From 30e1b837b3efe6fe0b4f0b959018329f1714ab55 Mon Sep 17 00:00:00 2001 From: Shubh Maheshwari Date: Wed, 4 Dec 2024 23:10:21 -0800 Subject: [PATCH 1/2] Addiing plotting function for skeleton --- src/plot_3.py | 23 +- src/plot_main_diagram.py | 643 +++++++++++++++++++++++++++++++++++++++ src/plot_series.py | 639 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 1304 insertions(+), 1 deletion(-) create mode 100644 src/plot_main_diagram.py create mode 100644 src/plot_series.py diff --git a/src/plot_3.py b/src/plot_3.py index 2e1f9b4..cbf18b9 100644 --- a/src/plot_3.py +++ b/src/plot_3.py @@ -226,7 +226,7 @@ def update_smpl_multi_view_callback(self,samples,video_name=None): shift_x = sample_ind - len(samples)//2 shift_x = -self.ps_data['bbox'][0]*shift_x*4 - + # shift_x = -1.5*shift_x biomechanical = sample.osim.vertices + np.array([shift_x,0,0.0])*self.ps_data['bbox'] biomechanical_joints = sample.osim.joints + np.array([shift_x,0,0.0])*self.ps_data['bbox'] @@ -332,6 +332,26 @@ def callback_render(self): + image_dir = os.path.join(video_name.replace('.mp4', "_images_transparent")) + os.makedirs(image_dir,exist_ok=True) + print(f'Rendering images:') + for i in tqdm(range(self.ps_data['T'])): + + for j in range(len(self.ps_data['ps_biomechanical_list'])): + self.ps_data['ps_biomechanical_list'][j].update_vertex_positions(self.ps_data['biomechanical'][j][i]) + self.ps_data['ps_biomechanical_joints_list'][j].update_point_positions(self.ps_data['biomechanical_joints'][j][i]) + + + + image_path = os.path.join(image_dir,f"{os.path.basename(video_name).replace('.mp4','')}_{i}.png") + # print(f"Saving plot to :{image_path}") + ps.set_screenshot_extension(".png") + ps.screenshot(image_path,transparent_bg=True) + + + + + image_dir = os.path.join(video_name.replace('.mp4', "_images")) os.makedirs(image_dir,exist_ok=True) print(f'Rendering images:') @@ -431,6 +451,7 @@ def load_subject(sample_path,retrieval_path=None): cur_sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True, start_frame=segment[0], end_frame=segment[1]) samples.append(cur_sample) + break if len(samples) == 0: sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) diff --git a/src/plot_main_diagram.py b/src/plot_main_diagram.py new file mode 100644 index 0000000..6c6593e --- /dev/null +++ b/src/plot_main_diagram.py @@ -0,0 +1,643 @@ +import os +import sys +import json +import numpy as np +from tqdm import tqdm +import polyscope as ps +import polyscope.imgui as psim + +from utils import * +from dataloader import OpenCapDataLoader,MultiviewRGB +from smpl_loader import SMPLRetarget +# from osim import OSIMSequence +# Load LaiArnoldModified2017 +from osim import OSIMSequence + +from scipy.spatial.transform import Rotation as sRotation + +class Visualizer: + def __init__(self): + + ps.init() + + ps.remove_all_structures() + # Set camera + ps.set_automatically_compute_scene_extents(True) + ps.set_navigation_style("free") + # ps.set_view_projection_mode("orthographic") + ps.set_ground_plane_mode('tile') + ps.set_background_color(np.array([201,233,246])/255) + + def read_display_size(self): + polyscope_ini_path = os.path.join(os.getcwd(),'.polyscope.ini') + if not os.path.exists(polyscope_ini_path): + self.display_size = (1280,720) + ps.warning("Polyscope.ini not found, defaulting to 1280x720") + else: + with open(polyscope_ini_path) as f: + display_data = json.load(f) + + self.display_size = (display_data['windowWidth'], display_data['windowHeight']) + + + return self.display_size + + + + def callback(self): + + ########### Checks ############ + # Ensure self.t lies between + self.ps_data['t'] %= self.ps_data['T'] + + + + ### Update animation based on self.t + t = self.ps_data['t'] + + for i in range(len(self.ps_data['biomechanical'])): + T = self.ps_data['biomechanical'][i].shape[0] + self.ps_data['ps_biomechanical_list'][i].update_vertex_positions(self.ps_data['biomechanical'][i][self.ps_data['t'] % T ]) + self.ps_data['ps_biomechanical_joints_list'][i].update_point_positions(self.ps_data['biomechanical_joints'][i][self.ps_data['t'] % T ]) + + if not self.ps_data['is_paused']: + self.ps_data['t'] += 1 + + + # Check keyboards for inputs + + # Check for spacebar press to toggle pause + if psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_Space)) or psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_Space)): + + self.ps_data['is_paused'] = not self.ps_data['is_paused'] + + # Left arrow pressed + if psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_LeftArrow)) or psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_LeftArrow)): + self.ps_data['t'] -= 1 + + if psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_RightArrow)) or psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_RightArrow)): + self.ps_data['t'] += 1 + + # Input text + changed, self.ps_data["ui_text"] = psim.InputText("- Coach Instructions", self.ps_data["ui_text"]) + + # Create a floater to show the timestep and adject self.t accordingly + changed, self.ps_data['t'] = psim.SliderInt("", self.ps_data['t'], v_min=0, v_max=self.ps_data['T']) + psim.SameLine() + + # Create a render button which when pressed will create a .mp4 file + if psim.Button("<"): + self.ps_data['t'] -= 1 + + psim.SameLine() + if psim.Button("Play Video" if self.ps_data['is_paused'] else "Pause Video"): + self.ps_data['is_paused'] = not self.ps_data['is_paused'] + + psim.SameLine() + if psim.Button(">"): + self.ps_data['t'] += 1 + + # psim.SameLine() + if psim.Button("Render Video"): + self.callback_render() + + if(psim.TreeNode("Load other samples")): + + # psim.TextUnformatted("Load Optimized samples") + + changed = psim.BeginCombo("- Experiement", self.ps_data["session_options_selected"]) + if changed: + for val in self.ps_data["session_options"]: + _, selected = psim.Selectable(val, selected=self.ps_data["session_options_selected"]==val) + if selected: + self.ps_data["session_options_selected"] = val + + sample_path = os.path.join(INPUT_DIR,f"OpenCapData_{self.ps_data['session_options_selected']}","MarkerData") + other_session_trials_details = [ os.path.join(sample_path, x) for x in os.listdir(sample_path)] + other_session_trials_details = [OpenCapDataLoader.get_label(os.path.basename(x)) for x in other_session_trials_details if os.path.isfile(x)] + + self.ps_data['category_options'] = list(set([x[0] for x in other_session_trials_details])) + self.ps_data['trial_options'] = [ x[1] for x in other_session_trials_details if x[0] == self.ps_data['category_options_selected']] + + psim.EndCombo() + + changed = psim.BeginCombo("- Trial", self.ps_data["trial_options_selected"]) + if changed: + for val in self.ps_data["trial_options"]: + _, selected = psim.Selectable(val, selected=self.ps_data["trial_options_selected"]==val) + if selected: + self.ps_data["trial_options_selected"] = val + + + # Load info about other trial samples. + sample_path = os.path.join(INPUT_DIR,f"OpenCapData_{self.ps_data['session_options_selected']}","MarkerData") + other_session_trials_details = [ os.path.join(sample_path, x) for x in os.listdir(sample_path)] + other_session_trials_details = [OpenCapDataLoader.get_label(os.path.basename(x)) for x in other_session_trials_details