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smpl3dclothrec_v4.py
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"""
cloth 3d model reconstruction based on SMPL body model
------------------------------------------------------------
(c) copyright 2019 [email protected]
In : 2D VTON of cloth to SMPL silhouette
SMPL template model params file (pkl)
2D matched cloth image file and mask
Out: plk or npz file for subset of SMPL vertices and displacement vector
Note: the Texture (2D warped cloth) and related 2D vertex and face information is obtained
with original SMPL and camera parameters
For in-advance tesrt purpose of part 3. we could move the pose and apply the displacement vector
we apply the pose and shape params for target user but with same texture and vertices and faces defitnion
template (source: pose and shape) target (pose and shape)
--------------------------------------------------------------------------
SMPL- p smpltemplate.pkl results/viton/smpl/000000.pkl
camera-p smpltemplate.pkl results/viton/smpl/000000.pkl
3D body-v smpl with template param smpl with target params
3D cloth-v displacement obtained use displacemt obtained at template
texture results/viton/2dwarp/00000_1.png same
texture-v cam projected onto the texture same as template (not new vertices)
texture-f model.f same
lightening only for cloth-related vertices same
"""
from __future__ import print_function
import graphutil as graphutil
import boundary_matching
import sys
from os.path import join, exists, abspath, dirname
from os import makedirs
import logging
import cPickle as pickle
import time
import cv2
from PIL import Image
import numpy as np
import chumpy as ch
from opendr.camera import ProjectPoints
from smpl_webuser.serialization import load_model
from smpl_webuser.verts import verts_decorated
from render_model import render_model
import inspect # for debugging
import matplotlib.pyplot as plt
from opendr.lighting import SphericalHarmonics, LambertianPointLight
from opendr.geometry import VertNormals, Rodrigues
from opendr.renderer import TexturedRenderer
import json
from smpl_webuser.lbs import global_rigid_transformation
_LOGGER = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
# To understand and verify the SMPL itself
def _examine_smpl_template(model, detail=False):
print(">> SMPL Template <<<<<<<<<<<<<<<<<<<<<<")
print(type(model))
print(dir(model))
#print('kintree_table', model.kintree_table)
print('pose:', model.pose)
if detail:
print('posedirs:', model.posedirs)
print('betas:', model.betas)
print('shape(model):', model.shape)
if detail:
print('shapedirs:', model.shapedirs)
# print('bs_style:', model.bs_style) # f-m-n
#print('f:', model.f)
print('V template :', type(model.v_template))
print('V template :', model.v_template.shape)
#print('weights:', model.weoptimize_on_jointsights)
print('W type:', type(model.weights))
print('W shape:', model.weights.r.shape)
if detail:
print('W value:')
print(model.weights.r)
#parts = np.count_nonzero(model.weights.r, axis =1)
parts = np.argmax(model.weights.r, axis=1)
print(" :", parts.shape, parts[:6000])
#print('J:', model.J)
#print('v_template:', model.v_template)
#print('J_regressor:', model.J_regressor)
# To understand and verify the paramters
def _examine_smpl_params(params):
print(type(params))
print(params.keys())
print('camera params')
camera = params['cam']
print(" - type:", type(camera))
#print(" - members:", dir(camera))
print(" - cam.t:", camera.t.r) # none-zero, likely only nonzero z
print(" - cam.rt:", camera.rt.r) # zero (fixed)
# print(" - cam.camera_mtx:", camera.camera_mtx) #
print(" - cam.k:", camera.k.r) #
print(" - cam.c:", camera.c.r) #
print(" - cam.f:", camera.f.r) #
# print(params['f'].shape) # 2
print('>> pose')
pose = params['pose']
print("\t\ttype:", type(pose))
print('\t\tshape:', pose.shape) # 72
# convert within
#pose = pose % (2.0*np.pi)
print('\t\tvalues (in degree):')
print(pose*180.0/np.pi) # degree
print('>> betas')
betas = params['betas']
print('\ttype:', type(betas))
print('\tshape:', betas.shape) # 10
# print('\tvalues:', params['betas']) # 10
#
#
#
def build_smplbody_surface(model, pose, betas, cam):
