def retrack_remap_rbfn():
grip_dir = os.environ['GRIP_DATA']
movie_fn,_ = QApp.app.loadFilename('Choose a movie to open', grip_dir, 'Movie Files (*.mp4 *.mov *.avi *.flv *.mpg)')
txt_fn,_ = QApp.app.loadFilename('Choose a text file of frame indices to open', grip_dir, 'Text Files (*.txt)')
md = MovieReader.open_file(movie_fn, audio=False)
lines = map(str.strip,(open(txt_fn,'r').readlines()))
mapping_file = {}
for l in lines:
pose_name,frame_number,group_names = l.split(':')
for gn in group_names.split(','):
mapping_file.setdefault(gn,{})[pose_name] = int(frame_number)
print mapping_file.keys()
print mapping_file
update_rbfn(md, mapping_file=mapping_file)
def dirty_cb(dirty):
if '/root/ui/attrs/movie_filename' in dirty:
fn = State.getKey('/root/ui/attrs/movie_filename')
global g_md
g_md = MovieReader.open_file(fn)
QApp.app.qtimeline.setRange(0,g_md['vmaxframe'])
for dk in dirty:
if dk.startswith('/root/ui/attrs/'):
QApp.app.refresh()
global g_mode, g_frame, g_rbfn
if g_mode == 1 and not '/root/sliders/attrs' in dirty: # RBFN view; changing frame sets all the sliders; we avoid that case
for key in dirty:
if key.startswith('/root/sliders/attrs'):
si = g_rbfn['slider_names'].index(key[len('/root/sliders/attrs/'):])
group,gn,pn,slider_indices,slider_names,pose_splits = rbfn_info_from_frame(g_frame[g_mode])
print 'rbfn slider value changed:',key,si,'from',group['slider_data'][pn][si],'to',State.getKey(key)
group['slider_data'][pn][si] = State.getKey(key)
rbfn_view_cb(g_frame[g_mode]) # TODO, force an update of the geo
def import_movie_frames():
movie_fn, _ = QApp.app.loadFilename(
'Choose a movie to open', cwd(),
'Movie Files (*.mp4 *.mov *.avi *.flv *.mpg)')
if movie_fn == '': return # cancel
set_cwd(movie_fn)
txt_fn, _ = QApp.app.loadFilename(
'Choose a text file of frame indices to open', cwd(),
'Text Files (*.txt)')
md = MovieReader.open_file(movie_fn, audio=False)
images, shapes = [], []
if txt_fn == '':
frames = range(0, md['vmaxframe'], 100)
#if txt_fn == '': frames = range(30000, 38300, 100)
else:
frames = [int(l.split(':')[1]) for l in open(txt_fn, 'r').readlines()]
for fi in frames:
print fi, '/', frames[-1]
MovieReader.readFrame(md, fi)
add_image(
np.frombuffer(md['vbuffer'],
dtype=np.uint8).reshape(md['vheight'], md['vwidth'],
3).copy())
State.push('Import movie frames')
def generateSkeleton(cacheId=622,
x3d_filename='',
perc=0.9,
triangleThreshold=1000,
thresholdDistance=25.,
useFrames=range(0, 514),
numComps=30,
labelGraphThreshold=4,
stepSize=1):
directory = os.path.join(os.environ['GRIP_DATA'],
'140113_A2_GRIP_GenPeople')
c3d_filename = 'ROM.c3d'
cameraB = 7292, 34, (47, 2.3, -0.2), (67, 788, 79)
cameraA = 5384, 49.8, (5.4, 1.1,
-0.7), (4, 1135, 0
) #5045,52,(5.6,0.9,-0.7),(0,1130,0)
camera = cameraA
startFrame = 0
tempDir = os.environ['GRIP_TEMP']
#import logging
#logging.basicConfig(level=logging.DEBUG)
from IO import C3D
graph_out_fn = None
# labelGraphThreshold = 4
# stepSize = 1
if True: # bo data
c = C3D.read(os.path.join(directory, c3d_filename))
c3d_frames, c3d_fps = c['frames'], c['fps']
pointLabels = c['labels']
print 'c3d fps = ', c3d_fps
numFramesVisiblePerPoint = np.sum(c3d_frames[:, :, 3] == 0, axis=0)
numPointsVisiblePerFrame = np.sum(c3d_frames[:, :, 3] == 0, axis=1)
print 'threshold', 0.40 * len(c3d_frames)
goodPoints = np.where(
numFramesVisiblePerPoint > 0.90 * len(c3d_frames))[0]
goodFrames = np.where(
np.sum(c3d_frames[:, goodPoints,
3] == 0, axis=1) == len(goodPoints))[0]
print len(goodPoints), len(
goodFrames) # 290 x 6162 (80%), 283 x 8729 (90%), 275x10054 (96%)
frames = c3d_frames[goodFrames, :, :][:, goodPoints, :][:, :, :3]
pointLabels = [pointLabels[g] for g in goodPoints]
#badPoint = pointLabels.index('BoDense:A_Neck_1')
data_fn = 'W90-28-10.IO'
skel_out_fn = None
triangleThreshold = 1000.
