''' Preprocessing Tranformers Based on sci-kit's API By Omid Alemi Created on June 12, 2017 ''' import copy import pandas as pd import numpy as np import transforms3d as t3d import scipy.ndimage.filters as filters from sklearn.base import BaseEstimator, TransformerMixin from analysis.pymo.rotation_tools import Rotation, euler2expmap, euler2expmap2, expmap2euler, euler_reorder, unroll from analysis.pymo.Quaternions import Quaternions from analysis.pymo.Pivots import Pivots class MocapParameterizer(BaseEstimator, TransformerMixin): def __init__(self, param_type = 'euler'): ''' param_type = {'euler', 'quat', 'expmap', 'position', 'expmap2pos'} ''' self.param_type = param_type def fit(self, X, y=None): return self def transform(self, X, y=None): print("MocapParameterizer: " + self.param_type) if self.param_type == 'euler': return X elif self.param_type == 'expmap': return self._to_expmap(X) elif self.param_type == 'quat': return X elif self.param_type == 'position': return self._to_pos(X) elif self.param_type == 'expmap2pos': return self._expmap_to_pos(X) else: raise 'param types: euler, quat, expmap, position, expmap2pos' # return X def inverse_transform(self, X, copy=None): if self.param_type == 'euler': return X elif self.param_type == 'expmap': return self._expmap_to_euler(X) elif self.param_type == 'quat': raise 'quat2euler is not supported' elif self.param_type == 'position': # raise 'positions 2 eulers is not supported' print('positions 2 eulers is not supported') return X else: raise 'param types: euler, quat, expmap, position' def _to_pos(self, X): '''Converts joints rotations in Euler angles to joint positions''' Q = [] for track in X: channels = [] titles = [] euler_df = track.values # Create a new DataFrame to store the exponential map rep pos_df = pd.DataFrame(index=euler_df.index) # Copy the root rotations into the new DataFrame # rxp = '%s_Xrotation'%track.root_name # ryp = '%s_Yrotation'%track.root_name # rzp = '%s_Zrotation'%track.root_name # pos_df[rxp] = pd.Series(data=euler_df[rxp], index=pos_df.index) # pos_df[ryp] = pd.Series(data=euler_df[ryp], index=pos_df.index) # pos_df[rzp] = pd.Series(data=euler_df[rzp], index=pos_df.index) # List the columns that contain rotation channels rot_cols = [c for c in euler_df.columns if ('rotation' in c)] # List the columns that contain position channels pos_cols = [c for c in euler_df.columns if ('position' in c)] # List the joints that are not end sites, i.e., have channels joints = (joint for joint in track.skeleton) tree_data = {} for joint in track.traverse(): parent = track.skeleton[joint]['parent'] rot_order = track.skeleton[joint]['order'] #print("rot_order:" + joint + " :" + rot_order) # Get the rotation columns that belong to this joint rc = euler_df[[c for c in rot_cols if joint in c]] # Get the position columns that belong to this joint pc = euler_df[[c for c in pos_cols if joint in c]] # Make sure the columns are organized in xyz order if rc.shape[1] < 3: euler_values = np.zeros((euler_df.shape[0], 3)) rot_order = "XYZ" else: euler_values = np.pi/180.0*np.transpose(np.array([track.values['%s_%srotation'%(joint, rot_order[0])], track.values['%s_%srotation'%(joint, rot_order[1])], track.values['%s_%srotation'%(joint, rot_order[2])]])) if pc.shape[1] < 3: pos_values = np.asarray([[0,0,0] for f in pc.iterrows()]) else: pos_values =np.asarray([[f[1]['%s_Xposition'%joint], f[1]['%s_Yposition'%joint], f[1]['%s_Zposition'%joint]] for f in pc.iterrows()]) quats = Quaternions.from_euler(np.asarray(euler_values), order=rot_order.lower(), world=False) tree_data[joint]=[ [], # to store the rotation matrix [] # to store the calculated position ] if track.root_name == joint: tree_data[joint][0] = quats#rotmats # tree_data[joint][1] = np.add(pos_values, track.skeleton[joint]['offsets']) tree_data[joint][1] = pos_values else: # for every frame i, multiply this joint's rotmat to the rotmat of its parent tree_data[joint][0] = tree_data[parent][0]*quats# np.matmul(rotmats, tree_data[parent][0]) # add the position channel to the offset and store it in k, for every frame i k = pos_values + np.asarray(track.skeleton[joint]['offsets']) # multiply k to the rotmat of the parent for every frame i q = tree_data[parent][0]*k #np.matmul(k.reshape(k.shape[0],1,3), tree_data[parent][0]) # add q to the position of the parent, for every frame i tree_data[joint][1] = tree_data[parent][1] + q #q.reshape(k.shape[0],3) + tree_data[parent][1] # Create the corresponding columns in the new DataFrame pos_df['%s_Xposition'%joint] = pd.Series(data=[e[0] for e in tree_data[joint][1]], index=pos_df.index) pos_df['%s_Yposition'%joint] = pd.Series(data=[e[1] for e in tree_data[joint][1]], index=pos_df.index) pos_df['%s_Zposition'%joint] = pd.Series(data=[e[2] for e in tree_data[joint][1]], index=pos_df.index) new_track = track.clone() new_track.values = pos_df Q.append(new_track) return Q def _expmap2rot(self, expmap): theta = np.linalg.norm(expmap, axis=1, keepdims=True) nz = np.nonzero(theta)[0] expmap[nz,:] = expmap[nz,:]/theta[nz] nrows=expmap.shape[0] x = expmap[:,0] y = expmap[:,1] z = expmap[:,2] s = np.sin(theta*0.5).reshape(nrows) c = np.cos(theta*0.5).reshape(nrows) rotmats = np.zeros((nrows, 3, 3)) rotmats[:,0,0] = 2*(x*x-1)*s*s+1 rotmats[:,0,1] = 2*x*y*s*s-2*z*c*s rotmats[:,0,2] = 2*x*z*s*s+2*y*c*s rotmats[:,1,0] = 2*x*y*s*s+2*z*c*s rotmats[:,1,1] = 2*(y*y-1)*s*s+1 rotmats[:,1,2] = 2*y*z*s*s-2*x*c*s rotmats[:,2,0] = 2*x*z*s*s-2*y*c*s rotmats[:,2,1] = 2*y*z*s*s+2*x*c*s rotmats[:,2,2] = 2*(z*z-1)*s*s+1 return rotmats def _expmap_to_pos(self, X): '''Converts joints rotations in expmap notation to joint positions''' Q = [] for track in X: channels = [] titles = [] exp_df = track.values # Create a new DataFrame to store the exponential map rep pos_df = pd.DataFrame(index=exp_df.index) # Copy the root rotations into the new DataFrame # rxp = '%s_Xrotation'%track.root_name # ryp = '%s_Yrotation'%track.root_name # rzp = '%s_Zrotation'%track.root_name # pos_df[rxp] = pd.Series(data=euler_df[rxp], index=pos_df.index) # pos_df[ryp] = pd.Series(data=euler_df[ryp], index=pos_df.index) # pos_df[rzp] = pd.Series(data=euler_df[rzp], index=pos_df.index) # List the columns that contain rotation channels exp_params = [c for c in exp_df.columns if ( any(p in c for p in ['alpha', 'beta','gamma']) and 'Nub' not in c)] # List the joints that are not end sites, i.e., have channels joints = (joint for joint in track.skeleton) tree_data = {} for joint in track.traverse(): parent = track.skeleton[joint]['parent'] if 'Nub' not in joint: r = exp_df[[c for c in exp_params if joint in c]] # Get the columns that belong to this joint expmap = r.values #expmap = [[f[1]['%s_alpha'%joint], f[1]['%s_beta'%joint], f[1]['%s_gamma'%joint]] for f in r.iterrows()] else: expmap = np.zeros((exp_df.shape[0], 3)) # Convert the eulers to rotation matrices #rotmats = np.asarray([Rotation(f, 'expmap').rotmat for f in expmap]) #angs = np.linalg.norm(expmap,axis=1, keepdims=True) rotmats = self._expmap2rot(expmap) tree_data[joint]=[ [], # to store the rotation matrix [] # to store the calculated position ] pos_values = np.zeros((exp_df.shape[0], 3)) if track.root_name == joint: tree_data[joint][0] = rotmats # tree_data[joint][1] = np.add(pos_values, track.skeleton[joint]['offsets']) tree_data[joint][1] = pos_values else: # for every frame i, multiply this joint's rotmat to the rotmat of its parent tree_data[joint][0] = np.matmul(rotmats, tree_data[parent][0]) # add the position channel to the offset and store it in k, for every frame i k = pos_values + track.skeleton[joint]['offsets'] # multiply k to the rotmat of the parent for every frame i q = np.matmul(k.reshape(k.shape[0],1,3), tree_data[parent][0]) # add q to the position of the parent, for every frame i tree_data[joint][1] = q.reshape(k.shape[0],3) + tree_data[parent][1] # Create the corresponding columns in the new DataFrame pos_df['%s_Xposition'%joint] = pd.Series(data=tree_data[joint][1][:,0], index=pos_df.index) pos_df['%s_Yposition'%joint] = pd.Series(data=tree_data[joint][1][:,1], index=pos_df.index) pos_df['%s_Zposition'%joint] = pd.Series(data=tree_data[joint][1][:,2], index=pos_df.index) new_track = track.clone() new_track.values = pos_df Q.append(new_track) return Q def _to_expmap(self, X): '''Converts Euler angles to Exponential Maps''' Q = [] for track in X: channels = [] titles = [] euler_df = track.values # Create a new DataFrame to store the exponential map rep exp_df = euler_df.copy()# pd.DataFrame(index=euler_df.index) # Copy the root positions into the new DataFrame #rxp = '%s_Xposition'%track.root_name #ryp = '%s_Yposition'%track.root_name #rzp = '%s_Zposition'%track.root_name #exp_df[rxp] = pd.Series(data=euler_df[rxp], index=exp_df.index) #exp_df[ryp] = pd.Series(data=euler_df[ryp], index=exp_df.index) #exp_df[rzp] = pd.Series(data=euler_df[rzp], index=exp_df.index) # List the columns that contain rotation channels rots = [c for c in euler_df.columns if ('rotation' in c and 'Nub' not in c)] # List the joints that are not end sites, i.e., have channels joints = (joint for joint in track.skeleton if 'Nub' not in joint) for joint in joints: #print(joint) r = euler_df[[c for c in rots if joint in c]] # Get the columns that belong to this joint rot_order = track.skeleton[joint]['order'] r1_col = '%s_%srotation'%(joint, rot_order[0]) r2_col = '%s_%srotation'%(joint, rot_order[1]) r3_col = '%s_%srotation'%(joint, rot_order[2]) exp_df.drop([r1_col, r2_col, r3_col], axis=1, inplace=True) euler = [[f[1][r1_col], f[1][r2_col], f[1][r3_col]] for f in r.iterrows()] #exps = [Rotation(f, 'euler', from_deg=True, order=rot_order).to_expmap() for f in euler] # Convert the eulers to exp maps exps = unroll(np.array([euler2expmap(f, rot_order, True) for f in euler])) # Convert the exp maps to eulers # exps = np.array([euler2expmap(f, rot_order, True) for f in euler]) # Convert the exp maps to eulers #exps = euler2expmap2(euler, rot_order, True) # Convert the eulers to exp maps # Create the corresponding columns in the new DataFrame exp_df.insert(loc=0, column='%s_gamma'%joint, value=pd.Series(data=[e[2] for e in exps], index=exp_df.index)) exp_df.insert(loc=0, column='%s_beta'%joint, value=pd.Series(data=[e[1] for