if os.path.isfile(x)] + + self.ps_data['trial_options'] = [ x[1] for x in other_session_trials_details if x[0] == self.ps_data['category_options_selected']] + psim.EndCombo() + + changed = psim.BeginCombo("- Suggested Samples", self.ps_data["retrieval_options_selected"]) + if changed: + for val in self.ps_data["retrieval_options"]: + _, selected = psim.Selectable(val, selected=self.ps_data["retrieval_options_selected"]==val) + if selected: + self.ps_data["retrieval_options_selected"] = val + psim.EndCombo() + + + + if(psim.Button("Load Optimized samples")): + sample_path = os.path.join(INPUT_DIR,f"OpenCapData_{self.ps_data['session_options_selected']}") + sample_path = os.path.join(sample_path, "MarkerData") + sample_path = os.path.join(sample_path,f"{self.ps_data['category_options_selected']}{self.ps_data['trial_options_selected']}.trc") + sample = load_subject(sample_path) + + retrieval_path = os.path.join(self.ps_data['retrieval_dir'], self.ps_data['retrieval_options_selected']) + sample = load_retrived_samples(sample,retrieval_path) + self.update_smpl_multi_view_callback(sample) + psim.TreePop() + + + # psim.End() + + + def update_smpl_multi_view_callback(self,samples,video_name=None): + + + + for sample_ind, sample in enumerate(samples): + assert hasattr(sample,'rgb'), "Error loading RGB Data. Don't know the camera details. Cannot render in multiple views" + assert hasattr(sample,'osim'), "Error loading OSIM Data. Cannot render biomechanical data" + + + # Initialize ps_data for polyscope + if not hasattr(self,'ps_data') : + + ps.init() + self.ps_data = {} + self.ps_data['T'] = 196 + target = sample.joints_np + self.ps_data['bbox'] = target.max(axis=(0,1)) - target.min(axis=(0,1)) + self.ps_data['object_position'] = sample.joints_np[0,0] + + + self.ps_data['ps_biomechanical_list'] = [] + self.ps_data['ps_biomechanical_joints_list'] = [] + self.ps_data['biomechanical'] = [] + self.ps_data['biomechanical_joints'] = [] + self.ps_data['com_curve'] = [] + + + + ps.remove_all_structures() + # camera_position = np.array([0,0,3*self.ps_data['bbox'][0]]) + # camera_position = np.array([7*self.ps_data['bbox'][0],0.0*self.ps_data['bbox'][1],0]) + self.ps_data['object_position'] + camera_position = np.array([0,-0.5*self.ps_data['bbox'][1],-7*self.ps_data['bbox'][0]]) + self.ps_data['object_position'] + look_at_position = np.array([0,-0.5*self.ps_data['bbox'][1],0]) + self.ps_data['object_position'] + ps.look_at(camera_position,look_at_position) + + + if not hasattr(self,'ps_data') or 'ps_cams' not in self.ps_data: + ps_cams = [] + # Set indivdual cameras + for i,camera in enumerate(sample.rgb.cameras): + intrinsics = ps.CameraIntrinsics(fov_vertical_deg=camera['fov_x'], fov_horizontal_deg=camera['fov_y']) + # extrinsics = ps.CameraExtrinsics(mat=np.eye(4)) + extrinsics = ps.CameraExtrinsics(root=camera['position'], look_dir=camera['look_dir'], up_dir=camera['up_dir']) + params = ps.CameraParameters(intrinsics, extrinsics) + ps_cam = ps.register_camera_view(f"Cam{i}", params,enabled=False) + # print("Camera:",params.get_view_mat()) + ps_cams.append(ps_cam) + + + # Create random colors of each segment + # colors = np.random.random((sample.segments.shape[0],3)) + # mesh_colors = np.zeros((verts.shape[0],3)) + # mesh_colors[:,1] = 0.3 # Default color is light blue + # mesh_colors[:,2] = 1 # Default color is light blue + # for i,segment in enumerate(sample.segments): + # mesh_colors[segment[0]:segment[1]] = colors[i:i+1] + + # Map all polyscope objects to ps_data + + self.ps_data['ps_cams'] = ps_cams + + + T = 150 if "FinalFinalHigh" in sample.osim_file else len(sample.osim.vertices) + step_siz = 1 + num_samples = list(range(0,T,step_siz)) + + hip_joints = [] + name = os.path.basename(sample.osim_file).split('.')[0] + for t in num_samples: + + shift_x = t//step_siz - len(num_samples)//2 + shift_x = 1.5*shift_x/90 -0.3 + print(t, shift_x) + + biomechanical_joints = sample.osim.joints[t] + biomechanical_joints[:,0] += shift_x + + + rot_x = 0 + rot_y = -90 if "t2m" in sample.osim_file or "mdm" in sample.osim_file else 0 + rot_z = 0 + + + R = sRotation.from_euler('xyz',[rot_x,rot_y,rot_z],degrees=True).as_matrix() + + biomechanical_joints = (biomechanical_joints - biomechanical_joints.mean(0,keepdims=True))@R.T + biomechanical_joints.mean(0,keepdims=True) + + + hip_joint = biomechanical_joints[0,:] + hip_joints.append(hip_joint) + + hip_joints = np.array(hip_joints) + edges = np.array([ [i,i+1] for i in range(hip_joints.shape[0]-1)]) + + # ps_com_curve = ps.register_curve_network(f"{sample_ind}-{name} com",hip_joints,edges, color=np.array([0,200,0])/255,radius=3e-3,enabled=True) + ps_com_curve = ps.register_curve_network(f"{sample_ind}-{name} com",hip_joints,edges, color=np.array([255,100,100])/255,radius=3e-3,enabled=True) + + T = 150 if "FinalFinalHigh" in sample.osim_file else len(sample.osim.vertices) + step_siz = T//5 + num_samples = list(range(0,T,step_siz)) + # num_samples = [0, 40,60, 80, 110, 150] + for t in num_samples: + + shift_x = t//step_siz - len(num_samples)//2 + shift_x = shift_x*0.7 + print(t, shift_x) + + # print(self.ps_data['bbox'].shape) + # print(np.array([[shift_x,0,0.0]]).shape) + # print((np.array([[shift_x,0,0.0]])*self.ps_data['bbox']).shape) + + # biomechanical = sample.osim.vertices[t] + np.array([[shift_x,0,0.0]])*self.ps_data['bbox'] + biomechanical = sample.osim.vertices[t].copy() + biomechanical[:,0] += shift_x + + rot_x = 0 + rot_y = 90 if "t2m" in sample.osim_file or "mdm" in sample.osim_file else 0 + rot_z = 0 + + + R = sRotation.from_euler('xyz',[rot_x,rot_y,rot_z],degrees=True).as_matrix() + + biomechanical = (biomechanical - biomechanical.mean(0,keepdims=True))@R.T + biomechanical.mean(0,keepdims=True) + + + + self.ps_data['biomechanical'].append(biomechanical) + + + + # ps_biomechanical = ps.register_surface_mesh(f"{t}-{name} mesh",biomechanical,sample.osim.faces,transparency=0.5 ,color=np.array([127,127,255])/255,smooth_shade=True,material='wax',enabled=True) + ps_biomechanical = ps.register_surface_mesh(f"{t}-{name} mesh",biomechanical,sample.osim.faces ,color=np.array([255,255,255])/255,smooth_shade=True,material='wax',enabled=True) + + + + + self.ps_data['ps_biomechanical_list'].append(ps_biomechanical) + + + + + # # Find the timestep with knee flexion and set it as the initial timestep + # deepest_squat_index = biomechanical_joints[:,0,1].argmin() + + # self.ps_data['ps_biomechanical_list'][-1].update_vertex_positions(biomechanical[deepest_squat_index]) + # self.ps_data['ps_biomechanical_joints_list'][-1].update_point_positions(biomechanical_joints[deepest_squat_index]) + + + # Map all the rendering information + self.ps_data['video_name'] = video_name + self.ps_data['label'] = sample.label + self.ps_data['recordAttempt'] = sample.recordAttempt + self.ps_data['fps'] = sample.fps + + # Animation details + self.ps_data['t'] = 0 + self.ps_data['T'] = max(self.ps_data['T'], sample.osim.vertices.shape[0]) + self.ps_data['is_paused'] = False + self.ps_data['ui_text'] = "Enter Instructions here" + + + self.ps_data['session_options_selected'] = sample.openCapID + self.ps_data['session_options'] = [ x.replace("OpenCapData_","") for