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import pandas as pd
import numpy as np
import torch
from analysis.aistplusplus_api.convert_mat_to_euler import rot_mats_to_eulers
import glob
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
%matplotlib
import pickle
# seqs = [x[:-1].split("_") for x in open("analysis/base_filenames.txt", "r").readlines()]
seqs = [x[:-1] for x in open("analysis/base_filenames.txt", "r").readlines()]
# seqs = [{"genre":x[0], "situation":x[1], "camera":x[2], "dancer":x[3], "musicId":x[4], "choreo":x[5]} for x in seqs]
#
# df = pd.DataFrame(seqs)
data_dir="data/scaled_features/"
# data = np.load(data_dir + seqs[10]+".joint_angles_scaled.npy")
def get_scaling(seq_id):
smpl_thing = pickle.load(open("../aistpp_data/aist_plusplus_final/motions/"+seq_id+".pkl", "rb"))
smpl_poses = smpl_thing['smpl_poses']
smpl_scaling = smpl_thing['smpl_scaling']
smpl_trans = smpl_thing['smpl_trans']
return smpl_scaling[0]
max_deriv = lambda seq: np.diff(np.load(data_dir + seq+".joint_angles_scaled.npy")).max()
seqs = sorted(seqs, key=max_deriv)
seqs2 = sorted(seqs, key=lambda seq: get_scaling(seq))
#%%
seqs2[-35]
get_scaling(seqs2[-35])
to_check_coz_big = []
with open("ones_to_check.txt", "a") as f:
for seq in list(reversed(seqs2))[33:]:
if get_scaling(seq) > 96:
to_check_coz_big.append(seq)
# seq = seq.split("_")
# seq[2] = "c01"
# seq = "_".join(seq)
# f.writelines(seq+"\n")
with open("ones_to_check2.txt", "w") as f:
for seq in list(reversed(seqs))[14:]:
if max_deriv(seq) > 20 and seq not in to_check_coz_big:
seq = seq.split("_")
seq[2] = "c01"
seq = "_".join(seq)
f.writelines(seq+"\n")
np.diff(np.load(data_dir + seqs[-15]+".joint_angles_scaled.npy")).max()
data = np.load(data_dir + seqs[-11]+".joint_angles_scaled.npy")
#%%
# with open("bad_ones.txt", "w") as f:
# for seq in [x.split("/")[-1][:-4] for x in glob.glob("../aistplusplus_api/visualization/bad/*")]:
# seq = seq.split("_")
# seq[2] = "cAll"
# seq = "_".join(seq)
# f.writelines(seq+"\n")
# f.writelines(seq+"\n")
train_ones = [x[:-1] for x in open("analysis/aistpp_base_filenames_train_filtered.txt", "r").readlines()]
bad_ones = [x[:-1] for x in open("analysis/aistpp_bad_ones.txt", "r").readlines()]
with open("analysis/aistpp_base_filenames_train_filtered.txt", "w") as f:
for line in train_ones:
if line not in bad_ones:
f.writelines(line+"\n")
#%%
# np.diff(data).max()
plt.plot(np.diff(data[:900]).max(1))
# plt.plot(np.diff(data).mean(1))
#gHO_sFM_cAll_d20_mHO5_ch13 from 350
#gBR_sFM_cAll_d05_mBR4_ch13 up to 900
#gWA_sBM_cAll_d27_mWA4_ch08 except the end
#%%
# seqs.remove(max_diff_seq)
max_diff = 0
max_diff_seq = ""
for seq in seqs:
data = np.load(data_dir + seq+".joint_angles_scaled.npy")
diff = np.diff(data).max()
if diff > max_diff:
max_diff = diff
max_diff_seq = seq
max_diff
max_diff_seq
data = np.load(data_dir + max_diff_seq+".joint_angles_scaled.npy")
#%%
#
# plt.ion()
# plt.show()
# for i in range(data.shape[1]):
# plt.gca().clear()
# plt.plot(data[:,i])
# plt.draw()
# plt.pause(0.03)
#
# plt.plot(np.diff(data[:,0]))
transform = pickle.load(open(data_dir+"/"+"pkl_joint_angles_mats_scaler"+'.pkl', "rb"))
