def learn_frame(im_depth, skel_pos, offsets_1, offsets_2):
''' Get frame data '''
skel_pos = pts_to_surface(skel_pos, im_depth, thresh=50)
''' Compute features and labels '''
im_labels = get_per_pixel_joints(im_depth, skel_pos, 'geodesic')
im_labels[(im_depth==0)*(im_labels>N_MSR_JOINTS)] = N_MSR_JOINTS+1
# im_labels[(im_depth>0)*(im_labels==255)] = N_MSR_JOINTS+1
mask = (im_labels<N_MSR_JOINTS)
pixel_loc = np.nonzero(mask)
pixel_labels = im_labels[pixel_loc]
# embed()
features = calculate_rf_features(im_depth, offsets_1, offsets_2, mask=mask)
''' Visualize '''
if 1:
im_labels = np.repeat(im_labels[:,:,None], 3, -1)
im_labels = display_skeletons(im_labels, skel_pos, (20,), skel_type='Low')
cv2.putText(im_labels, "Blue=Truth", (10, 210), cv2.FONT_HERSHEY_DUPLEX, .5, (int(im_labels.max()/2), 0, 0))
cv2.putText(im_labels, "Green=Predict", (10, 230), cv2.FONT_HERSHEY_DUPLEX, .5, (0, int(im_labels.max()/2), 0))
cv2.imshow("feature_space", im_labels/im_labels.max().astype(np.float))
ret = cv2.waitKey(10)
if ret > 0: exit
return features, pixel_labels
def learn_frame(im_depth, skel_pos, offsets_1, offsets_2):
''' Get frame data '''
skel_pos = pts_to_surface(skel_pos, im_depth, thresh=50)
''' Compute features and labels '''
im_labels = get_per_pixel_joints(im_depth, skel_pos, 'geodesic')
im_labels[(im_depth == 0) * (im_labels > N_MSR_JOINTS)] = N_MSR_JOINTS + 1
# im_labels[(im_depth>0)*(im_labels==255)] = N_MSR_JOINTS+1
mask = (im_labels < N_MSR_JOINTS)
pixel_loc = np.nonzero(mask)
pixel_labels = im_labels[pixel_loc]
# embed()
features = calculate_rf_features(im_depth, offsets_1, offsets_2, mask=mask)
''' Visualize '''
if 1:
im_labels = np.repeat(im_labels[:, :, None], 3, -1)
im_labels = display_skeletons(im_labels,
skel_pos, (20, ),
skel_type='Low')
cv2.putText(im_labels, "Blue=Truth", (10, 210),
cv2.FONT_HERSHEY_DUPLEX, .5,
(int(im_labels.max() / 2), 0, 0))
cv2.putText(im_labels, "Green=Predict", (10, 230),
cv2.FONT_HERSHEY_DUPLEX, .5,
(0, int(im_labels.max() / 2), 0))
cv2.imshow("feature_space",
im_labels / im_labels.max().astype(np.float))
ret = cv2.waitKey(10)
if ret > 0: exit
return features, pixel_labels
def plotUsers(image, users):
# embed()
# if type(users) == dict:
# users = [u for u in users.values()]
# for u in users:
# uvw = [-1]
for u in users.keys():
if users[u]['tracked']:
xyz = users[u]['com']
uvw = skel2depth(np.array([xyz]), image.shape)[0]
''' Only plot if there are valid coordinates (not [0,0,0])'''
if uvw[0] > 0:
if users[u]['tracked'] and len(
users[u]['jointPositions'].keys()) > 0:
'''Colorize COM'''
cv2.rectangle(image, tuple([uvw[0] - 3, uvw[1] - 3]),
tuple([uvw[0] + 3, uvw[1] + 3]), (4000))
'''Plot skeleton'''
pts = [j for j in users[u]['jointPositions'].values()]
skel = skel2depth(np.array(pts), image.shape)
from pyKinectTools.utils.SkeletonUtils import display_skeletons
image = display_skeletons(image,
skel,
color=(image.max(), 0, 0),
skel_type='Kinect')
def plotUsers(image, users):
# embed()
# if type(users) == dict:
# users = [u for u in users.values()]
# for u in users:
# uvw = [-1]
for u in users.keys():
if users[u]['tracked']:
xyz = users[u]['com']
uvw = skel2depth(np.array([xyz]), image.shape)[0]
''' Only plot if there are valid coordinates (not [0,0,0])'''
if uvw[0] > 0:
if users[u]['tracked'] and len(users[u]['jointPositions'].keys()) > 0:
'''Colorize COM'''
cv2.rectangle(image, tuple([uvw[0]-3, uvw[1]-3]), tuple([uvw[0]+3, uvw[1]+3]), (4000))
'''Plot skeleton'''
pts = [j for j in users[u]['jointPositions'].values()]
skel = skel2depth(np.array(pts), image.shape)
from pyKinectTools.utils.SkeletonUtils import display_skeletons
image = display_skeletons(image, skel, color=(image.max(),0,0), skel_type='Kinect')
def infer_pose(self, depth):
'''
depth : depth image
'''
# Compute features and labels
# embed()
features = calculate_rf_features(depth, self.offsets[0],
self.offsets[1])
pixel_loc = np.nonzero(depth > 0)
pred = self.rf.predict(features)
im_predict = np.ones_like(depth) + N_MSR_JOINTS + 1
im_predict[pixel_loc] = pred
skel_pos_pred = pose_mean_shift(im_predict)
''' Visualize '''
if 1:
cv2.imshow("prediction1", im_predict / float(im_predict.max()))
im_predict = np.repeat(depth[:, :, None].astype(np.float), 3, -1)
im_predict[depth > 0] -= depth[depth > 0].min()
im_predict /= float(im_predict.max() / 255.)
im_predict = im_predict.astype(np.uint8)
# im_predict = display_skeletons(im_predict, skel_pos, (255,0,0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos_pred,
(0, 255, 0), SKEL_DISPLAY_MODE)
cv2.putText(im_predict, "Blue=Truth", (10, 210),
cv2.FONT_HERSHEY_DUPLEX, .5,
(int(im_predict.max() / 2), 0, 0))
cv2.putText(im_predict, "Green=Predict", (10, 230),
cv2.FONT_HERSHEY_DUPLEX, .5,
(0, int(im_predict.max() / 2), 0))
cv2.imshow("prediction", im_predict)
ret = cv2.waitKey(10)
# if ret > 0: break
return skel_pos_pred, im_predict
def infer_pose(self,depth):
'''
depth : depth image
'''
# Compute features and labels
# embed()
features = calculate_rf_features(depth, self.offsets[0], self.offsets[1])
pixel_loc = np.nonzero(depth>0)
pred = self.rf.predict(features)
im_predict = np.ones_like(depth)+N_MSR_JOINTS+1
im_predict[pixel_loc] = pred
skel_pos_pred = pose_mean_shift(im_predict)
''' Visualize '''
if 1:
cv2.imshow("prediction1", im_predict/float(im_predict.max()))
im_predict = np.repeat(depth[:,:,None].astype(np.float), 3, -1)
im_predict[depth>0] -= depth[depth>0].min()
im_predict /= float(im_predict.max()/255.)
im_predict = im_predict.astype(np.uint8)
# im_predict = display_skeletons(im_predict, skel_pos, (255,0,0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos_pred, (0,255,0), SKEL_DISPLAY_MODE)
cv2.putText(im_predict, "Blue=Truth", (10, 210), cv2.FONT_HERSHEY_DUPLEX, .5, (int(im_predict.max()/2), 0, 0))
cv2.putText(im_predict, "Green=Predict", (10, 230), cv2.FONT_HERSHEY_DUPLEX, .5, (0, int(im_predict.max()/2), 0))
cv2.imshow("prediction", im_predict)
ret = cv2.waitKey(10)
# if ret > 0: break
return skel_pos_pred, im_predict
def get_per_pixel_joints(im_depth, skel, alg='geodesic', radius=5):
'''
Find the closest joint to each pixel using geodesic distances.