x in os.listdir(INPUT_DIR)] + + # Load info about other trial samples. + other_session_trials_details = [ os.path.join(os.path.dirname(sample.osim_file), x) for x in os.listdir(os.path.dirname(sample.osim_file))] + other_session_trials_details = [OpenCapDataLoader.get_label(os.path.basename(x)) for x in other_session_trials_details if os.path.isfile(x)] + + self.ps_data['category_options_selected'] = sample.label + self.ps_data['category_options'] = list(set([x[0] for x in other_session_trials_details])) + + + self.ps_data['trial_options_selected'] = sample.recordAttempt_str + self.ps_data['trial_options'] = [ x[1] for x in other_session_trials_details if x[0] == self.ps_data['category_options_selected']] + + self.ps_data['retrieval_options_selected'] = os.path.basename(sample.osim_file) + self.ps_data['retrieval_dir'] = os.path.dirname(sample.osim_file) + self.ps_data['retrieval_options'] = os.listdir(self.ps_data['retrieval_dir']) + + + # Take a screenshot of the initial setup + ps.show() + if video_name is not None: + image_path = video_name.replace('.mp4', "_series.png") + # print(f"Saving plot to :{image_path}") + ps.set_screenshot_extension(".png") + ps.screenshot(image_path,transparent_bg=True) + + + os._exit(0) + + + + def callback_render(self): + + + + video_name = self.ps_data['video_name'] + + if video_name is None: + ps.warning("Location to render not specefied. Setting to /render as default") + video_name = os.path.join(os.getcwd(),"render") + + if not video_name.endswith('.mp4'): + video_name = video_name + "mp4" + + + + + + image_dir = os.path.join(video_name.replace('.mp4', "_images_transparent")) + os.makedirs(image_dir,exist_ok=True) + print(f'Rendering images:') + for i in tqdm(range(self.ps_data['T'])): + + for j in range(len(self.ps_data['ps_biomechanical_list'])): + self.ps_data['ps_biomechanical_list'][j].update_vertex_positions(self.ps_data['biomechanical'][j][i]) + self.ps_data['ps_biomechanical_joints_list'][j].update_point_positions(self.ps_data['biomechanical_joints'][j][i]) + + + + image_path = os.path.join(image_dir,f"{os.path.basename(video_name).replace('.mp4','')}_{i}.png") + # print(f"Saving plot to :{image_path}") + ps.set_screenshot_extension(".png") + ps.screenshot(image_path,transparent_bg=True) + + + + + + image_dir = os.path.join(video_name.replace('.mp4', "_images")) + os.makedirs(image_dir,exist_ok=True) + print(f'Rendering images:') + for i in tqdm(range(self.ps_data['T'])): + + for j in range(len(self.ps_data['ps_biomechanical_list'])): + self.ps_data['ps_biomechanical_list'][j].update_vertex_positions(self.ps_data['biomechanical'][j][i]) + self.ps_data['ps_biomechanical_joints_list'][j].update_point_positions(self.ps_data['biomechanical_joints'][j][i]) + + + + image_path = os.path.join(image_dir,f"smpl_{i}.png") + # print(f"Saving plot to :{image_path}") + ps.set_screenshot_extension(".png") + ps.screenshot(image_path,transparent_bg=False) + + + image_path = os.path.join(image_dir,f"smpl_\%d.png") + video_name = os.path.abspath(video_name) + palette_path = os.path.join(image_dir,f"smpl.png") + frame_rate = self.ps_data['fps']//2 # Slowed down by 2x + os.system(f"ffmpeg -y -framerate {frame_rate} -i {image_path} -vf palettegen {palette_path}") + os.system(f"ffmpeg -y -framerate {frame_rate} -i {image_path} -i {palette_path} -lavfi paletteuse -q:v 5 {video_name}") + # os.system(f"ffmpeg -y -framerate {frame_rate} -i {image_path} -i {palette_path} -lavfi paletteuse {video_name.replace('mp4','gif')}") + + print(f"Running Command:",f"ffmpeg -y -framerate {frame_rate} -i {image_path} -i {palette_path} -lavfi paletteuse {video_name}") + + + + + + # Initialize 3D objects from a sample and set callback for + def render_smpl_multi_view_callback(self,samples,video_name=None): + self.update_smpl_multi_view_callback(samples,video_name=video_name) + ps.show() + ps.set_user_callback(self.callback) + ps.show() + + + +def render(samples, vis,video_name=None): + """ + Render dataset samples + + + @params + sample_path: Filepath of input + video_dir: Folder to store Render results for the complete worflow + + + Load input (currently .trc files) and save all the rendering videos + images (retargetting to smp, getting input text, per frame annotations etc.) + """ + + + if video_name is not None: + vis.update_smpl_multi_view_callback(samples,video_name=video_name) + vis.callback_render() + assert os.path.isfile(video_name), f"FFMPEG unable to render video:{video_name}" + else: + # Visualize each view + vis.render_smpl_multi_view_callback(samples,video_name=None) + + + +def load_subject(sample_path,retrieval_path=None): + sample = OpenCapDataLoader(sample_path) + + # Load Video + sample.rgb = MultiviewRGB(sample) + + print(f"Session ID: {sample.name} SubjectID:{sample.rgb.session_data['subjectID']} Action:{sample.label}") + + osim_path = os.path.dirname(os.path.dirname(sample.sample_path)) + osim_path = os.path.join(osim_path,'OpenSimData','Model', 'LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim') + osim_geometry_path = os.path.join(DATA_DIR,'OpenCap_LaiArnoldModified2017_Geometry') + + + ###################### Subject Details #################################################### + mot_path = os.path.dirname(os.path.dirname(sample.sample_path)) + mot_path = os.path.join(mot_path,'OpenSimData','Kinematics',sample.label+ sample.recordAttempt_str + '.mot') + mot_path = os.path.abspath(mot_path) + print("Loading User motion file:",mot_path) + sample.osim_file = mot_path + + + samples = [] + # Load Segments + if os.path.exists(os.path.join(DATA_DIR,"squat-segmentation-data", sample.openCapID+'.npy')): + segments = np.load(os.path.join(DATA_DIR,"squat-segmentation-data", sample.openCapID+'.npy'),allow_pickle=True).item() + if os.path.basename(sample.sample_path).split('.')[0] in segments: + segments = segments[sample.label+ sample.recordAttempt_str] + + + for segment in segments: + cur_sample = copy.deepcopy(sample) + cur_sample.joints_np = cur_sample.joints_np[segment[0]:segment[1]] + cur_sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True, start_frame=segment[0], end_frame=segment[1]) + + samples.append(cur_sample) + break + + if len(samples) == 0: + sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + samples.append(sample) + + return samples + +def load_retrived_samples(session, retrieval_path): + ###################### GENERATION DETAILS #################################################### + + # mot_path = os.path.dirname(os.path.dirname(sample.sample_path)) + # mot_path = os.path.join(mot_path,'OpenSimData','VQVAE7_Temporal_Kinematics',sample.label+ sample.recordAttempt_str + '.mot') + # print("Loading Reconstrction file:",mot_path) + + # sample.osim_pred = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/constrained_mot_0.002/12.mot" + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_196/entry_2.mot" + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_temporal_consistency_v2/entry_9.mot" + + + assert retrieval_path and os.path.isfile(retrieval_path), f"Unable to load .mot file:{retrieval_path}" + + mot_path = os.path.abspath(retrieval_path) + print("Loading Generatrion file:",mot_path) + + + trc_path = os.path.join(DATA_DIR,"Data", session, "MarkerData") + trc_file = [os.path.join(trc_path,x) for x in os.listdir(trc_path) if 'sqt' in x.lower() and x.endswith('.trc') ][0] + sample = OpenCapDataLoader(trc_file) + + # Load Video + sample.rgb = MultiviewRGB(sample) + + + osim_path = os.path.join(DATA_DIR,"Data", session, "OpenSimData","Model","LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim") + osim_geometry_path = os.path.join(DATA_DIR,'OpenCap_LaiArnoldModified2017_Geometry') + + sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + print("MOT DATA:",sample.osim.motion.shape) + print("Pelivs:",np.rad2deg(sample.osim.motion[::10,1:3])) + print("KNEE Left:",np.rad2deg(sample.osim.motion[::10,10])) + print("TIME:",sample.osim.motion[::10,0]) + # sample.osim.vertices[:,:,2] -= 1 + sample.osim_file = retrieval_path + return sample + + +def load_dynamics_data(retrieval_path): + ###################### GENERATION DETAILS #################################################### + + # mot_path = os.path.dirname(os.path.dirname(sample.sample_path)) + # mot_path = os.path.join(mot_path,'OpenSimData','VQVAE7_Temporal_Kinematics',sample.label+ sample.recordAttempt_str + '.mot') + # print("Loading Reconstrction file:",mot_path) + + # sample.osim_pred = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/constrained_mot_0.002/12.mot" + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_196/entry_2.mot" + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_temporal_consistency_v2/entry_9.mot" + + + assert retrieval_path and os.path.isfile(retrieval_path), f"Unable to load .mot file:{retrieval_path}" + + + mot_path = os.path.abspath(retrieval_path) + print("Loading Generatrion file:",mot_path) + + + trc_path = os.path.join(DATA_DIR,"Data", session, "MarkerData") + trc_file = [os.path.join(trc_path,x) for x in os.listdir(trc_path) if 'sqt' in x.lower() and x.endswith('.trc') ][0] + sample = OpenCapDataLoader(trc_file) + + # Load Video + sample.rgb = MultiviewRGB(sample) + + + osim_path = os.path.join(DATA_DIR,"Data", session, "OpenSimData","Model","LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim") + osim_geometry_path = os.path.join(DATA_DIR,'OpenCap_LaiArnoldModified2017_Geometry') + + sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + print("MOT DATA:",sample.osim.motion.shape) + print("Pelivs:",np.rad2deg(sample.osim.motion[::10,1:3])) + print("KNEE Left:",np.rad2deg(sample.osim.motion[::10,10])) + print("TIME:",sample.osim.motion[::10,0]) + # sample.osim.vertices[:,:,2] -= 1 + sample.osim_file = retrieval_path + return sample + +# Load file and render skeleton for each video +def render_dataset(sample_path=None): + video_dir = RENDER_DIR + + vis = Visualizer() + + for subject in os.listdir(INPUT_DIR): + for trial_path in os.listdir(os.path.join(INPUT_DIR,subject,'MarkerData')): + trial_path = os.path.join(INPUT_DIR,subject,'MarkerData',trial_path) + retrieval_path = os.path.join(INPUT_DIR,subject,'MarkerData',trial_path) + + +if __name__ == "__main__": + + import copy + + + # compare_files = ["/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/Data/d66330dc-7884-4915-9dbb-0520932294c4/MarkerData/SQT01.trc", + # "MCS_DATA/LIMO/ComAcc/mot_visualization/latents_subject_run_000cffd9-e154-4ce5-a075-1b4e1fd66201/entry_17_ComAcc.mot", + # "MCS_DATA/LIMO/FinalFinalHigh/mot_visualization/latents_subject_run_d2020b0e-6d41-4759-87f0-5c158f6ab86a/entry_19_FinalFinalHigh.mot"] + + compare_files = sys.argv[1:] + + + + if len(sys.argv) == 0: # Skipping for now. Will fix later to generate videos + render_dataset() + else: + samples = [] + for i in range(0, min(len(compare_files),4) ): + # file_path = sys.argv[i] + file_path = compare_files[i] + if file_path.endswith('.trc'): + sample = load_subject(file_path) + samples.extend(sample) + elif 'OpenSimData/Dynamics' in file_path: # For Dynamics data + + session = file_path + for i in range(4): + session = os.path.dirname(session) + session = os.path.basename(session) + print(session) + sample = load_retrived_samples(session, file_path) + samples.append(sample) + + + elif file_path.endswith('.mot'): # For baseline + generated results + session = os.path.basename(os.path.dirname(file_path)) + session = session.replace("latents_subject_run_","") + sample = load_retrived_samples(session,file_path) + + samples.append(sample) + break + vis = Visualizer() + video_name = None + video_name = sys.argv[-1] if sys.argv[-1].endswith('mp4') else None + render(samples,vis,video_name) + diff --git a/src/plot_series.py b/src/plot_series.py new file mode 100644 index 0000000..ecf4c27 --- /dev/null +++ b/src/plot_series.py @@ -0,0 +1,639 @@ +import os +import sys +import json +import numpy as np +from tqdm import tqdm +import polyscope as ps +import polyscope.imgui as psim + +from utils import * +from dataloader import OpenCapDataLoader,MultiviewRGB +from smpl_loader import SMPLRetarget +# from osim import OSIMSequence +# Load LaiArnoldModified2017 +from osim import OSIMSequence + +from scipy.spatial.transform import Rotation as sRotation + +class Visualizer: + def __init__(self): + + ps.init() + + ps.remove_all_structures() + # Set camera + ps.set_automatically_compute_scene_extents(True) + ps.set_navigation_style("free") + ps.set_view_projection_mode("orthographic") + ps.set_ground_plane_mode('tile_reflection') + + def read_display_size(self): + polyscope_ini_path = os.path.join(os.getcwd(),'.polyscope.ini') + if not os.path.exists(polyscope_ini_path): + self.display_size = (1280,720) + ps.warning("Polyscope.ini not found, defaulting to 1280x720") + else: + with open(polyscope_ini_path) as f: + display_data = json.load(f) + + self.display_size = (display_data['windowWidth'], display_data['windowHeight']) + + + return self.display_size + + + + def callback(self): + + ########### Checks ############ + # Ensure self.t lies between + self.ps_data['t'] %= self.ps_data['T'] + + + + ### Update animation based on self.t + t = self.ps_data['t'] + + for i in range(len(self.ps_data['biomechanical'])): + T = self.ps_data['biomechanical'][i].shape[0] + self.ps_data['ps_biomechanical_list'][i].update_vertex_positions(self.ps_data['biomechanical'][i][self.ps_data['t'] % T ]) + self.ps_data['ps_biomechanical_joints_list'][i].update_point_positions(self.ps_data['biomechanical_joints'][i][self.ps_data['t'] % T ]) + + if not self.ps_data['is_paused']: + self.ps_data['t'] += 1 + + + # Check keyboards for inputs + + # Check for spacebar press to toggle pause + if psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_Space)) or psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_Space)): + + self.ps_data['is_paused'] = not self.ps_data['is_paused'] + + # Left arrow pressed + if psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_LeftArrow)) or psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_LeftArrow)): + self.ps_data['t'] -= 1 + + if psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_RightArrow)) or psim.IsKeyPressed(psim.GetKeyIndex(psim.ImGuiKey_RightArrow)): + self.ps_data['t'] += 1 + + # Input text + changed, self.ps_data["ui_text"] = psim.InputText("- Coach Instructions", self.ps_data["ui_text"]) + + # Create a floater to show