else: # orn data
# cacheId = 622
# perc = 0.9
# triangleThreshold = 1000. # Bone threshold
# thresholdDistance = 25. # Joint threshold
# useFrames = range(2370, 3500)
# useFrames = range(2650, 3500)
# useFrames = range(2600, 3480)
# useFrames = range(2650, 3480)
# useFrames = range(0, 2000)
# useFrames = range(0, 514)
#useFrames = [] #range(0, 1000)
#useFrames.extend(range(2650, 3480))
#useFrames.extend(range(4824, 5253))
# numComps = 30
# useFrames = range(0, 333)
# useFrames = range(4824, 5253)
print 'CacheId:', cacheId
print 'Good point percentage:', perc
print 'Triangle threshold:', triangleThreshold
print 'Distance threshold:', thresholdDistance
print 'Frames:', useFrames[0], '-', useFrames[-1]
_, x3d_data = IO.load(x3d_filename)
data_fn = 'W90-28-8.romtracks_T%d.IO' % cacheId
location = '/root/tracks'
# location = '/root/skeleton/reconstruction/collection/c3ds'
c3d_frames = x3d_data[location]['x3ds']
print c3d_frames.shape
c3d_frames = np.transpose(c3d_frames, axes=(1, 0, 2))
#frames = frames[:, blueIds, :]
print c3d_frames.shape
pointLabels = x3d_data[location]['x3ds_labels']
if False:
goodPoints = np.arange(c3d_frames.shape[1])
goodFrames = np.arange(len(c3d_frames))
else:
numFramesVisiblePerPoint = np.sum(c3d_frames[useFrames, :, 3] == 0,
axis=0)
numPointsVisiblePerFrame = np.sum(c3d_frames[useFrames, :, 3] == 0,
axis=1)
goodPoints = np.where(
numFramesVisiblePerPoint > perc * len(useFrames))[0]
goodFrames = np.where(
np.sum(c3d_frames[:, goodPoints,
3] == 0, axis=1) == len(goodPoints))[0]
print '# Good points: %d | # Good frames: %d' % (len(goodPoints),
len(goodFrames))
print goodFrames[:4]
frames = c3d_frames[goodFrames, :, :][:, goodPoints, :][:, :, :3]
pointLabels = [int(pointLabels[g]) for g in goodPoints]
skel_out_fn = None
graph_out_fn = None
data = frames[::stepSize, :, :].copy()
first_time_only = not os.path.exists(os.path.join(tempDir, data_fn))
if first_time_only: # generate the file
M = ASFReader.greedyTriangles(
data,
numComps,
triangleThreshold=triangleThreshold,
thresholdDistance=thresholdDistance**2) # only every Nth frame
IO.save(os.path.join(tempDir, 'M90_T%d.IO' % cacheId), M)
_, M = IO.load(os.path.join(tempDir, 'M90_T%d.IO' % cacheId))
stabilizedPointToGroup, stabilizedPointResiduals, stabilizedFrames = ASFReader.assignAndStabilize(
data,
M['RTs'][M['triIndices'][:28]],
thresholdDistance=thresholdDistance**2)
W = {
'stabilizedPointToGroup': stabilizedPointToGroup,
'stabilizedPointResiduals': stabilizedPointResiduals,
'stabilizedFrames': stabilizedFrames
}
IO.save(os.path.join(tempDir, data_fn), W)
else:
_data = IO.load(os.path.join(tempDir, data_fn))[1]
stabilizedPointToGroup = _data['stabilizedPointToGroup']
stabilizedPointResiduals = _data['stabilizedPointResiduals']
stabilizedFrames = _data['stabilizedFrames']
print 'numFrames = %d' % len(stabilizedFrames)
print 'number of labelled points %d' % np.sum(stabilizedPointToGroup != -1)
print 'RMS of labelled points %fmm' % np.sqrt(
np.mean(
stabilizedPointResiduals[np.where(stabilizedPointToGroup != -1)]))
first_time_only = True
print stabilizedPointToGroup
num_groups = max(stabilizedPointToGroup) + 1
stabilized_groups = [
np.where(stabilizedPointToGroup == gi)[0] for gi in range(num_groups)
]
if first_time_only:
if True: # tighten the fit
# thresh = [10,10,9,9] #,10,10,9,7,9,9,6,9,9,9,]
thresh = [
thresholdDistance, thresholdDistance, thresholdDistance - 1,
thresholdDistance - 1, thresholdDistance - 2,
thresholdDistance - 2
]
# thresh = [20, 20, 19, 19, 10, 10, 9, 9]
for t in thresh:
#stabilizedPointToGroup[badPoint] = -1 # unlabel
RTs = ASFReader.stabilizeAssignment(data,
stabilizedPointToGroup)
stabilizedPointToGroup, stabilizedPointResiduals, stabilizedFrames = ASFReader.assignAndStabilize(
data, RTs, thresholdDistance=float(t)**2)
print 'number of labelled points %d' % np.sum(
stabilizedPointToGroup != -1)
print 'RMS of labelled points %fmm' % np.sqrt(
np.mean(stabilizedPointResiduals[np.where(
stabilizedPointToGroup != -1)]))
else:
RTs = ASFReader.stabilizeAssignment(data, stabilizedPointToGroup)
stabilizedPointToGroup, stabilizedPointResiduals, stabilizedFrames = ASFReader.assignAndStabilize(
data, RTs, thresholdDistance=10.**2)
global animJoints, stablePointsGroups, displayFrames, groupRepresentatives
stablePointsData = ASFReader.sharedStablePoints(RTs, threshold=3.**2)
stablePointsGroups = [sp[0] for sp in stablePointsData]
stablePoints = np.array([sp[2] for sp in stablePointsData],
dtype=np.float32)
print 'num stable points', len(stablePoints)
def residual(gi, leaf_indices, RTs):
'''given a group and a list of attachment points, choose the best attachment point and return the residual.'''