---Paramaters---
im_depth : should be masked depth image
skel : in image coords
alg : 'geodesic' or 'circular'
radius : [only for circular]
'''
height, width = im_depth.shape
distance_ims = np.empty([height, width, N_SKEL_JOINTS])
MAX_VALUE = 32000
# Get rid of edges around joints
edge_thresh = 200#10
gradients = np.gradient(im_depth)
mag = np.sqrt(gradients[0]**2+gradients[1]**2)
im_depth[mag>edge_thresh] = 0
joints_out_of_bounds = np.abs(im_depth[skel[:,0], skel[:,1]] - skel[:,2]) > 100
# Only look at major joints
for i, j in zip(SKEL_JOINTS, range(N_SKEL_JOINTS)):
if skel[i] in joints_out_of_bounds:
distance_ims[:,:,j] = MAX_VALUE
continue
pos = skel[i]
if alg == 'geodesic':
x = np.maximum(np.minimum(pos[1], height-1), 0)
y = np.maximum(np.minimum(pos[0], width-1), 0)
# if j == 0:
# pts, min_map = generateKeypoints(np.ascontiguousarray(im_depth), centroid=[x,y], iterations=10, scale=6)
im_dist = distance_map(np.ascontiguousarray(im_depth), centroid=[x,y], scale=6)
distance_ims[:,:,j] = im_dist
if 0:
radius = 5
x = np.maximum(np.minimum(pos[1], height-1-radius), radius)
y = np.maximum(np.minimum(pos[0], width-1-radius), radius)
# print x,y
pts = circle(x,y, radius)
# print pts[0]
max_ = distance_ims[distance_ims[:,:,j]<MAX_VALUE, j].max()
distance_ims[distance_ims[:,:,j]>=MAX_VALUE,j] = max_+100
distance_ims[pts[0],pts[1],j] = max_+100
figure(1)
subplot(3,5,j+1)
imshow(distance_ims[:,:,j])
axis('off')
# subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.1, hspace=.1)
tight_layout()
subplots_adjust(left=.001, bottom=.001, wspace=.003, hspace=.003)
elif alg == 'circular':
x = np.maximum(np.minimum(pos[1], height-1-radius), radius)
y = np.maximum(np.minimum(pos[0], width-1-radius), radius)
pts = draw.circle(x,y, radius)
closest_pos[pts] = i
else:
print "No algorithm type in 'get_per_pixel_joints'"
if alg == 'geodesic':
closest_pos = np.argmin(distance_ims, -1).astype(np.uint8)
closest_pos_all = np.argsort(distance_ims, -1).astype(np.uint8)
elif alg == 'circular':
closest_pos = np.zeros_like(im_depth)
#Change all background pixels
# mask = im_depth!=0
# mask = (distance_ims.max(-1)<32000)
mask = im_depth!=0
closest_pos[-mask] = N_MSR_JOINTS+1
if 0:
# Reorder for occlusions
skel_depths = np.array([skel[i,2] for i in SKEL_JOINTS])
skel_XYZ = np.array([skel[i] for i in SKEL_JOINTS])
pos_order = np.argsort(skel_depths).tolist()
pos_order.reverse()
residual = im_depth[mask] - skel_depths[closest_pos[mask]]
im_tmp = np.zeros_like(im_depth)
im_tmp[mask] = residual
closest_arg = np.argmin(distance_ims+skel_depths, -1)
closest_arg[-mask] = -1
# If geo dist < euc distance then kill
# Go from back to front. Only care about things ahead
from scipy.spatial import distance
# euclid_dists = distance.squareform(distance.pdist(depth2world(skel_XYZ, [240,320])))
euclid_dists = distance.squareform(distance.pdist(skel_XYZ))
geo_dists = np.zeros_like(euclid_dists)
for i in xrange(len(SKEL_JOINTS)):
p = pos_order[i]
# If a joint is behind it, don't care -- set to infinity
geo_dists[p,:] = [distance_ims[skel[j][0],skel[j][1],p] if j in pos_order[i:] else np.inf for j in SKEL_JOINTS]
# Check if it's incorrect at least 2x
removed_joints = []
err = (geo_dists*2. < euclid_dists)
for i in xrange(len(err)):
if err[i].sum(0) >= 1:
# removed_joints += [i]
distance_ims[:,:,i] = MAX_VALUE
closest_pos_e = np.argmin(distance_ims, -1).astype(np.uint8)
closest_pos_e[-mask] = N_MSR_JOINTS+1
subplot(2,2,1)
closest_pos = display_skeletons(closest_pos, skel, color=(23,), skel_type='Low')
imshow(closest_pos); axis('off')
subplot(2,2,2)
imshow(closest_pos_e); axis('off')
subplot(2,2,3)
imshow(closest_pos_e==closest_pos); axis('off')
# blank = np.ones(300,300)
# removed_text = ''
# for i in removed_joints:
# removed_text += str(i)+" "
# cv2.putText()
tight_layout()
subplots_adjust(left=.001, bottom=.001, wspace=.003, hspace=.003)
show()
# embed()
# closest_pos_e[im_depth==0] = N_MSR_JOINTS+1
# closest_pos2[-mask] = N_MSR_JOINTS+1
# closest_pos2[closest_pos>=32000] = N_MSR_JOINTS+1
# closest_arg[im_depth==0] = N_MSR_JOINTS+1
# figure(1); imshow(closest_arg);
# figure(3); imshow(closest_pos2)
# # Reorder
# im_order = np.zeros_like(im_depth)-1
# for p in range(len(pos_order)):
# mask_tmp = closest_pos==p
# dists_tmp = distance_ims[mask_tmp, p] + skel[pos_order[p],2]
# im_order[mask_tmp] = np.maximum(im_order[mask_tmp], dists_tmp)
# figure(100)
# imshow(closest_pos)
# figure(101)
# imshow(im_depth)
# show()
# embed()
return closest_pos
def compute_features(name, vis=False, person_rez=[144,72]):
'''
---Parameters---
filename : base filename for depth/color/skel
vis: turn visualize on?
