def retargetJointLocation(cls, loc):
L.info('Retarget the joint location to fit the human model')
newLoc = {}
newLoc['root'] = loc['root'] # Start from here
# Do normalization for each bone
refArmature = Retarget.getReferenceArmature()
for bone in Retarget.bones:
boneLength = refArmature[bone]
joint1 = Retarget.posepriorJointMapping[bone][
0] # TODO: change this ugly code later
joint2 = Retarget.posepriorJointMapping[bone][1]
src = loc[joint1]
tgt = loc[joint2]
vec = tgt - src
# print('%.2f' % vec.length)
vec.normalize()
newSrc = newLoc[joint1]
newTgt = newSrc + vec * boneLength
newLoc[joint2] = newTgt
return newLoc
def apply(self):
'''
Apply constraint to connect bone and empty control objects
'''
self.constraintName = '%s_%s' % (self.type, self.target)
model = models.humanModel()
L.info('Setting up constraint %s, %s' %
(self.boneName, self.target))
bone = model.pose.bones.get(self.boneName)
if not bone:
L.error('The bone %s can not be found' % self.boneName)
c = bone.constraints.get(self.constraintName)
if c:
L.info('Constraint already exist')
else:
c = bone.constraints.new(self.type)
targetObject = bpy.data.objects.get(self.target)
if not targetObject:
L.error('The target object %s can not be found' % self.target)
c.target = targetObject
for k in self.parameters:
L.debug('Setup parameter %s:%s' % (k, self.parameters[k]))
if k not in dir(c):
L.error('Parameter %s is not valid' % k)
else:
setattr(c, k, self.parameters[k])
def addTail(cls, loc):
# Use this to add a tail bone to structure
# Create a tail for rotation control
# vec1 = loc['shoulder.l'] - loc['shoulder.r']
L.info('Add the tail position to the armature')
vec1 = loc['shoulder.r'] - loc['shoulder.l']
vec2 = loc['neck'] - loc['root']
vec = vec1.cross(vec2)
# It is not necessary to check the tail orientation, since we know the left and right of the joints
# orintationVec = [
# loc['hip.r'] - loc['root'],
# loc['hip.l'] - loc['root'],
# # loc['knee.l'] - loc['hip.l'],
# # loc['knee.r'] - loc['hip.r']
# loc['foot.l'] - loc['ankle.l'],
# loc['foot.r'] - loc['ankle.r']
# ]
# sameDirection = [dot(v, vec)/(v.length * vec.length) for v in orintationVec]
# if sum(sameDirection) > 0:
# vec = -vec
vec.normalize()
loc['tail'] = loc['root'] + 5 * vec
def addTail(cls, loc):
# Use this to add a tail bone to structure
# Create a tail for rotation control
# vec1 = loc['shoulder.l'] - loc['shoulder.r']
L.info('Add the tail position to the armature')
vec1 = loc['shoulder.r'] - loc['shoulder.l']
vec2 = loc['neck'] - loc['root']
vec = vec1.cross(vec2)
# It is not necessary to check the tail orientation, since we know the left and right of the joints
# orintationVec = [
# loc['hip.r'] - loc['root'],
# loc['hip.l'] - loc['root'],
# # loc['knee.l'] - loc['hip.l'],
# # loc['knee.r'] - loc['hip.r']
# loc['foot.l'] - loc['ankle.l'],
# loc['foot.r'] - loc['ankle.r']
# ]
# sameDirection = [dot(v, vec)/(v.length * vec.length) for v in orintationVec]
# if sum(sameDirection) > 0:
# vec = -vec
vec.normalize()
loc['tail'] = loc['root'] + 5 * vec
def apply(self):
'''
Apply constraint to connect bone and empty control objects
'''
self.constraintName = '%s_%s' % (self.type, self.target)
model = models.humanModel()
L.info('Setting up constraint %s, %s' % (self.boneName, self.target))
bone = model.pose.bones.get(self.boneName)
if not bone:
L.error('The bone %s can not be found' % self.boneName)
c = bone.constraints.get(self.constraintName)
if c:
L.info('Constraint already exist')
else:
c = bone.constraints.new(self.type)
targetObject = bpy.data.objects.get(self.target)
if not targetObject:
