이정은 / actionGAN

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JeungEunLee
cd939190 1 parent fa8be2da

data augmentated and size/rotate experiment : because output looks folded

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Showing 20 changed files with 515 additions and 387 deletions
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from glob import glob\n",
"import os \n",
"\n",
"motionpatch_location = './motionpatch/*.png'\n",
"output_location = './smallone/'\n",
"count = 0\n",
"for f in glob(motionpatch_location):\n",
" count += 1\n",
" image = cv2.imread(f)\n",
" small = cv2.resize(image,dsize=(25,25))\n",
" dst = os.path.join(output_location +str(count)+\".png\")\n",
" cv2.imwrite(dst,small)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
......@@ -20,6 +20,49 @@
" dst = os.path.join(output_location +str(count)+\".png\")\n",
" cv2.imwrite(dst,small)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" def __init__(self, data_files):\n",
" IMAGE_WIDTH = 25\n",
" IMAGE_HEIGHT = 25\n",
" self.image_mode = 'RGB'\n",
" image_channels = 3\n",
" self.data_files = data_files\n",
" self.shape = len(data_files), IMAGE_WIDTH, IMAGE_HEIGHT, image_channels\n",
" \n",
" def get_image(iself,image_path, width, height, mode):\n",
" image = Image.open(image_path)\n",
" image = Image.im2double(image)\n",
" return np.array(image)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"import numpy as np\n",
"from matplotlib import pyplot\n",
"\n",
"imgloc = './smallone/5004.png'\n",
"image = Image.open(imgloc)\n",
"dst = os.path.join(\"./samples/5004.png\")\n",
"pyplot.imsave(dst,image)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
......
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%your motion_patch location
ori = imread('/home/rfj/바탕화면/actionGAN/motion_patch/S001C001P001R001A020.png');
ori = imread('/home/rfj/바탕화면/actionGAN/DCGAN/new_motionpatch/sample_111.png');
ori = im2double(ori);
ori = ori(:,:,:);
......
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clear;
path_name = '/media/rfj/EEA4441FA443E923/nturgb_skeletones/';
fileID = fopen('/home/rfj/바탕화면/actionGAN/skeletone_INDEX/good_stand_2.txt','r');
formatSpec = '%s';
sizeA = [20 Inf];
perfect_list = fscanf(fileID,formatSpec,sizeA);
perfect_list = perfect_list.';
fclose(fileID);
L = length(perfect_list);
for K = 1:L
file_name = char(perfect_list(K,:));
name = strcat(path_name,file_name(1:20),'.skeleton');
[token,remainder] = strtok(file_name,'A');
class = str2num(remainder(2:4));
bodyinfo = read_skeleton_file(name);
frame_num = size(bodyinfo,2);
try
%initialize
cur_subject_x = zeros(frame_num, 25);
cur_subject_y = zeros(frame_num, 25);
cur_subject_z = zeros(frame_num, 25);
tot_x = zeros(frame_num,25);
tot_y = zeros(frame_num,25);
tot_z = zeros(frame_num,25);
joint_5 = zeros(1,3);
joint_9 = zeros(1,3);
joint_1 = zeros(1,3);
joint_3 = zeros(1,3);
%get total joints information
for FN = 1:frame_num
cur_body = bodyinfo(FN).bodies(1);
joints = cur_body.joints;
for JN = 1:25