person_rez : all hog/hof features should be the same dimensions, so we resize the people to this resolution
---Return---
features: features in a dictionary
'''
''' Get filenames '''
depth_file = name + "depth.bin"
color_file = name + "rgb.avi"
skeleton_file = name + "skeleton.txt"
''' Read data from each video/sequence '''
try:
depthIms, maskIms = read_MSR_depth_ims(depth_file)
colorIms = read_MSR_color_ims(color_file)
skels_world, skels_im = read_MSR_skeletons(skeleton_file)
except:
print "Error reading data"
return -1
#print depthIms.shape, colorIms.shape
framecount = np.minimum(depthIms.shape[0], colorIms.shape[0])
dataset_features = {'framecount':framecount, 'hog':[], 'hof':[], 'skel_image':[], 'skel_world':[]}
grayIm_prev = None
''' View all data'''
for frame in xrange(framecount):
depth = depthIms[frame]
mask = maskIms[frame]
color = colorIms[frame]
# Skeleton in world (w) and image (i) coordinates
skel_w = skels_world[frame]
skel_i = world2depth(skel_w, rez=[240,320])
''' Calculate hogs '''
grayIm = (rgb2gray(color) * 255).astype(np.uint8)
hogIm = np.zeros_like(depth)
person_mask, bounding_boxes, labels = extract_people(grayIm, mask>0)
rez = grayIm[bounding_boxes[0]].shape
#hog_input_im = sm.imresize(grayIm[bounding_boxes[0]], person_rez)
hog_input_im = cv2.resize(grayIm[bounding_boxes[0]], (person_rez[1], person_rez[0]))
hogData, hogImBox = hog(hog_input_im, orientations=4, visualise=True)
#hogIm[bounding_boxes[0]] = sm.imresize(hogImBox, [rez[0],rez[1]])
hogIm[bounding_boxes[0]] = cv2.resize(hogImBox, (rez[1],rez[0]))
# hogIm[bounding_boxes[0]] = hogImBox
hogIm *= person_mask
''' Calculate HOF '''
hofIm = np.zeros_like(depth)
if grayIm_prev is None:
grayIm_prev = np.copy(grayIm)
continue
else:
flow = getFlow(grayIm_prev[bounding_boxes[0]], grayIm[bounding_boxes[0]])
rez = flow.shape
bounding_boxes = (bounding_boxes[0][0], bounding_boxes[0][1], slice(0,2))
#hof_input_im = np.dstack([sm.imresize(flow[0], [person_rez[0],person_rez[1]]),
# sm.imresize(flow[1], [person_rez[0],person_rez[1]])])i
hof_input_im = np.dstack([cv2.resize(flow[0], (person_rez[1],person_rez[0])), cv2.resize(flow[1], (person_rez[1], person_rez[0]))])
hofData, hofImBox = hof(hof_input_im, orientations=5, visualise=True)
#hofIm[bounding_boxes[:2]] = sm.imresize(hofImBox, [rez[0],rez[1]])
hofIm[bounding_boxes[:2]] = cv2.resize(hogImBox, (rez[1],rez[0]))
hofIm *= person_mask
grayIm_prev = np.copy(grayIm)
''' Add features '''
dataset_features['hog'] += [hogData]
dataset_features['hof'] += [hofData]
dataset_features['skel_image'] += [skel_i]
dataset_features['skel_world'] += [skel_w]
''' Plot skeletons on color image'''
if vis:
color = display_skeletons(color, skel_i)
''' Visualization '''
cv2.imshow("Depth", depth/float(depth.max()))
cv2.imshow("HOG", hogIm/float(hogIm.max()))
cv2.imshow("RGB", color)
cv2.imshow("RGB masked", color*(mask[:,:,None]>0))
cv2.imshow("HOF", hofIm/float(hofIm.max()))
ret = cv2.waitKey(10)
if ret >= 0:
break
print "Done calculating ", name
return dataset_features
def main(visualize=False,
learn=False,
actions=None,
subjects=None,
n_frames=220):
# learn = True
# learn = False
if actions is []:
actions = [2]
if subjects is []:
subjects = [2]
# actions = [1]
# actions = [1, 2, 3, 4, 5]
# subjects = [1]
if 1:
MHAD = True
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/',
kinect=1,
actions=actions,
subjects=subjects,
reps=[1],
get_depth=True,
get_color=True,
get_skeleton=True,
fill_images=False)
else:
MHAD = False
cam = KinectPlayer(base_dir='./',
device=1,
bg_subtraction=True,
get_depth=True,
get_color=True,
get_skeleton=True,
fill_images=False)
bg = Image.open(
'/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_A.tif')
# cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_B.tif')
cam.bgSubtraction.backgroundModel = np.array(bg.getdata()).reshape(
[240, 320]).clip(0, 4500)
height, width = cam.depthIm.shape
skel_previous = None
face_detector = FaceDetector()
hand_detector = HandDetector(cam.depthIm.shape)
# curve_detector = CurveDetector(cam.depthIm.shape)
# Video writer
# video_writer = cv2.VideoWriter("/Users/colin/Desktop/test.avi", cv2.cv.CV_FOURCC('M','J','P','G'), 15, (320,240))
# Save Background model
# im = Image.fromarray(cam.depthIm.astype(np.int32), 'I')
# im.save("/Users/Colin/Desktop/k2.png")
# Setup pose database
append = True
append = False
# pose_database = PoseDatabase("PoseDatabase.pkl", learn=learn, search_joints=[0,4,7,10,13], append=append)
pose_database = PoseDatabase("PoseDatabase.pkl",
learn=learn,
search_joints=[0, 2, 4, 5, 7, 10, 13],
append=append)
# Setup Tracking
skel_init, joint_size, constraint_links, features_joints, skel_parts, convert_to_kinect = get_14_joint_properties(
)
constraint_values = []
for c in constraint_links:
constraint_values += [
np.linalg.norm(skel_init[c[0]] - skel_init[c[1]], 2)
]
constraint_values = np.array(constraint_values)
skel_current = None #skel_init.copy()
skel_previous = None #skel_current.copy()
# Evaluation
accuracy_all_db = []
accuracy_all_track = []
joint_accuracy_db = []
joint_accuracy_track = []
# geo_accuracy = []
# color_accuracy = []
# lbp_accuracy = []
frame_count = 0
frame_rate = 1
if not MHAD:
cam.next(350)
frame_prev = 0
# try:
if 1:
while cam.next(frame_rate): # and frame_count < n_frames:
if frame_count - frame_prev > 100:
print ""
print "Frame #{0:d}".format(frame_count)
frame_prev = frame_count
if not MHAD:
if len(cam.users) == 0:
continue
else:
# cam.users = [np.array(cam.users[0]['jointPositions'].values())]
if np.any(cam.users[0][0] == -1):
continue
cam.users[0][:, 1] *= -1
cam.users_uv_msr = [
cam.camera_model.world2im(cam.users[0], [240, 320])
]
# Apply mask to image
if MHAD:
mask = cam.get_person(2) > 0
else:
mask = cam.get_person() > 0
if np.all(mask == False):
continue
im_depth = cam.depthIm
cam.depthIm[cam.depthIm > 3000] = 0
im_color = cam.colorIm * mask[:, :, None]
cam.colorIm *= mask[:, :, None]
pose_truth = cam.users[0]
pose_truth_uv = cam.users_uv_msr[0]
# Get bounding box around person
box = nd.find_objects(mask)[0]
d = 20
# Widen box
box = (slice(np.maximum(box[0].start-d, 0), \
np.minimum(box[0].stop+d, height-1)), \
slice(np.maximum(box[1].start-d, 0), \
np.minimum(box[1].stop+d, width-1)))
box_corner = [box[0].start, box[1].start]
''' ---------- ----------------------------------- --------'''
''' ----------- Feature Detector centric approach ---------'''
''' ---------- ----------------------------------- --------'''
