rand_mse = np.linalg.norm(random_vectors, axis=2)
factor = mse / rand_mse
random_vectors *= factor[:, :, np.newaxis]
rotated_generated_vectors = reference_data - random_vectors
mse2 = np.linalg.norm(reference_data - rotated_generated_vectors, axis=2)
shuffled_rotated_reference, shuffled_and_rotated_generated = shuffle(
reference_data, rotated_generated_vectors, random_state=42)
joint_names = list(np_file['joint_names'])
activity = np_file['activity'].item().split("/")[-1]
delta_t = np_file['delta_t'].item()
fh.create_folder(normal_path)
fh.create_folder(shuffled_path)
fh.create_folder(rotated_path)
fh.create_folder(shuffled_and_rotated_path)
sp.print_stats(generated_data,
reference_data,
joint_names,
activity,
delta_t,
loss_test_wrapper,
save_matrices=False,
target_path=normal_path)
sp.print_stats(rotated_generated_vectors,
reference_data,
joint_names,
from Helpers import DataPreprocessor
from Helpers import ModelWrappers
from Helpers import Models
import torch
import sys
import time
from Helpers import FileHelpers
og_folder = "TEMP_REMOVE"
FileHelpers.create_folder(og_folder)
FileHelpers.create_folder(og_folder + "/unity_motion_export")
FileHelpers.create_folder(og_folder + "/stats")
FileHelpers.create_folder(og_folder + "/videos_out")
def test_folder_func(folder_name, FF_BATCH_SIZE):
STACKCOUNT = 15
TARGET_FPS = 20
take_last = lambda x: og_folder + "/" + x.split('/')[-1].split('.')[
0] + "_trained_on_" + folder_name.split("/")[-2]
# eval_files = [ "E:/Master/Converted Mocap/Eyes_Japan_Dataset/hamada/greeting-01-hello-hamada_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/kudo/jump-12-boxer step-kudo_poses.npz",
# "E:/Master/Converted Mocap/KIT/576/MarcusS_AdrianM11_poses.npz",
# "E:/Master/Converted Mocap/KIT/513/balance_on_beam06_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/hamada/gesture_etc-14-apologize-hamada_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/kanno/walk-01-normal-kanno_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/takiguchi/pose-10-recall blackmagic-takiguchi_poses.npz",
# "E:/Master/Converted Mocap/TotalCapture/s1/freestyle2_poses.npz",
if idx == 0:
pass
else:
colorclip = ColorClip(size=(clip1.w, clip1.h), color=[1, 1, 1], duration=1)
clip = concatenate_videoclips([colorclip, clip])
clips.append(clip)
final_clip = concatenate_videoclips(clips) # Overlay the text clip on the first video clip
final_clip.write_videofile(target, codec='libx264')
file_path = "E:\\Systemordner\\Dokumente\\Pycharm\\Master\\sparse_body_pose_prediction\\moglow_dropout\\unity_motion_export\\UNTITYEXPORT\\"
image_folder = ""
numbers = [45, 720, 734, # bwd
338, 1148, 2112, # circle
650, 763, 2308, # diagonal
976, 1514, 2016, # fwd
12, 13, 772 # sideways
]
methods = ["RNN2", "REF", "GLOW", "IK"]
for number in numbers:
file_names = [file_path + "WALKING_" + method + "_" + str(number) + "_trained_on_WALKING.mp4" for method in methods]
out_file_path = "shuffled_videos\\"
FileHelpers.create_folder(out_file_path)
concatenate_animations([file_names[0],file_names[1],file_names[2],file_names[3]], image_folder, out_file_path + str(number) + "_A.mp4")
concatenate_animations([file_names[3],file_names[2],file_names[1],file_names[0]], image_folder, out_file_path + str(number) + "_B.mp4")
concatenate_animations([file_names[2],file_names[3],file_names[0],file_names[1]], image_folder, out_file_path + str(number) + "_C.mp4")
concatenate_animations([file_names[1],file_names[0],file_names[3],file_names[2]], image_folder, out_file_path + str(number) + "_D.mp4")
from matplotlib.ticker import FuncFormatter
import matplotlib.pyplot as plt
import numpy as np
from Helpers import FileHelpers, ModelWrappers
from Helpers import DataPreprocessor
from Helpers import StatsPrinter
target_folder = "stats/"
training_prep_folder = "E:/Master/Sorted Mocap/WALKING/"
from sklearn.utils import shuffle
FileHelpers.create_folder(target_folder)
numbers = [
45,
720,
734, # bwd
338,
1148,
2112, # circle
650,
763,
2308, # diagonal
976,
1514,
2016, # fwd
12,
13,
772 # sideways
]
motiondict = {
45: "bwd",