the timestep and adject self.t accordingly + changed, self.ps_data['t'] = psim.SliderInt("", self.ps_data['t'], v_min=0, v_max=self.ps_data['T']) + psim.SameLine() + + # Create a render button which when pressed will create a .mp4 file + if psim.Button("<"): + self.ps_data['t'] -= 1 + + psim.SameLine() + if psim.Button("Play Video" if self.ps_data['is_paused'] else "Pause Video"): + self.ps_data['is_paused'] = not self.ps_data['is_paused'] + + psim.SameLine() + if psim.Button(">"): + self.ps_data['t'] += 1 + + # psim.SameLine() + if psim.Button("Render Video"): + self.callback_render() + + if(psim.TreeNode("Load other samples")): + + # psim.TextUnformatted("Load Optimized samples") + + changed = psim.BeginCombo("- Experiement", self.ps_data["session_options_selected"]) + if changed: + for val in self.ps_data["session_options"]: + _, selected = psim.Selectable(val, selected=self.ps_data["session_options_selected"]==val) + if selected: + self.ps_data["session_options_selected"] = val + + sample_path = os.path.join(INPUT_DIR,f"OpenCapData_{self.ps_data['session_options_selected']}","MarkerData") + other_session_trials_details = [ os.path.join(sample_path, x) for x in os.listdir(sample_path)] + other_session_trials_details = [OpenCapDataLoader.get_label(os.path.basename(x)) for x in other_session_trials_details if os.path.isfile(x)] + + self.ps_data['category_options'] = list(set([x[0] for x in other_session_trials_details])) + self.ps_data['trial_options'] = [ x[1] for x in other_session_trials_details if x[0] == self.ps_data['category_options_selected']] + + psim.EndCombo() + + changed = psim.BeginCombo("- Trial", self.ps_data["trial_options_selected"]) + if changed: + for val in self.ps_data["trial_options"]: + _, selected = psim.Selectable(val, selected=self.ps_data["trial_options_selected"]==val) + if selected: + self.ps_data["trial_options_selected"] = val + + + # Load info about other trial samples. + sample_path = os.path.join(INPUT_DIR,f"OpenCapData_{self.ps_data['session_options_selected']}","MarkerData") + other_session_trials_details = [ os.path.join(sample_path, x) for x in os.listdir(sample_path)] + other_session_trials_details = [OpenCapDataLoader.get_label(os.path.basename(x)) for x in other_session_trials_details if os.path.isfile(x)] + + self.ps_data['trial_options'] = [ x[1] for x in other_session_trials_details if x[0] == self.ps_data['category_options_selected']] + psim.EndCombo() + + changed = psim.BeginCombo("- Suggested Samples", self.ps_data["retrieval_options_selected"]) + if changed: + for val in self.ps_data["retrieval_options"]: + _, selected = psim.Selectable(val, selected=self.ps_data["retrieval_options_selected"]==val) + if selected: + self.ps_data["retrieval_options_selected"] = val + psim.EndCombo() + + + + if(psim.Button("Load Optimized samples")): + sample_path = os.path.join(INPUT_DIR,f"OpenCapData_{self.ps_data['session_options_selected']}") + sample_path = os.path.join(sample_path, "MarkerData") + sample_path = os.path.join(sample_path,f"{self.ps_data['category_options_selected']}{self.ps_data['trial_options_selected']}.trc") + sample = load_subject(sample_path) + + retrieval_path = os.path.join(self.ps_data['retrieval_dir'], self.ps_data['retrieval_options_selected']) + sample = load_retrived_samples(sample,retrieval_path) + self.update_smpl_multi_view_callback(sample) + psim.TreePop() + + + # psim.End() + + + def update_smpl_multi_view_callback(self,samples,video_name=None): + + + + for sample_ind, sample in enumerate(samples): + assert hasattr(sample,'rgb'), "Error loading RGB Data. Don't know the camera details. Cannot render in multiple views" + assert hasattr(sample,'osim'), "Error loading OSIM Data. Cannot render biomechanical data" + + + # Initialize ps_data for polyscope + if not hasattr(self,'ps_data') : + + ps.init() + self.ps_data = {} + self.ps_data['T'] = 196 + target = sample.joints_np + self.ps_data['bbox'] = target.max(axis=(0,1)) - target.min(axis=(0,1)) + self.ps_data['object_position'] = sample.joints_np[0,0] + + + self.ps_data['ps_biomechanical_list'] = [] + self.ps_data['ps_biomechanical_joints_list'] = [] + self.ps_data['biomechanical'] = [] + self.ps_data['biomechanical_joints'] = [] + self.ps_data['com_curve'] = [] + + + + ps.remove_all_structures() + # camera_position = np.array([0,0,3*self.ps_data['bbox'][0]]) + # camera_position = np.array([7*self.ps_data['bbox'][0],0.0*self.ps_data['bbox'][1],0]) + self.ps_data['object_position'] + camera_position = np.array([0,-0.5*self.ps_data['bbox'][1],-7*self.ps_data['bbox'][0]]) + self.ps_data['object_position'] + look_at_position = np.array([0,-0.5*self.ps_data['bbox'][1],0]) + self.ps_data['object_position'] + ps.look_at(camera_position,look_at_position) + + + if not hasattr(self,'ps_data') or 'ps_cams' not in self.ps_data: + ps_cams = [] + # Set indivdual cameras + for i,camera in enumerate(sample.rgb.cameras): + intrinsics = ps.CameraIntrinsics(fov_vertical_deg=camera['fov_x'], fov_horizontal_deg=camera['fov_y']) + # extrinsics = ps.CameraExtrinsics(mat=np.eye(4)) + extrinsics = ps.CameraExtrinsics(root=camera['position'], look_dir=camera['look_dir'], up_dir=camera['up_dir']) + params = ps.CameraParameters(intrinsics, extrinsics) + ps_cam = ps.register_camera_view(f"Cam{i}", params,enabled=False) + # print("Camera:",params.get_view_mat()) + ps_cams.append(ps_cam) + + + # Create random colors of each segment + # colors = np.random.random((sample.segments.shape[0],3)) + # mesh_colors = np.zeros((verts.shape[0],3)) + # mesh_colors[:,1] = 0.3 # Default color is light blue + # mesh_colors[:,2] = 1 # Default color is light blue + # for i,segment in enumerate(sample.segments): + # mesh_colors[segment[0]:segment[1]] = colors[i:i+1] + + # Map all polyscope objects to ps_data + + self.ps_data['ps_cams'] = ps_cams + + + T = 150 if "FinalFinalHigh" in sample.osim_file else len(sample.osim.vertices) + step_siz = T//7 + num_samples = list(range(0,T,step_siz)) + + hip_joints = [] + name = os.path.basename(sample.osim_file).split('.')[0] + for t in num_samples: + + shift_x = t//step_siz - len(num_samples)//2 + shift_x = shift_x*1.2 + + print(t, shift_x) + + biomechanical_joints = sample.osim.joints[t] + biomechanical_joints[:,0] += shift_x + + + rot_x = 0 + rot_y = 90 if "t2m" in sample.osim_file or "mdm" in sample.osim_file else 0 + rot_z = 0 + + + R = sRotation.from_euler('xyz',[rot_x,rot_y,rot_z],degrees=True).as_matrix() + + biomechanical_joints = (biomechanical_joints - biomechanical_joints.mean(0,keepdims=True))@R.T + biomechanical_joints.mean(0,keepdims=True) + + + hip_joint = biomechanical_joints[1,:] + hip_joints.append(hip_joint) + + hip_joints = np.array(hip_joints) + edges = np.array([ [i,i+1] for i in range(hip_joints.shape[0]-1)]) + + ps_com_curve = ps.register_curve_network(f"{sample_ind}-{name} com",hip_joints,edges,color=np.array([255,255,0])/255,radius=1e-3,enabled=True) + + + for t in num_samples: + + shift_x = t//step_siz - len(num_samples)//2 + shift_x = shift_x*1.2 + + print(t, shift_x) + + # print(self.ps_data['bbox'].shape) + # print(np.array([[shift_x,0,0.0]]).shape) + # print((np.array([[shift_x,0,0.0]])*self.ps_data['bbox']).shape) + + # biomechanical = sample.osim.vertices[t] + np.array([[shift_x,0,0.0]])*self.ps_data['bbox'] + biomechanical = sample.osim.vertices[t].copy() + biomechanical[:,0] += shift_x + + rot_x = 0 + rot_y = 90 if "t2m" in sample.osim_file or "mdm" in sample.osim_file else 0 + rot_z = 0 + + + R = sRotation.from_euler('xyz',[rot_x,rot_y,rot_z],degrees=True).as_matrix() + + biomechanical = (biomechanical - biomechanical.mean(0,keepdims=True))@R.T + biomechanical.mean(0,keepdims=True) + + + + self.ps_data['biomechanical'].append(biomechanical) + + + + ps_biomechanical = ps.register_surface_mesh(f"{t}-{name} mesh",biomechanical,sample.osim.faces,transparency=0.5,color=np.array([127,127,255])/255,smooth_shade=True,material='wax',enabled=True) + + + + + self.ps_data['ps_biomechanical_list'].append(ps_biomechanical) + + + + + # # Find the timestep with knee flexion and set it as the initial timestep + # deepest_squat_index = biomechanical_joints[:,0,1].argmin() + + # self.ps_data['ps_biomechanical_list'][-1].update_vertex_positions(biomechanical[deepest_squat_index]) + # self.ps_data['ps_biomechanical_joints_list'][-1].update_point_positions(biomechanical_joints[deepest_squat_index]) + + + # Map all the rendering information + self.ps_data['video_name'] = video_name + self.ps_data['label'] = sample.label + self.ps_data['recordAttempt'] = sample.recordAttempt + self.ps_data['fps'] = sample.fps + + # Animation details + self.ps_data['t'] = 0 + self.ps_data['T'] = max(self.ps_data['T'], sample.osim.vertices.shape[0]) + self.ps_data['is_paused'] = False + self.ps_data['ui_text'] = "Enter Instructions here" + + + self.ps_data['session_options_selected'] = sample.openCapID + self.ps_data['session_options'] = [ x.replace("OpenCapData_","") for x in os.listdir(INPUT_DIR)] + + # Load info about other trial samples. + other_session_trials_details = [ os.path.join(os.path.dirname(sample.osim_file), x) for x in os.listdir(os.path.dirname(sample.osim_file))] + other_session_trials_details = [OpenCapDataLoader.get_label(os.path.basename(x)) for x in other_session_trials_details if os.path.isfile(x)] + + self.ps_data['category_options_selected'] = sample.label + self.ps_data['category_options'] = list(set([x[0] for x in other_session_trials_details])) + + + self.ps_data['trial_options_selected'] = sample.recordAttempt_str + self.ps_data['trial_options'] = [ x[1] for x in other_session_trials_details if x[0] == self.ps_data['category_options_selected']] + + self.ps_data['retrieval_options_selected'] = os.path.basename(sample.osim_file) + self.ps_data['retrieval_dir'] = os.path.dirname(sample.osim_file) + self.ps_data['retrieval_options'] = os.listdir(self.ps_data['retrieval_dir']) + + + # Take a screenshot of the initial setup + ps.show() + if video_name is not None: + image_path = video_name.replace('.mp4', "_series.png") + # print(f"Saving plot to :{image_path}") + ps.set_screenshot_extension(".png") + ps.screenshot(image_path,transparent_bg=True) + + + os._exit(0) + + + + def callback_render(self): + + + + video_name = self.ps_data['video_name'] + + if video_name is None: + ps.warning("Location to render not specefied. Setting to /render as default") + video_name = os.path.join(os.getcwd(),"render") + + if not video_name.endswith('.mp4'): + video_name = video_name + "mp4" + + + + + + image_dir = os.path.join(video_name.replace('.mp4', "_images_transparent")) + os.makedirs(image_dir,exist_ok=True) + print(f'Rendering images:') + for i in tqdm(range(self.ps_data['T'])): + + for j in range(len(self.ps_data['ps_biomechanical_list'])): + self.ps_data['ps_biomechanical_list'][j].update_vertex_positions(self.ps_data['biomechanical'][j][i]) + self.ps_data['ps_biomechanical_joints_list'][j].update_point_positions(self.ps_data['biomechanical_joints'][j][i]) + + + + image_path = os.path.join(image_dir,f"{os.path.basename(video_name).replace('.mp4','')}_{i}.png") + # print(f"Saving plot to :{image_path}") + ps.set_screenshot_extension(".png") + ps.screenshot(image_path,transparent_bg=True) + + + + + + image_dir = os.path.join(video_name.replace('.mp4', "_images")) + os.makedirs(image_dir,exist_ok=True) + print(f'Rendering images:') + for i in tqdm(range(self.ps_data['T'])): + + for j in range(len(self.ps_data['ps_biomechanical_list'])): + self.ps_data['ps_biomechanical_list'][j].update_vertex_positions(self.ps_data['biomechanical'][j][i]) + self.ps_data['ps_biomechanical_joints_list'][j].update_point_positions(self.ps_data['biomechanical_joints'][j][i]) + + + + image_path = os.path.join(image_dir,f"smpl_{i}.png") + # print(f"Saving plot to :{image_path}") + ps.set_screenshot_extension(".png") + ps.screenshot(image_path,transparent_bg=False) + + + image_path = os.path.join(image_dir,f"smpl_\%d.png") + video_name = os.path.abspath(video_name) + palette_path = os.path.join(image_dir,f"smpl.png") + frame_rate = self.ps_data['fps']//2 # Slowed down by 2x + os.system(f"ffmpeg -y -framerate {frame_rate} -i {image_path} -vf palettegen {palette_path}") + os.system(f"ffmpeg -y -framerate {frame_rate} -i {image_path} -i {palette_path} -lavfi paletteuse -q:v 5 {video_name}") + # os.system(f"ffmpeg -y -framerate {frame_rate} -i {image_path} -i {palette_path} -lavfi paletteuse {video_name.replace('mp4','gif')}") + + print(f"Running Command:",f"ffmpeg -y -framerate {frame_rate} -i {image_path} -i {palette_path} -lavfi paletteuse {video_name}") + + + + + + # Initialize 3D objects from a sample and set callback for + def render_smpl_multi_view_callback(self,samples,video_name=None): + self.update_smpl_multi_view_callback(samples,video_name=video_name) + ps.show() + ps.set_user_callback(self.callback) + ps.show() + + + +def render(samples, vis,video_name=None): + """ + Render dataset samples + + + @params + sample_path: Filepath of input + video_dir: Folder to store Render results for the complete worflow + + + Load input (currently .trc files) and save all the rendering videos + images (retargetting to smp, getting input text, per frame annotations etc.) + """ + + + if video_name is not None: + vis.update_smpl_multi_view_callback(samples,video_name=video_name) + vis.callback_render() + assert os.path.isfile(video_name), f"FFMPEG unable to render video:{video_name}" + else: + # Visualize each view + vis.render_smpl_multi_view_callback(samples,video_name=None) + + + +def load_subject(sample_path,retrieval_path=None): + sample = OpenCapDataLoader(sample_path) + + # Load Video + sample.rgb = MultiviewRGB(sample) + + print(f"Session ID: {sample.name} SubjectID:{sample.rgb.session_data['subjectID']} Action:{sample.label}") + + osim_path = os.path.dirname(os.path.dirname(sample.sample_path)) + osim_path = os.path.join(osim_path,'OpenSimData','Model', 'LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim') + osim_geometry_path = os.path.join(DATA_DIR,'OpenCap_LaiArnoldModified2017_Geometry') + + + ###################### Subject Details #################################################### + mot_path = os.path.dirname(os.path.dirname(sample.sample_path)) + mot_path = os.path.join(mot_path,'OpenSimData','Kinematics',sample.label+ sample.recordAttempt_str + '.mot') + mot_path = os.path.abspath(mot_path) + print("Loading User motion file:",mot_path) + sample.osim_file = mot_path + + + samples = [] + # Load Segments + if os.path.exists(os.path.join(DATA_DIR,"squat-segmentation-data", sample.openCapID+'.npy')): + segments = np.load(os.path.join(DATA_DIR,"squat-segmentation-data", sample.openCapID+'.npy'),allow_pickle=True).item() + if os.path.basename(sample.sample_path).split('.')