''' ---- Calculate Detectors ---- '''
# Face detection
face_detector.run(im_color[box])
# Skin detection
hand_markers = hand_detector.run(im_color[box], n_peaks=3)
# Calculate Geodesic Extrema
im_pos = cam.camera_model.im2PosIm(
cam.depthIm * mask)[box] * mask[box][:, :, None]
geodesic_markers = geodesic_extrema_MPI(im_pos,
iterations=5,
visualize=False)
_, geo_map = geodesic_extrema_MPI(im_pos,
iterations=1,
visualize=True)
geodesic_markers_pos = im_pos[geodesic_markers[:, 0],
geodesic_markers[:, 1]]
markers = list(geodesic_markers) + list(
hand_markers) #+ list(lop_markers) + curve_markers
markers = np.array([list(x) for x in markers])
''' ---- Database lookup ---- '''
if 1:
pts_mean = im_pos[(im_pos != 0)[:, :, 2]].mean(0)
if learn:
# Normalize pose
pose_uv = cam.users_uv[0]
if np.any(pose_uv == 0):
print "skip"
frame_count += frame_rate
continue
pose_database.update(pose_truth - pts_mean)
else:
# Concatenate markers
markers = list(geodesic_markers) + hand_markers
markers = np.array([list(x) for x in markers])
# Normalize pose
pts = im_pos[markers[:, 0], markers[:, 1]]
pts = np.array([x for x in pts if x[0] != 0])
pts -= pts_mean
# Get closest pose
pose = pose_database.query(pts, knn=5)
# embed()
for i in range(5):
pose_tmp = cam.camera_model.world2im(
pose[i] + pts_mean, cam.depthIm.shape)
cam.colorIm = display_skeletons(cam.colorIm,
pose_tmp,
skel_type='Kinect',
color=(0, i * 40 + 50,
0))
pose = pose[0]
# im_pos -= pts_mean
# R,t = IterativeClosestPoint(pose, im_pos.reshape([-1,3])-pts_mean, max_iters=5, min_change=.001, pt_tolerance=10000)
# pose = np.dot(R.T, pose.T).T - t
# pose = np.dot(R, pose.T).T + t
pose += pts_mean
pose_uv = cam.camera_model.world2im(
pose, cam.depthIm.shape)
# print pose
surface_map = nd.distance_transform_edt(
-nd.binary_erosion(mask[box]),
return_distances=False,
return_indices=True)
try:
pose_uv[:, :2] = surface_map[:, pose_uv[:, 0] -
box_corner[0],
pose_uv[:, 1] -
box_corner[1]].T + [
box_corner[0],
box_corner[1]
]
except:
pass
pose = cam.camera_model.im2world(pose_uv,
cam.depthIm.shape)
# print pose
''' ---- Tracker ---- '''
# surface_map = nd.distance_transform_edt(-mask[box], return_distances=False, return_indices=True)
# surface_map = nd.distance_transform_edt(im_pos[:,:,2]==0, return_distances=False, return_indices=True)
if skel_previous is None:
# if 1:
skel_previous = pose.copy()
skel_current = pose.copy()
skel_previous_uv = pose_uv.copy()
skel_current_uv = pose_uv.copy()
for _ in range(1):
# ---- (Step 1A) Find feature coordespondences ----
try:
skel_previous_uv[:, :
2] = surface_map[:,
skel_previous_uv[:, 0] -
box_corner[0],
skel_previous_uv[:, 1] -
box_corner[1]].T + [
box_corner[0],
box_corner[1]
]
except:
pass
skel_current = cam.camera_model.im2world(
skel_previous_uv, cam.depthIm.shape)
# Alternative method: use kdtree
## Calc euclidian distance between each pixel and all joints
px_corr = np.zeros(
[im_pos.shape[0], im_pos.shape[1],
len(skel_current)])
# for i,s in enumerate(pose):
# for i,s in enumerate(skel_current):
# px_corr[:,:,i] = np.sqrt(np.sum((im_pos - s)**2, -1))# / joint_size[i]**2
# for i,s in enumerate(pose_uv):
# Geodesics
for i, s in enumerate(skel_previous_uv):
''' Problem: need to constrain pose_uv to mask '''
_, geo_map = geodesic_extrema_MPI(
im_pos, [s[0] - box_corner[0], s[1] - box_corner[1]],
iterations=1,
visualize=True)
px_corr[:, :, i] = geo_map
subplot(2, 7, i + 1)
# imshow(geo_map, vmin=0, vmax=2000)
# axis('off')
px_corr[geo_map == 0, i] = 9999
cv2.imshow('gMap', (px_corr.argmin(-1) + 1) / 15. * mask[box])
## Handle occlusions by argmax'ing over set of skel parts
# visible_configurations = list(it.product([0,1], repeat=5))[1:]
visible_configurations = [
# [0,1,1,1,1],
# [1,0,0,0,0],
[1, 1, 1, 1, 1]
]
px_visibility_label = np.zeros([
im_pos.shape[0], im_pos.shape[1],
len(visible_configurations)
],
dtype=np.uint8)
visible_scores = np.ones(len(visible_configurations)) * np.inf
# Try each occlusion configuration set
for i, v in enumerate(visible_configurations):
visible_joints = list(
it.chain.from_iterable(skel_parts[np.array(v) > 0]))
px_visibility_label[:, :, i] = np.argmin(
px_corr[:, :, visible_joints],
-1) #.reshape([im_pos.shape[0], im_pos.shape[1]])
visible_scores[i] = np.min(px_corr[:, :, visible_joints],
-1).sum()
# Choose best occlusion configuration
occlusion_index = np.argmin(visible_scores)
occlusion_configuration = visible_configurations[
occlusion_index]
occlusion_set = list(
it.chain.from_iterable(skel_parts[np.array(
visible_configurations[occlusion_index]) > 0]))
# Choose label for pixels based on occlusion configuration
px_label = px_visibility_label[:, :,
occlusion_index] * mask[box]
px_label_flat = px_visibility_label[:, :, occlusion_index][
mask[box]].flatten()
visible_joints = [
1 if x in occlusion_set else 0 for x in range(len(pose))
]
# print visible_joints
# Project distance to joint's radius
px_joint_displacement = im_pos[
mask[box]] - skel_current[px_label_flat]
px_joint_magnitude = np.sqrt(
np.sum(px_joint_displacement**2, -1))
joint_mesh_pos = skel_current[
px_label_flat] + px_joint_displacement * (
joint_size[px_label_flat] / px_joint_magnitude)[:,
None]
px_joint_displacement = joint_mesh_pos - im_pos[mask[box]]
# Ensure pts aren't too far away
px_joint_displacement[np.abs(px_joint_displacement) > 500] = 0
# embed()
if 0:
x = im_pos.copy() * 0
x[mask[box]] = joint_mesh_pos
for i in range(3):
subplot(1, 4, i + 1)
imshow(x[:, :, i])
axis('off')
subplot(1, 4, 4)
imshow((px_label + 1) * mask[box])
# Calc the correspondance change in position for each joint
correspondence_displacement = np.zeros([len(skel_current), 3])
ii = 0
for i, _ in enumerate(skel_current):
if i in occlusion_set:
labels = px_label_flat == i
correspondence_displacement[i] = np.sum(
px_joint_displacement[px_label_flat == ii], 0
) / np.sum(
px_joint_displacement[px_label_flat == ii] != 0)
ii += 1
correspondence_displacement = np.nan_to_num(
correspondence_displacement)
# print correspondence_displacement
# Viz correspondences
if 0:
x = im_pos.copy() * 0
x[mask[box]] = px_joint_displacement
for i in range(3):
subplot(1, 4, i + 1)
imshow(x[:, :, i])
axis('off')
subplot(1, 4, 4)
imshow((px_label + 1) * mask[box])
# embed()
# for j in range(3):
# for i in range(14):
# subplot(3,14,j*14+i+1)
# imshow(x[:,:,j]*((px_label==i)*mask[box]))
# axis('off')
show()
# ---- (Step 2) Update pose state, x ----
lambda_p = .0
lambda_c = 1.