n_betas = betas.shape[0]
viz = False
# 2. build body model
sv = verts_decorated( # surface vertices
trans=ch.zeros(3),
pose=ch.array(pose),
v_template=model.v_template,
J=model.J_regressor,
betas=ch.array(betas),
shapedirs=model.shapedirs[:, :, :n_betas],
weights=model.weights,
kintree_table=model.kintree_table,
bs_style=model.bs_style,
f=model.f,
bs_type=model.bs_type,
posedirs=model.posedirs,
want_Jtr=not viz) # need J_transformed for reposing based on vertices
return sv
#
# 3D Body shape => 2D Body shape => cloth 2D Shape
#
# in order to have the cooresponding body vertex for each cloth vertex, we deform body shape
# here we do the 2D mask based and then later will get the 3D coordinates
#
# model : smpl mesh structure (esp. faces)
# body_sv : 3d body vertices
# cam : camera paramter
# imMask : mask for body + cloth area
# return: clothed2dvt : for body + cloth area
#
def construct_clothed2d_from_body(model, body_sv, j2d, cam, mask):
h, w = mask.shape
# 1. extract edge vertices
# cam should be set with cam.v = body_sv.r
# 1.1 construct face_visibility map in 3D body shape
f_normal, v_normal = graphutil.calc_normal_vectors(cam.v.r, model.f)
face_visibility = graphutil.build_face_visibility(f_normal, cam)
# 1.2. extract edge vertices
check_edge_vertices = True
'''
graph analysis data structure: vertex, edge, vs face
face to edges: mdoel.f
face to vertices: model.f
vertex to edges: graph
vertex to faces: graph
edge to vertices: graph
edge to faces: graph
graph[start_v][end_v][0, 1, 2 (contour label)]
'''
graph, longest_contour_label, con_length = graphutil.build_edge_graph_dict(
cam.v, model.f, face_visibility)
# num_body_vertices = np.count_nonzero(
# graph[:, :, 2] == longest_contour_label)
num_edge_vertices = np.amax(con_length)
#print("edge v number:", num_edge_vertices)
# if save_edge_vertices:
edge_vertices = np.zeros([num_edge_vertices, 2], dtype='int32')
# visualization of contour
img_contour = np.zeros([h, w], dtype='uint8')
i = 0
if check_edge_vertices or save_edge_vertices:
for v_s in range(cam.v.shape[0]):
# for v_e in range(v_s):
for v_e in graph[v_s]:
# if graph[v_s, v_e, 2] == longest_contour_label: # > 0:
if graph[v_s][v_e][2] == longest_contour_label: # > 0:
if check_edge_vertices:
sx, sy = cam.r[v_s] # projected coordinate
ex, ey = cam.r[v_e]
edge_vertices[i, 0], edge_vertices[i, 1] = int(
sx), int(sy)
i = i + 1
cv2.line(img_contour, (int(sx), int(sy)), (int(
ex), int(ey)), graph[v_s][v_e][2], thickness=1)
# boudnary matching
# Body Part
##############################################
# 1.1 read boundary matching input files
#img_idx = 1
#maskfile = "../results/10k/segmentation/10kgt_%04d.png"%img_idx
#mask = cv2.imread(maskfile, cv2.IMREAD_UNCHANGED)
# if mask is None:
# print("cannot open", maskfile), exit()
'''
edge_vertices_path ='edge_vertices_%04d.pkl'%img_idx
with open(edge_vertices_path, 'rb') as f:
edge_vertices = pickle.load(f)
'''
# 1.3 boudnary matching
#neck_xy = (j2d[12,0], j2d[12,1])
neck_y = j2d[12, 1]
lsh_y = j2d[9, 1]
rsh_y = j2d[8, 1]
top_y = int((neck_y + lsh_y + rsh_y)/3.0)
nearest_list, img_allcontours = boundary_matching.boundary_match(
mask, edge_vertices, top_y, step=5)
# print(j2d), print(j2d[12,:2]), exit()
# joints matching added
# j2d will be added for source and tgt ....
njoints = j2d.shape[0]
# 2. transform
# 2.1 adaptation of matching data
nboundarypts = len(nearest_list)
npts = nboundarypts + njoints
srcPts = np.zeros([1, npts, 2], dtype='float32')
tgtPts = np.zeros([1, npts, 2], dtype='float32')
for i in range(nboundarypts):
srcPts[0, i, :] = nearest_list[i][0]
tgtPts[0, i, :] = nearest_list[i][1]
#print(tgtPts[0,i,:], srcPts[0,i,:])
for i in range(nboundarypts, npts):
srcPts[0, i, :] = j2d[i-nboundarypts, :]
tgtPts[0, i, :] = j2d[i-nboundarypts, :]
# 2.2 estimate TPS params
tps = boundary_matching.estimateTPS(srcPts, tgtPts, 10)
# 3. deform the boundary 2D vertices using TPS
'''
body2dvt1 = edge_vertices.astype('float32').reshape(1, -1,2)
print('>>> Edge vertices <<<;-')
print('type:', body2dvt1.dtype)
print('shape:', body2dvt1.shape)