tot_x(FN,JN) = joints(JN).x;
tot_y(FN,JN) = joints(JN).y;
tot_z(FN,JN) = joints(JN).z;
end
end
%Orientation normalization 1 : in space
%get median values
M_x = median(tot_x);
M_y = median(tot_y);
M_z = median(tot_z);
%set 3 points for make plane
joint_5 = [M_x(5) M_y(5) M_z(5)];
joint_9 = [M_x(9) M_y(9) M_z(9)];
joint_1 = [M_x(1) M_y(1) M_z(1)];
joint_3 = [M_x(3) M_y(3) M_z(3)];
%find RIGID TRNASFORMATION matrix
d1 = joint_1 - joint_5;
d2 = joint_1 - joint_9;
n1 = cross(d1,d2); % because we will parallel transform, don't need to find belly
u1 = n1/norm(n1);
u2 = [0 0 1];
cs1 = dot(u1,u2)/norm(u1)*norm(u2);
ss1 = sqrt(1-cs1.^2);
v1 = cross(u1,u2)/norm(cross(u1,u2));
R1 = [v1(1)*v1(1)*(1-cs1)+cs1 v1(1)*v1(2)*(1-cs1)-v1(3)*ss1 v1(1)*v1(3)*(1-cs1)+v1(2)*ss1];
R1(2,:) = [v1(1)*v1(2)*(1-cs1)+v1(3)*ss1 v1(2)*v1(2)*(1-cs1)+cs1 v1(2)*v1(3)*(1-cs1)-v1(1)*ss1];
R1(3,:) = [v1(1)*v1(3)*(1-cs1)-v1(2)*ss1 v1(2)*v1(3)*(1-cs1)+v1(1)*ss1 v1(3)*v1(3)*(1-cs1)+cs1];
%1-3 number tolls to parallel x axis. Rigid transformation on plane surface
%Z axis coords oyler angle transform
t = joint_3 - joint_1;
d3 = R1(1,:) * t.';
d3(1,2) = R1(2,:) * t.';
d3(1,3) = R1(3,:) * t.';
u3 = d3(1:2)/norm(d3(1:2));
v3 = [u3(1) -u3(2)];
v3(2,:) = [u3(2) u3(1)];
u4 = [1 0].';
csss = v3\u4;
cs2 = csss(1);
ss2 = csss(2);
R2 = [cs2 -ss2 0];
R2(2,:) = [ss2 cs2 0];
R2(3,:) = [0 0 1];
%apply rigid transformation
for FN = 1:frame_num
cur_body = bodyinfo(FN).bodies(1);
joints = cur_body.joints;
for JN = 1:25
a = R1(1,:) * [joints(JN).x joints(JN).y joints(JN).z].';
b = R1(2,:) * [joints(JN).x joints(JN).y joints(JN).z].';
c = R1(3,:) * [joints(JN).x joints(JN).y joints(JN).z].';
cur_subject_x(FN,JN) = R2(1,:) * [a b c].';
cur_subject_y(FN,JN) = R2(2,:) * [a b c].';
cur_subject_z(FN,JN) = R2(3,:) * [a b c].';
end
end
%orientation normalize 2 in plane surface
if cur_subject_x(1,4) < cur_subject_x(1,1)
cur_subject_x = 0 - cur_subject_x;
end
if cur_subject_y(1,9) > cur_subject_y(1,5)
cur_subject_y = 0 - cur_subject_y;
end
% for save origin subjects before data augment
clear_subject_x = cur_subject_x;
clear_subject_y = cur_subject_y;
clear_subject_z = cur_subject_z;
% Left <-> Right Change : 2option
for LR = 1:2
if LR == 1
augment_y = clear_subject_y;
else
augment_y = 0 - clear_subject_y;
end
%Height change : 3option
for HE = 1:3
if HE == 1
augment_x = clear_subject_x.* 1.2;
elseif HE==2
augment_x = clear_subject_x.* 1.0;
else
augment_x = clear_subject_x.* 0.8;
end
%Give Gaussian Random Variable : 0.01 - 6times
for RV = 1:6
%3. Gaussian Random filter 0.1
cur_subject_x = augment_x + 0.01.*randn(frame_num,25);
cur_subject_y = augment_y + 0.01.*randn(frame_num,25);
cur_subject_z = clear_subject_z + 0.01.*randn(frame_num,25);
% NORMALIZATION
cur_subject_x = cur_subject_x - min(cur_subject_x(:));
max_tall = max(cur_subject_x(:)) .*2;
cur_subject_x = cur_subject_x ./ max_tall;
cur_subject_y = cur_subject_y - min(cur_subject_y(:));
cur_subject_y = cur_subject_y ./ max_tall;
