line[0] == "limits" + line = line[1:] + assert len(line) == 2 + mini = float(line[0][1:]) + maxi = float(line[1][:-1]) + limits.append((mini, maxi)) + + line, idx = read_line(content, idx) + + assert line[0] == "end" + joints[name] = Joint(name, direction, length, axis, dof, limits) + + # read hierarchy + assert line[0] == ":hierarchy" + + line, idx = read_line(content, idx) + + assert line[0] == "begin" + + while True: + line, idx = read_line(content, idx) + if line[0] == "end": + break + assert len(line) >= 2 + for joint_name in line[1:]: + joints[line[0]].children.append(joints[joint_name]) + for nm in line[1:]: + joints[nm].parent = joints[line[0]] + + return joints + +def parse_amc(file_path): + with open(file_path) as f: + content = f.read().splitlines() + + for idx, line in enumerate(content): + if line == ":DEGREES": + content = content[idx + 1 :] + break + + frames = [] + idx = 0 + line, idx = read_line(content, idx) + assert line[0].isnumeric(), line + EOF = False + while not EOF: + joint_degree = {} + while True: + line, idx = read_line(content, idx) + if line is None: + EOF = True + break + if line[0].isnumeric(): + break + joint_degree[line[0]] = [float(deg) for deg in line[1:]] + frames.append(joint_degree) + return frames + + +#################################################### +####################### MAIN ####################### +#################################################### +sns.set_style('whitegrid') +plt.rcParams.update({'font.size': 18}) +plt.rcParams.update({'figure.figsize': (7.0, 4.0)}) +colors = [(0.368, 0.507, 0.71), (0.881, 0.611, 0.142), (0.923, 0.386,0.209), + (0.56, 0.692, 0.195),(0.528, 0.471, 0.701), (0.772, 0.432,0.102), + (0.572, 0.586, 0.) ] + +data_path = f"{HITCHHIKING_ROOT_DIR}/assets/datasets/motion_capture_dataset" +BASE_DIR = Path(f'{data_path}/all_asfamc') +OUT_PATH = Path(f'{data_path}').mkdir_p() +DS_FULL = Path(f'{data_path}/datasets_full.csv') +DS_SAMPLED = Path(f'{data_path}/datasets_sampled_760.csv') +SAMPLED_10K = Path(f'{data_path}/datasets_10k.csv') +T_POSE_PATH = Path(f'{data_path}/motion_T_pose.csv') +NUMPY_DS = Path(f'{data_path}/dataset/') + + +def download_dataset(): + zip_file = f"{data_path}/allasfamc.zip" + + if not Path(zip_file).exists(): + # download + print('\n\nDownloading dataset') + url = "wget http://mocap.cs.cmu.edu/allasfamc.zip" + bash_command = f"wget {url} -P {data_path}" + os.system(bash_command) + + # extract only airplane + if not BASE_DIR.exists(): + print('Extracting dataset') + bash_command = f'unzip {zip_file} -d {data_path}' + os.system(bash_command) + +def load(): + def get_joint_pos_rot_arr(joints, amc_path, frame, joint_names): + c_joints = joints + c_motion = parse_amc(amc_path)[frame] + c_joints['root'].set_motion(c_motion) + d = c_joints['root'].to_dict() + + out_arr = np.array([d[j].coordinate.flatten().tolist() + d[j].rot_in_parent.flatten().tolist() for j in joint_names]) + return out_arr.flatten().tolist() + + if not BASE_DIR.exists(): + download_dataset() + + # All motion clips + if not DS_FULL.exists(): + print('Creating full dataset') + def get_nframe(amc_path): + with open(amc_path) as f: + content = f.read().splitlines() + for line in content[::-1]: + if line[0].isnumeric(): + return int(line) + + datasets_df = pd.DataFrame({'path': list(BASE_DIR.glob('subjects/*/*.amc'))}) + datasets_df['Subject'] = datasets_df['path'].map(lambda x: x.parent.stem) + datasets_df['Activity'] = datasets_df['path'].map(lambda