print('x:', np.amin(body2dvt1[:,:,0]), np.amax(body2dvt1[:,:,0]))
print('y:', np.amin(body2dvt1[:,:,1]), np.amax(body2dvt1[:,:,1]))
print(body2dvt1)
transformed = tps.applyTransformation(body2dvt1)
print(transformed)
'''
body2dvt_save = cam.r.copy()
body2dvt = cam.r.copy().reshape(1, -1, 2).astype('float32')
'''
print('>>> all vertices <<<;-')
print('type:', body2dvt.dtype)
print('shape:', body2dvt.shape)
print('x:', np.amin(body2dvt[:,:,0]), np.amax(body2dvt[:,:,0]))
print('y:', np.amin(body2dvt[:,:,1]), np.amax(body2dvt[:,:,1]))
print(body2dvt)
'''
transformed = tps.applyTransformation(body2dvt)
# print(transformed)
clothed2dvt = transformed[1].reshape(-1, 2)
body2dvt = body2dvt.reshape(-1, 2)
return clothed2dvt
def construct_clothed3d_from_clothed2d_depth(body_sv, cam, clothed2d):
# 1. get the dept for body vertex
bodydepth = graphutil.build_depthmap2(body_sv.r, cam)
check_depthmap = False
if check_depthmap:
# depth in reverse way
plt.suptitle('depthmap')
plt.subplot(1, 2, 1)
plt.imshow(img[:, :, ::-1]) # , cmap='gray')
plt.subplot(1, 2, 2)
depthmap = graphutil.build_depthimage(
body_sv.r, model.f, bodydepth, cam, height=h, width=w)
#plt.imshow(depthmap, cmap='gray')
plt.imshow(depthmap)
plt.draw()
plt.show()
# plt.imshow(depthmap, cmap='gray_r') # the closer to camera, the brighter
_ = raw_input('quit?')
#exit()
# 2. modify the depth for clothed
# @TODO
# 3. unproject to 3D
# uv space? pixels coordinated!!
clothuvd = np.zeros(body_sv.r.shape)
clothuvd[:, 0] = clothed2d[:, 0]
clothuvd[:, 1] = clothed2d[:, 1]
# @TODO for now simply use the same depth as body ^^;
clothuvd[:, 2] = bodydepth
cloth3d = cam.unproject_points(clothuvd)
# sv.r = cloth3d # now the model is not body but cloth
return cloth3d
# convert numpy to json for a single person joint
# connvert
# 1) uint8 image to float texture image
# 2) normalize the vertices
# optionally,
# 3) coloring the backsize if face visibiltiy is not None)
# ***note ****: texture coordinate is UP-side Down, and x-y normalized
# j
# def prepare_texture(imv2d, faces, im4texture, skin_color_b, skin_color_g, skin_color_r):
def prepare_texture(imv2d, faces, im4texture):
# add arms to textures
"""img_skin = np.zeros(
[im4texture.shape[0], im4texture.shape[1], 3], dtype=np.uint8)
# img_skin.fill(255) # make skin color
img_skin[:, :, 0] = int(skin_color_b)
img_skin[:, :, 1] = int(skin_color_g)
img_skin[:, :, 2] = int(skin_color_r)
# target cloth + skin-colored painting
im4texture = im4texture + img_skin * (im4texture == 0)"""
# texture = overlayed images of 2d and projected.
texture = im4texture.astype(float)/255.0 # uint8 to float
#print('dtype of img:', img.dtype)
#print('dtype of pjt_texture:', pjt_texture.dtype)
th, tw = texture.shape[0:2]
'''
pjt_texture[:,:,:] = (1.0, .0, .0) #
#pjt_texture[:,:int(tw/2),:] = (1.0, 0., 0.) # B, G, R
pjt_texture[:,int(tw/4):int(3*tw/4),:] = (1.0, 1.0, 1.0) # B, G, R
'''
#print("th, tw:", th, tw)
texture_v2d = np.stack(
(imv2d[:, 0]/tw, imv2d[:, 1]/th), axis=-1) # uv normalize
# 5. project the body model with texture renderer
# 3. reprojection
# print(type(cam.v))
# print(cam.v.r.shape)
#print("textured:", type(pjt_texture), 'dtype:', pjt_texture.dtype, "shape:", pjt_texture.shape)
# print('max:', np.amax(pjt_texture[:, :, 0]), np.amax(
# pjt_texture[:, :, 1]), np.amax(pjt_texture[:, :, 2]))
# print('meam:', np.mean(pjt_texture[:, :, 0]), np.mean(
# pjt_texture[:, :, 1]), np.mean(pjt_texture[:, :, 2]))
# apply the visibility map for texturing
return texture, texture_v2d
def prepare_body_texture(imv2d, c3dw, im_path, im_parse_path):
"""
LIP labels
[(0, 0, 0), # 0=Background
(128, 0, 0), # 1=Hat
(255, 0, 0), # 2=Hair
(0, 85, 0), # 3=Glove
(170, 0, 51), # 4=Sunglasses
(255, 85, 0), # 5=UpperClothes
(0, 0, 85), # 6=Dress
(0, 119, 221), # 7=Coat
(85, 85, 0), # 8=Socks
(0, 85, 85), # 9=Pants
(85, 51, 0), # 10=Jumpsuits
(52, 86, 128), # 11=Scarf
(0, 128, 0), # 12=Skirt
(0, 0, 255), # 13=Face
(51, 170, 221), # 14=LeftArm
(0, 255, 255), # 15=RightArm
(85, 255, 170), # 16=LeftLeg
(170, 255, 85), # 17=RightLeg
(255, 255, 0), # 18=LeftShoe
(255, 170, 0) # 19=RightShoe
(189, 170, 160) # 20=Skin/Neck
]
"""