cur_subject_z = cur_subject_z - min(cur_subject_z(:));
cur_subject_z = cur_subject_z ./ max_tall;
%Write image
motionpatch = cur_subject_x;
motionpatch(:,:,2) = cur_subject_y;
motionpatch(:,:,3) = cur_subject_z;
new_file_name = strcat('/home/rfj/바탕화면/actionGAN/DCGAN/new_motionpatch_halfsize/',file_name(1:20),'_',num2str(LR),num2str(HE),num2str(RV),'.png');
imwrite(motionpatch,new_file_name);
end
end
end
catch
name
end
end
clear;
path_name = '/media/rfj/EEA4441FA443E923/nturgb_skeletones/';
fileID = fopen('/home/rfj/바탕화면/actionGAN/skeletone_INDEX/good_stand_2.txt','r');
formatSpec = '%s';
sizeA = [20 Inf];
perfect_list = fscanf(fileID,formatSpec,sizeA);
perfect_list = perfect_list.';
fclose(fileID);
L = length(perfect_list);
for K = 1:L
file_name = char(perfect_list(K,:));
name = strcat(path_name,file_name(1:20),'.skeleton');
[token,remainder] = strtok(file_name,'A');
class = str2num(remainder(2:4));
bodyinfo = read_skeleton_file(name);
frame_num = size(bodyinfo,2);
try
%initialize
cur_subject_x = zeros(frame_num, 25);
cur_subject_y = zeros(frame_num, 25);
cur_subject_z = zeros(frame_num, 25);
tot_x = zeros(frame_num,25);
tot_y = zeros(frame_num,25);
tot_z = zeros(frame_num,25);
joint_5 = zeros(1,3);
joint_9 = zeros(1,3);
joint_1 = zeros(1,3);
joint_3 = zeros(1,3);
%get total joints information
for FN = 1:frame_num
cur_body = bodyinfo(FN).bodies(1);
joints = cur_body.joints;
for JN = 1:25
tot_x(FN,JN) = joints(JN).x;
tot_y(FN,JN) = joints(JN).y;
tot_z(FN,JN) = joints(JN).z;
end
end
%Orientation normalization 1 : in space
%get median values
M_x = median(tot_x);
M_y = median(tot_y);
M_z = median(tot_z);
%set 3 points for make plane
joint_5 = [M_x(5) M_y(5) M_z(5)];
joint_9 = [M_x(9) M_y(9) M_z(9)];
joint_1 = [M_x(1) M_y(1) M_z(1)];
joint_3 = [M_x(3) M_y(3) M_z(3)];
%find RIGID TRNASFORMATION matrix
d1 = joint_1 - joint_5;
d2 = joint_1 - joint_9;
n1 = cross(d1,d2); % because we will parallel transform, don't need to find belly
u1 = n1/norm(n1);
u2 = [0 0 1];
cs1 = dot(u1,u2)/norm(u1)*norm(u2);
ss1 = sqrt(1-cs1.^2);
v1 = cross(u1,u2)/norm(cross(u1,u2));
R1 = [v1(1)*v1(1)*(1-cs1)+cs1 v1(1)*v1(2)*(1-cs1)-v1(3)*ss1 v1(1)*v1(3)*(1-cs1)+v1(2)*ss1];
R1(2,:) = [v1(1)*v1(2)*(1-cs1)+v1(3)*ss1 v1(2)*v1(2)*(1-cs1)+cs1 v1(2)*v1(3)*(1-cs1)-v1(1)*ss1];
R1(3,:) = [v1(1)*v1(3)*(1-cs1)-v1(2)*ss1 v1(2)*v1(3)*(1-cs1)+v1(1)*ss1 v1(3)*v1(3)*(1-cs1)+cs1];
%1-3 number tolls to parallel x axis. Rigid transformation on plane surface
%Z axis coords oyler angle transform
t = joint_3 - joint_1;
d3 = R1(1,:) * t.';
d3(1,2) = R1(2,:) * t.';
d3(1,3) = R1(3,:) * t.';
u3 = d3(1:2)/norm(d3(1:2));
v3 = [u3(1) -u3(2)];
v3(2,:) = [u3(2) u3(1)];
u4 = [0 1].'; % decide orientation in plane
csss = v3\u4;
cs2 = csss(1);
ss2 = csss(2);
R2 = [cs2 -ss2 0];
R2(2,:) = [ss2 cs2 0];
R2(3,:) = [0 0 1];
%apply rigid transformation
for FN = 1:frame_num
cur_body = bodyinfo(FN).bodies(1);
joints = cur_body.joints;
for JN = 1:25
a = R1(1,:) * [joints(JN).x joints(JN).y joints(JN).z].';
b = R1(2,:) * [joints(JN).x joints(JN).y joints(JN).z].';