L.error('The target object %s can not be found' % self.target)
c.target = targetObject
for k in self.parameters:
L.debug('Setup parameter %s:%s' % (k, self.parameters[k]))
if k not in dir(c):
L.error('Parameter %s is not valid' % k)
else:
setattr(c, k, self.parameters[k])
def main():
import random
import tenon.logging as L
# L.fileLevel(L.DEBUG) # default is ERROR
cachedir = os.path.abspath(
os.path.join(rootdir, 'cache/examples/random_light'))
# Set up the light configuration
radius = 12
nLight = 16
z = 10
lamps = [tenon.obj.Lamp.create('light%d' % v) for v in range(10)]
for j in range(10):
for i in range(len(lamps)):
lamp = lamps[i]
# Compute the location of light source, put the light evenly
lampObj = tenon.obj.get(lamp.name)
lampObj.location = tenon.util.sphereLocation(
radius, 360 / nLight * i, 0)
lampObj.location[2] += z # Set the z of the light source
lamp.energy = random.gauss(1, 1.5)
filename = os.path.join(cachedir, 'random_lighting_%d.png' % j)
L.info('Rendered file: %s', filename)
tenon.render.write(filename)
def retargetJointLocation(cls, loc):
L.info('Retarget the joint location to fit the human model')
newLoc = {}
newLoc['root'] = loc['root'] # Start from here
# Do normalization for each bone
refArmature = Retarget.getReferenceArmature()
for bone in Retarget.bones:
boneLength = refArmature[bone]
joint1 = Retarget.posepriorJointMapping[bone][0] # TODO: change this ugly code later
joint2 = Retarget.posepriorJointMapping[bone][1]
src = loc[joint1]
tgt = loc[joint2]
vec = tgt - src
# print('%.2f' % vec.length)
vec.normalize()
newSrc = newLoc[joint1]
newTgt = newSrc + vec * boneLength
newLoc[joint2] = newTgt
return newLoc
def readJointLocCSV(cls, csvFile, retarget=True):
'''
Return a dictionary containing location for each joint
'''
csvFile = os.path.expanduser(csvFile)
if not os.path.isfile(csvFile):
L.error('Joint file %s does not exist.' % csvFile)
return None
L.info('Read joint information from csv file %s' % csvFile)
px = []
py = []
pz = []
jointNames = []
with open(csvFile) as f:
headline = f.readline()
assert (headline.strip().lower() == 'name,x,y,z')
line = f.readline()
while line:
[name, x, y, z] = line.strip().split(',')
px.append(float(x))
py.append(float(y))
pz.append(float(z))
jointNames.append(name)
line = f.readline()
# Load joint location from csv file.
loc = {}
for i in range(len(px)):
jointName = jointNames[i]
# Swap y an z axis
x = px[i] - px[0]
y = pz[i] - pz[0]
z = py[i] - py[0] # swap y and z
z = -z
loc[jointName] = mathutils.Vector((x, y, z))
# Add tail
Retarget.addTail(loc)
# Move the shoulder down
# Do this before length normalization, because this operation can affect bone length
vec = loc['neck'] - loc['root']
offset = vec * 0.32
# offset = -vec * 0
# offJoints = ['shoulder.l', 'shoulder.r', 'elbow.l', 'elbow.r', 'wrist.l', 'wrist.r']
offJoints = ['neck', 'headTop']
for j in offJoints:
loc[j] += offset
# Make the bone length fit the human model
if retarget:
loc = Retarget.retargetJointLocation(loc)
return loc
def readJointLocCSV(cls, csvFile, retarget=True):
'''
Return a dictionary containing location for each joint
'''
csvFile = os.path.expanduser(csvFile)
if not os.path.isfile(csvFile):
L.error('Joint file %s does not exist.' % csvFile)
return None
L.info('Read joint information from csv file %s' % csvFile)
px = []; py = []; pz = []; jointNames = []
with open(csvFile) as f:
headline = f.readline()
assert(headline.strip().lower() == 'name,x,y,z')
line = f.readline()
while line:
[name,x,y,z] = line.strip().split(',')
px.append(float(x))
py.append(float(y))
pz.append(float(z))
jointNames.append(name)
line = f.readline()