skel_prev_difference = (skel_current - skel_previous)
# print skel_prev_difference
skel_current = skel_previous \
+ lambda_p * skel_prev_difference \
- lambda_c * correspondence_displacement#\
# ---- (Step 3) Add constraints ----
if 1:
# A: Link lengths / geometry
# skel_current = link_length_constraints(skel_current, constraint_links, constraint_values, alpha=.5)
skel_current = geometry_constraints(skel_current,
joint_size,
alpha=0.5)
skel_current = collision_constraints(
skel_current, constraint_links)
skel_img_box = (cam.camera_model.world2im(
skel_current, cam.depthIm.shape) -
[box[0].start, box[1].start, 0]
) #/mask_interval
skel_img_box = skel_img_box.clip([0, 0, 0], [
box[0].stop - box[0].start - 1,
box[1].stop - box[1].start - 1, 9999
])
# skel_img_box = skel_img_box.clip([0,0,0], [cam.depthIm.shape[0]-1, cam.depthIm.shape[1]-1, 9999])
# B: Ray-cast constraints
# embed()
skel_current, skel_current_uv = ray_cast_constraints(
skel_current, skel_img_box, im_pos, surface_map,
joint_size)
# skel_img_box -= [box[0].start, box[1].start, 0]
# # Map back from mask to image
# try:
# skel_current_uv[:,:2] = surface_map[:, skel_img_box[:,0], skel_img_box[:,1]].T# + [box_corner[0], box_corner[1]]
# except:
# pass
prob = link_length_probability(skel_current,
constraint_links,
constraint_values, 100)
# print "Prob:", np.mean(prob), np.min(prob), prob
print frame_count
thresh = .05
if np.min(prob) < thresh: # and frame_count > 1:
print 'Resetting pose'
for c in constraint_links[prob < thresh]:
for cc in c:
skel_current_uv[c] = pose_uv[c] - [
box[0].start, box[1].start, 0
]
skel_current[c] = pose[c]
# skel_current_uv = pose_uv.copy() - [box[0].start, box[1].start, 0]
# skel_current = pose.copy()
skel_current_uv = skel_current_uv + [
box[0].start, box[1].start, 0
]
skel_current = cam.camera_model.im2world(
skel_current_uv, cam.depthIm.shape)
else:
skel_current_uv = (cam.camera_model.world2im(
skel_current, cam.depthIm.shape))
# skel_img_box = skel_img_box.clip([0,0,0], [cam.depthIm.shape[0]-1, cam.depthIm.shape[1]-1, 9999])
# Update for next round
skel_previous = skel_current.copy()
skel_previous_uv = skel_current_uv.copy()
''' ---- Accuracy ---- '''
# embed()
if 1 and not learn:
# pose_truth = cam.users[0]
error_db = pose_truth - pose
error_track = pose_truth - skel_current
# print "Error", error
error_l2_db = np.sqrt(np.sum(error_db**2, 1))
error_l2_track = np.sqrt(np.sum(error_track**2, 1))
joint_accuracy_db += [error_l2_db]
joint_accuracy_track += [error_l2_track]
accuracy_db = np.sum(error_l2_db < 150) / 14.
accuracy_track = np.sum(error_l2_track < 150) / 14.
print "Current db:", accuracy_db, error_l2_db.mean()
print "Current track:", accuracy_track, error_l2_track.mean()
print ""
accuracy_all_db += [accuracy_db]
accuracy_all_track += [accuracy_track]
# print "Running avg:", np.mean(accuracy_all)
# print "Joint avg (per-joint):", np.mean(joint_accuracy_all, -1)
# print "Joint avg (overall):", np.mean(joint_accuracy_all)
''' --- Visualization --- '''
display_markers(cam.colorIm,
hand_markers[:2],
box,
color=(0, 250, 0))
if len(hand_markers) > 2:
display_markers(cam.colorIm, [hand_markers[2]],
box,
color=(0, 200, 0))
display_markers(cam.colorIm,
geodesic_markers,
box,
color=(200, 0, 0))
# display_markers(cam.colorIm, curve_markers, box, color=(0,100,100))
# display_markers(cam.colorIm, lop_markers, box, color=(0,0,200))
cam.colorIm = display_skeletons(cam.colorIm,
pose_truth_uv,
skel_type='Kinect',
color=(0, 255, 0))
cam.colorIm = display_skeletons(cam.colorIm,
pose_uv,
skel_type='Kinect')
cam.colorIm = display_skeletons(cam.colorIm,
skel_current_uv,
skel_type='Kinect',
color=(0, 0, 255))
# cam.visualize(color=True, depth=False)
cam.visualize(color=True, depth=True)
# embed()
# ------------------------------------------------------------
# video_writer.write((geo_clf_map/float(geo_clf_map.max())*255.).astype(np.uint8))
# video_writer.write(cam.colorIm[:,:,[2,1,0]])
frame_count += frame_rate
# except:
# pass
print "-- Results for subject {:d} action {:d}".format(
subjects[0], actions[0])
print "Running avg (db):", np.mean(accuracy_all_db)
print "Running avg (track):", np.mean(accuracy_all_track)
print "Joint avg (overall db):", np.mean(joint_accuracy_db)
print "Joint avg (overall track):", np.mean(joint_accuracy_track)
# print 'Done'
embed()
return
def compute_features(name, vis=False, person_rez=[144, 72]):
'''
---Parameters---
filename : base filename for depth/color/skel
vis: turn visualize on?