c = R1(3,:) * [joints(JN).x joints(JN).y joints(JN).z].';
cur_subject_x(FN,JN) = R2(1,:) * [a b c].';
cur_subject_y(FN,JN) = R2(2,:) * [a b c].';
cur_subject_z(FN,JN) = R2(3,:) * [a b c].';
end
end
%orientation normalize 2 (with plane surface)
if cur_subject_y(1,4) < cur_subject_y(1,1)
cur_subject_y = 0 - cur_subject_y;
end
if cur_subject_x(1,9) > cur_subject_x(1,5)
cur_subject_x = 0 - cur_subject_x;
end
% for save origin subjects before data augment
clear_subject_x = cur_subject_x;
clear_subject_y = cur_subject_y;
clear_subject_z = cur_subject_z;
% Left <-> Right Change : 2option
for LR = 1:2
if LR == 1
augment_x = clear_subject_x;
else
augment_x = 0 - clear_subject_x;
end
%Height change : 3option
for HE = 1:3
if HE == 1
augment_y = clear_subject_y.* 1.2;
elseif HE==2
augment_y = clear_subject_y.* 1.0;
else
augment_y = clear_subject_y.* 0.8;
end
%Give Gaussian Random Variable : 0.01 - 6times
for RV = 1:6
%3. Gaussian Random filter 0.1
cur_subject_x = augment_x + 0.01.*randn(frame_num,25);
cur_subject_y = augment_y + 0.01.*randn(frame_num,25);
cur_subject_z = clear_subject_z + 0.01.*randn(frame_num,25);
% NORMALIZATION
cur_subject_y = cur_subject_y - min(cur_subject_y(:));
max_tall = max(cur_subject_y(:));
cur_subject_y = cur_subject_y ./ max_tall;
cur_subject_x = cur_subject_x - min(cur_subject_x(:));
cur_subject_x = cur_subject_x ./ max_tall;
cur_subject_z = cur_subject_z - min(cur_subject_z(:));
cur_subject_z = cur_subject_z ./ max_tall;
%Write image
motionpatch = cur_subject_x;
motionpatch(:,:,2) = cur_subject_y;
motionpatch(:,:,3) = cur_subject_z;
new_file_name = strcat('/home/rfj/바탕화면/actionGAN/DCGAN/new_motionpatch_rotate90/',file_name(1:20),'_',num2str(LR),num2str(HE),num2str(RV),'.png');
imwrite(motionpatch,new_file_name);
end
end
end
catch
name
end
end
......@@ -7,7 +7,7 @@
clear;
name = '/home/rfj/바탕화면/skeletones/S001C001P002R002A020.skeleton'
name = '/home/rfj/바탕화면/actionGAN/sample_skeletones/S001C001P001R002A020.skeleton'
bodyinfo = read_skeleton_file(name);
frame_num = size(bodyinfo,2);
......@@ -37,6 +37,7 @@ for FN = 1:frame_num
end
end
%Orientation normalization 1 : in space
%get median values
M_x = median(tot_x);
M_y = median(tot_y);
......@@ -109,89 +110,59 @@ end
if cur_subject_y(1,9) > cur_subject_y(1,5)
cur_subject_y = 0 - cur_subject_y;
end
%get current median
CM_x=median(cur_subject_x);
CM_y=median(cur_subject_y);
CM_z=median(cur_subject_z);
%for transform bellybutton to 0.5,0.5 (Except X) but it doesn't work
belly_button = 0.5 - CM_y(2);
belly_button(2) = 0.5 - CM_z(2);
% normalize with x... <- HERE! WANT TO PARALLEL TRANSFORM
... but if I plus belly_button for x and y axis , it dosn't work
cur_subject_x = cur_subject_x - min(cur_subject_x(:));
max_tall = max(cur_subject_x(:));
cur_subject_x = cur_subject_x ./ max_tall;
cur_subject_y = cur_subject_y - min(cur_subject_y(:));
cur_subject_y = cur_subject_y ./ max_tall;
cur_subject_z = cur_subject_z - min(cur_subject_z(:));
cur_subject_z = cur_subject_z ./ max_tall;