# get full target body texture except upper-clothes
im = Image.open(im_path)
im_parse = Image.open(im_parse_path)
parse_array = np.array(im_parse)
parse_top = (parse_array == 1) + \
(parse_array == 2) + \
(parse_array == 4) + \
(parse_array == 8) + \
(parse_array == 13) + \
(parse_array == 20)
im_top = im * parse_top - (1 - parse_top) # [-1,1], fill 0 for other parts
parse_cloth = (parse_array == 0) + \
(parse_array == 3) + \
(parse_array == 5) + \
(parse_array == 6) + \
(parse_array == 7) + \
(parse_array == 10) + \
(parse_array == 11) + \
(parse_array == 14) + \
(parse_array == 15) + \
(parse_array == 20)
im_cloth = c3dw * parse_cloth - (1 - parse_cloth) # [-1,1], fill 0 for other parts
parse_bottom = (parse_array == 9) + \
(parse_array == 12) + \
(parse_array == 16) + \
(parse_array == 17) + \
(parse_array == 18) + \
(parse_array == 19) + \
(parse_array == 20)
im_bottom = im * parse_bottom - (1 - parse_bottom) # [-1,1], fill 0 for other parts
# texture = overlayed images of 2d and projected.
full_body = im_top + im_cloth + im_bottom
h, w = full_body.shape[0:2]
h_ext = h * 3//2
im_body_ext = np.zeros([h_ext, w, 3], dtype='uint8')
im_body_ext[:h, :, :] = full_body
# texture = im_body_ext.astype(float)/255.0 # uint8 to float
imHuman = cv2.imread(im_path)
texture = imHuman.astype(float)/255.0 # uint8 to float
th, tw = texture.shape[0:2]
texture_v2d = np.stack(
(imv2d[:, 0]/tw, imv2d[:, 1]/th), axis=-1) # uv normalize
"""plt.subplot(1, 3, 1)
plt.axis('off')
plt.imshow(im_cloth)
plt.title('cloth')
plt.subplot(1, 3, 2)
plt.axis('off')
plt.imshow(im_top)
plt.title('body')
plt.subplot(1, 3, 3)
plt.axis('off')
plt.imshow(full_body)
plt.title('texture')
plt.show()
_ = raw_input("next?")"""
return texture, texture_v2d
#
# texture processing with alpha blending
def prepare_texture_with_alpha(pjt_v, pjt_f, img, mask, target_label):
alpha = np.zeros(mask.shape)
# 1.0 for fully opaque, 0.0 for transparent
alpha[mask == target_label] = 1.0
rgb = img.astype(float)/255.0 # uint8 to float
rgba = cv2.merge((rgb, alpha))
print('shapes:', img.shape, rgb.shape, alpha.shape, rgba.shape)
th, tw = rgba.shape[0:2]
pjt_v[:, 0] = pjt_v[:, 0]/tw # uv normalize
pjt_v[:, 1] = pjt_v[:, 1]/th # uv normalize
return rgba # [:,:,:3]
# create V, A, U, f: geom, bright, cam, renderer
def build_texture_renderer(U, V, f, vt, ft, texture, w, h, ambient=0.0, near=0.5, far=20000, background_image=None):
# add lighting
A = SphericalHarmonics(vn=VertNormals(v=V, f=f),
components=[0., 0., 0., 0., 0., 0., 0., 0., 0.],
light_color=ch.ones(3)) + ambient
"""A = LambertianPointLight(
f=f,
v=V,
num_verts=len(V),
light_pos=ch.array([-500,-500,-500]),
vc=np.ones_like(V.r),
# light_color=ch.array([0.7, 0.7, 0.7])) + 0.3
light_color=ch.array([1.0, 1.0, 1.0])) + 0.5 # brighter"""
if background_image is not None:
R = TexturedRenderer(vc=A, camera=U, f=f, bgcolor=[0.0, 0.0, 0.0],
texture_image=texture, vt=vt, ft=ft,
frustum={'width': w, 'height': h, 'near': near, 'far': far}, background_image=background_image)
else:
R = TexturedRenderer(vc=A, camera=U, f=f, bgcolor=[0.0, 0.0, 0.0],
texture_image=texture, vt=vt, ft=ft,
frustum={'width': w, 'height': h, 'near': near, 'far': far})
return R
# display 3d model
def show_3d_model(cam, _texture, texture_v2d, faces, normalImage=False):
#h, w = imTexture.shape[:2]
h, w = _texture.shape[:2]
dist = 20.0
if normalImage:
texture = prepare_texture(texture_v2d, faces, _texture)
else:
texture = _texture
# 1. build texture renderer
texture_renderer = build_texture_renderer(cam, cam.v, faces, texture_v2d, faces,
texture[::-1, :, :], w, h, 1.0, near=0.5, far=20 + dist)
#textured_cloth2d = texture_renderer.r
# plt.figure()
plt.subplot(1, 5, 1)
plt.axis('off')
plt.imshow(texture[:, :, ::-1])
plt.title('input')
rot_axis = 1
rotation = ch.zeros(3)
rotation[rot_axis] = np.pi/4
img0 = texture_renderer.r[:, :, ::-1]*255.0
img0 = img0.astype('uint8')
for i in range(4):
plt.subplot(1, 5, i+2)
# plt.imshow(pjt_R.r)
plt.imshow(texture_renderer.r)
plt.axis('off')
# plt.draw()
# plt.show()
#plt.title('angle =%f'%yaw)
plt.title('%.0f degree' % (i*45))
cam.v = cam.v.dot(Rodrigues(rotation))
plt.show()