[0] in segments: + segments = segments[sample.label+ sample.recordAttempt_str] + + + for segment in segments: + cur_sample = copy.deepcopy(sample) + cur_sample.joints_np = cur_sample.joints_np[segment[0]:segment[1]] + cur_sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True, start_frame=segment[0], end_frame=segment[1]) + + samples.append(cur_sample) + break + + if len(samples) == 0: + sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + samples.append(sample) + + return samples + +def load_retrived_samples(session, retrieval_path): + ###################### GENERATION DETAILS #################################################### + + # mot_path = os.path.dirname(os.path.dirname(sample.sample_path)) + # mot_path = os.path.join(mot_path,'OpenSimData','VQVAE7_Temporal_Kinematics',sample.label+ sample.recordAttempt_str + '.mot') + # print("Loading Reconstrction file:",mot_path) + + # sample.osim_pred = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/constrained_mot_0.002/12.mot" + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_196/entry_2.mot" + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_temporal_consistency_v2/entry_9.mot" + + + assert retrieval_path and os.path.isfile(retrieval_path), f"Unable to load .mot file:{retrieval_path}" + + mot_path = os.path.abspath(retrieval_path) + print("Loading Generatrion file:",mot_path) + + + trc_path = os.path.join(DATA_DIR,"Data", session, "MarkerData") + trc_file = [os.path.join(trc_path,x) for x in os.listdir(trc_path) if 'sqt' in x.lower() and x.endswith('.trc') ][0] + sample = OpenCapDataLoader(trc_file) + + # Load Video + sample.rgb = MultiviewRGB(sample) + + + osim_path = os.path.join(DATA_DIR,"Data", session, "OpenSimData","Model","LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim") + osim_geometry_path = os.path.join(DATA_DIR,'OpenCap_LaiArnoldModified2017_Geometry') + + sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + print("MOT DATA:",sample.osim.motion.shape) + print("Pelivs:",np.rad2deg(sample.osim.motion[::10,1:3])) + print("KNEE Left:",np.rad2deg(sample.osim.motion[::10,10])) + print("TIME:",sample.osim.motion[::10,0]) + # sample.osim.vertices[:,:,2] -= 1 + sample.osim_file = retrieval_path + return sample + + +def load_dynamics_data(retrieval_path): + ###################### GENERATION DETAILS #################################################### + + # mot_path = os.path.dirname(os.path.dirname(sample.sample_path)) + # mot_path = os.path.join(mot_path,'OpenSimData','VQVAE7_Temporal_Kinematics',sample.label+ sample.recordAttempt_str + '.mot') + # print("Loading Reconstrction file:",mot_path) + + # sample.osim_pred = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/constrained_mot_0.002/12.mot" + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_196/entry_2.mot" + + # mot_path = "/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/mot_visualization/normal_latents_temporal_consistency_v2/entry_9.mot" + + + assert retrieval_path and os.path.isfile(retrieval_path), f"Unable to load .mot file:{retrieval_path}" + + + mot_path = os.path.abspath(retrieval_path) + print("Loading Generatrion file:",mot_path) + + + trc_path = os.path.join(DATA_DIR,"Data", session, "MarkerData") + trc_file = [os.path.join(trc_path,x) for x in os.listdir(trc_path) if 'sqt' in x.lower() and x.endswith('.trc') ][0] + sample = OpenCapDataLoader(trc_file) + + # Load Video + sample.rgb = MultiviewRGB(sample) + + + osim_path = os.path.join(DATA_DIR,"Data", session, "OpenSimData","Model","LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim") + osim_geometry_path = os.path.join(DATA_DIR,'OpenCap_LaiArnoldModified2017_Geometry') + + sample.osim = OSIMSequence.from_files(osim_path, mot_path, geometry_path=osim_geometry_path,ignore_fps=True ) + print("MOT DATA:",sample.osim.motion.shape) + print("Pelivs:",np.rad2deg(sample.osim.motion[::10,1:3])) + print("KNEE Left:",np.rad2deg(sample.osim.motion[::10,10])) + print("TIME:",sample.osim.motion[::10,0]) + # sample.osim.vertices[:,:,2] -= 1 + sample.osim_file = retrieval_path + return sample + +# Load file and render skeleton for each video +def render_dataset(sample_path=None): + video_dir = RENDER_DIR + + vis = Visualizer() + + for subject in os.listdir(INPUT_DIR): + for trial_path in os.listdir(os.path.join(INPUT_DIR,subject,'MarkerData')): + trial_path = os.path.join(INPUT_DIR,subject,'MarkerData',trial_path) + retrieval_path = os.path.join(INPUT_DIR,subject,'MarkerData',trial_path) + + +if __name__ == "__main__": + + import copy + + + # compare_files = ["/media/shubh/Elements/RoseYu/UCSD-OpenCap-Fitness-Dataset/MCS_DATA/Data/d66330dc-7884-4915-9dbb-0520932294c4/MarkerData/SQT01.trc", + # "MCS_DATA/LIMO/ComAcc/mot_visualization/latents_subject_run_000cffd9-e154-4ce5-a075-1b4e1fd66201/entry_17_ComAcc.mot", + # "MCS_DATA/LIMO/FinalFinalHigh/mot_visualization/latents_subject_run_d2020b0e-6d41-4759-87f0-5c158f6ab86a/entry_19_FinalFinalHigh.mot"] + + compare_files = sys.argv[1:] + + + + if len(sys.argv) == 0: # Skipping for now. Will fix later to generate videos + render_dataset() + else: + samples = [] + for i in range(0, min(len(compare_files),4) ): + # file_path = sys.argv[i] + file_path = compare_files[i] + if file_path.endswith('.trc'): + sample = load_subject(file_path) + samples.extend(sample) + elif 'OpenSimData/Dynamics' in file_path: # For Dynamics data + + session = file_path + for i in range(4): + session = os.path.dirname(session) + session = os.path.basename(session) + print(session) + sample = load_retrived_samples(session, file_path) + samples.append(sample) + + + elif file_path.endswith('.mot'): # For baseline + generated results + session = os.path.basename(os.path.dirname(file_path)) + session = session.replace("latents_subject_run_","") + sample = load_retrived_samples(session,file_path) + + samples.append(sample) + break + vis = Visualizer() + video_name = None + video_name = sys.argv[-1] if sys.argv[-1].endswith('mp4') else None + render(samples,vis,video_name) + From 4d10c12d2ec5b2b8177553fe1ecd68496440307f Mon Sep 17 00:00:00 2001 From: Shubh Maheshwari Date: Fri, 6 Dec 2024 09:39:21 -0800 Subject: [PATCH 2/2] Updating website to BIGE --- docs/index.html | 90 ++-- .../images/Sports_Analytic_Mockup_1_V4.png | Bin 0 -> 612442 bytes docs/ucsd-opencap-dataset.html | 453 ++++++++++++++++++ 3 files changed, 513 insertions(+), 30 deletions(-) create mode 100644 docs/static/images/Sports_Analytic_Mockup_1_V4.png create mode 100644 docs/ucsd-opencap-dataset.html diff --git a/docs/index.html b/docs/index.html index 5298f96..2371d98 100644 --- a/docs/index.html +++ b/docs/index.html @@ -3,12 +3,12 @@ + content="BIGE : Biomechanics-informed GenAI for Exercise Science"> - Style-based controllable motion generation + BIGE : Biomechanics-informed GenAI for Exercise Science - @@ -75,7 +91,7 @@