tmp = [(ASFReader.transform_pair_residual(RTs[gi], RTs[gj]), gj)
for gj in leaf_indices]
return min(tmp)
# make a skeleton from stabilizedPointToGroup
root_group = 0
leaf_nodes = set([root_group])
skel_group_indices = [root_group]
skel_joint_parents = [-1]
groups = set(range(stabilizedPointToGroup.max() + 1))
groups.remove(root_group)
RTs = ASFReader.stabilizeAssignment(data, stabilizedPointToGroup)
joints = []
joints.append(np.mean(data[0, stabilized_groups[root_group]], axis=0))
bones = []
bones.append([])
G = np.eye(3, 4, dtype=np.float32)
G[:, 3] = np.mean(data[0, stabilized_groups[root_group]], axis=0)
Gs = [G]
while groups:
residuals = [(residual(gi, leaf_nodes, RTs), gi) for gi in groups]
(((res, O), parent), group) = min(residuals)
groups.remove(group)
leaf_nodes.add(group)
skel_group_indices.append(group)
pi = skel_group_indices.index(parent)
skel_joint_parents.append(pi)
joint_world = np.float32(O)
joints.append(joint_world)
bones.append([np.mean(data[0, stabilized_groups[group]], axis=0) - O])
bones[pi].append(joint_world - joints[pi])
print group, parent
G = np.eye(3, 4, dtype=np.float32)
G[:, 3] = O
Gs.append(G)
print skel_group_indices
print skel_joint_parents
numJoints = len(skel_joint_parents)
jointNames = map(str, skel_group_indices)
jointIndex = dict(zip(jointNames, range(len(jointNames))))
jointParents = skel_joint_parents
jointChans = [0, 1, 2] + [3, 4, 5] * numJoints
jointChanSplits = [0, 3, 6]
for x in range(numJoints - 1):
jointChanSplits.append(jointChanSplits[-1])
jointChanSplits.append(jointChanSplits[-1] + 3)
dofNames = [
jointNames[ji] +
[':tx', ':ty', ':tz', ':rx', ':ry', ':rz'][jointChans[di]]
for ji in range(numJoints)
for di in range(jointChanSplits[2 * ji], jointChanSplits[2 * ji + 2])
]
numDofs = len(dofNames)
def mult_inv(Gs_pi, Gs_gi):
# Gs_pi^-1 Gs_gi = Ls_gi
R = np.linalg.inv(Gs_pi[:3, :3])
ret = np.dot(R, Gs_gi)
ret[:, 3] -= np.dot(R, Gs_pi[:, 3])
return ret
Ls = np.float32([
mult_inv(Gs[pi], Gs[gi]) if pi != -1 else Gs[gi]
for gi, pi in enumerate(skel_joint_parents)
])
Bs = bones
print map(len, Bs)
markerParents = [
skel_group_indices.index(gi) for gi in stabilizedPointToGroup
if gi != -1
]
markerNames = [('%d' % pi) for pi, gi in enumerate(stabilizedPointToGroup)
if gi != -1]
labelNames = [('%d' % pointLabels[pi])
for pi, gi in enumerate(stabilizedPointToGroup) if gi != -1]
markerOffsets = [
np.dot(Gs[skel_group_indices.index(gi)][:3, :3].T,
data[0][pi] - Gs[skel_group_indices.index(gi)][:3, 3])
for pi, gi in enumerate(stabilizedPointToGroup) if gi != -1
]
skel_dict = {
'name': 'skeleton',
'numJoints': int(numJoints),
'jointNames': jointNames, # list of strings
'jointIndex': jointIndex, # dict of string:int
'jointParents': np.int32(jointParents),
'jointChans': np.int32(jointChans), # 0 to 5 : tx,ty,tz,rx,ry,rz
'jointChanSplits': np.int32(jointChanSplits),
'chanNames': dofNames, # list of strings
'chanValues': np.zeros(numDofs, dtype=np.float32),
'numChans': int(numDofs),
'Bs': Bs,
'Ls': np.float32(Ls),
'Gs': np.float32(Gs),
'markerParents': np.int32(markerParents),
'markerNames': markerNames,
'markerOffsets': np.float32(markerOffsets),
'markerWeights': np.ones(len(markerNames), dtype=np.float32),
'rootMat': np.eye(3, 4, dtype=np.float32),
'labelNames': labelNames
}
if graph_out_fn is not None and labelGraphThreshold != -1:
print 'Generating labelling graph...'
from GCore import Label as GLabel
c3d_data = c3d_frames[goodFrames, :, :][:, goodPoints, :][:, :, :]
c3d_data = c3d_data[::stepSize, :, :]
# graph = GLabel.graph_from_c3ds(skel_dict, markerNames, c3d_data, threshold=3)
graph = GLabel.graph_from_c3ds(skel_dict,
markerNames,
c3d_data,
threshold=labelGraphThreshold)
IO.save(graph_out_fn, {'/root/graph': {'label_graph': graph}})
print 'Labelling graph saved to:', graph_out_fn
if skel_out_fn is not None: IO.save(skel_out_fn, skel_dict)
def test_skeleton(sd):
'''TODO, write some code to verify that a dict actually is a skeleton.'''
assert isinstance(sd['name'], str), 'name key should be a string'
numJoints = sd['numJoints']
assert isinstance(numJoints, int), 'numJoints key should be an int'
animJoints = None
showStabilized = False
if showStabilized:
displayFrames = stabilizedFrames
pointToGroup = stabilizedPointToGroup
else: # show animated
displayFrames = frames #c3d_frames[:,:,:3]
displayLabels = pointLabels
if first_time_only: # generate the file
framesRTs = ASFReader.stabilizeAssignment(displayFrames,
stabilizedPointToGroup)
IO.save(
os.path.join(tempDir, 'tmp90-28.IO'), {
'framesRTs': framesRTs,
'stabilizedPointToGroup': stabilizedPointToGroup,
'stablePoints': stablePoints,
'stablePointsGroups': stablePointsGroups
})