# calcuated the local coordinates at each vetex.
#
# z : normal to the vertex
# x : the smallest indexed neighbor vertex based unit vector
# y : the remianing axis in right handed way, ie. z x x => y
def setup_vertex_local_coord(faces, vertices):
# 1.1 normal vectors (1st axis) at each vertex
_, axis_z = graphutil.calc_normal_vectors(vertices, faces)
# 1.2 get 2nd axis
axis_x = graphutil.find2ndaxis(faces, axis_z, vertices)
# 1.3 get 3rd axis
# matuir contribution. np.cross support row-vectorization
axis_y = np.cross(axis_z[:, :], axis_x[:, :])
return axis_x, axis_y, axis_z
#
# reporesent the displacement (now in global coord) into local coordinates
#
# model: smpl mesh structure
# v0 : reference vertex surface, ie. the body
# v*****array: vertext index array for interest
# d : displacement, ie. v = v0 + d
#
def compute_displacement_at_vertex(model, v0, d_global):
debug = False
# 1.setup local coordinate system to each vertex
axis_x, axis_y, axis_z = setup_vertex_local_coord(model.f, v0)
# 2. express displacement in 3 axises
#dlocal = np.concatenate(np.dot(d, axis_x), np.dot(d, axis_y), np.dot(d, axis_z))
xl = np.sum(d_global*axis_x, axis=1)
yl = np.sum(d_global*axis_y, axis=1)
zl = np.sum(d_global*axis_z, axis=1)
d_local = np.stack((xl, yl, zl), axis=-1)
print('dlocal shape:', xl.shape, yl.shape, zl.shape, d_local.shape)
if debug: # verifying d_global = xs * axis_x + ys* axis_y + z*axis_z
# get global coorindate vector
xg = xl[:, None]*axis_x
yg = yl[:, None]*axis_y
zg = zl[:, None]*axis_z
dg = xg + yg + zg
# check the error
err = np.absolute(dg - d_global)
print('d, e x:', np.amax(d_global[:, 0]), np.amax(
err[:, 0]), np.mean(d_global[:, 0]), np.mean(err[:, 0]))
print('d, e y:', np.amax(d_global[:, 1]), np.amax(
err[:, 1]), np.mean(d_global[:, 1]), np.mean(err[:, 1]))
print('d, e z:', np.amax(d_global[:, 2]), np.amax(
err[:, 2]), np.mean(d_global[:, 2]), np.mean(err[:, 2]))
'''
print('d 0:', np.amax(d_global[:,0]), np.amin(d_global[:,0]))
print('error0:', np.amax(err[:,0]), np.amin(err[:,0]))
print('d 1:', np.amax(d_global[:,1]), np.amin(d_global[:,1]))
print('error1:', np.amax(err[:,1]), np.amin(err[:,1]))
print('d 2:', np.amax(d_global[:,2]), np.amin(d_global[:,2]))
print('error2:', np.amax(err[:,2]), np.amin(err[:,2]))
'''
return d_local
#
# get subset numpy 2-D array of triangles
# whose all 3 vertices or one or them are included in the target vertices set
#
#
def getSubsetFaces(ifaces, set_v, smpl_model, allinclusion):
# get arm/hand vertices
"""set_hand = []
for i in range(smpl_model.shape[0]):
if smpl_model.weights_prior[i][13] > 0 or smpl_model.weights_prior[i][14] > 0 or smpl_model.weights_prior[i][16] > 0 or smpl_model.weights_prior[i][17] > 0 or smpl_model.weights_prior[i][18] > 0 or smpl_model.weights_prior[i][19] > 0 or smpl_model.weights_prior[i][20] > 0 or smpl_model.weights_prior[i][21] > 0 or smpl_model.weights_prior[i][22] > 0 or smpl_model.weights_prior[i][23] > 0:
set_hand.append(i)"""
flags = np.zeros(ifaces.shape[0], dtype=np.bool)
for i in range(ifaces.shape[0]):
#v1, v2, v3 = ifaces[i]
mask_v = np.isin(ifaces[i], set_v)
if (mask_v[0] == True) and (mask_v[1] == True) and (mask_v[2] == True):
flags[i] = True
# else:
# flags[i] = False
# add the hand parts
"""hand_v = np.isin(ifaces[i], set_hand)
if (hand_v[0] == True) and (hand_v[1] == True) and (hand_v[2] == True):
flags[i] = True"""
return ifaces[flags, :]