# Load joint location from csv file.
loc = {}
for i in range(len(px)):
jointName = jointNames[i]
# Swap y an z axis
x = px[i] - px[0]
y = pz[i] - pz[0]
z = py[i] - py[0] # swap y and z
z = -z
loc[jointName] = mathutils.Vector((x, y, z))
# Add tail
Retarget.addTail(loc)
# Move the shoulder down
# Do this before length normalization, because this operation can affect bone length
vec = loc['neck'] - loc['root']
offset = vec * 0.32
# offset = -vec * 0
# offJoints = ['shoulder.l', 'shoulder.r', 'elbow.l', 'elbow.r', 'wrist.l', 'wrist.r']
offJoints = ['neck', 'headTop']
for j in offJoints:
loc[j] += offset
# Make the bone length fit the human model
if retarget:
loc = Retarget.retargetJointLocation(loc)
return loc
def run(taskfile, scenefile):
if not tenon.inblender():
import tenon.logging as L
blender = tenon.setting.blender
# cmd = '%s %s --background --python %s > /dev/null 2>&1' % (blender, scenefile, taskfile)
cmd = '%s %s --background --python %s >> blender_stdout.log' % (blender, scenefile, taskfile)
# Redirect the output of blender. The default output is not very useful
L.info('Start tenon on scene file %s', 'file://%s' % os.path.abspath(scenefile))
print(cmd)
os.system(cmd)
def main():
import tenon.logging as L
import bpy
camera = tenon.obj.get('Camera')
# check demo.blend to get the camera name
for i in range(10):
camera.location.x += 1
# L.info(camera.location)
figname = './data/demo/%04d.png' % i
tenon.render.write(figname)
# TODO: Generate html doc from source code
L.info('Write to %s successful' % figname)
def check_texture():
import bpy, os
texs = bpy.data.textures
def check_exist(file):
return os.path.isfile(file)
for t in texs:
L.info('Check texture {}'.format(t))
if isinstance(t, bpy.types.ImageTexture) and t.image.source == 'FILE':
filepath = bpy.path.abspath(t.image.filepath)
exist = check_exist(filepath)
L.info('File {} exist {}'.format(t.image.filepath, exist))
def main():
import tenon.logging as L
import bpy
camera = tenon.obj.get('Camera')
# check demo.blend to get the camera name
for i in range(10):
camera.location.x += 1
# L.info(camera.location)
figname = './data/demo/%04d.png' % i
tenon.render.write(figname)
# TODO: Generate html doc from source code
L.info('Write to %s successful' % figname)
def postprocess_img(i, az):
import matplotlib.pyplot as plt
import numpy as np
import tenon.logging as L
filename_no_ext = '%04d_az%d' % (i, az)
joint_file = os.path.join(output_dir, 'full', 'joints', '%s.csv' % filename_no_ext)
fullimg_filename = os.path.join(output_dir, 'full', 'imgs', '%s.png' % filename_no_ext)
# cropped_filename = os.path.join(output_dir, 'cropped', '%s.png' % filename_no_ext)
depth_filename = os.path.join(output_dir, 'full', 'depth', '%s.png' % filename_no_ext)
X = []; Y = []; labels = []
with open(joint_file) as f:
for l in f:
[label, x, y] = l.strip().split(',')
X.append(int(x))
Y.append(int(y))
labels.append(label)
L.debug('label %s, x: %s, y: %s', label, x, y)
# Get scale and translation parameters
depth = plt.imread(depth_filename);
depth = depth[:,:,0]
fullimg = plt.imread(fullimg_filename)
orig_coords = np.array([X, Y]).T
cropper = lsppose.Cropper()
[cropped_img, cropped_coords] = cropper.crop_img(fullimg, depth, orig_coords, labels)
combine_img = cropper.add_bg(cropped_img)
L.info('Crop file %s', fullimg_filename)
if L.isDebug() and False:
plt.subplot(1,2,1)
plt.imshow(cropped_img)
plt.subplot(1,2,2)
plt.imshow(combine_img)
plt.show()
cropped_filename = os.path.join(output_dir, 'crop', 'nobg', '%s.png' % filename_no_ext)
cropped_filename_bg = os.path.join(output_dir, 'crop', 'imgs', '%s.png' % filename_no_ext)
cropped_joints = os.path.join(output_dir, 'crop', 'joints', '%s.csv' % filename_no_ext)