for k, v in IO.load(os.path.join(tempDir,
'tmp90-28.IO'))[1].iteritems():
locals()[k] = v
animJoints = ASFReader.unstabilize(stablePoints,
framesRTs[stablePointsGroups])
print 'animJoints shape', animJoints.shape
pointToGroup = -np.ones(displayFrames.shape[1], dtype=np.int32)
print goodPoints.shape, pointToGroup.shape, stabilizedPointToGroup.shape
pointToGroup = stabilizedPointToGroup
#pointToGroup[goodPoints] = stabilizedPointToGroup # for displayFrames = c3d_frames[:,:,:3]
groupRepresentatives = ASFReader.groupRepresentatives(
data, stabilizedPointToGroup)
numJoints = len(stablePoints)
boneEdges = np.array(range(2 * numJoints), dtype=np.int32)
boneVertices = np.zeros((numJoints * 2, 3), dtype=np.float32)
boneVertices[::2] = stablePoints
boneVertices[1::2] = displayFrames[
0, groupRepresentatives[stablePointsGroups]]
#import cv2
#movie = cv2.VideoCapture(directory+movieFilename)
#frameOk, frameData = movie.read()
#global md
#md = {'buffer':frameData, 'height':frameData.shape[0], 'width':frameData.shape[1]}
global app, win, view, frame, points, joints, bones
app = QtGui.QApplication(sys.argv)
app.setStyle('plastique')
win = QtGui.QMainWindow()
win.setFocusPolicy(QtCore.Qt.StrongFocus) # get keyboard events
win.setWindowTitle('Imaginarium Skeleton Reconstruction Test %d' % cacheId)
panel = GViewer.QGLPanel()
view = panel.view
view.setMinimumWidth(640)
view.setMinimumHeight(480)
win.setCentralWidget(panel)
timelineDock = QtGui.QDockWidget('Timeline')
timeline = UI.QTimeline(win)
timeline.cb = setFrame
timeline.setRange(0, goodFrames[-1])
timelineDock.setWidget(timeline)
timelineDock.setFeatures(QtGui.QDockWidget.DockWidgetMovable
| QtGui.QDockWidget.DockWidgetFloatable)
frame = startFrame
view.addCamera(UI.QGLViewer.Camera('default'))
grid = GLGrid()
view.primitives.append(grid)
points = GLPoints3D(displayFrames[frame])
from colorsys import hsv_to_rgb
colorTable = np.array([
hsv_to_rgb((h * 0.618033988749895) % 1, 0.5, 0.95)
for h in xrange(max(pointToGroup) + 2)
],
dtype=np.float32)
colorTable[-1] = 0
points.colours = colorTable[pointToGroup]
#points.names = displayLabels
#points.pointSize = 3
view.primitives.append(points)
joints = GLPoints3D(stablePoints)
joints.names = map(str, xrange(len(stablePoints)))
view.primitives.append(joints)
bones = GLBones(boneVertices, boneEdges)
view.primitives.append(bones)
win.addDockWidget(QtCore.Qt.BottomDockWidgetArea, timelineDock)
win.show()
global md, img, g_detectingDots, g_readingMovie
md, img, g_detectingDots = None, None, False
g_readingMovie = False
if g_readingMovie:
md = MovieReader.open_file(os.path.join(directory, movieFilename))
img = np.frombuffer(md['vbuffer'],
dtype=np.uint8).reshape(md['vheight'],
md['vwidth'], 3)
view.setImageData(md['vbuffer'], md['vheight'], md['vwidth'], 3)
global allSkels
allSkels = []
app.connect(app, QtCore.SIGNAL('lastWindowClosed()'), app.quit)
sys.exit(app.exec_())
def import_movie_cb():
grip_dir = os.environ['GRIP_DATA']
movie_fn, _ = QApp.app.loadFilename('Choose a movie to open', grip_dir, 'Movie Files (*.mp4 *.mov *.avi *.flv *.mpg)')
global md
md = MovieReader.open_file(movie_fn)
def main():
global State, mats, movieFilenames, primitives
global movies, primitives2D, deinterlacing, detectingWands
import IO
import sys, os
deinterlacing = False
detectingWands = False
detectingTiara = False
dot_detections = None
detections_filename = None
frame_offsets = None
firstFrame, lastFrame = 0, 5000
drawDotSize = 4.0
fovX, (ox,
oy), pan_tilt_roll, tx_ty_tz, distortion = 50., (0,
0), (0, 0,
0), (0, 1250,
0), (0,
0)
mats = []
grip_directory = os.environ['GRIP_DATA']
if 0:
fovX, (ox, oy), pan_tilt_roll, tx_ty_tz, distortion = 37.9, (0, 0), (
-66.0, 3.5, -0.2), (4850, 1330, 3280), (0, 0) # roughed in
K, RT = Calibrate.composeK(fovX, ox, oy), Calibrate.composeRT(
Calibrate.composeR(pan_tilt_roll), tx_ty_tz, 0)
mat0 = [
K[:3, :3], RT[:3, :4],
np.dot(K, RT)[:3, :], distortion, -np.dot(RT[:3, :3].T, RT[:3, 3]),
[1920, 1080]
]
fovX, (ox, oy), pan_tilt_roll, tx_ty_tz, distortion = 55.8, (0, 0), (
-103.6, 3.5, -0.3), (2980, 1380, -2180), (0, 0) # roughed in
K, RT = Calibrate.composeK(fovX, ox, oy), Calibrate.composeRT(
Calibrate.composeR(pan_tilt_roll), tx_ty_tz, 0)
mat1 = [
K[:3, :3], RT[:3, :4],
np.dot(K, RT)[:3, :], distortion, -np.dot(RT[:3, :3].T, RT[:3, 3]),
[1920, 1080]
]
fovX, (ox, oy), pan_tilt_roll, tx_ty_tz, distortion = 49.3, (0, 0), (
27.9, 4.0, -0.2), (-5340, 1150, 5030), (0, 0) # roughed in
K, RT = Calibrate.composeK(fovX, ox, oy), Calibrate.composeRT(
Calibrate.composeR(pan_tilt_roll), tx_ty_tz, 0)
mat2 = [
K[:3, :3], RT[:3, :4],
np.dot(K, RT)[:3, :], distortion, -np.dot(RT[:3, :3].T, RT[:3, 3]),