x: x.stem.split('_')[-1].lower()) + datasets_df['asf_path'] = datasets_df['path'].map(lambda x: x.parent / (x.parent.stem + '.asf')) + datasets_df['n_frames'] = datasets_df['path'].map(get_nframe) + datasets_df.to_csv(DS_FULL, index=False) + + if not DS_SAMPLED.exists(): + print('Creating sampled dataset') + datasets_df = pd.read_csv(DS_FULL) + datasets_df = datasets_df[datasets_df['n_frames'] > 100] # remove motion clips with less than 100 frames + + # Sample 760(pepe) or 865(zhou) + # sample 30 frames from each motion clip + print('Creating 760 motions sampled dataset') + N_motion_samples = 760 + N_frames_sampled = 30 + datasets_s = datasets_df.sample(N_motion_samples, random_state=0).reset_index(drop=True) + datasets_s['sampled_frames'] = datasets_s['n_frames'].map(lambda n_frame: random.sample(range(n_frame), N_frames_sampled)) + datasets_s.to_csv(DS_SAMPLED, index=False) + + + # create dataset with 10k entries + if not SAMPLED_10K.exists(): + print('Creating 10k motions sampled dataset') + datasets_s = pd.read_csv(DS_SAMPLED, converters={'sampled_frames': pd.eval}) + + N_SAMPLES = 10000 + + N_motion_samples = len(datasets_s) + N_frames_sampled = len(datasets_s['sampled_frames'][0]) + N_total_sampled_frames = N_motion_samples * N_frames_sampled + + # Sample 10k frames + print('Sampling 10k frames dataset') + ix_sampled = random.sample(range(N_total_sampled_frames), N_SAMPLES) + + # Get the corresponding metadata + motion_sampled = [ix // N_frames_sampled for ix in ix_sampled] + frame_sampled = [ix % N_frames_sampled for ix in ix_sampled] + paths = datasets_s['path'].to_numpy()[motion_sampled] + asf_paths = datasets_s['asf_path'].to_numpy()[motion_sampled] + frames = [ datasets_s['sampled_frames'][m][f] for m,f in zip(motion_sampled, frame_sampled)] + subjects = [datasets_s['Subject'][m] for m in motion_sampled] + activities = [datasets_s['Activity'][m] for m in motion_sampled] + + # Save to csv + dataset_10k = pd.DataFrame({'path': paths, 'frame': frames, 'Subject': subjects, 'Activity': activities, 'asf_path': asf_paths}) + joint_names = list(parse_asf(dataset_10k['asf_path'][0]).keys()) + dic_of_joints = {asf: parse_asf(asf) for asf in tqdm(dataset_10k['asf_path'].unique(), desc='Parsing asf files')} + + tqdm.pandas(desc='Computing joint pos and rot for each motion') + dataset_10k['pos_rot'] = dataset_10k.progress_apply(lambda row: get_joint_pos_rot_arr(dic_of_joints[row.asf_path], row.path, row.frame, joint_names), axis=1) + + print('Saving dataset') + dataset_10k.to_csv(SAMPLED_10K, index=False) + + + + # 1. Capture first frame in T-Pose (87_02) + if not T_POSE_PATH.exists(): + print('Creating T-Pose') + subject, activity = '87', '02' + tpose_amc_path = BASE_DIR / 'subjects' / subject / f'{subject}_{activity}.amc' + tpose_asf_path = BASE_DIR / 'subjects' / subject / f'{subject}.asf' + assert tpose_amc_path.exists(), 'T-Pose amc path does not exist' + assert tpose_asf_path.exists(), 'T-Pose asf path does not exist' + + joints = parse_asf(tpose_asf_path) # dict of joints: forward kinematics + + joint_names = list(joints.keys()) + + t_pose_pos_rot = get_joint_pos_rot_arr(joints, tpose_amc_path, frame=0, joint_names=joint_names) + + t_pose_df = pd.DataFrame({'Subject': ['87'], 'Activity': ['02'], 'path': [tpose_amc_path], 'asf_path': [tpose_asf_path], 'pos_rot': [t_pose_pos_rot]}) + t_pose_df.to_csv(T_POSE_PATH, index=False) + + + + # load T_pose + if not NUMPY_DS.exists(): + print('Saving