e in exps], index=exp_df.index)) exp_df.insert(loc=0, column='%s_alpha'%joint, value=pd.Series(data=[e[0] for e in exps], index=exp_df.index)) #print(exp_df.columns) new_track = track.clone() new_track.values = exp_df Q.append(new_track) return Q def _expmap_to_euler(self, X): Q = [] for track in X: channels = [] titles = [] exp_df = track.values # Create a new DataFrame to store the exponential map rep #euler_df = pd.DataFrame(index=exp_df.index) euler_df = exp_df.copy() # Copy the root positions into the new DataFrame #rxp = '%s_Xposition'%track.root_name #ryp = '%s_Yposition'%track.root_name #rzp = '%s_Zposition'%track.root_name #euler_df[rxp] = pd.Series(data=exp_df[rxp], index=euler_df.index) #euler_df[ryp] = pd.Series(data=exp_df[ryp], index=euler_df.index) #euler_df[rzp] = pd.Series(data=exp_df[rzp], index=euler_df.index) # List the columns that contain rotation channels exp_params = [c for c in exp_df.columns if ( any(p in c for p in ['alpha', 'beta','gamma']) and 'Nub' not in c)] # List the joints that are not end sites, i.e., have channels joints = (joint for joint in track.skeleton if 'Nub' not in joint) for joint in joints: r = exp_df[[c for c in exp_params if joint in c]] # Get the columns that belong to this joint euler_df.drop(['%s_alpha'%joint, '%s_beta'%joint, '%s_gamma'%joint], axis=1, inplace=True) expmap = [[f[1]['%s_alpha'%joint], f[1]['%s_beta'%joint], f[1]['%s_gamma'%joint]] for f in r.iterrows()] # Make sure the columsn are organized in xyz order rot_order = track.skeleton[joint]['order'] #euler_rots = [Rotation(f, 'expmap').to_euler(True, rot_order) for f in expmap] # Convert the exp maps to eulers euler_rots = [expmap2euler(f, rot_order, True) for f in expmap] # Convert the exp maps to eulers # Create the corresponding columns in the new DataFrame euler_df['%s_%srotation'%(joint, rot_order[0])] = pd.Series(data=[e[0] for e in euler_rots], index=euler_df.index) euler_df['%s_%srotation'%(joint, rot_order[1])] = pd.Series(data=[e[1] for e in euler_rots], index=euler_df.index) euler_df['%s_%srotation'%(joint, rot_order[2])] = pd.Series(data=[e[2] for e in euler_rots], index=euler_df.index) new_track = track.clone() new_track.values = euler_df Q.append(new_track) return Q class Mirror(BaseEstimator, TransformerMixin): def __init__(self, axis="X", append=True): """ Mirrors the data """ self.axis = axis self.append = append def fit(self, X, y=None): return self def transform(self, X, y=None): print("Mirror: " + self.axis) Q = [] if self.append: for track in X: Q.append(track) for track in X: channels = [] titles = [] if self.axis == "X": signs = np.array([1,-1,-1]) if self.axis == "Y": signs = np.array([-1,1,-1]) if self.axis == "Z": signs = np.array([-1,-1,1]) euler_df = track.values # Create a new DataFrame to store the exponential map rep new_df = pd.DataFrame(index=euler_df.index) # Copy the root positions into the new DataFrame rxp = '%s_Xposition'%track.root_name ryp = '%s_Yposition'%track.root_name rzp = '%s_Zposition'%track.root_name new_df[rxp] = pd.Series(data=-signs[0]*euler_df[rxp], index=new_df.index) new_df[ryp] = pd.Series(data=-signs[1]*euler_df[ryp], index=new_df.index) new_df[rzp] = pd.Series(data=-signs[2]*euler_df[rzp], index=new_df.index) # List the columns that contain rotation channels rots = [c for c in euler_df.columns if ('rotation' in c and 'Nub' not in c)] #lft_rots = [c for c in euler_df.columns if ('Left' in c and 'rotation' in c and 'Nub' not in c)] #rgt_rots = [c for c in euler_df.columns if ('Right' in c and 'rotation' in c and 'Nub' not in c)] lft_joints = (joint for joint in track.skeleton if 'Left' in joint and 'Nub' not in joint) rgt_joints = (joint for joint in track.skeleton if 'Right' in joint and 'Nub' not in joint) new_track = track.clone() for lft_joint in lft_joints: #lr = euler_df[[c for c in rots if lft_joint + "_" in c]] #rot_order = track.skeleton[lft_joint]['order'] #lft_eulers = [[f[1]['%s_Xrotation'%lft_joint], f[1]['%s_Yrotation'%lft_joint], f[1]['%s_Zrotation'%lft_joint]] for f in lr.iterrows()] rgt_joint = lft_joint.replace('Left', 'Right') #rr = euler_df[[c for c in rots if rgt_joint + "_" in c]] #rot_order = track.skeleton[rgt_joint]['order'] # rgt_eulers = [[f[1]['%s_Xrotation'%rgt_joint], f[1]['%s_Yrotation'%rgt_joint], f[1]['%s_Zrotation'%rgt_joint]] for f in rr.iterrows()] # Create the corresponding columns in the new DataFrame new_df['%s_Xrotation'%lft_joint] = pd.Series(data=signs[0]*track.values['%s_Xrotation'%rgt_joint], index=new_df.index) new_df['%s_Yrotation'%lft_joint] = pd.Series(data=signs[1]*track.values['%s_Yrotation'%rgt_joint], index=new_df.index) new_df['%s_Zrotation'%lft_joint] = pd.Series(data=signs[2]*track.values['%s_Zrotation'%rgt_joint], index=new_df.index) new_df['%s_Xrotation'%rgt_joint] = pd.Series(data=signs[0]*track.values['%s_Xrotation'%lft_joint], index=new_df.index) new_df['%s_Yrotation'%rgt_joint] = pd.Series(data=signs[1]*track.values['%s_Yrotation'%lft_joint], index=new_df.index) new_df['%s_Zrotation'%rgt_joint] = pd.Series(data=signs[2]*track.values['%s_Zrotation'%lft_joint], index=new_df.index) # List