% 이미지 저장
motionpatch = cur_subject_x;
motionpatch(:,:,2) = cur_subject_y;
motionpatch(:,:,3) = cur_subject_z;
new_file_name = strcat('/home/rfj/바탕화면/sample.png');
imwrite(motionpatch,new_file_name);
% read image after write
ori = imread('/home/rfj/바탕화면/sample.png');
ori = im2double(ori);
ori = ori(:,:,:);
dx = [];
dy = [];
dz = [];
for f = 1:numel(ori(:,1,1))
for j = 1:25
dx = [dx;ori(f,j,1)];
dy = [dy;ori(f,j,2)];
dz = [dz;ori(f,j,3)];
% for save origin subjects before data augment
clear_subject_x = cur_subject_x;
clear_subject_y = cur_subject_y;
clear_subject_z = cur_subject_z;
% Left <-> Right Change : 2option
for LR = 1:2
if LR == 1
augment_y = clear_subject_y;
else
augment_y = 0 - clear_subject_y;
end
end
a = [1 0 0]; % Red 척추 1,2,3,4,20
b = [0 0 1]; % Blue 오른팔 8,9,10,11,23,24
c = [0 1 0]; % Green왼팔 5,6,7,21,22 (여기서 5번이 빠짐. 넣고싶으면 나중에 24 joint가 아니라 25 joint로 추가)
d = [1 1 0]; % Yellow 오른다리 16,17,18,19
e = [0 1 1]; % Skyblue 왼다리 12,13,14,15
colors = [a;a;a;a;c;c;c;c;b;b;b;b;e;e;e;e;d;d;d;d;a;c;c;b;b];
scatter3(dx,dy,dz,100,'filled');
connecting_joints= ...
[2 1 21 3 21 5 6 7 21 9 10 11 1 13 14 15 1 17 18 19 2 8 8 12 12];
for jj=1:25:numel(dx)% 1부터 8개씩 numel = 열갯수..?
current = [];
current(:,1) = dy(jj:jj+24) ;
current(:,2) = dz(jj:jj+24) ;
current(:,3) = dx(jj:jj+24) ;
scatter3(current(:,1),current(:,2),current(:,3),100,colors(:,:),'filled');
for j =1:25
k=connecting_joints(j);
line([current(j,1) current(k,1)], [current(j,2) current(k,2)] , [current(j,3) current(k,3)])
%Height change : 3option
for HE = 1:3
if HE == 1
augment_x = clear_subject_x.* 1.2;
elseif HE==2
augment_x = clear_subject_x.* 1.0;
else
augment_x = clear_subject_x.* 0.8;
end
%Give Gaussian Random Variable : 0.01 - 6times
for RV = 1:6
%3. Gaussian Random filter 0.1
cur_subject_x = augment_x + 0.01.*randn(frame_num,25);
cur_subject_y = augment_y + 0.01.*randn(frame_num,25);
cur_subject_z = clear_subject_z + 0.01.*randn(frame_num,25);
% NORMALIZATION
cur_subject_x = cur_subject_x - min(cur_subject_x(:));
max_tall = max(cur_subject_x(:));
cur_subject_x = cur_subject_x ./ max_tall;
cur_subject_y = cur_subject_y - min(cur_subject_y(:));
cur_subject_y = cur_subject_y ./ max_tall;
cur_subject_z = cur_subject_z - min(cur_subject_z(:));
cur_subject_z = cur_subject_z ./ max_tall;
%Write image
motionpatch = cur_subject_x;
motionpatch(:,:,2) = cur_subject_y;
motionpatch(:,:,3) = cur_subject_z;
new_file_name = strcat('/home/rfj/바탕화면/actionGAN/DCGAN/new_motionpatch/sample_',num2str(LR),num2str(HE),num2str(RV),'.png');
imwrite(motionpatch,new_file_name);
end
end
set(gca,'Xdir','reverse','Ydir','reverse')
xlim([0 1]);
xlabel('x')
ylim([0 1]);
ylabel('y')
zlim([0 1]);
zlabel('z')
drawnow
pause(0.01)
end
\ No newline at end of file
end
......
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......@@ -4,7 +4,6 @@ S001C001P005R002A020
S001C001P007R001A020
S001C001P008R002A020
S001C002P002R002A020
S001C001P001R001A020
S001C002P003R002A020
S001C002P005R001A020
S001C002P005R002A020
......