person_rez : all hog/hof features should be the same dimensions, so we resize the people to this resolution
---Return---
features: features in a dictionary
'''
''' Get filenames '''
depth_file = name + "depth.bin"
color_file = name + "rgb.avi"
skeleton_file = name + "skeleton.txt"
''' Read data from each video/sequence '''
try:
depthIms, maskIms = read_MSR_depth_ims(depth_file)
colorIms = read_MSR_color_ims(color_file)
skels_world, skels_im = read_MSR_skeletons(skeleton_file)
except:
print "Error reading data"
return -1
#print depthIms.shape, colorIms.shape
framecount = np.minimum(depthIms.shape[0], colorIms.shape[0])
dataset_features = {
'framecount': framecount,
'hog': [],
'hof': [],
'skel_image': [],
'skel_world': []
}
grayIm_prev = None
''' View all data'''
for frame in xrange(framecount):
depth = depthIms[frame]
mask = maskIms[frame]
color = colorIms[frame]
# Skeleton in world (w) and image (i) coordinates
skel_w = skels_world[frame]
skel_i = world2depth(skel_w, rez=[240, 320])
''' Calculate hogs '''
grayIm = (rgb2gray(color) * 255).astype(np.uint8)
hogIm = np.zeros_like(depth)
person_mask, bounding_boxes, labels = extract_people(grayIm, mask > 0)
rez = grayIm[bounding_boxes[0]].shape
#hog_input_im = sm.imresize(grayIm[bounding_boxes[0]], person_rez)
hog_input_im = cv2.resize(grayIm[bounding_boxes[0]],
(person_rez[1], person_rez[0]))
hogData, hogImBox = hog(hog_input_im, orientations=4, visualise=True)
#hogIm[bounding_boxes[0]] = sm.imresize(hogImBox, [rez[0],rez[1]])
hogIm[bounding_boxes[0]] = cv2.resize(hogImBox, (rez[1], rez[0]))
# hogIm[bounding_boxes[0]] = hogImBox
hogIm *= person_mask
''' Calculate HOF '''
hofIm = np.zeros_like(depth)
if grayIm_prev is None:
grayIm_prev = np.copy(grayIm)
continue
else:
flow = getFlow(grayIm_prev[bounding_boxes[0]],
grayIm[bounding_boxes[0]])
rez = flow.shape
bounding_boxes = (bounding_boxes[0][0], bounding_boxes[0][1],
slice(0, 2))
#hof_input_im = np.dstack([sm.imresize(flow[0], [person_rez[0],person_rez[1]]),
# sm.imresize(flow[1], [person_rez[0],person_rez[1]])])i
hof_input_im = np.dstack([
cv2.resize(flow[0], (person_rez[1], person_rez[0])),
cv2.resize(flow[1], (person_rez[1], person_rez[0]))
])
hofData, hofImBox = hof(hof_input_im,
orientations=5,
visualise=True)
#hofIm[bounding_boxes[:2]] = sm.imresize(hofImBox, [rez[0],rez[1]])
hofIm[bounding_boxes[:2]] = cv2.resize(hogImBox, (rez[1], rez[0]))
hofIm *= person_mask
grayIm_prev = np.copy(grayIm)
''' Add features '''
dataset_features['hog'] += [hogData]
dataset_features['hof'] += [hofData]
dataset_features['skel_image'] += [skel_i]
dataset_features['skel_world'] += [skel_w]
''' Plot skeletons on color image'''
if vis:
color = display_skeletons(color, skel_i)
''' Visualization '''
cv2.imshow("Depth", depth / float(depth.max()))
cv2.imshow("HOG", hogIm / float(hogIm.max()))
cv2.imshow("RGB", color)
cv2.imshow("RGB masked", color * (mask[:, :, None] > 0))
cv2.imshow("HOF", hofIm / float(hofIm.max()))
ret = cv2.waitKey(10)
if ret >= 0:
break
print "Done calculating ", name
return dataset_features
def main(visualize=False, learn=False):
# Init both cameras
# fill = True
fill = False
get_color = True
cam = KinectPlayer(base_dir='./', device=1, bg_subtraction=True, get_depth=True, get_color=get_color, get_skeleton=True, fill_images=fill)
cam2 = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=get_color, get_skeleton=True, fill_images=fill)
# Transformation matrix from first to second camera
data = pickle.load(open("Registration.dat", 'r'))
transform_c1_to_c2 = data['transform']
# Get random forest parameters
if learn:
rf = RFPose(offset_max=100, feature_count=300)
else:
rf = RFPose()
rf.load_forest()
ii = 0
# cam.next(200)
all_joint_ims_z = []
all_joint_ims_c = []
while cam.next() and ii < 200:
# Update frames
cam2.sync_cameras(cam)
if ii%10 != 0:
ii += 1
continue
# Transform skels from cam1 to cam2
cam_skels = [np.array(cam.users[s]['jointPositions'].values()) for s in cam.users.keys()]
# Get rid of bad skels
cam_skels = [s for s in cam_skels if np.all(s[0] != -1)]
if len(cam_skels) == 0:
continue
ii+=1
# Save images
if 1:
joint_ims_z = []
joint_ims_c = []
dx = 10
skel_tmp = skel2depth(cam_skels[0], [240,320])
for j_pos in skel_tmp:
# embed()
joint_ims_z += [cam.depthIm[j_pos[0]-dx:j_pos[0]+dx, j_pos[1]-dx:j_pos[1]+dx]]
joint_ims_c += [cam.colorIm[j_pos[0]-dx:j_pos[0]+dx, j_pos[1]-dx:j_pos[1]+dx]]
if len(joint_ims_z) > 0:
all_joint_ims_z += [joint_ims_z]
all_joint_ims_c += [joint_ims_c]
if 0:
cam2_skels = transform_skels(cam_skels, transform_c1_to_c2, 'image')
try:
depth = cam2.get_person()
if learn:
rf.add_frame(depth, cam2_skels[0])
else:
rf.infer_pose(depth)
if visualize:
cam2.depthIm = display_skeletons(cam2.depthIm, cam2_skels[0], (5000,), skel_type='Low')
skel1 = kinect_to_msr_skel(skel2depth(cam_skels[0], [240,320]))
cam.depthIm = display_skeletons(cam.depthIm, skel1, (5000,), skel_type='Low')
cam.visualize()
cam2.visualize()
except:
pass
embed()
if learn:
print "Starting forest"
rf.learn_forest()
print 'Done'
def main_infer(rf_name=None):
'''
'''
if rf_name is None:
import os
files = os.listdir('Saved_Params/')
rf_name = 'Saved_Params/' + files[-1]
print "Classifier file:",rf_name
# Load classifier data
data = pickle.load(open(rf_name))
rf = data['rf']
offsets_1, offsets_2 = data['offsets']
''' Read data from each video/sequence '''
depthIms = []
skels_world = []
if 1:
# VP = KinectPlayer(base_dir='/Users/colin/Data/Office_25Feb2013/', device=2, get_depth=True, get_color=False, get_skeleton=True, get_mask=False)
VP = KinectPlayer(base_dir='/Users/colin/Data/Room_close_26Feb13/', device=1, get_depth=True, get_color=False, get_skeleton=True, get_mask=False)
_,_ = VP.get_n_skeletons(50)
depthIms, skels_world = VP.get_n_skeletons(100)
# depthIms = np.array(depthIms)[:,:,::-1]
else:
# name = 'a01_s01_e02_'
name = 'a01_s10_e02_'
# name = 'a02_s06_e02_'
# name = 'a05_s02_e02_'
depth_file = name + "depth.bin"
color_file = name + "rgb.avi"
skeleton_file = name + "skeleton.txt"
try:
depthIms, maskIms = read_MSR_depth_ims(depth_file)
depthIms *= maskIms
depthIms /= 10
# colorIms = read_MSR_color_ims(color_file)
skels_world, skels_im = read_MSR_skeletons(skeleton_file)
skels_world[:,2]/=10
except:
print "Error reading data"
''' Process data '''
all_pred_ims = []
for i in xrange(1, len(depthIms), 1):
# try:
if 1:
print i
''' Get frame data '''
im_depth = depthIms[i]
# im_depth[160:, :] = 0
skel_pos = skel2depth(skels_world[i], rez=[240,320])
# ''' --- NOTE THIS 10 PX OFFSET IN THE MSR DATASET !!! --- '''
# skel_pos[:,0] -= 10
skel_pos_pred, im_predict = infer_pose(im_depth, rf, offsets_1, offsets_2)
# Overlay skeletons
if 1:
# colorIm = colorIms[i]
# im_predict = colorIm
cv2.imshow("forest_prediction", im_predict/float(im_predict.max()))
im_predict = np.repeat(im_depth[:,:,None].astype(np.float), 3, -1)
# embed()
im_predict[im_depth>0] -= im_depth[im_depth>0].min()
im_predict /= float(im_predict.max()/255.)