Style-based controllable motion generat - @@ -85,12 +101,12 @@

Style-based controllable motion generat - - Poster + Slidies @@ -105,7 +121,7 @@

Style-based controllable motion generat - @@ -121,15 +137,22 @@

Style-based controllable motion generat +
-
@@ -159,24 +182,36 @@

Summary

+ +

- Recommending clinically optimal body motion can significantly improve an athlete's performance. The goal of our work is to suggest optimal body poses an athlete should perform. To distinguish between optimal and sub-optimal pose we use the Movement Competency Screen (MCS) to guide the generative model to produce motion with high fidelity and diversity. + Proper movements enhance mobility, coordination, and muscle activation, which are crucial for + performance, injury prevention, and overall fitness. However, traditional simulation tools rely + on strong modeling assumptions, are difficult to set up and computationally expensive. On the + other hand, generative AI approaches provide efficient alternatives to motion generation. But they + often lack physiological relevance and do not incorporate biomechanical constraints, limiting their + practical applications in sports and exercise science. To address these limitations.

  • - We prosose a novel constraint composition formulation to incorporate multiple constraints like MCS Score and text prompt. + We propose a novel framework, BIGE, that combines bio-mechanically meaningful scoring metrics with generative modeling.

  • - Furthermore, we release a new dataset (OpenCap Dataset) encompassing more than 3000 high-quality human motion samples from 13 different primitive movements like Squats, Pull-ups, and Push-ups with their corresponding MCS Score labeled by experts. + BIGE integrates a differentiable surrogate model for muscle activation to reverse optimize the latent space of the generative model

  • - Our experiments show qualitative and quantitative improvement over baselines by incorporating constrain composition. + Enables the retrieval of physiologically valid motions through targeted search. +

    +
  • +
  • +

    + Through extensive experiments on squat exercise data, our framework demonstrates superior performance in generating diverse, physically plausible motions while maintaining high fidelity to clinician-defined objectives compared to existing approaches.

@@ -184,7 +219,7 @@

Summary

- + +

Comparisions

-

Constraint composition

Constraint composition

alt="Interpolation end reference image."/>
- -

Stylized generatation

Stylized generatation

Compared to motion skeleton extracted from other methods, by incorporating structural cues, ours is more effective at embedding skeleton from incomplete mesh sequence.

- + - - + -->

BibTeX

@article{,
   author    = {Bhamidipati, Panini and Maheshwari, Shubh and Yu, Rose},
-  title     = {Style-based controllable motion generation},
+  title     = {BIGE : Biomechanics-informed GenAI for Exercise Science},
   journal   = {},
   year      = {2024},
 }
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+ A novel constraint composition for motion diffusion to recommend clinically optimal body poses. +

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Summary

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+ Recommending clinically optimal body motion can significantly improve an athlete's performance. The goal of our work is to suggest optimal body poses an athlete should perform. To distinguish between optimal and sub-optimal pose we use the Movement Competency Screen (MCS) to guide the generative model to produce motion with high fidelity and diversity. +

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    + We prosose a novel constraint composition formulation to incorporate multiple constraints like MCS Score and text prompt. +

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    + Furthermore, we release a new dataset (OpenCap Dataset) encompassing more than 3000 high-quality human motion samples from 13 different primitive movements like Squats, Pull-ups, and Push-ups with their corresponding MCS Score labeled by experts. +

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    + Our experiments show qualitative and quantitative improvement over baselines by incorporating constrain composition. +

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Dataset

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The datset comprises of over 3000 high-quality motion samples and 13 action labels. Each video is accompanied a "MCS Score," ranging from 1 to 5. This score serves as + a qualitative measure, with 1 indicating suboptimal movement and 5 representing an exemplary + execution of the specific action. +

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Temporal Segmentation

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For the temporal segmentation, that is to find the start and the end frame for each exercise iteration, we found out that the angular velocity of the joints is a good indicator. +

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+ The attached video shows an activity being performed. The color of the mesh changes for every iteraction. + The top right plot shows the DTW metric similarity between different selected segments. + The middle plot in the image shows the angular velocity for each joint ( in deg/s ) for each timestep. The red peaks in the bottom plot showcase the timestep at which an iteration ends. +

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The uniqueness of the OpenCap dataset lies in its focus on capturing + a diverse range of human motions, with an emphasis on activities commonly associated with physical + fitness. To ensure the dataset’s richness and authenticity, the motion samples were meticulously + recorded by observing athletes from the University of California, San Diego (UCSD) performing + various sports movements. +

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Generating Style Tags

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    + The style tags encompass both the exercise category (such as push-ups and squats) and the MCS scores assigned by experts. +

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    + We train 2 classifiers to predict the category and the MCS score. +

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+ Extracted Curve Skeleton +

+ The above table shows the accuracy between classifiers with different window size based. +

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Comparisions

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Constraint composition

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+ Interpolation end reference image. +
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Stylized generatation

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+ Interpolation end reference image. +

+ Compared to motion skeleton extracted from other methods, by incorporating structural cues, ours is more effective at embedding skeleton from incomplete mesh sequence. +

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BibTeX

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@article{,
+  author    = {Bhamidipati, Panini and Maheshwari, Shubh and Yu, Rose},
+  title     = {Style-based controllable motion generation},
+  journal   = {},
+  year      = {2024},
+}
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