the joints that are not left or right, i.e. are on the trunk joints = (joint for joint in track.skeleton if 'Nub' not in joint and 'Left' not in joint and 'Right' not in joint) for joint in joints: #r = euler_df[[c for c in rots if joint in c]] # Get the columns that belong to this joint #rot_order = track.skeleton[joint]['order'] #eulers = [[f[1]['%s_Xrotation'%joint], f[1]['%s_Yrotation'%joint], f[1]['%s_Zrotation'%joint]] for f in r.iterrows()] # Create the corresponding columns in the new DataFrame new_df['%s_Xrotation'%joint] = pd.Series(data=signs[0]*track.values['%s_Xrotation'%joint], index=new_df.index) new_df['%s_Yrotation'%joint] = pd.Series(data=signs[1]*track.values['%s_Yrotation'%joint], index=new_df.index) new_df['%s_Zrotation'%joint] = pd.Series(data=signs[2]*track.values['%s_Zrotation'%joint], index=new_df.index) new_track.values = new_df Q.append(new_track) return Q def inverse_transform(self, X, copy=None, start_pos=None): return X class EulerReorder(BaseEstimator, TransformerMixin): def __init__(self, new_order): """ Add a """ self.new_order = new_order def fit(self, X, y=None): self.orig_skeleton = copy.deepcopy(X[0].skeleton) print(self.orig_skeleton) return self def transform(self, X, y=None): Q = [] for track in X: channels = [] titles = [] euler_df = track.values # Create a new DataFrame to store the exponential map rep new_df = pd.DataFrame(index=euler_df.index) # Copy the root positions into the new DataFrame rxp = '%s_Xposition'%track.root_name ryp = '%s_Yposition'%track.root_name rzp = '%s_Zposition'%track.root_name new_df[rxp] = pd.Series(data=euler_df[rxp], index=new_df.index) new_df[ryp] = pd.Series(data=euler_df[ryp], index=new_df.index) new_df[rzp] = pd.Series(data=euler_df[rzp], index=new_df.index) # List the columns that contain rotation channels rots = [c for c in euler_df.columns if ('rotation' in c and 'Nub' not in c)] # List the joints that are not end sites, i.e., have channels joints = (joint for joint in track.skeleton if 'Nub' not in joint) new_track = track.clone() for joint in joints: r = euler_df[[c for c in rots if joint in c]] # Get the columns that belong to this joint rot_order = track.skeleton[joint]['order'] euler = [[f[1]['%s_Xrotation'%(joint)], f[1]['%s_Yrotation'%(joint)], f[1]['%s_Zrotation'%(joint)]] for f in r.iterrows()] new_euler = [euler_reorder(f, rot_order, self.new_order, True) for f in euler] #new_euler = euler_reorder2(np.array(euler), rot_order, self.new_order, True) # Create the corresponding columns in the new DataFrame new_df['%s_%srotation'%(joint, self.new_order[0])] = pd.Series(data=[e[0] for e in new_euler], index=new_df.index) new_df['%s_%srotation'%(joint, self.new_order[1])] = pd.Series(data=[e[1] for e in new_euler], index=new_df.index) new_df['%s_%srotation'%(joint, self.new_order[2])] = pd.Series(data=[e[2] for e in new_euler], index=new_df.index) new_track.skeleton[joint]['order'] = self.new_order new_track.values = new_df Q.append(new_track) return Q def inverse_transform(self, X, copy=None, start_pos=None): return X # Q = [] # # for track in X: # channels = [] # titles = [] # euler_df = track.values # # # Create a new DataFrame to store the exponential map rep # new_df = pd.DataFrame(index=euler_df.index) # # # Copy the root positions into the new DataFrame # rxp = '%s_Xposition'%track.root_name # ryp = '%s_Yposition'%track.root_name # rzp = '%s_Zposition'%track.root_name # new_df[rxp] = pd.Series(data=euler_df[rxp], index=new_df.index) # new_df[ryp] = pd.Series(data=euler_df[ryp], index=new_df.index) # new_df[rzp] = pd.Series(data=euler_df[rzp], index=new_df.index) # # # List the columns that contain rotation channels # rots = [c for c in euler_df.columns if ('rotation' in c and 'Nub' not in c)] # # # List the joints that are not end sites, i.e., have channels # joints = (joint for joint in track.skeleton if 'Nub' not in joint) # # new_track = track.clone() # for joint in joints: # r = euler_df[[c for c in rots if joint in c]] # Get the columns that belong to this joint # rot_order = track.skeleton[joint]['order'] # new_order = self.orig_skeleton[joint]['order'] # print("rot_order:" + str(rot_order)) # print("new_order:" + str(new_order)) # # euler = [[f[1]['%s_%srotation'%(joint, rot_order[0])], f[1]['%s_%srotation'%(joint, rot_order[1])], f[1]['%s_%srotation'%(joint, rot_order[2])]] for f in r.iterrows()] # #new_euler = [euler_reorder(f, rot_order, new_order, True) for f in euler] # new_euler = euler_reorder2(np.array(euler), rot_order, self.new_order, True) # # # Create the corresponding columns in the new DataFrame # new_df['%s_%srotation'%(joint, new_order[0])] = pd.Series(data=[e[0] for e in new_euler], index=new_df.index) # new_df['%s_%srotation'%(joint, new_order[1])] = pd.Series(data=[e[1] for e in new_euler], index=new_df.index) # new_df['%s_%srotation'%(joint, new_order[2])] = pd.Series(data=[e[2] for e in new_euler], index=new_df.index) # # new_track.skeleton[joint]['order'] = new_order # # new_track.values = new_df # Q.append(new_track) # return Q