#
# calculate pixel position of SMPL joints
#
# cam: camera ie. projector
# model: smpl basic mdoel
# sv: surfac vectors (opendr)
# betas : body shape, why needed?
# h: projection image height
# w: projection image width
def calculate_joints(cam, model, sv, betas, h, w):
# 1. get the joint locations
# , 12 ] # index in Jtr # @TODO correct neck
smpl_ids = [8, 5, 2, 1, 4, 7, 21, 19, 17, 16, 18, 20]
# lsh,lelb, lwr, neck
# make the SMPL joints depend on betas
Jdirs = np.dstack([model.J_regressor.dot(model.shapedirs[:, :, i])
for i in range(len(betas))])
J_onbetas = ch.array(Jdirs).dot(betas) + \
model.J_regressor.dot(model.v_template.r)
# get joint positions as a function of model pose, betas and trans
(_, A_global) = global_rigid_transformation(
sv.pose, J_onbetas, model.kintree_table, xp=ch)
Jtr = ch.vstack([g[:3, 3] for g in A_global]) + sv.trans
# add joints, with corresponding to a vertex...
neck_id = 3078 # 2951 #3061 # viton's bewtween shoulder
Jtr = ch.vstack((Jtr, sv[neck_id]))
smpl_ids.append(len(Jtr) - 1)
# head_id = 411
nose_id = 331 # nose vertex id
Jtr = ch.vstack((Jtr, sv[nose_id]))
smpl_ids.append(len(Jtr) - 1)
lear_id = 516
Jtr = ch.vstack((Jtr, sv[lear_id]))
smpl_ids.append(len(Jtr) - 1)
rear_id = 3941 # 422# 226 #396
Jtr = ch.vstack((Jtr, sv[rear_id]))
smpl_ids.append(len(Jtr) - 1)
leye_id = 125 # 220 # 125
Jtr = ch.vstack((Jtr, sv[leye_id]))
smpl_ids.append(len(Jtr) - 1)
reye_id = 3635
Jtr = ch.vstack((Jtr, sv[reye_id]))
smpl_ids.append(len(Jtr) - 1)
# 2. project SMPL joints on the image plane using the estimated camera
cam.v = Jtr
joints_np_wo_confidence = cam.r[smpl_ids] # get the projected value
# print(joints_np_wo_confidence)
joints_np = np.zeros([18, 3])
joints_np[:, :2] = joints_np_wo_confidence
joints_np[:, 2] = 1.0
for i in range(joints_np.shape[0]):
if joints_np[i, 0] < 0 or joints_np[i, 0] > (w-1) or joints_np[i, 1] < 0 or joints_np[i, 1] > (h-1):
joints_np[i, 2] = 0.0
# print(joints_np)
return joints_np
def cvt_joints_np2json(joints_np):
# 1. re-ordering
# same as viton2lsp_joint and reamining
order = [13, 12, 8, 7, 6, 9, 10, 11, 2, 1, 0, 3, 4, 5, 14, 15, 16, 17]
# 2. build dictionary
oneperson = {"face_keypoints": [],
"pose_keypoints": joints_np[order].flatten().tolist(),
"hand_right_keypoints": [],
"hand_left_keypoints": []}
people = [oneperson]
joints_json = {"version": 1.0, "people": people}
return joints_json
def get_skin_color_from_image(im_path, im_parse_path):
imBody = cv2.imread(im_path)
imBodySegm = cv2.imread(im_parse_path)
# neck/skin
skin_color_b = np.mean(imBody[imBodySegm[:, :, 0] == 20, 0])
skin_color_g = np.mean(imBody[imBodySegm[:, :, 1] == 20, 1])
skin_color_r = np.mean(imBody[imBodySegm[:, :, 2] == 20, 2])
print('skin color:', skin_color_r, skin_color_g, skin_color_b)
skin_color = np.mean([skin_color_b, skin_color_g, skin_color_r])
# face
kin_color_b = np.mean(imBody[imBodySegm[:, :, 0] == 13, 0])
kin_color_g = np.mean(imBody[imBodySegm[:, :, 1] == 13, 1])
kin_color_r = np.mean(imBody[imBodySegm[:, :, 2] == 13, 2])
print('face color:', kin_color_r, kin_color_g, kin_color_b)