[1920, 1080]
]
fovX, (ox, oy), pan_tilt_roll, tx_ty_tz, distortion = 50.6, (0, 0), (
-156.6, 4.9, 0.2), (-105, 1400, -4430), (0, 0) # roughed in
K, RT = Calibrate.composeK(fovX, ox, oy), Calibrate.composeRT(
Calibrate.composeR(pan_tilt_roll), tx_ty_tz, 0)
mat3 = [
K[:3, :3], RT[:3, :4],
np.dot(K, RT)[:3, :], distortion, -np.dot(RT[:3, :3].T, RT[:3, 3]),
[1920, 1080]
]
mats = [mat0, mat1, mat2, mat3]
xcp_filename = '154535_Cal168_Floor_Final.xcp'
directory = os.path.join(grip_directory, 'REFRAME')
movieFilenames = [
'001E0827_01.MP4', '001F0813_01.MP4', '001G0922_01.MP4',
'001H0191_01.MP4'
]
#mats,movieFilenames = mats[:1],movieFilenames[:1] # restrict to single-view
frame_offsets = [119 + 160, 260, 339, 161]
small_blur, large_blur = 1, 25
min_dot_size = 1.0
max_dot_size = 20.0
circularity_threshold = 3.0
threshold_bright, threshold_dark_inv = 250, 250 #135,135
elif 0:
xcp_filename = '201401211653-4Pico-32_Quad_Dialogue_01_Col_wip_01.xcp'
detections_filename = 'detections.dat'
detectingTiara = True
pan_tilt_roll = (0, 0, 90)
distortion = (0.291979, 0.228389)
directory = os.path.join(os.environ['GRIP_DATA'], 'ted')
movieFilenames = [
'201401211653-4Pico-32_Quad_Dialogue_01_%d.mpg' % xi
for xi in range(1)
]
firstFrame = 511
small_blur, large_blur = 1, 20
min_dot_size = 1.0
max_dot_size = 16.0
circularity_threshold = 3.0
threshold_bright, threshold_dark_inv = 0, 170
elif 1:
xcp_filename = '50_Grip_RoomCont_AA_02.xcp'
detections_filename = 'detections.dat'
pan_tilt_roll = (0, 0, 0)
distortion = (0.291979, 0.228389)
directory = os.path.join(os.environ['GRIP_DATA'], '151110')
movieFilenames = ['50_Grip_RoomCont_AA_02.v2.mov']
firstFrame = 0
small_blur, large_blur = 1, 20
min_dot_size = 1.0
max_dot_size = 16.0
circularity_threshold = 3.0
threshold_bright, threshold_dark_inv = 170, 170
attrs = dict([(v, eval(v)) for v in [
'small_blur', 'large_blur', 'threshold_bright', 'threshold_dark_inv',
'circularity_threshold', 'min_dot_size', 'max_dot_size'
]])
primitives2D = QGLViewer.makePrimitives2D(([], []), ([], []))
primitives = []
if len(movieFilenames) is 1:
# TODO: time_base, timecode
K, RT = Calibrate.composeK(fovX, ox, oy), Calibrate.composeRT(
Calibrate.composeR(pan_tilt_roll), tx_ty_tz, 0)
mats = [[
K[:3, :3], RT[:3, :4],
np.dot(K, RT)[:3, :], distortion, -np.dot(RT[:3, :3].T, RT[:3, 3]),
[1920, 1080]
]]
camera_ids = ['video']
movies = [
MovieReader.open_file(os.path.join(directory, movieFilenames[0]),
audio=False)
]
else: # hard coded cameras
if xcp_filename.endswith('.xcp'):
if detectingTiara: # gruffalo
c3d_filename = os.path.join(
directory,
'201401211653-4Pico-32_Quad_Dialogue_01_Col_wip_02.c3d')
from IO import C3D
c3d_dict = C3D.read(c3d_filename)
global c3d_frames
c3d_frames, c3d_fps, c3d_labels = c3d_dict['frames'], c3d_dict[
'fps'], c3d_dict['labels']
c3d_subject = '' #'TedFace'
which = np.where(
[s.startswith(c3d_subject) for s in c3d_labels])[0]
c3d_frames = c3d_frames[:, which, :]
c3d_labels = [c3d_labels[i] for i in which]
print len(c3d_frames)
xcp, xcp_data = ViconReader.loadXCP(
os.path.join(directory, xcp_filename))
mats.extend(xcp)
elif xcp_filename.endswith('.cal'):
from IO import OptitrackReader
xcp, xcp_data = OptitrackReader.load_CAL(
os.path.join(directory, xcp_filename))
mats = xcp
print 'mats', len(mats), len(movieFilenames)
assert (len(mats) == len(movieFilenames))
camera_ids = []
movies = []
for ci, mf in enumerate(movieFilenames):
fo = 0 if frame_offsets is None else frame_offsets[ci]
movies.append(
MovieReader.open_file(os.path.join(directory, mf),
audio=False,
frame_offset=fo))
camera_ids = ['cam_%d' % ci for ci in xrange(len(mats))]
print len(mats), len(movies), len(camera_ids)
primitives.append(GLPoints3D([]))
primitives.append(GLPoints3D([]))
primitives.append(GLPoints3D([]))
primitives[0].colour = (0, 1, 1, 0.5) # back-projected "cyan" points
primitives[1].colour = (0, 0, 1, 0.5)
primitives[1].pointSize = 5
primitives[2].colour = (1, 0, 0, 0.99)
if len(movieFilenames) != 1 and detections_filename != None:
try:
dot_detections = IO.load(detections_filename)[1]
except:
numFrames = len(c3d_frames) # TODO HACK HACK
dot_detections = movies_to_detections(movies, range(numFrames),
deinterlacing, attrs)
IO.save(detections_filename, dot_detections)
if detectingTiara:
x3ds_seq = {}
for fi in dot_detections.keys():
frame = c3d_frames[(fi - 55) % len(c3d_frames)]
which = np.array(np.where(frame[:, 3] == 0)[0], dtype=np.int32)
x3ds_seq[fi] = np.concatenate((VICON_tiara_x3ds + np.array([150,-100,0],dtype=np.float32),frame[which,:3])), \
np.concatenate((np.arange(len(VICON_tiara_x3ds),dtype=np.int32),which+len(VICON_tiara_x3ds)))
dot_labels = get_labels(dot_detections.keys(),