class JointSelector(BaseEstimator, TransformerMixin): ''' Allows for filtering the mocap data to include only the selected joints ''' def __init__(self, joints, include_root=False): self.joints = joints self.include_root = include_root def fit(self, X, y=None): selected_joints = [] selected_channels = [] if self.include_root: selected_joints.append(X[0].root_name) selected_joints.extend(self.joints) for joint_name in selected_joints: selected_channels.extend([o for o in X[0].values.columns if (joint_name + "_") in o and 'Nub' not in o]) self.selected_joints = selected_joints self.selected_channels = selected_channels self.not_selected = X[0].values.columns.difference(selected_channels) self.not_selected_values = {c:X[0].values[c].values[0] for c in self.not_selected} self.orig_skeleton = X[0].skeleton return self def transform(self, X, y=None): print("JointSelector") Q = [] for track in X: t2 = track.clone() for key in track.skeleton.keys(): if key not in self.selected_joints: parent = t2.skeleton[key]['parent'] if parent in t2.skeleton: t2.skeleton[parent]['children'].remove(key) t2.skeleton.pop(key) t2.values = track.values[self.selected_channels] Q.append(t2) return Q def inverse_transform(self, X, copy=None): Q = [] for track in X: t2 = track.clone() t2.skeleton = self.orig_skeleton for d in self.not_selected: t2.values[d] = self.not_selected_values[d] Q.append(t2) return Q class Numpyfier(BaseEstimator, TransformerMixin): ''' Just converts the values in a MocapData object into a numpy array Useful for the final stage of a pipeline before training ''' def __init__(self): pass def fit(self, X, y=None): self.org_mocap_ = X[0].clone() self.org_mocap_.values.drop(self.org_mocap_.values.index, inplace=True) return self def transform(self, X, y=None): print("Numpyfier") Q = [] for track in X: Q.append(track.values.values) #print("Numpyfier:" + str(track.values.columns)) return np.array(Q) def inverse_transform(self, X, copy=None): Q = [] for track in X: new_mocap = self.org_mocap_.clone() time_index = pd.to_timedelta([f for f in range(track.shape[0])], unit='s') # print(self.org_mocap_.values.columns) # import pdb;pdb.set_trace() new_df = pd.DataFrame(data=track, index=time_index, columns=self.org_mocap_.values.columns) new_mocap.values = new_df Q.append(new_mocap) return Q class Slicer(BaseEstimator, TransformerMixin): ''' Slice the data into intervals of equal size ''' def __init__(self, window_size, overlap=0.5): self.window_size = window_size self.overlap = overlap pass def fit(self, X, y=None): self.org_mocap_ = X[0].clone() self.org_mocap_.values.drop(self.org_mocap_.values.index, inplace=True) return self def transform(self, X, y=None): print("Slicer") Q = [] for track in X: vals = track.values.values nframes = vals.shape[0] overlap_frames = (int)(self.overlap*self.window_size) n_sequences = (nframes-overlap_frames)//(self.window_size-overlap_frames) if n_sequences>0: y = np.zeros((n_sequences, self.window_size, vals.shape[1])) # extract sequences from the input data for i in range(0,n_sequences): frameIdx = (self.window_size-overlap_frames) * i Q.append(vals[frameIdx:frameIdx+self.window_size,:]) return np.array(Q) def inverse_transform(self, X, copy=None): Q = [] for track in X: new_mocap = self.org_mocap_.clone() time_index = pd.to_timedelta([f for f in range(track.shape[0])], unit='s') new_df = pd.DataFrame(data=track, index=time_index, columns=self.org_mocap_.values.columns) new_mocap.values = new_df Q.append(new_mocap) return Q class RootTransformer(BaseEstimator, TransformerMixin): def __init__(self, method, position_smoothing=0, rotation_smoothing=0): """ Accepted methods: abdolute_translation_deltas pos_rot_deltas """ self.method = method self.position_smoothing=position_smoothing self.rotation_smoothing=rotation_smoothing def fit(self, X, y=None): return self def transform(self, X, y=None): print("RootTransformer") Q = [] for track in X: if self.method == 'abdolute_translation_deltas': new_df = track.values.copy() xpcol = '%s_Xposition'%track.root_name ypcol = '%s_Yposition'%track.root_name zpcol = '%s_Zposition'%track.root_name dxpcol = '%s_dXposition'%track.root_name dzpcol = '%s_dZposition'%track.root_name x=track.values[xpcol].copy() z=track.values[zpcol].copy() if self.position_smoothing>0: x_sm = filters.gaussian_filter1d(x, self.position_smoothing, axis=0, mode='nearest') z_sm = filters.gaussian_filter1d(z, self.position_smoothing, axis=0, mode='nearest') dx = pd.Series(data=x_sm, index=new_df.index).diff() dz = pd.Series(data=z_sm, index=new_df.index).diff() new_df[xpcol] = x-x_sm new_df[zpcol] = z-z_sm else: dx = x.diff() dz = z.diff() new_df.drop([xpcol, zpcol], axis=1, inplace=True) dx[0] = dx[1] dz[0] = dz[1] new_df[dxpcol] = dx new_df[dzpcol] = dz new_track = track.clone() new_track.values = new_df # end of abdolute_translation_deltas elif self.method == 'pos_rot_deltas': new_track = track.clone() # Absolute columns xp_col = '%s_Xposition'%track.root_name yp_col = '%s_Yposition'%track.root_name zp_col = '%s_Zposition'%track.root_name #rot_order = track.skeleton[track.root_name]['order'] #%(joint, rot_order[0]) rot_order = track.skeleton[track.root_name]['order'] r1_col = '%s_%srotation'%(track.root_name, rot_order[0]) r2_col = '%s_%srotation'%(track.root_name, rot_order[1]) r3_col = '%s_%srotation'%(track.root_name, rot_order[2]) # Delta columns dxp_col = '%s_dXposition'%track.root_name dzp_col = '%s_dZposition'%track.root_name dxr_col = '%s_dXrotation'%track.root_name dyr_col = '%s_dYrotation'%track.root_name dzr_col = '%s_dZrotation'%track.root_name positions = np.transpose(np.array([track.values[xp_col], track.values[yp_col], track.values[zp_col]])) rotations = np.pi/180.0*np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]])) """ Get Trajectory and smooth it""" trajectory_filterwidth = self.position_smoothing reference = positions.copy()*np.array([1,0,1]) if trajectory_filterwidth>0: reference = filters.gaussian_filter1d(reference, trajectory_filterwidth, axis=0, mode='nearest') """ Get Root Velocity """ velocity = np.diff(reference, axis=0) velocity = np.vstack((velocity[0,:], velocity)) """ Remove Root Translation """ positions = positions-reference """ Get Forward Direction along the x-z plane, assuming character is facig z-forward """ #forward = [Rotation(f, 'euler', from_deg=True, order=rot_order).rotmat[:,2] for f in rotations] # get the z-axis of the rotation matrix, assuming character is facig z-forward #print("order:" + rot_order.lower()) quats = Quaternions.from_euler(rotations, order=rot_order.lower(), world=False) forward = quats*np.array([[0,0,1]]) forward[:,1] = 0 """ Smooth Forward Direction """ direction_filterwidth = self.rotation_smoothing if direction_filterwidth>0: forward = filters.gaussian_filter1d(forward, direction_filterwidth, axis=0, mode='nearest') forward = forward / np.sqrt((forward**2).sum(axis=-1))[...,np.newaxis] """ Remove Y Rotation """ target = np.array([[0,0,1]]).repeat(len(forward), axis=0) rotation = Quaternions.between(target, forward)[:,np.newaxis] positions = (-rotation[:,0]) * positions new_rotations = (-rotation[:,0]) * quats velocity = (-rotation[:,0]) * velocity """ Get Root Rotation """ #print(rotation[:,0]) rvelocity = Pivots.from_quaternions(rotation[1:] * -rotation[:-1]).ps rvelocity = np.vstack((rvelocity[0], rvelocity)) eulers = np.array([t3d.euler.quat2euler(q, axes=('s'+rot_order.lower()[::-1]))[::-1] for q in new_rotations])*180.0/np.pi new_df = track.values.copy() root_pos_x = pd.Series(data=positions[:,0], index=new_df.index) root_pos_y = pd.Series(data=positions[:,1], index=new_df.index) root_pos_z = pd.Series(data=positions[:,2], index=new_df.index) root_pos_x_diff = pd.Series(data=velocity[:,0], index=new_df.index) root_pos_z_diff = pd.Series(data=velocity[:,2], index=new_df.index) root_rot_1 = pd.Series(data=eulers[:,0], index=new_df.index) root_rot_2 = pd.Series(data=eulers[:,1], index=new_df.index) root_rot_3 = pd.Series(data=eulers[:,2], index=new_df.index) root_rot_y_diff = pd.Series(data=rvelocity[:,0], index=new_df.index) #new_df.drop([xr_col, yr_col, zr_col, xp_col, zp_col], axis=1, inplace=True) new_df[xp_col] = root_pos_x new_df[yp_col] = root_pos_y new_df[zp_col] = root_pos_z new_df[dxp_col] = root_pos_x_diff new_df[dzp_col] = root_pos_z_diff new_df[r1_col] = root_rot_1 new_df[r2_col] = root_rot_2 new_df[r3_col] = root_rot_3 #new_df[dxr_col] = root_rot_x_diff new_df[dyr_col] = root_rot_y_diff #new_df[dzr_col] = root_rot_z_diff new_track.values = new_df elif self.method == 'hip_centric': new_track = track.clone() # Absolute columns xp_col = '%s_Xposition'%track.root_name yp_col = '%s_Yposition'%track.root_name zp_col = '%s_Zposition'%track.root_name xr_col = '%s_Xrotation'%track.root_name yr_col = '%s_Yrotation'%track.root_name zr_col = '%s_Zrotation'%track.root_name new_df = track.values.copy() all_zeros = np.zeros(track.values[xp_col].values.shape) new_df[xp_col] = pd.Series(data=all_zeros, index=new_df.index) new_df[yp_col] = pd.Series(data=all_zeros, index=new_df.index) new_df[zp_col] = pd.Series(data=all_zeros, index=new_df.index) new_df[xr_col] = pd.Series(data=all_zeros, index=new_df.index) new_df[yr_col] = pd.Series(data=all_zeros, index=new_df.index) new_df[zr_col] = pd.Series(data=all_zeros, index=new_df.index) new_track.values = new_df #print(new_track.values.columns) Q.append(new_track) return Q def inverse_transform(self, X, copy=None, start_pos=None): Q = [] #TODO: simplify this implementation startx = 0 startz = 0 if start_pos is not None: startx, startz = start_pos for track in X: new_track = track.clone() if self.method == 'abdolute_translation_deltas': new_df = new_track.values xpcol = '%s_Xposition'%track.root_name ypcol = '%s_Yposition'%track.root_name zpcol = '%s_Zposition'%track.root_name dxpcol = '%s_dXposition'%track.root_name dzpcol = '%s_dZposition'%track.root_name dx = track.values[dxpcol].values dz = track.values[dzpcol].values recx = [startx] recz = [startz] for i in range(dx.shape[0]-1): recx.append(recx[i]+dx[i+1]) recz.append(recz[i]+dz[i+1]) # recx = [recx[i]+dx[i+1] for i in range(dx.shape[0]-1)] # recz = [recz[i]+dz[i+1] for i in range(dz.shape[0]-1)] # recx = dx[:-1] + dx[1:] # recz = dz[:-1] + dz[1:] if self.position_smoothing > 0: new_df[xpcol] = pd.Series(data=new_df[xpcol]+recx, index=new_df.index) new_df[zpcol] = pd.Series(data=new_df[zpcol]+recz, index=new_df.index) else: new_df[xpcol] = pd.Series(data=recx, index=new_df.index) new_df[zpcol] = pd.Series(data=recz, index=new_df.index) new_df.drop([dxpcol, dzpcol], axis=1, inplace=True) new_track.values = new_df # end of abdolute_translation_deltas elif self.method == 'pos_rot_deltas': # Absolute columns rot_order = track.skeleton[track.root_name]['order'] xp_col = '%s_Xposition'%track.root_name yp_col = '%s_Yposition'%track.root_name zp_col = '%s_Zposition'%track.root_name xr_col = '%s_Xrotation'%track.root_name yr_col = '%s_Yrotation'%track.root_name zr_col = '%s_Zrotation'%track.root_name r1_col = '%s_%srotation'%(track.root_name, rot_order[0]) r2_col = '%s_%srotation'%(track.root_name, rot_order[1]) r3_col = '%s_%srotation'%(track.root_name, rot_order[2]) # Delta columns dxp_col = '%s_dXposition'%track.root_name dzp_col = '%s_dZposition'%track.root_name dyr_col = '%s_dYrotation'%track.root_name positions = np.transpose(np.array([track.values[xp_col], track.values[yp_col], track.values[zp_col]])) rotations = np.pi/180.0*np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]])) quats = Quaternions.from_euler(rotations, order=rot_order.lower(), world=False) new_df = track.values.copy() dx = track.values[dxp_col].values dz = track.values[dzp_col].values dry = track.values[dyr_col].values #rec_p = np.array([startx, 0, startz])+positions[0,:] rec_ry = Quaternions.id(quats.shape[0]) rec_xp = [0] rec_zp = [0] #rec_r = Quaternions.id(quats.shape[0]) for i in range(dx.shape[0]-1): #print(dry[i]) q_y = Quaternions.from_angle_axis(np.array(dry[i+1]), np.array([0,1,0])) rec_ry[i+1] = q_y*rec_ry[i] #print("dx: + " + str(dx[i+1])) dp = rec_ry[i+1]*np.array([dx[i+1], 0, dz[i+1]]) rec_xp.append(rec_xp[i]+dp[0,0]) rec_zp.append(rec_zp[i]+dp[0,2]) rec_r=rec_ry*quats pp=rec_ry*positions rec_xp = rec_xp + pp[:,0] rec_zp = rec_zp + pp[:,2] eulers = np.array([t3d.euler.quat2euler(q, axes=('s'+rot_order.lower()[::-1]))[::-1] for q in rec_r])*180.0/np.pi new_df = track.values.copy() root_rot_1 = pd.Series(data=eulers[:,0], index=new_df.index) root_rot_2 = pd.Series(data=eulers[:,1], index=new_df.index) root_rot_3 = pd.Series(data=eulers[:,2], index=new_df.index) new_df[xp_col] = pd.Series(data=rec_xp, index=new_df.index) new_df[zp_col] = pd.Series(data=rec_zp, index=new_df.index) new_df[r1_col] = pd.Series(data=root_rot_1, index=new_df.index) new_df[r2_col] = pd.Series(data=root_rot_2, index=new_df.index) new_df[r3_col] = pd.Series(data=root_rot_3, index=new_df.index) new_df.drop([dyr_col, dxp_col, dzp_col], axis=1, inplace=True) new_track.values = new_df #print(new_track.values.columns) Q.append(new_track) return Q class RootCentricPositionNormalizer(BaseEstimator, TransformerMixin): def __init__(self): pass def fit(self, X, y=None): return self def transform(self, X, y=None): Q = [] for track in X: new_track = track.clone() rxp = '%s_Xposition'%track.root_name ryp = '%s_Yposition'%track.root_name rzp = '%s_Zposition'%track.root_name projected_root_pos = track.values[[rxp, ryp, rzp]] projected_root_pos.loc[:,ryp] = 0 # we want the root's projection on the floor plane as the ref new_df = pd.DataFrame(index=track.values.index) all_but_root = [joint for joint in track.skeleton if track.root_name not in joint] # all_but_root = [joint for joint in track.skeleton] for joint in all_but_root: new_df['%s_Xposition'%joint] = pd.Series(data=track.values['%s_Xposition'%joint]-projected_root_pos[rxp], index=new_df.index) new_df['%s_Yposition'%joint] = pd.Series(data=track.values['%s_Yposition'%joint]-projected_root_pos[ryp], index=new_df.index) new_df['%s_Zposition'%joint] = pd.Series(data=track.values['%s_Zposition'%joint]-projected_root_pos[rzp], index=new_df.index) # keep the root as it is now new_df[rxp] = track.values[rxp] new_df[ryp] = track.values[ryp] new_df[rzp] = track.values[rzp] new_track.values = new_df Q.append(new_track) return Q def inverse_transform(self, X, copy=None): Q = [] for track in X: new_track = track.clone() rxp = '%s_Xposition'%track.root_name ryp = '%s_Yposition'%track.root_name rzp = '%s_Zposition'%track.root_name projected_root_pos = track.values[[rxp, ryp, rzp]] projected_root_pos.loc[:,ryp] = 0 # we want the root's projection on the floor plane as the ref new_df = pd.DataFrame(index=track.values.index) for joint in track.skeleton: new_df['%s_Xposition'%joint] = pd.Series(data=track.values['%s_Xposition'%joint]+projected_root_pos[rxp], index=new_df.index) new_df['%s_Yposition'%joint] = pd.Series(data=track.values['%s_Yposition'%joint]+projected_root_pos[ryp], index=new_df.index) new_df['%s_Zposition'%joint] = pd.Series(data=track.values['%s_Zposition'%joint]+projected_root_pos[rzp], index=new_df.index) new_track.values = new_df Q.append(new_track) return Q class Flattener(BaseEstimator, TransformerMixin): def __init__(self): pass def fit(self, X, y=None): return self def transform(self, X, y=None): return