plt.imsave(cropped_filename, cropped_img)
plt.imsave(cropped_filename_bg, combine_img)
L.info('Cropped image %s\nCropped joint info %s', L.prettify_filename(cropped_filename_bg), \
L.prettify_filename(cropped_joints))
L.debug('Shape of cropped_coords %s', cropped_coords.shape)
with open(cropped_joints, 'w') as f:
for i in range(cropped_coords.shape[0]):
f.write('%.2f,%.2f\n' % (cropped_coords[i,0], cropped_coords[i,1]))
def check_texture():
import bpy, os
texs = bpy.data.textures
def check_exist(file):
return os.path.isfile(file)
for t in texs:
L.info('Check texture {}'.format(t))
if isinstance(t, bpy.types.ImageTexture) and t.image.source == 'FILE':
filepath = bpy.path.abspath(t.image.filepath)
exist = check_exist(filepath)
L.info('File {} exist {}'.format(t.image.filepath, exist))
def render_scene(self, output_dir, filename_no_ext):
imgfilename = os.path.join(output_dir, 'imgs/%s.png' % filename_no_ext)
L.info('Render file to %s', L.prettify_filename(imgfilename))
tenon.render.write(imgfilename)
depth_filename = os.path.join(output_dir, 'depth/%s.png' % filename_no_ext)
tenon.render.DepthMode.enable()
tenon.render.write(depth_filename)
tenon.render.DepthMode.disable()
paint_filename = os.path.join(output_dir, 'parts/%s.png' % filename_no_ext)
tenon.render.PaintMode.enable(models.humanModel())
tenon.render.write(paint_filename)
# Also save the joint annotation and part annotation
joint_filename = os.path.join(output_dir, 'joints/%s.csv' % filename_no_ext)
joints = JointInfo.export()
JointInfo.serializeJointInfo(joint_filename, joints)
def blender():
lsppose_ = lsppose.Util()
lsppose_.rootdir = rootdir
import tenon.util as U
L.info('Switch logging level to INFO')
camera = tenon.obj.get('Camera') # Unused in this demo
scene = lsppose_.setup_scene()
radius = camera.location.length # Keep the radius fixed
el = 0
for i in range(143, 2001):
lsppose_.update_scene(scene, i)
for az in range(0, 360, 90):
loc = U.sphere_location(radius, az, el)
camera.location = loc
filename_no_ext = '%04d_az%d' % (i, az)
lsppose_.render_scene(os.path.join(output_dir, 'full'), filename_no_ext)
def main():
util = lsppose.Util()
models = lsppose.Models()
util.rootdir = rootdir
import tenon.logging as L
# outputdir = '//../cache/lsp_synthesized'
L.setLevel(tenon.logging.INFO)
L.info('Switch logging level to INFO')
tenon.render.write('init.png')
camera = tenon.obj.get('Camera') # Unused in this demo
scene = util.setup_scene()
objs = [
# models.humanModel(),
models.bodyMesh(),
models.upperCloth(),
models.lowerCloth(),
models.hair(),
models.eye()
]
schedule_idx = range(1, 20001)
schedule_idx = [73, 2]
for i in schedule_idx:
util.update_scene(scene, i)
imgfilename = os.path.join(outputdir, 'imgs/%04d.png' % i)
tenon.render.write(imgfilename)
L.info('Synthetic image: %s' % L.prettify_filename(imgfilename))
depth_filename = os.path.join(outputdir, 'depth/%04d.png' % i)
tenon.render.DepthMode.enable()
tenon.render.write(depth_filename)
L.info('Depth: %s' % L.prettify_filename(depth_filename))
tenon.render.DepthMode.disable()
paint_filename = os.path.join(outputdir, 'parts/%04d.png' % i)
for obj in objs:
tenon.render.PaintMode.enable(obj)
tenon.render.write(paint_filename)
L.info('Semantic parts: %s' % L.prettify_filename(paint_filename))
for obj in objs:
tenon.render.PaintMode.disable(obj)
# Also save the joint annotation and part annotation
joint_filename = os.path.join(outputdir, 'joints/%04d.csv' % i)
joints = lsppose.JointInfo.export()
lsppose.JointInfo.serializeJointInfo(joint_filename, joints)
def main():
util = lsppose.Util()
models = lsppose.Models()
util.rootdir = rootdir
import tenon.logging as L