x3ds_seq,
dot_detections,
mats,
x2d_threshold=0.05)
calibration_fi = 546 - 2 - 6
RT = tighten_calibration(x3ds_seq[calibration_fi],
dot_labels[calibration_fi], mats)
for v in c3d_frames:
v[:, :3] = np.dot(v[:, :3], RT[:3, :3].T) + RT[:, 3]
if True:
dot_detections = IO.load(detections_filename)[1]
x3ds_seq = {}
for fi in dot_detections.keys():
frame = c3d_frames[(fi - 55) % len(c3d_frames)]
which = np.array(np.where(frame[:, 3] == 0)[0],
dtype=np.int32)
x3ds_seq[fi] = np.concatenate((VICON_tiara_x3ds + np.array([0,1000,0],dtype=np.float32),frame[which,:3])), \
np.concatenate((np.arange(len(VICON_tiara_x3ds),dtype=np.int32),which+len(VICON_tiara_x3ds)))
#dot_labels = get_labels(dot_detections.keys(), x3ds_seq, dot_detections, mats, x2d_threshold = 0.05)
if detectingTiara:
primitives.append(GLPoints3D(VICON_tiara_x3ds + [0, 1000, 0]))
primitives[-1].pointSize = 5
global track3d, prev_frame, booting, trackGraph
track3d = Label.Track3D(mats[:len(movies)],
x2d_threshold=0.03,
x3d_threshold=5.0,
min_rays=3,
boot_interval=2) #tilt_threshold = 0.01, gruffalo
trackGraph = Label.TrackGraph()
prev_frame = 0
booting = 1
from UI import QApp
from PySide import QtGui
from GCore import State
# Modified the options parameter for fields to be the range of acceptable values for the box
# Previously would crash if small_blur got too low
QApp.fields = {
'image filter': [
('small_blur', 'Small blur radius',
'This is part of the image filter which controls the size of smallest detected features.',
'int', small_blur, {
"min": 0,
"max": None
}),
('large_blur', 'Large blur radius',
'This is part of the image filter which controls the size of largest detected features.',
'int', large_blur, {
"min": 0,
"max": None
}),
('threshold_bright', 'threshold_bright',
'This is part of the image filter which controls the size of smallest detected features.',
'int', threshold_bright, {
"min": 0,
"max": 255
}),
('threshold_dark_inv', 'threshold_dark_inv',
'This is part of the image filter which controls the size of largest detected features.',
'int', threshold_dark_inv, {
"min": 0,
"max": 255
}),
('circularity_threshold', 'circularity_threshold',
'How circular?.', 'float', circularity_threshold, {
"min": 0,
"max": 100
}),
('min_dot_size', 'min_dot_size',
'min_dot_size smallest detected features.', 'float', min_dot_size,
{
"min": 0,
"max": 100
}),
('max_dot_size', 'max_dot_size',
'max_dot_size largest detected features.', 'float', max_dot_size,
{
"min": 0,
"max": 100
}),
]
}
State.addKey('dotParams', {'type': 'image filter', 'attrs': attrs})
State.setSel('dotParams')
appIn = QtGui.QApplication(sys.argv)
appIn.setStyle('plastique')
win = QApp.QApp()
win.setWindowTitle('Imaginarium Dots Viewer')
QGLViewer.makeViewer(primitives=primitives,
primitives2D=primitives2D,
timeRange=(firstFrame, lastFrame),
callback=setFrame,
mats=mats,
camera_ids=camera_ids,
movies=movies,
pickCallback=picked,
appIn=appIn,
win=win)
def main(x2d_filename, xcp_filename, c3d_filename=None):
'''Generate a 3D view of an x2d file, using the calibration.'''
global x2d_frames, mats, Ps, c3d_frames, primitives, primitives2D, track3d, prev_frame, track_orn, orn_graph, boot, orn_mapper, mar_mapper
prev_frame = None
c3d_frames = None
if c3d_filename != None:
c3d_dict = C3D.read(c3d_filename)
c3d_frames, c3d_fps, c3d_labels = c3d_dict['frames'], c3d_dict[
'fps'], c3d_dict['labels']
mats, xcp_data = ViconReader.loadXCP(xcp_filename)
camera_ids = [int(x['DEVICEID']) for x in xcp_data]
print 'loading 2d'
x2d_dict = ViconReader.loadX2D(x2d_filename)
x2d_frames = x2d_dict['frames']
cameras_info = ViconReader.extractCameraInfo(x2d_dict)
print 'num frames', len(x2d_frames)
Ps = [m[2] / (m[0][0, 0]) for m in mats]
track3d = Label.Track3D(mats)
primitives = QGLViewer.makePrimitives(vertices=[], altVertices=[])
primitives2D = QGLViewer.makePrimitives2D(([], [0]))
global g_all_skels, md
directory = os.path.join(os.environ['GRIP_DATA'], '151110')
_, orn_skel_dict = IO.load(os.path.join(directory, 'orn.skel'))
movie_fn = os.path.join(directory, '50_Grip_RoomCont_AA_02.v2.mov')
md = MovieReader.open_file(movie_fn,
audio=True,
frame_offset=0,
volume_ups=10)
asf_filename = os.path.join(directory, 'Martha.asf')
amc_filename = os.path.join(directory, 'Martha.amc')
asf_dict = ASFReader.read_ASF(asf_filename)
mar_skel_dict = ASFReader.asfDict_to_skelDict(asf_dict)
mar_skel_dict['anim_dict'] = ASFReader.read_AMC(amc_filename, asf_dict)
for k in ('geom_Vs', 'geom_vsplits', 'geom_Gs'):
mar_skel_dict[k] = orn_skel_dict[k].copy()
mar_skel_dict['shape_weights'] = orn_skel_dict['shape_weights']
mar_skel_dict['geom_dict'] = orn_skel_dict['geom_dict']