im_predict = im_predict.astype(np.uint8)
# im_predict = np.repeat(im_predict[:,:,None], 3, -1)
# im_predict /= float(im_predict.max())*255
# im_predict = im_predict.astype(np.uint8)
im_predict = display_skeletons(im_predict, skel_pos, (255,0,0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos_pred, (0,255,0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos, (255,0,0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos_pred, (0,255,0), SKEL_DISPLAY_MODE)
# embed()
# max_ = (im_predict * (im_predict < 255)).max()
all_pred_ims += [im_predict]
''' Visualize '''
if 1:
cv2.putText(im_predict, "Blue=Truth", (10, 210), cv2.FONT_HERSHEY_DUPLEX, .5, (int(im_predict.max()/2), 0, 0))
cv2.putText(im_predict, "Green=Predict", (10, 230), cv2.FONT_HERSHEY_DUPLEX, .5, (0, int(im_predict.max()/2), 0))
cv2.imshow("prediction", im_predict)
ret = cv2.waitKey(10)
if ret > 0: break
time.sleep(.5)
# except:
# print "Frame failed:", i
# break
embed()
def main(visualize=False, learn=False, patch_size=32, n_frames=2500):
if 1:
get_color = True
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/',
kinects=[1],
actions=[1],
subjects=[1],
get_depth=True,
get_color=True,
get_skeleton=True,
fill_images=False)
# cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
# cam.bgSubtraction.backgroundModel = sm.imread('/Users/colin/Data/CIRL_28Feb2013/depth/59/13/47/device_1/depth_59_13_47_4_13_35507.png').clip(0, 4500)
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/Office_Background_B.tif')
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_B.tif')
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/Wall_Background_B.tif')
# cam.bgSubtraction.backgroundModel = np.array(bg.getdata()).reshape([240,320]).clip(0, 4500)
# embed()
# cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
elif 0:
get_color = False
cam = EVALPlayer(base_dir='/Users/colin/Data/EVAL/',
bg_subtraction=True,
get_depth=True,
get_skeleton=True,
fill_images=False)
elif 0:
get_color = False
cam = MSRPlayer(base_dir='/Users/colin/Data/MSR_DailyActivities/Data/',
actions=[1],
subjects=[1, 2, 3, 4, 5],
bg_subtraction=True,
get_depth=True,
get_color=True,
get_skeleton=True,
fill_images=False)
embed()
height, width = cam.depthIm.shape
skel_names = np.array(['head', 'neck', 'torso', 'l_shoulder', 'l_elbow', 'l_hand', \
'r_shoulder', 'r_elbow', 'r_hand', 'l_hip', 'l_knee', 'l_foot',\
'r_hip', 'r_knee', 'r_foot'])
# skel_init, joint_size, constraint_links, features_joints,convert_to_kinect = get_11_joint_properties()
skel_init, joint_size, constraint_links, features_joints, skel_parts, convert_to_kinect = get_13_joint_properties(
)
# skel_init, joint_size, constraint_links, features_joints,skel_parts, convert_to_kinect = get_14_joint_properties()
# skel_init, joint_size, constraint_links, features_joints,convert_to_kinect = get_15_joint_properties()
constraint_values = []
for c in constraint_links:
constraint_values += [
np.linalg.norm(skel_init[c[0]] - skel_init[c[1]], 2)
]
constraint_values = np.array(constraint_values)
skel_current = skel_init.copy()
skel_previous = skel_current.copy()
face_detector = FaceDetector()
hand_template = sm.imread('/Users/colin/Desktop/fist.png')[:, :, 2]
hand_template = (255 - hand_template) / 255.
if height == 240:
hand_template = cv2.resize(hand_template, (10, 10))
else:
hand_template = cv2.resize(hand_template, (20, 20))
frame_count = 0
if get_color and height == 240:
cam.next(220)
accuracy_measurements = {'overall': [], 'per_joint': []}
# Video writer
# print '1'
video_writer = cv2.VideoWriter("/Users/colin/Desktop/test.avi",
cv2.cv.CV_FOURCC('M', 'J', 'P', 'G'), 15,
(320, 240))
# print '1'
# embed()
while cam.next(1) and frame_count < n_frames:
print ""
print "Frame #{0:d}".format(frame_count)
# Get rid of bad skeletons
if type(cam.users) == dict:
cam_skels = [
np.array(cam.users[s]['jointPositions'].values())
for s in cam.users
]
else:
cam_skels = [np.array(s) for s in cam.users]
cam_skels = [s for s in cam_skels if np.all(s[0] != -1)]
# Check for skeletons
# if len(cam_skels) == 0:
# continue
# Apply mask to image
mask = cam.get_person() > 0
# if mask is False:
if 1:
if len(cam_skels) > 0:
# cam.colorIm = display_skeletons(cam.colorIm[:,:,2], skel_msr_im, (255,), skel_type='Kinect')[:,:,None]
cam.colorIm[:, :, 1] = display_skeletons(
cam.colorIm[:, :, 2],
skel2depth(cam_skels[0], cam.depthIm.shape), (255, ),
skel_type='Kinect')
## Max P=31 for LBPs becuase of datatype
# tmp = local_binary_pattern(-cam.depthIm, 1, 10)#*(cam.foregroundMask>0)
# embed()
# tmp = local_occupancy_pattern(cam.depthIm, 31, 20, px_diff_thresh=100)*(cam.foregroundMask>0)
# cv2.imshow("LBP", np.abs(tmp.astype(np.float))/float(tmp.max()))
cam.colorIm = cam.colorIm[:, :, [0, 2, 1]]
cam.visualize()
continue
# Anonomize
# c_masked = cam.colorIm*mask[:,:,None]
# d_masked = cam.depthIm*mask
# c_masked_neg = cam.colorIm*(-mask[:,:,None])
im_depth = cam.depthIm
if get_color:
im_color = cam.colorIm
im_color *= mask[:, :, None]
im_color = np.ascontiguousarray(im_color)
im_color = im_color[:, :, [2, 1, 0]]
if len(cam_skels) > 0:
skel_msr_xyz = cam_skels[0]
skel_msr_im = skel2depth(cam_skels[0], cam.depthIm.shape)
box = nd.find_objects(mask)[0]
d = 20
box = (slice(np.maximum(box[0].start-d, 0), \
np.minimum(box[0].stop+d, height-1)), \
slice(np.maximum(box[1].start-d, 0), \
np.minimum(box[1].stop+d, width-1)))
# Face and skin detection
if get_color:
face_detector.run(im_color[box])
im_skin = rgb2lab(cam.colorIm[box].astype(np.int16))[:, :, 1]
# im_skin = skimage.exposure.equalize_hist(im_skin)
# im_skin = skimage.exposure.rescale_intensity(im_skin, out_range=[0,1])
im_skin *= im_skin > face_detector.min_threshold
im_skin *= im_skin < face_detector.max_threshold
# im_skin *= face_detector>.068
skin_match_c = nd.correlate(im_skin, hand_template)
# Display Predictions - Color Based matching
optima = peak_local_max(skin_match_c,
min_distance=20,
num_peaks=3,
exclude_border=False)
# Visualize
if len(optima) > 0:
optima_values = skin_match_c[optima[:, 0], optima[:, 1]]
optima_thresh = np.max(optima_values) / 2
optima = optima.tolist()
for i, o in enumerate(optima):
if optima_values[i] < optima_thresh:
optima.pop(i)
break
joint = np.array(o) + [box[0].start, box[1].start]
circ = np.array(circle(joint[0], joint[1], 5)).T
circ = circ.clip([0, 0], [height - 1, width - 1])
cam.colorIm[circ[:, 0], circ[:, 1]] = (