# outputdir = '//../cache/lsp_synthesized'
L.setLevel(tenon.logging.INFO)
L.info('Switch logging level to INFO')
tenon.render.write('init.png')
camera = tenon.obj.get('Camera') # Unused in this demo
scene = util.setup_scene()
objs = [
# models.humanModel(),
models.bodyMesh(),
models.upperCloth(),
models.lowerCloth(),
models.hair(),
models.eye()
]
schedule_idx = range(1, 20001)
schedule_idx = [73, 2]
for i in schedule_idx:
util.update_scene(scene, i)
imgfilename = os.path.join(outputdir, 'imgs/%04d.png' % i)
tenon.render.write(imgfilename)
L.info('Synthetic image: %s' % L.prettify_filename(imgfilename))
depth_filename = os.path.join(outputdir, 'depth/%04d.png' % i)
tenon.render.DepthMode.enable()
tenon.render.write(depth_filename)
L.info('Depth: %s' % L.prettify_filename(depth_filename))
tenon.render.DepthMode.disable()
paint_filename = os.path.join(outputdir, 'parts/%04d.png' % i)
for obj in objs:
tenon.render.PaintMode.enable(obj)
tenon.render.write(paint_filename)
L.info('Semantic parts: %s' % L.prettify_filename(paint_filename))
for obj in objs:
tenon.render.PaintMode.disable(obj)
# Also save the joint annotation and part annotation
joint_filename = os.path.join(outputdir, 'joints/%04d.csv' % i)
joints = lsppose.JointInfo.export()
lsppose.JointInfo.serializeJointInfo(joint_filename, joints)
def render_scene(self, output_dir, filename_no_ext):
imgfilename = os.path.join(output_dir,
'imgs/%s.png' % filename_no_ext)
L.info('Render file to %s', L.prettify_filename(imgfilename))
tenon.render.write(imgfilename)
depth_filename = os.path.join(output_dir,
'depth/%s.png' % filename_no_ext)
tenon.render.DepthMode.enable()
tenon.render.write(depth_filename)
tenon.render.DepthMode.disable()
paint_filename = os.path.join(output_dir,
'parts/%s.png' % filename_no_ext)
tenon.render.PaintMode.enable(models.humanModel())
tenon.render.write(paint_filename)
# Also save the joint annotation and part annotation
joint_filename = os.path.join(output_dir,
'joints/%s.csv' % filename_no_ext)
joints = JointInfo.export()
JointInfo.serializeJointInfo(joint_filename, joints)
def main():
import random
import tenon.logging as L
# L.fileLevel(L.DEBUG) # default is ERROR
cachedir = os.path.abspath(os.path.join(rootdir, 'cache/examples/random_light'))
# Set up the light configuration
radius = 12
nLight = 16
z = 10
lamps = [tenon.obj.Lamp.create('light%d' % v) for v in range(10)]
for j in range(10):
for i in range(len(lamps)):
lamp = lamps[i]
# Compute the location of light source, put the light evenly
lampObj = tenon.obj.get(lamp.name)
lampObj.location = tenon.util.sphereLocation(radius, 360 / nLight * i, 0)
lampObj.location[2] += z # Set the z of the light source
lamp.energy = random.gauss(1, 1.5)
filename = os.path.join(cachedir, 'random_lighting_%d.png' % j)
L.info('Rendered file: %s', filename)
tenon.render.write(filename)
import sys, os
rootdir = os.path.expanduser('~/Dropbox/workspace/graphics_for_vision/tenon')
tenonpath = os.path.join(rootdir, 'code/tenon')
pwd = os.path.join(rootdir, 'code/tenon/examples/demo_lsp')
for v in [tenonpath, pwd]:
sys.path.append(os.path.expanduser(v))
import tenon
import lsppose
# output_dir = os.path.join(rootdir, 'cache/lsp_synthesized/circular_demo')
output_dir = os.path.expanduser('~/nosync/circular_demo/')
import tenon.logging as L
L.setLevel(L.INFO)
# L.setLevel(L.DEBUG)
L.info('Output folder is %s', L.prettify_filename(output_dir))
def blender():
lsppose_ = lsppose.Util()
lsppose_.rootdir = rootdir
import tenon.util as U
L.info('Switch logging level to INFO')
camera = tenon.obj.get('Camera') # Unused in this demo
scene = lsppose_.setup_scene()
radius = camera.location.length # Keep the radius fixed