orn_vss = ViconReader.loadVSS(os.path.join(directory, 'Orn.vss'))
orn_vss_chan_mapping = [
orn_vss['chanNames'].index(n) for n in orn_skel_dict['chanNames']
]
orn_anim_dict = orn_skel_dict['anim_dict']
orn_vss_anim = np.zeros(
(orn_anim_dict['dofData'].shape[0], orn_vss['numChans']),
dtype=np.float32)
orn_vss_anim[:, orn_vss_chan_mapping] = orn_anim_dict['dofData']
orn_anim_dict['dofData'] = orn_vss_anim
orn_vss['anim_dict'] = orn_anim_dict
for x in [
'geom_dict', 'geom_Vs', 'geom_vsplits', 'geom_Gs', 'shape_weights'
]:
orn_vss[x] = orn_skel_dict[x]
orn_skel_dict = orn_vss
g_all_skels = {}
orn_mesh_dict, orn_skel_mesh, orn_geom_mesh = orn_t = Character.make_geos(
orn_skel_dict)
g_all_skels['orn'] = (orn_skel_dict, orn_t)
orn_skel_dict['chanValues'][:] = 0
Character.updatePoseAndMeshes(orn_skel_dict, orn_skel_mesh, orn_geom_mesh)
mar_mesh_dict, mar_skel_mesh, mar_geom_mesh = mar_t = Character.make_geos(
mar_skel_dict)
g_all_skels['mar'] = (mar_skel_dict, mar_t)
#ted_mesh_dict, ted_skel_mesh, ted_geom_mesh = ted_t = Character.make_geos(ted_skel_dict)
#g_all_skels['ted'] = (ted_skel_dict, ted_t)
#ted_skel_dict['chanValues'][0] += 1000
#Character.updatePoseAndMeshes(ted_skel_dict, ted_skel_mesh, ted_geom_mesh)
mnu = orn_skel_dict['markerNamesUnq']
mns = orn_skel_dict['markerNames']
effectorLabels = np.array([mnu.index(n) for n in mns], dtype=np.int32)
orn_graph = Label.graph_from_skel(orn_skel_dict, mnu)
boot = -10
track_orn = Label.TrackModel(orn_skel_dict, effectorLabels, mats)
#ted = GLSkel(ted_skel_dict['Bs'], ted_skel_dict['Gs']) #, mvs=ted_skel_dict['markerOffsets'], mvis=ted_skel_dict['markerParents'])
#ted = GLSkeleton(ted_skel_dict['jointNames'],ted_skel_dict['jointParents'], ted_skel_dict['Gs'][:,:,3])
#ted.setName('ted')
#ted.color = (1,1,0)
#orn = GLSkeleton(orn_skel_dict['jointNames'],orn_skel_dict['jointParents'], orn_skel_dict['Gs'][:,:,3])
#orn.setName('orn')
#orn.color = (0,1,1)
#square = GLMeshes(names=['square'],verts=[[[0,0,0],[1000,0,0],[1000,1000,0],[0,1000,0]]],vts=[[[0,0],[1,0],[1,1],[0,1]]],faces=[[[0,1,2,3]]],fts=[[[0,1,2,3]]])
#square.setImageData(np.array([[[0,0,0],[255,255,255]],[[255,255,255],[0,0,0]]],dtype=np.uint8))
#orn_geom_mesh.setImageData(np.array([[[0,0,0],[255,255,255]],[[255,255,255],[0,0,0]]],dtype=np.uint8))
P = Calibrate.composeP_fromData((60.8, ), (-51.4, 14.7, 3.2),
(6880, 2860, 5000),
0) # roughed in camera for 151110
ks = (0.06, 0.0)
mat = Calibrate.makeMat(P, ks, (1080, 1920))
orn_mapper = Opengl.ProjectionMapper(mat)
orn_mapper.setGLMeshes(orn_geom_mesh)
orn_geom_mesh.setImage((md['vbuffer'], (md['vheight'], md['vwidth'], 3)))
mar_mapper = Opengl.ProjectionMapper(mat)
mar_mapper.setGLMeshes(mar_geom_mesh)
mar_geom_mesh.setImage((md['vbuffer'], (md['vheight'], md['vwidth'], 3)))
global g_screen
g_screen = Opengl.make_quad_distortion_mesh()
QGLViewer.makeViewer(mat=mat,md=md,layers = {\
#'ted':ted, 'orn':orn,
#'ted_skel':ted_skel_mesh,'ted_geom':ted_geom_mesh,\
#'square':square,
'orn_skel':orn_skel_mesh,'orn_geom':orn_geom_mesh,\
'mar_skel':mar_skel_mesh,'mar_geom':mar_geom_mesh,\
},
primitives=primitives, primitives2D=primitives2D, timeRange=(0, len(x2d_frames) - 1, 4, 25.0), callback=intersectRaysCB, mats=mats,camera_ids=camera_ids)
def retrack_refresh_rbfn():
grip_dir = os.environ['GRIP_DATA']
movie_fn,_ = QApp.app.loadFilename('Choose a movie to open', grip_dir, 'Movie Files (*.mp4 *.mov *.avi *.flv *.mpg)')
md = MovieReader.open_file(movie_fn, audio=False)
update_rbfn(md)
print 'generating ted skel and anim'
ASFReader.convertASFAMC_to_SKELANIM(asf_filename, amc_filename,
skelFilename, animFilename)
ted_skel = IO.load(skelFilename)[1]
ted_anim = IO.load(animFilename)[1]
ted_xcp_mats, ted_xcp_data = ViconReader.loadXCP(xcp_filename)
if True: # facial animation
global ted_geom, ted_geom2, ted_shape, tony_geom, tony_shape, tony_geom2, tony_obj, ted_obj, diff_geom, c3d_frames
global tony_shape_vector, tony_shape_mat, ted_lo_rest, ted_lo_mat, c3d_points
global md, movies
ted_dir = os.path.join(os.environ['GRIP_DATA'], 'ted')
wavFilename = os.path.join(ted_dir, '32T01.WAV')
md = MovieReader.open_file(wavFilename)
c3d_filename = os.path.join(
ted_dir, '201401211653-4Pico-32_Quad_Dialogue_01_Col_wip_02.c3d')
c3d_dict = C3D.read(c3d_filename)
c3d_frames, c3d_fps, c3d_labels = c3d_dict['frames'], c3d_dict[
'fps'], c3d_dict['labels']
if False: # only for cleaned-up data
c3d_subject = 'TedFace'
which = np.where([s.startswith(c3d_subject)
for s in c3d_labels])[0]
c3d_frames = c3d_frames[:, which, :]
c3d_labels = [c3d_labels[i] for i in which]
print c3d_labels
if False: # this is for the cleaned-up data (don't apply the other offset...)