face_color = np.mean([kin_color_b, kin_color_g, kin_color_r])
# left-arm
in_color_b = np.mean(imBody[imBodySegm[:, :, 0] == 14, 0])
in_color_g = np.mean(imBody[imBodySegm[:, :, 1] == 14, 1])
in_color_r = np.mean(imBody[imBodySegm[:, :, 2] == 14, 2])
print('left-arm color:', in_color_r, in_color_g, in_color_b)
larm_color = np.mean([in_color_b, in_color_g, in_color_r])
# right-arm
n_color_b = np.mean(imBody[imBodySegm[:, :, 0] == 15, 0])
n_color_g = np.mean(imBody[imBodySegm[:, :, 1] == 15, 1])
n_color_r = np.mean(imBody[imBodySegm[:, :, 2] == 15, 2])
print('right-arm color:', n_color_r, n_color_g, n_color_b)
rarm_color = np.mean([n_color_b, n_color_g, n_color_r])
brightest_color = np.max([skin_color, face_color, larm_color, rarm_color])
if face_color == brightest_color:
skin_color_b = kin_color_b
skin_color_g = kin_color_g
skin_color_r = kin_color_r
print("face color is chosen.")
elif larm_color == brightest_color:
skin_color_b = in_color_b
skin_color_g = in_color_g
skin_color_r = in_color_r
print("left-arm color is chosen.")
elif rarm_color == brightest_color:
skin_color_b = n_color_b
skin_color_g = n_color_g
skin_color_r = n_color_r
print("right-arm color is chosen.")
else:
print("skin color is chosen.")
return skin_color_b, skin_color_g, skin_color_r
#
# cloth 3D model reconstrction using 2d cloth (mapped onto template) and template
#
def cloth3drec_core(model, # SMPL model
cam, # camera model, Chv
betas, # shape coef, numpy
n_betas, # num of PCA
pose, # angles, 27x3 numpy
imCloth, # img numpy
imClothMask, # img numpy
human_path, # img numpy
human_segm_path, # img numpy
viz=False): # visualize or not
# get human image and segm
imBody = cv2.imread(human_path)
if imBody is None:
print("cannot open", human_path), exit()
imBodySegm = cv2.imread(human_segm_path, cv2.IMREAD_UNCHANGED)
if imBodySegm is None:
print("cannot open", human_segm_path), exit()
for which in [cam, betas, pose, imCloth, imClothMask, imBody, imBodySegm, model]:
if which is None:
print(retrieve_name(which), 'is None')
exit()
h, w = imCloth.shape[0:2]
h_ext = h * 3//2
print(imClothMask.shape)
print(len(imClothMask.shape))
if len(imClothMask.shape) > 2: # ie. 3 ch to 1 ch
imClothMask = cv2.cvtColor(imClothMask, cv2.COLOR_BGR2GRAY)
# get skin/face color for hand/skin area painting
# skin_color_b, skin_color_g, skin_color_r = get_skin_color_from_image(imBody, imBodySegm)
print(imBodySegm.shape)
print(len(imBodySegm.shape))
if len(imBodySegm.shape) > 2: # ie. 3 ch to 1 ch
imBodySegm = cv2.cvtColor(imBodySegm, cv2.COLOR_BGR2GRAY)
# h vs h_ext
# half body image has size of h x w
# rendering needs full body texture image : size of h_ext x w
'''
# 1. Pose to standard pose
if True: # make standard pose for easier try-on
pose[:] = 0.0
pose[0] = np.pi
# lsh = 16 rsh = 17 67.5 degree rotation around z axis
pose[16*3+2] = -7/16.0*np.pi
pose[17*3+2] = +7/16.0*np.pi
betas[:] = 0.0
#cam.t = [0. , 0., 20.] - cam.t: [ 0. 0. 20.] # [-3.12641449e-03 4.31656201e-01 2.13035413e+01]
cam.t = [0., 0.4, 25.]
cam.rt = [0., 0., 0.]
cam.k = [0., 0., 0., 0., 0.]
cam.f = [5000., 5000.]