samped dataset w.r.t T-Pose as numpy') + NUMPY_DS.mkdir_p() + tpose_df = pd.read_csv(T_POSE_PATH, converters={'pos_rot': pd.eval}) + dataset_df = pd.read_csv(SAMPLED_10K, converters={'pos_rot': pd.eval}) + + N_size_dataset = len(dataset_df) + N_joints = len(tpose_df['pos_rot'][0]) // 12 + + t_pose_pos_rot = np.array(tpose_df['pos_rot'][0]).reshape(-1, 12) + dataset_pos_rot = np.stack([np.array(pos_rot).reshape(-1,12) for pos_rot in dataset_df['pos_rot']]) + + # T-pose + t_pose_pos = t_pose_pos_rot[:,:3].reshape((1, N_joints, 3, 1)) + t_pose_rot = t_pose_pos_rot[:,3:].reshape((1, N_joints, 3, 3)) + + # Dataset + dataset_pos = dataset_pos_rot[:, :, :3, None] + dataset_rots = dataset_pos_rot[:, :, 3:].reshape((N_size_dataset, N_joints, 3,3)) + dataset_rot_tpose_2_rot = np.matmul(np.linalg.inv(t_pose_rot), dataset_rots) + + tpose_df.to_csv(NUMPY_DS / 'tpose.csv', index=False) + dataset_df.to_csv(NUMPY_DS / 'dataset.csv', index=False) + np.save(NUMPY_DS / 'tpose_pos.npy', t_pose_pos) + np.save(NUMPY_DS / 'tpose_rot.npy', t_pose_rot) + np.save(NUMPY_DS / 'ds_pos.npy', dataset_pos) + np.save(NUMPY_DS / 'ds_rot_tpose_2_rot.npy', dataset_rot_tpose_2_rot) + + +def plot_distances(): + ds_rot_tpose_2_rot = Rotation.from_matrix(np.load(NUMPY_DS / 'ds_rot_tpose_2_rot.npy').reshape(-1 ,3, 3)) # N*Njoints, 3, 3 = 10k*31, 3, 3 + + so3_dist = Rotation.magnitude(ds_rot_tpose_2_rot) + so3_d_mean = so3_dist.mean() + so3_d_std = so3_dist.std() + + + quat_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_quat(canonical=True) - np.array([[0,0,0,1]]), axis=-1) + quat_d_mean = quat_dist.mean() + quat_d_std = quat_dist.std() + + + euler_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_euler(seq='xyz'), axis=-1) + euler_d_mean = euler_dist.mean() + euler_d_std = euler_dist.std() + + + exp_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_rotvec(), axis=-1) + exp_d_mean = exp_dist.mean() + exp_d_std = exp_dist.std() + + plt.hist(so3_dist, bins=100, label='SO3', fc=colors[0]+(0.5,)) + plt.hist(quat_dist, bins=100, label='Quaternions', fc=colors[1]+(0.5,)) + plt.axvline(3.14, color=colors[0], linestyle='dashed') + plt.axvline(np.sqrt(2), color=colors[1], linestyle='dashed') + plt.locator_params(axis='y', nbins=3) + + # log y axis + plt.xlabel('Distance') + plt.ylabel('Frequency') + plt.legend(loc='upper right') + plt.yscale('log') + plt.tight_layout() + out_p = os.path.join(HITCHHIKING_ROOT_DIR, "results", f"mocap_analsysis.pdf") + plt.savefig(out_p, bbox_inches="tight") + plt.show() + + +def plot_skeleton(): + out_p = os.path.join(HITCHHIKING_ROOT_DIR, "results", f"mocap_sceleton.pdf") + dataset_10k = pd.read_csv(DS_SAMPLED, converters={'sampled_frames': pd.eval}) + + asf_path, amc_path, sampled_frames = dataset_10k[['asf_path', 'path', 'sampled_frames']].iloc[33] + + c_joints = parse_asf(asf_path) + c_motion = parse_amc(amc_path)[sampled_frames[2]] + c_joints['root'].set_motion(c_motion) + + + def draw(joint): + joints = joint.to_dict() + fig = plt.figure() + ax = Axes3D(fig,auto_add_to_figure=False) + fig.add_axes(ax) + + ax.set_xlim3d(-30, 30) + ax.set_ylim3d(-20, 20) + ax.set_zlim3d(-20, 20) + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_zticks([]) + ax.set_axis_off() + + + for joint in joints.values(): + child = joint + if child.parent is not None: + parent = child.parent + xs = [child.coordinate[0, 0], parent.coordinate[0, 0]] + ys = [child.coordinate[1, 0], parent.coordinate[1, 0]] + zs = [child.coordinate[2, 0], parent.coordinate[2, 0]] + plt.plot(zs, xs, ys, 'k', linewidth=0.7) + + + + xs, ys, zs = [], [], [] + for joint in joints.values(): + xs.append(joint.coordinate[0, 0]) + ys.append(joint.coordinate[1, 0]) + zs.append(joint.coordinate[2, 0]) + plt.plot(zs, xs, ys, 'b.') + + #set looking angle 33 , -5 0 + ax.view_init(33, -5) + plt.savefig(out_p, bbox_inches="tight") + plt.show() + + draw(c_joints['root']) + + +if __name__ == "__main__": + if not NUMPY_DS.exists(): + load() + + plot_distances() + + plot_skeleton() \ No newline at end of file From 68aef5b41877b2df27a83499aeaf13735a2117e6 Mon Sep 17 00:00:00 2001 From: AndReGeist Date: Wed, 3 Apr 2024 11:37:11 +0200 Subject: [PATCH 4/4] used black --- visu/figure_16.py | 226 +++++++++++++++++++++++++--------------------- 1 file changed, 121 insertions(+), 105 deletions(-) diff --git a/visu/figure_16.py b/visu/figure_16.py index 877b462..de90d37 100644 --- a/visu/figure_16.py +++ b/visu/figure_16.py @@ -12,7 +12,6 @@ from hitchhiking_rotations import HITCHHIKING_ROOT_DIR - #################################################### ######### DEFINE AMC_PARSER FUNCTIONALITY ######### #################################################### @@ -91,9 +90,7 @@ def set_motion(self, motion): # R^p = pRl * R^l * lRp: (right to left) rot_back_to_parent <- rotate_in_local <- rot_from_parent_to_local self.rot_in_parent = self.C.dot(euler2mat(*rotation)).dot(self.Cinv) # R^p self.matrix = self.parent.matrix.dot(self.rot_in_parent) - self.coordinate = self.parent.coordinate + self.length * self.matrix.dot( - self.direction - ) + self.coordinate = self.parent.coordinate + self.length * self.matrix.dot(self.direction) for child in self.children: child.set_motion(motion) @@ -140,6 +137,7 @@ def pretty_print(self): print("parent:", self.parent) print("children:", self.children) + def read_line(stream, idx): if idx >= len(stream): return None, idx @@ -147,6 +145,7 @@ def read_line(stream, idx): idx += 1 return line, idx + def parse_asf(file_path): """read joint data only""" with open(file_path) as f: @@ -233,6 +232,7 @@ def parse_asf(file_path): return joints + def parse_amc(file_path): with open(file_path) as f: content = f.read().splitlines() @@ -264,21 +264,27 @@ def parse_amc(file_path): #################################################### ####################### MAIN ####################### #################################################### -sns.set_style('whitegrid') -plt.rcParams.update({'font.size': 18}) -plt.rcParams.update({'figure.figsize': (7.0, 4.0)}) -colors = [(0.368, 0.507, 0.71), (0.881, 0.611, 0.142), (0.923, 0.386,0.209), - (0.56, 0.692, 0.195),(0.528, 0.471, 0.701), (0.772, 0.432,0.102), - (0.572, 0.586, 0.) ] +sns.set_style("whitegrid") +plt.rcParams.update({"font.size": 18}) +plt.rcParams.update({"figure.figsize": (7.0, 4.0)}) +colors = [ + (0.368, 0.507, 0.71), + (0.881, 0.611, 0.142), + (0.923, 0.386, 0.209), + (0.56, 0.692, 0.195), + (0.528, 0.471, 0.701), + (0.772, 0.432, 0.102), + (0.572, 0.586, 0.0), +] data_path = f"{HITCHHIKING_ROOT_DIR}/assets/datasets/motion_capture_dataset" -BASE_DIR = Path(f'{data_path}/all_asfamc') -OUT_PATH = Path(f'{data_path}').mkdir_p() -DS_FULL = Path(f'{data_path}/datasets_full.csv') -DS_SAMPLED = Path(f'{data_path}/datasets_sampled_760.csv') -SAMPLED_10K = Path(f'{data_path}/datasets_10k.csv') -T_POSE_PATH = Path(f'{data_path}/motion_T_pose.csv') -NUMPY_DS = Path(f'{data_path}/dataset/') +BASE_DIR = Path(f"{data_path}/all_asfamc") +OUT_PATH = Path(f"{data_path}").mkdir_p() +DS_FULL = Path(f"{data_path}/datasets_full.csv") +DS_SAMPLED = Path(f"{data_path}/datasets_sampled_760.csv") +SAMPLED_10K = Path(f"{data_path}/datasets_10k.csv") +T_POSE_PATH = Path(f"{data_path}/motion_T_pose.csv") +NUMPY_DS = Path(f"{data_path}/dataset/") def download_dataset(): @@ -286,25 +292,28 @@ def download_dataset(): if not Path(zip_file).exists(): # download - print('\n\nDownloading dataset') + print("\n\nDownloading dataset") url = "wget http://mocap.cs.cmu.edu/allasfamc.zip" bash_command = f"wget {url} -P {data_path}" os.system(bash_command) # extract only airplane if not BASE_DIR.exists(): - print('Extracting dataset') - bash_command = f'unzip {zip_file} -d {data_path}' + print("Extracting dataset") + bash_command = f"unzip {zip_file} -d {data_path}" os.system(bash_command) + def load(): def get_joint_pos_rot_arr(joints, amc_path, frame, joint_names): c_joints = joints c_motion = parse_amc(amc_path)[frame] - c_joints['root'].set_motion(c_motion) - d = c_joints['root'].to_dict() + c_joints["root"].set_motion(c_motion) + d = c_joints["root"].to_dict() - out_arr = np.array([d[j].coordinate.flatten().tolist() + d[j].rot_in_parent.flatten().tolist() for j in joint_names]) + out_arr = np.array( + [d[j].coordinate.flatten().tolist() + d[j].rot_in_parent.flatten().tolist() for j in joint_names] + ) return out_arr.flatten().tolist() if not BASE_DIR.exists(): @@ -312,7 +321,8 @@ def get_joint_pos_rot_arr(joints, amc_path, frame, joint_names): # All motion clips if not DS_FULL.exists(): - print('Creating full dataset') + print("Creating full dataset") + def get_nframe(amc_path): with open(amc_path) as f: content = f.read().splitlines() @@ -320,73 +330,78 @@ def get_nframe(amc_path): if line[0].isnumeric(): return int(line) - datasets_df = pd.DataFrame({'path': list(BASE_DIR.glob('subjects/*/*.amc'))}) - datasets_df['Subject'] = datasets_df['path'].map(lambda x: x.parent.stem) - datasets_df['Activity'] = datasets_df['path'].map(lambda x: x.stem.split('_')[-1].lower()) - datasets_df['asf_path'] = datasets_df['path'].map(lambda x: x.parent / (x.parent.stem + '.asf')) - datasets_df['n_frames'] = datasets_df['path'].map(get_nframe) + datasets_df = pd.DataFrame({"path": list(BASE_DIR.glob("subjects/*/*.amc"))}) + datasets_df["Subject"] = datasets_df["path"].map(lambda x: x.parent.stem) + datasets_df["Activity"] = datasets_df["path"].map(lambda x: x.stem.split("_")[-1].lower()) + datasets_df["asf_path"] = datasets_df["path"].map(lambda x: x.parent / (x.parent.stem + ".asf")) + datasets_df["n_frames"] = datasets_df["path"].map(get_nframe) datasets_df.to_csv(DS_FULL, index=False) if not DS_SAMPLED.exists(): - print('Creating sampled dataset') + print("Creating sampled dataset") datasets_df = pd.read_csv(DS_FULL) - datasets_df = datasets_df[datasets_df['n_frames'] > 100] # remove motion clips with less than 100 frames + datasets_df = datasets_df[datasets_df["n_frames"] > 100] # remove motion clips with less than 100 frames # Sample 760(pepe) or 865(zhou) # sample 30 frames from each motion clip - print('Creating 760 motions sampled dataset') + print("Creating 760 motions sampled dataset") N_motion_samples = 760 N_frames_sampled = 30 datasets_s = datasets_df.sample(N_motion_samples, random_state=0).reset_index(drop=True) - datasets_s['sampled_frames'] = datasets_s['n_frames'].map(lambda n_frame: random.sample(range(n_frame), N_frames_sampled)) + datasets_s["sampled_frames"] = datasets_s["n_frames"].map( + lambda n_frame: random.sample(range(n_frame), N_frames_sampled) + ) datasets_s.to_csv(DS_SAMPLED, index=False) - # create dataset with 10k entries if not SAMPLED_10K.exists(): - print('Creating 10k motions sampled dataset') - datasets_s = pd.read_csv(DS_SAMPLED, converters={'sampled_frames': pd.eval}) + print("Creating 10k motions sampled dataset") + datasets_s = pd.read_csv(DS_SAMPLED, converters={"sampled_frames": pd.eval}) N_SAMPLES = 10000 N_motion_samples = len(datasets_s) - N_frames_sampled = len(datasets_s['sampled_frames'][0]) + N_frames_sampled = len(datasets_s["sampled_frames"][0]) N_total_sampled_frames = N_motion_samples * N_frames_sampled # Sample 10k frames - print('Sampling 10k frames dataset') + print("Sampling 10k frames dataset") ix_sampled = random.sample(range(N_total_sampled_frames), N_SAMPLES) # Get the corresponding metadata motion_sampled = [ix // N_frames_sampled for ix in ix_sampled] frame_sampled = [ix % N_frames_sampled for ix in ix_sampled] - paths = datasets_s['path'].to_numpy()[motion_sampled] - asf_paths = datasets_s['asf_path'].to_numpy()[motion_sampled] - frames = [ datasets_s['sampled_frames'][m][f] for m,f in zip(motion_sampled, frame_sampled)] - subjects = [datasets_s['Subject'][m] for m in motion_sampled] - activities = [datasets_s['Activity'][m] for m in motion_sampled] + paths = datasets_s["path"].to_numpy()[motion_sampled] + asf_paths = datasets_s["asf_path"].to_numpy()[motion_sampled] + frames = [datasets_s["sampled_frames"][m][f] for m, f in zip(motion_sampled, frame_sampled)] + subjects = [datasets_s["Subject"][m] for m in motion_sampled] + activities = [datasets_s["Activity"][m] for m in motion_sampled] # Save to csv - dataset_10k = pd.DataFrame({'path': paths, 'frame': frames, 'Subject': subjects, 'Activity': activities, 'asf_path': asf_paths}) - joint_names = list(parse_asf(dataset_10k['asf_path'][0]).keys()) - dic_of_joints = {asf: parse_asf(asf) for asf in tqdm(dataset_10k['asf_path'].unique(), desc='Parsing asf files')} - - tqdm.pandas(desc='Computing joint pos and rot for each motion') - dataset_10k['pos_rot'] = dataset_10k.progress_apply(lambda row: get_joint_pos_rot_arr(dic_of_joints[row.asf_path], row.path, row.frame, joint_names), axis=1) - - print('Saving dataset') + dataset_10k = pd.DataFrame( + {"path": paths, "frame": frames, "Subject": subjects, "Activity": activities, "asf_path": asf_paths} + ) + joint_names = list(parse_asf(dataset_10k["asf_path"][0]).keys()) + dic_of_joints = { + asf: parse_asf(asf) for asf in tqdm(dataset_10k["asf_path"].unique(), desc="Parsing asf files") + } + + tqdm.pandas(desc="Computing joint pos and rot for each motion") + dataset_10k["pos_rot"] = dataset_10k.progress_apply( + lambda row: get_joint_pos_rot_arr(dic_of_joints[row.asf_path], row.path, row.frame, joint_names), axis=1 + ) + + print("Saving dataset") dataset_10k.to_csv(SAMPLED_10K, index=False) - - # 1. Capture first frame in T-Pose (87_02) if not T_POSE_PATH.exists(): - print('Creating T-Pose') - subject, activity = '87', '02' - tpose_amc_path = BASE_DIR / 'subjects' / subject / f'{subject}_{activity}.amc' - tpose_asf_path = BASE_DIR / 'subjects' / subject / f'{subject}.asf' - assert tpose_amc_path.exists(), 'T-Pose amc path does not exist' - assert tpose_asf_path.exists(), 'T-Pose asf path does not exist' + print("Creating T-Pose") + subject, activity = "87", "02" + tpose_amc_path = BASE_DIR / "subjects" / subject / f"{subject}_{activity}.amc" + tpose_asf_path = BASE_DIR / "subjects" / subject / f"{subject}.asf" + assert tpose_amc_path.exists(), "T-Pose amc path does not exist" + assert tpose_asf_path.exists(), "T-Pose asf path does not exist" joints = parse_asf(tpose_asf_path) # dict of joints: forward kinematics @@ -394,74 +409,79 @@ def get_nframe(amc_path): t_pose_pos_rot = get_joint_pos_rot_arr(joints, tpose_amc_path, frame=0, joint_names=joint_names) - t_pose_df = pd.DataFrame({'Subject': ['87'], 'Activity': ['02'], 'path': [tpose_amc_path], 'asf_path': [tpose_asf_path], 'pos_rot': [t_pose_pos_rot]}) + t_pose_df = pd.DataFrame( + { + "Subject": ["87"], + "Activity": ["02"], + "path": [tpose_amc_path], + "asf_path": [tpose_asf_path], + "pos_rot": [t_pose_pos_rot], + } + ) t_pose_df.to_csv(T_POSE_PATH, index=False) - - # load T_pose if not NUMPY_DS.exists(): - print('Saving samped dataset w.r.t T-Pose as numpy') + print("Saving samped dataset w.r.t T-Pose as numpy") NUMPY_DS.mkdir_p() - tpose_df = pd.read_csv(T_POSE_PATH, converters={'pos_rot': pd.eval}) - dataset_df = pd.read_csv(SAMPLED_10K, converters={'pos_rot': pd.eval}) + tpose_df = pd.read_csv(T_POSE_PATH, converters={"pos_rot": pd.eval}) + dataset_df = pd.read_csv(SAMPLED_10K, converters={"pos_rot": pd.eval}) N_size_dataset = len(dataset_df) - N_joints = len(tpose_df['pos_rot'][0]) // 12 + N_joints = len(tpose_df["pos_rot"][0]) // 12 - t_pose_pos_rot = np.array(tpose_df['pos_rot'][0]).reshape(-1, 12) - dataset_pos_rot = np.stack([np.array(pos_rot).reshape(-1,12) for pos_rot in dataset_df['pos_rot']]) + t_pose_pos_rot = np.array(tpose_df["pos_rot"][0]).reshape(-1, 12) + dataset_pos_rot = np.stack([np.array(pos_rot).reshape(-1, 12) for pos_rot in dataset_df["pos_rot"]]) # T-pose - t_pose_pos = t_pose_pos_rot[:,:3].reshape((1, N_joints, 3, 1)) - t_pose_rot = t_pose_pos_rot[:,3:].reshape((1, N_joints, 3, 3)) + t_pose_pos = t_pose_pos_rot[:, :3].reshape((1, N_joints, 3, 1)) + t_pose_rot = t_pose_pos_rot[:, 3:].reshape((1, N_joints, 3, 3)) # Dataset dataset_pos = dataset_pos_rot[:, :, :3, None] - dataset_rots = dataset_pos_rot[:, :, 3:].reshape((N_size_dataset, N_joints, 3,3)) + dataset_rots = dataset_pos_rot[:, :, 3:].reshape((N_size_dataset, N_joints, 3, 3)) dataset_rot_tpose_2_rot = np.matmul(np.linalg.inv(t_pose_rot), dataset_rots) - tpose_df.to_csv(NUMPY_DS / 'tpose.csv', index=False) - dataset_df.to_csv(NUMPY_DS / 'dataset.csv', index=False) - np.save(NUMPY_DS / 'tpose_pos.npy', t_pose_pos) - np.save(NUMPY_DS / 'tpose_rot.npy', t_pose_rot) - np.save(NUMPY_DS / 'ds_pos.npy', dataset_pos) - np.save(NUMPY_DS / 'ds_rot_tpose_2_rot.npy', dataset_rot_tpose_2_rot) + tpose_df.to_csv(NUMPY_DS / "tpose.csv", index=False) + dataset_df.to_csv(NUMPY_DS / "dataset.csv", index=False) + np.save(NUMPY_DS / "tpose_pos.npy", t_pose_pos) + np.save(NUMPY_DS / "tpose_rot.npy", t_pose_rot) + np.save(NUMPY_DS / "ds_pos.npy", dataset_pos) + np.save(NUMPY_DS / "ds_rot_tpose_2_rot.npy", dataset_rot_tpose_2_rot) def plot_distances(): - ds_rot_tpose_2_rot = Rotation.from_matrix(np.load(NUMPY_DS / 'ds_rot_tpose_2_rot.npy').reshape(-1 ,3, 3)) # N*Njoints, 3, 3 = 10k*31, 3, 3 + ds_rot_tpose_2_rot = Rotation.from_matrix( + np.load(NUMPY_DS / "ds_rot_tpose_2_rot.npy").reshape(-1, 3, 3) + ) # N*Njoints, 3, 3 = 10k*31, 3, 3 so3_dist = Rotation.magnitude(ds_rot_tpose_2_rot) so3_d_mean = so3_dist.mean() so3_d_std = so3_dist.std() - - quat_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_quat(canonical=True) - np.array([[0,0,0,1]]), axis=-1) + quat_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_quat(canonical=True) - np.array([[0, 0, 0, 1]]), axis=-1) quat_d_mean = quat_dist.mean() quat_d_std = quat_dist.std() - - euler_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_euler(seq='xyz'), axis=-1) + euler_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_euler(seq="xyz"), axis=-1) euler_d_mean = euler_dist.mean() euler_d_std = euler_dist.std() - exp_dist = np.linalg.norm(ds_rot_tpose_2_rot.as_rotvec(), axis=-1) exp_d_mean = exp_dist.mean() exp_d_std = exp_dist.std() - plt.hist(so3_dist, bins=100, label='SO3', fc=colors[0]+(0.5,)) - plt.hist(quat_dist, bins=100, label='Quaternions', fc=colors[1]+(0.5,)) - plt.axvline(3.14, color=colors[0], linestyle='dashed') - plt.axvline(np.sqrt(2), color=colors[1], linestyle='dashed') - plt.locator_params(axis='y', nbins=3) + plt.hist(so3_dist, bins=100, label="SO3", fc=colors[0] + (0.5,)) + plt.hist(quat_dist, bins=100, label="Quaternions", fc=colors[1] + (0.5,)) + plt.axvline(3.14, color=colors[0], linestyle="dashed") + plt.axvline(np.sqrt(2), color=colors[1], linestyle="dashed") + plt.locator_params(axis="y", nbins=3) # log y axis - plt.xlabel('Distance') - plt.ylabel('Frequency') - plt.legend(loc='upper right') - plt.yscale('log') + plt.xlabel("Distance") + plt.ylabel("Frequency") + plt.legend(loc="upper right") + plt.yscale("log") plt.tight_layout() out_p = os.path.join(HITCHHIKING_ROOT_DIR, "results", f"mocap_analsysis.pdf") plt.savefig(out_p, bbox_inches="tight") @@ -470,19 +490,18 @@ def plot_distances(): def plot_skeleton(): out_p = os.path.join(HITCHHIKING_ROOT_DIR, "results", f"mocap_sceleton.pdf") - dataset_10k = pd.read_csv(DS_SAMPLED, converters={'sampled_frames': pd.eval}) + dataset_10k = pd.read_csv(DS_SAMPLED, converters={"sampled_frames": pd.eval}) - asf_path, amc_path, sampled_frames = dataset_10k[['asf_path', 'path', 'sampled_frames']].iloc[33] + asf_path, amc_path, sampled_frames = dataset_10k[["asf_path", "path", "sampled_frames"]].iloc[33] c_joints = parse_asf(asf_path) c_motion = parse_amc(amc_path)[sampled_frames[2]] - c_joints['root'].set_motion(c_motion) - + c_joints["root"].set_motion(c_motion) def draw(joint): joints = joint.to_dict() fig = plt.figure() - ax = Axes3D(fig,auto_add_to_figure=False) + ax = Axes3D(fig, auto_add_to_figure=False) fig.add_axes(ax) ax.set_xlim3d(-30, 30) @@ -493,7 +512,6 @@ def draw(joint): ax.set_zticks([]) ax.set_axis_off() - for joint in joints.values(): child = joint if child.parent is not None: @@ -501,23 +519,21 @@ def draw(joint): xs = [child.coordinate[0, 0], parent.coordinate[0, 0]] ys = [child.coordinate[1, 0], parent.coordinate[1, 0]] zs = [child.coordinate[2, 0], parent.coordinate[2, 0]] - plt.plot(zs, xs, ys, 'k', linewidth=0.7) - - + plt.plot(zs, xs, ys, "k", linewidth=0.7) xs, ys, zs = [], [], [] for joint in joints.values(): xs.append(joint.coordinate[0, 0]) ys.append(joint.coordinate[1, 0]) zs.append(joint.coordinate[2, 0]) - plt.plot(zs, xs, ys, 'b.') + plt.plot(zs, xs, ys, "b.") - #set looking angle 33 , -5 0 + # set looking angle 33 , -5 0 ax.view_init(33, -5) plt.savefig(out_p, bbox_inches="tight") plt.show() - draw(c_joints['root']) + draw(c_joints["root"]) if __name__ == "__main__": @@ -526,4 +542,4 @@ def draw(joint): plot_distances() - plot_skeleton() \ No newline at end of file + plot_skeleton()