unscaled_data = transform.inverse_transform(data)
unscaled_data.shape
smpl_thing = rot_mats_to_eulers(np.expand_dims(unscaled_data, 1))
smpl_poses,smpl_scaling,smpl_trans = smpl_thing['smpl_poses'], smpl_thing['smpl_scaling'], smpl_thing['smpl_trans']
#%%
import glob, os
import pickle
# for file in glob.glob("../aistpp_data/aist_plusplus_final/motions/*"):
def get_scaling(seq_id):
smpl_thing = pickle.load(open("../aistpp_data/aist_plusplus_final/motions/"+seq_id+".pkl", "rb"))
smpl_poses = smpl_thing['smpl_poses']
smpl_scaling = smpl_thing['smpl_scaling']
smpl_trans = smpl_thing['smpl_trans']
return smpl_scaling[0]
# smpl_thing['smpl_poses'], smpl_thing['smpl_scaling'], smpl_thing['smpl_trans'] =
#%%
from analysis.utils import run_bash_command
from smplx import SMPL
import os
audio_file = "a"
seq_id = "a"
output_folder = "analysis/tmp"
root_dir="analysis/tmp"
def delete_images():
files = glob.glob(root_dir+'/img/*')
for f in files:
os.remove(f)
smpl = SMPL(model_path="../aistplusplus_api", gender='MALE', batch_size=1)
output = smpl.forward(
global_orient=torch.from_numpy(smpl_poses[:, 0:1]).float(),
body_pose=torch.from_numpy(smpl_poses[:, 1:]).float(),
transl=torch.from_numpy(smpl_trans).float(),
scaling=torch.from_numpy(smpl_scaling.reshape(1, 1)).float(),
)
keypoints3d = output.joints.detach().numpy()
keypoints3d = keypoints3d[:,:24] # the body joints (ignoring the extra head, feet and hand bones added onto it here https://github.com/vchoutas/smplx/blob/7547ee6656b942a68a97604d0cf7b6b834fad9eb/smplx/vertex_joint_selector.py)
# that file takes the position of the vertices corresponding to certain joints
# print(keypoints3d)
# Plot as images
delete_images()
fig = plt.figure()
plt.ion()
plt.show()
ax = Axes3D(fig)
# print(keypoints3d.shape)
# print(keypoints3d[0,:,2])
ax.scatter(keypoints3d[0,:,2], keypoints3d[0,:,0], keypoints3d[0,:,1])
plt.xlim([-100,100])
plt.ylim([-100,100])
ax.set_zlim([75,275])
ax.view_init(0, 0)
plt.draw()
plt.pause(0.001)
for i in range(1,len(keypoints3d)):
print(i)
ax.clear()
ax.scatter(keypoints3d[i,:,2], keypoints3d[i,:,0], keypoints3d[i,:,1])
plt.xlim([-100,100])
plt.ylim([-100,100])
ax.set_zlim([75,275])
ax.view_init(0, 0)
plt.draw()
plt.pause(0.001)
plt.savefig(root_dir+"/img/img_"+str(i)+".png")
video_file = output_folder+seq_id+".mp4"
video_file2 = output_folder+seq_id+"_music.mp4"
bash_command = "ffmpeg -y -r 60 -f image2 -s 1920x1080 -i "+root_dir+"/img/img_%d.png -vcodec libx264 -crf 25 -pix_fmt yuv420p "+video_file
run_bash_command(bash_command)
trim_audio=2
if audio_file is not None:
new_audio_file = output_folder+seq_id+".mp3"
bash_command = "ffprobe -v 0 -show_entries format=duration -of compact=p=0:nk=1 "+video_file
duration = float(run_bash_command(bash_command))
bash_command = "ffmpeg -y -i "+audio_file+" -ss "+str(trim_audio)+" -t "+str(duration)+" "+new_audio_file
run_bash_command(bash_command)
bash_command = "ffmpeg -y -i "+video_file+" -i "+new_audio_file+" "+video_file2
run_bash_command(bash_command)
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