offset = Calibrate.composeRT(Calibrate.composeR((0.0, 0.0, 0)),
(0, 0, -8), 0) # 0.902
def initialise(self, interface, attrs):
directory = self.resolvePath(attrs['directory'])
if not directory: return False
prefix = attrs['prefix']
prefixFilename = self.resolvePath(attrs['prefixFilename'])
if prefix and not prefixFilename: return
calibration = attrs['calibration']
calibrationFilename = self.resolvePath(attrs['calibrationFilename'])
calibrationLocation = self.resolvePath(attrs['calibrationLocation'])
if calibration and (not calibrationFilename
and not calibrationLocation):
return False
movieFilenames = []
try:
for file in os.listdir(directory):
if prefixFilename and not file.startswith(prefixFilename):
continue
if file.endswith('.avi') or file.endswith(
'.mov') or file.endswith('mp4'):
movieFilenames.append(os.path.join(directory, file))
except WindowsError as e:
self.logger.error('Could not find videos: % s' % str(e))
if not movieFilenames:
# TODO: Here we'll have to clear the cameras etc.
return False
# Windows will produce a wonky order, i.e. 1, 10, 11, .., 2, 3, ..
# Use natural sorting to rectify
movieFilenames.sort(key=self.alphaNumKey)
self.camera_ids = []
self.camera_names = []
self.movies = []
self.mats = []
vheights = []
vwidths = []
timecodes = []
hasTimecode = False
useTimecode = attrs['useTimecode'] if 'useTimecode' in attrs else True
offset = attrs['offset']
if 'offsets' in attrs and attrs['offsets']:
offsets = eval(attrs['offsets'])
else:
offsets = [offset] * len(movieFilenames)
for ci, mf in enumerate(movieFilenames):
self.logger.info('Loading MovieReader: %s' % mf)
movieData = MovieReader.open_file(mf,
audio=False,
frame_offset=offsets[ci])
if movieData['vbuffer'] is not None:
self.movies.append(movieData)
self.timecodeOffsets.append(0)
if 'timecode' in movieData and movieData['timecode']:
hasTimecode = True
timecodes.append(movieData['timecode'])
# Make sure we have all the cameras before continuing
if len(self.movies) != len(movieFilenames):
self.logger.error('Could not load all movies in sequence')
return
# Make sure we have as many time codes as movies (if we have any)
if hasTimecode and len(self.movies) != len(timecodes):
self.logger.error('Not all movie files have a time code')
return
# See if we can get the offsets using the time codes
if hasTimecode and useTimecode:
print 'Video timecodes:', timecodes
fps_all = [round(m['fps']) for m in self.movies]
print 'FPS:', fps_all
timecodeValues = [
Timecode.TCFtoInt(tc, fps)
for tc, fps in zip(timecodes, fps_all)
]
tcOrderDesc = [
timecodes.index(tc) for tc in sorted(timecodes, reverse=True)
]
# Set the first offset to 0
firstTcIndex = tcOrderDesc[0]
self.timecodeOffsets[firstTcIndex] = 0
largestTc = timecodes[firstTcIndex]
offsetStartIndex = 1
# We can also get the timecode destination from an incoming location, e.g. 2D detections
if 'timecodeLocation' in attrs and attrs['timecodeLocation']:
tcSyncTime = interface.attr(
'timecode', atLocation=attrs['timecodeLocation'])
if tcSyncTime is not None:
tcSyncValue = Timecode.TCFtoInt(tcSyncTime, fps_all[0])
if tcSyncValue < timecodeValues[firstTcIndex]:
self.logger.error(
'Sync timecode %s is smaller than video timecodes (%s).'
% (tcSyncTime, largestTc))
return
largestTc = tcSyncTime
offsetStartIndex = 0
self.timecode = largestTc
self.logger.info('Setting timecode to: %s' % (largestTc))
# Calculate the offset for each camera to get it up to speed with the target timecode
# TODO: Replace hard coded timecode fps and multiplier
timecodeFps, timecodeMultiplier = 25., 2.
for tcInd in tcOrderDesc[offsetStartIndex:]:
diff = Timecode.TCSub(largestTc, timecodes[tcInd], timecodeFps)
self.timecodeOffsets[tcInd] = Timecode.TCFtoInt(
diff, timecodeFps) * timecodeMultiplier
if self.timecodeOffsets:
print 'Video timecode offsets:', self.timecodeOffsets
self.camera_ids = [
'Camera %d' % ci for ci in xrange(len(movieFilenames))
]
self.movies = self.movies
if not calibrationLocation: calibrationLocation = interface.root()
if calibrationFilename or interface.hasAttr(
'mats', atLocation=calibrationLocation):
if calibrationFilename:
# TODO: Detect filetype, e.g. .cal and .xcp and handle accordingly
try:
self.mats, rawCalData = OptitrackReader.load_CAL(
calibrationFilename)
if not self.mats: return False
except IOError as e:
self.logger.error('Could not load calibration file: %s' %
str(e))
return False
else:
self.mats = interface.attr('mats',
atLocation=calibrationLocation)
if not self.mats:
self.logger.error('Could not find calibration mats: %s' %
calibrationLocation)
return False
else:
from GCore import Calibrate
for ci, (cid, md) in enumerate(zip(self.camera_ids, self.movies)):
if md is not None:
self.mats.append(
Calibrate.makeUninitialisedMat(
ci, (md['vheight'], md['vwidth'])))
for md in self.movies:
vheights.append(md['vheight'])
vwidths.append(md['vwidth'])
Ps = interface.getPsFromMats(self.mats)
self.attrs = {
'vheight': vheights,
'vwidth': vwidths,
'camera_ids': self.camera_ids,
'Ps': Ps,
'mats': self.mats,
'colour': eval(attrs['colour'])
}
if self.camera_names:
self.attrs['camera_names'] = self.camera_names
self.initialised = True
return True