el = 0
def main():
util = lsppose.Util()
models = lsppose.Models()
util.rootdir = rootdir
import tenon.logging as L
# outputdir = '//../cache/lsp_synthesized'
L.setLevel(tenon.logging.INFO)
L.info('Switch logging level to INFO')
tenon.render.write('init.png')
camera = tenon.obj.get('Camera') # Unused in this demo
scene = util.setup_scene()
objs = [
# models.humanModel(),
models.bodyMesh(),
models.upperCloth(),
models.lowerCloth(),
models.hair(),
models.eye()
]
schedule_idx = range(1, 20001)
schedule_idx = [2]
for i in schedule_idx:
util.update_scene(scene, i)
# imgfilename = os.path.join(outputdir, 'imgs/%04d.png' % i)
# tenon.render.write(imgfilename)
# L.info('Synthetic image: %s' % L.prettify_filename(imgfilename))
# depth_filename = os.path.join(outputdir, 'depth/%04d.png' % i)
# tenon.render.DepthMode.enable()
# tenon.render.write(depth_filename)
# L.info('Depth: %s' % L.prettify_filename(depth_filename))
# tenon.render.DepthMode.disable()
# paint_filename = os.path.join(outputdir, 'parts/%04d.png' % i)
# for obj in objs:
# tenon.render.PaintMode.enable(obj)
# tenon.render.write(paint_filename)
# L.info('Semantic parts: %s' % L.prettify_filename(paint_filename))
# for obj in objs:
# tenon.render.PaintMode.disable(obj)
# # Also save the joint annotation and part annotation
# joint_filename = os.path.join(outputdir, 'joints/%04d.csv' % i)
# joints = lsppose.JointInfo.export()
# lsppose.JointInfo.serializeJointInfo(joint_filename, joints)
import tenon.util as U
radius = camera.location.length # Keep the radius fixed
el = 0
# for angle in [0, 90, 180, 270]:
# for angle in range(0, 360, 10):
for angle in [240]:
for el in range(0, 90, 10):
loc = U.sphere_location(radius, angle, el)
camera.location = loc
imgfilename = os.path.join(outputdir, 'imgs/%04d_%d_%d.png' % (i, angle, el))
tenon.render.write(imgfilename)
L.info('Synthetic image: %s' % L.prettify_filename(imgfilename))
def main():
out_folders = {
'image': './lsp/images',
'depth': './lsp/depth',
'semantic': './lsp/semantic',
'2dpose': './lsp/2dpose'
}
for path in out_folders.values():
if not os.path.exists(path):
os.makedirs(path)
util = lsppose.Util()
models = lsppose.Models()
L.setLevel(tenon.logging.INFO)
L.info('Switch logging level to INFO')
L.info('Write a test image to init.png')
tenon.render.write('lsp/init.png')
camera = tenon.obj.get('Camera') # Unused in this demo
scene = util.setup_scene()
objs = [
# models.humanModel(),
models.bodyMesh(),
models.upperCloth(),
models.lowerCloth(),
models.hair(),
models.eye()
]
schedule_idx = range(1, 2001)
schedule_idx = [73]
for pose_id in schedule_idx:
posefolder = '../data/2015101415_v2'
util.update_scene(posefolder, scene, pose_id)
imgfilename = os.path.join(out_folders['image'], '%04d.png' % pose_id)
tenon.render.write(imgfilename)
L.info('Synthetic image: %s' % L.prettify_filename(imgfilename))
depth_filename = os.path.join(out_folders['depth'], '%04d.png' % pose_id)
tenon.render.DepthMode.enable()
tenon.render.write(depth_filename)
L.info('Depth: %s' % L.prettify_filename(depth_filename))
tenon.render.DepthMode.disable()
paint_filename = os.path.join(out_folders['semantic'], '%04d.png' % pose_id)
for obj in objs:
tenon.render.PaintMode.enable(obj)
tenon.render.write(paint_filename)
L.info('Semantic parts: %s' % L.prettify_filename(paint_filename))
for obj in objs:
tenon.render.PaintMode.disable(obj)
# Also save the joint annotation and part annotation
joint_filename = os.path.join(out_folders['2dpose'], '%04d.csv' % pose_id)
joints = lsppose.JointInfo.export()
lsppose.JointInfo.serializeJointInfo(joint_filename, joints)
L.info('2D joint location: %s' % L.prettify_filename(joint_filename))