cam.c = [ 96., 128.] # depending on the image size
print('Final pose and betas ')
print('pose:', pose.reshape([-1,3]))
print('betas:', betas)
'''
# 1. Prepare input images and masks
# 1.1 build template body model
body_sv = build_smplbody_surface(model, pose, betas, cam)
dist = np.abs(cam.t.r[2] - np.mean(body_sv.r, axis=0)[2])
im3CBlack = np.zeros([h_ext, w, 3], dtype=np.uint8)
imBackground = im3CBlack
imBodyRGB = (render_model(
body_sv.r, model.f, w, h_ext, cam, far=20 + dist, img=imBackground[:, :, ::-1]) * 255.).astype('uint8')
imBodyRGB_body = imBodyRGB.copy()
# 1.2 source cloth image and mask (extension for the same size as body silhouette)
imClothedMask = cv2.cvtColor(
imBodyRGB, cv2.COLOR_BGR2GRAY) # gray silhouette
imClothedMask[imClothedMask > 0] = 255 # binary (0, 1)
# imClothedMask[imClothMask[:,:] > 0] = 255 # union of body and .....
# blank background image
imClothMask_ext = np.zeros([h_ext, w], dtype='uint8')
imClothMask_ext[:h, :] = imClothMask[:, :]
imClothedMask[imClothMask_ext > 0] = 255 # union of body and .....
# blank background image
imCloth_ext = np.zeros([h_ext, w, 3], dtype='uint8')
imCloth_ext[:h, :, :] = imCloth[:, :, :]
imCloth_ext[imClothMask_ext <= 0] = 0 # black out
imBodyRGB[imClothMask_ext > 0] = imCloth_ext[imClothMask_ext > 0]
if viz: # show cloth overlayed on smpl
plt.imshow(imBodyRGB[:h, :, ::-1])
# plt.imshow(imClothedMask)
# print("overlaid")
plt.draw()
plt.show()
_ = raw_input('next?')
# 2. Derive 2D cloth vertics position from body vertices using 2-D deformation
j2d = calculate_joints(cam, model, body_sv, betas, h_ext, w)
j2d_wo_confidence = j2d[:, :2]
cam.v = body_sv
clothed2d = construct_clothed2d_from_body(
model, body_sv, j2d_wo_confidence, cam, imClothedMask)
if viz: # show the clothed 2d vertices
marksize = 1
# blank background image
imClothed2d = np.zeros([h_ext, w], dtype='uint8')
for i in range(clothed2d.shape[0]):
x, y = int(clothed2d[i, 0]), int(clothed2d[i, 1])
imClothed2d[y-marksize:y+marksize, x-marksize:x+marksize] = 255
#cv2.drawMarker(img[:,:, parts[i]], (x,y), 255, markerSize = 5)
plt.imshow(imClothed2d)
plt.draw()
plt.show()
_ = raw_input('next?')
# 3. 3D cloth vertices from 2d position and depth
clothed3d = construct_clothed3d_from_clothed2d_depth(
body_sv, cam, clothed2d)
cam.v = clothed3d # now camera project clothed 3D vertex not body's
# 4. Rendering texture
# 4.1 updatng the cloth mask and image at boundary
'''
imClothMask1d = imClothMask[t].flatten()
print(imClothMask1d.shape)
print(imClothMask1dv4Cloth)
'''
# extend the mask boundary for hide the mismatch beween mask and image for rendering texture
kernel = np.ones((3, 3), np.uint8)
imClothMask_ext_raw = imClothMask_ext.copy()
imClothMask_ext = cv2.dilate(imClothMask_ext, kernel, iterations=2)
# modify the boundary
imClothMask_ext_bndry = imClothMask_ext.copy()
imClothMask_ext_bndry[imClothMask_ext_raw > 0] = 0
imCloth_ext_bndry = imCloth_ext.copy()
imCloth_ext_bndry[:, :, 0] = cv2.dilate(
imCloth_ext[:, :, 0], kernel, iterations=2)
imCloth_ext_bndry[:, :, 1] = cv2.dilate(
imCloth_ext[:, :, 1], kernel, iterations=2)
imCloth_ext_bndry[:, :, 2] = cv2.dilate(
imCloth_ext[:, :, 2], kernel, iterations=2)
# imCloth_ext[imClothMask_ext_bndry > 0, :] = (255, 0, 0) # draw the boundary with Blue
imCloth_ext[imClothMask_ext_bndry > 0,
:] = imCloth_ext_bndry[imClothMask_ext_bndry > 0, :]
if viz:
plt.subplot(1, 2, 1)
plt.imshow(imCloth_ext[:, :, ::-1])
plt.subplot(1, 2, 2)
# plt.imshow(imCloth_ext_not_modified[:,:,::-1])
plt.imshow(imClothMask_ext_bndry)
plt.show()
_ = raw_input('next?')
# 4.2 Prepare Rendering Texture
# texture, texture_v2d = prepare_texture(cam.r, model.f, imCloth_ext, skin_color_b, skin_color_g, skin_color_r)
texture, texture_v2d = prepare_texture(cam.r, model.f, imCloth_ext)
# 5. Collect the vertices for the cloth surface