np.concatenate(X, axis=0) class ConstantsRemover(BaseEstimator, TransformerMixin): ''' For now it just looks at the first track ''' def __init__(self, eps = 1e-6, only_cols=None): self.eps = eps self.only_cols = only_cols def fit(self, X, y=None): stds = X[0].values.std() cols = X[0].values.columns.values if self.only_cols is not None: self.const_dims_ = [c for c in cols if ((stds[c] < self.eps).any()) and c in self.only_cols] else: self.const_dims_ = [c for c in cols if (stds[c] < self.eps).any()] # self.const_values_ = {c:X[0].values[c].values[0] for c in cols if (stds[c] < self.eps).any()} self.const_values_ = {c:X[0].values[c].values[0] for c in cols if self.const_dims_} return self def transform(self, X, y=None): Q = [] for track in X: t2 = track.clone() #for key in t2.skeleton.keys(): # if key in self.ConstDims_: # t2.skeleton.pop(key) #print(track.values.columns.difference(self.const_dims_)) t2.values.drop(self.const_dims_, axis=1, inplace=True) #t2.values = track.values[track.values.columns.difference(self.const_dims_)] Q.append(t2) return Q def inverse_transform(self, X, copy=None): Q = [] for track in X: t2 = track.clone() for d in self.const_dims_: t2.values[d] = self.const_values_[d] # t2.values.assign(d=pd.Series(data=self.const_values_[d], index = t2.values.index)) Q.append(t2) return Q class ListStandardScaler(BaseEstimator, TransformerMixin): def __init__(self, is_DataFrame=False): self.is_DataFrame = is_DataFrame def fit(self, X, y=None): if self.is_DataFrame: X_train_flat = np.concatenate([m.values for m in X], axis=0) else: X_train_flat = np.concatenate([m for m in X], axis=0) self.data_mean_ = np.mean(X_train_flat, axis=0) self.data_std_ = np.std(X_train_flat, axis=0) return self def transform(self, X, y=None): Q = [] for track in X: if self.is_DataFrame: normalized_track = track.copy() normalized_track.values = (track.values - self.data_mean_) / self.data_std_ else: normalized_track = (track - self.data_mean_) / self.data_std_ Q.append(normalized_track) if self.is_DataFrame: return Q else: return np.array(Q) def inverse_transform(self, X, copy=None): Q = [] for track in X: if self.is_DataFrame: unnormalized_track = track.copy() unnormalized_track.values = (track.values * self.data_std_) + self.data_mean_ else: unnormalized_track = (track * self.data_std_) + self.data_mean_ Q.append(unnormalized_track) if self.is_DataFrame: return Q else: return np.array(Q) class ListMinMaxScaler(BaseEstimator, TransformerMixin): def __init__(self, is_DataFrame=False): self.is_DataFrame = is_DataFrame def fit(self, X, y=None): if self.is_DataFrame: X_train_flat = np.concatenate([m.values for m in X], axis=0) else: X_train_flat = np.concatenate([m for m in X], axis=0) self.data_max_ = np.max(X_train_flat, axis=0) self.data_min_ = np.min(X_train_flat, axis=0) return self def transform(self, X, y=None): Q = [] for track in X: if self.is_DataFrame: normalized_track = track.copy() normalized_track.values = (track.values - self.data_min_) / (self.data_max_ - self.data_min_) else: normalized_track = (track - self.data_min_) / (self.data_max_ - self.data_min_) Q.append(normalized_track) if self.is_DataFrame: return Q else: return np.array(Q) def inverse_transform(self, X, copy=None): Q = [] for track in X: if self.is_DataFrame: unnormalized_track = track.copy() unnormalized_track.values = (track.values * (self.data_max_ - self.data_min_)) + self.data_min_ else: unnormalized_track = (track * (self.data_max_ - self.data_min_)) + self.data_min_ Q.append(unnormalized_track) if self.is_DataFrame: return Q else: return np.array(Q) class DownSampler(BaseEstimator, TransformerMixin): def __init__(self, tgt_fps, keep_all=False): self.tgt_fps = tgt_fps self.keep_all = keep_all def fit(self, X, y=None): return self def transform(self, X, y=None): Q = [] for track in X: orig_fps=round(1.0/track.framerate) rate = orig_fps//self.tgt_fps if orig_fps%self.tgt_fps!=0: print("error orig_fps (" + str(orig_fps) + ") is not dividable with tgt_fps (" + str(self.tgt_fps) + ")") else: print("downsampling with rate: " + str(rate)) #print(track.values.size) for ii in range(0,rate): new_track = track.clone() new_track.values = track.values[ii:-1:rate].copy() #print(new_track.values.size) #new_track = track[0:-1:self.rate] new_track.framerate = 1.0/self.tgt_fps Q.append(new_track) if not self.keep_all: break return Q def inverse_transform(self, X, copy=None): return X class ReverseTime(BaseEstimator, TransformerMixin): def __init__(self, append=True): self.append = append def fit(self, X, y=None): return self def transform(self, X, y=None): Q = [] if self.append: for track in X: Q.append(track) for track in X: new_track = track.clone() new_track.values = track.values[-1::-1] Q.append(new_track) return Q def inverse_transform(self, X, copy=None): return X #TODO: JointsSelector (x) #TODO: SegmentMaker #TODO: DynamicFeaturesAdder #TODO: ShapeFeaturesAdder #TODO: DataFrameNumpier (x) class TemplateTransform(BaseEstimator, TransformerMixin): def __init__(self): pass def fit(self, X, y=None): return self def transform(self, X, y=None): return X