0, 120 - 30 * i, 0
) #(255*(i==0),255*(i==1),255*(i==2))
markers = optima
# ---------------- Tracking Algorithm ----------------
# ---- Preprocessing ----
if get_color:
im_pos = rgbIm2PosIm(cam.depthIm * mask)[box] * mask[box][:, :,
None]
else:
im_pos = cam.posIm[box]
cam.depthIm[cam.depthIm == 4500] = 0
im_pos_mean = np.array([
im_pos[:, :, 0][im_pos[:, :, 2] != 0].mean(),
im_pos[:, :, 1][im_pos[:, :, 2] != 0].mean(),
im_pos[:, :, 2][im_pos[:, :, 2] != 0].mean()
],
dtype=np.int16)
# Zero-center
if skel_current[0, 2] == 0:
skel_current += im_pos_mean
skel_previous += im_pos_mean
# Calculate Geodesic Extrema
extrema = geodesic_extrema_MPI(im_pos, iterations=15, visualize=False)
if len(extrema) > 0:
for i, o in enumerate(extrema):
joint = np.array(o) + [box[0].start, box[1].start]
circ = np.array(circle(joint[0], joint[1], 5)).T
circ = circ.clip([0, 0], [height - 1, width - 1])
cam.colorIm[circ[:, 0], circ[:, 1]] = (0, 0, 200 - 10 * i)
# Calculate Z-surface
surface_map = nd.distance_transform_edt(-mask[box],
return_distances=False,
return_indices=True)
# Only sample some of the points
if 1:
mask_interval = 1
feature_radius = 10
else:
mask_interval = 3
feature_radius = 2
# Modify the box wrt the sampling
box = (slice(box[0].start, box[0].stop, mask_interval),
slice(box[1].start, box[1].stop, mask_interval))
im_pos_full = im_pos.copy()
im_pos = im_pos[::mask_interval, ::mask_interval]
box_height, box_width, _ = im_pos.shape
skel_img_box = world2rgb(
skel_current, cam.depthIm.shape) - [box[0].start, box[1].start, 0]
skel_img_box = skel_img_box.clip(
[0, 0, 0], [im_pos.shape[0] - 1, im_pos.shape[1] - 1, 9999])
feature_width = feature_radius * 2 + 1
all_features = [face_detector.face_position, optima, extrema]
total_feature_count = np.sum([len(f) for f in all_features])
# Loop through the rest of the constraints
for _ in range(1):
# ---- (Step 1A) Find feature coordespondences ----
color_feature_displacement = feature_joint_displacements(
skel_current,
im_pos,
all_features[1],
features_joints[1],
distance_thresh=500)
depth_feature_displacement = feature_joint_displacements(
skel_current,
im_pos,
all_features[2],
features_joints[2],
distance_thresh=500)
# Alternative method: use kdtree
## Calc euclidian distance between each pixel and all joints
px_corr = np.zeros(
[im_pos.shape[0], im_pos.shape[1],
len(skel_current)])
for i, s in enumerate(skel_current):
px_corr[:, :, i] = np.sqrt(np.sum((im_pos - s)**2,
-1)) # / joint_size[i]**2
## Handle occlusions by argmax'ing over set of skel parts
# visible_configurations = list(it.product([0,1], repeat=5))[1:]
visible_configurations = [
#[0,1,1,1,1],
#[1,0,0,0,0],
[1, 1, 1, 1, 1]
]
px_visibility_label = np.zeros([
im_pos.shape[0], im_pos.shape[1],
len(visible_configurations)
],
dtype=np.uint8)
visible_scores = np.ones(len(visible_configurations)) * np.inf
# Try each occlusion configuration set
for i, v in enumerate(visible_configurations):
visible_joints = list(
it.chain.from_iterable(skel_parts[np.array(v) > 0]))
px_visibility_label[:, :, i] = np.argmin(
px_corr[:, :, visible_joints],
-1) #.reshape([im_pos.shape[0], im_pos.shape[1]])
visible_scores[i] = np.min(px_corr[:, :, visible_joints],
-1).sum()
# Choose best occlusion configuration
occlusion_index = np.argmin(visible_scores)
occlusion_configuration = visible_configurations[occlusion_index]
occlusion_set = list(
it.chain.from_iterable(skel_parts[
np.array(visible_configurations[occlusion_index]) > 0]))
# Choose label for pixels based on occlusion configuration
px_label = px_visibility_label[:, :, occlusion_index] * mask[box]
px_label_flat = px_visibility_label[:, :, occlusion_index][
mask[box]].flatten()
visible_joints = [
1 if x in occlusion_set else 0
for x in range(len(skel_current))
]
# Project distance to joint's radius
px_joint_displacement = skel_current[px_label_flat] - im_pos[
mask[box]]
px_joint_magnitude = np.sqrt(np.sum(px_joint_displacement**2, -1))
joint_mesh_pos = skel_current[
px_label_flat] + px_joint_displacement * (
joint_size[px_label_flat] / px_joint_magnitude)[:, None]
px_joint_displacement = joint_mesh_pos - im_pos[mask[box]]
# Calc the correspondance change in position for each joint
correspondence_displacement = np.zeros([len(skel_current), 3])
ii = 0
for i, _ in enumerate(skel_current):
if i in occlusion_set:
labels = px_label_flat == i
correspondence_displacement[i] = np.mean(
px_joint_displacement[px_label_flat == ii], 0)
# correspondence_displacement[ii] = np.sum(px_joint_displacement[px_label_flat==ii], 0)
ii += 1
correspondence_displacement = np.nan_to_num(
correspondence_displacement)
# ---- (Step 2) Update pose state, x ----
lambda_p = .0
lambda_c = .3
lambda_cf = .3
lambda_df = .0
skel_prev_difference = (skel_current - skel_previous)
# embed()
skel_current = skel_previous \
+ lambda_p * skel_prev_difference \
- lambda_c * correspondence_displacement\
- lambda_cf * color_feature_displacement\
- lambda_df * depth_feature_displacement
# ---- (Step 3) Add constraints ----
# A: Link lengths / geometry
skel_current = link_length_constraints(skel_current,
constraint_links,
constraint_values,
alpha=.5)
skel_current = geometry_constraints(skel_current,
joint_size,
alpha=0.5)
# skel_current = collision_constraints(skel_current, constraint_links)
skel_img_box = (world2rgb(skel_current, cam.depthIm.shape) -
[box[0].start, box[1].start, 0]) / mask_interval
skel_img_box = skel_img_box.clip(
[0, 0, 0],
[cam.depthIm.shape[0] - 1, cam.depthIm.shape[1] - 1, 9999])
# B: Ray-cast constraints
skel_current, skel_img_box = ray_cast_constraints(
skel_current, skel_img_box, im_pos_full, surface_map,
joint_size)
# # Map back from mask to image
skel_img = skel_img_box + [box[0].start, box[1].start, 0]
# Update for next round
skel_previous = skel_current.copy()
# skel_previous = skel_init.copy()
# ---------------------Accuracy --------------------------------------
# Compute accuracy wrt standard Kinect data
# skel_im_error = skel_msr_im[:,[1,0]] - skel_img[[0,2,3,4,5,6,7,8,9,10,11,12,13,14],:2]
skel_current_kinect = convert_to_kinect(skel_current)
try:
skel_msr_im_box = np.array([
skel_msr_im[:, 1] - box[0].start,
skel_msr_im[:, 0] - box[1].start
]).T.clip([0, 0], [box_height - 1, box_width - 1])
skel_xyz_error = im_pos[
skel_msr_im_box[:, 0],
skel_msr_im_box[:,
1]] - skel_current_kinect #skel_current[[0,2,3,4,5,6,7,8,9,10,11,12],:]
skel_l2 = np.sqrt(np.sum(skel_xyz_error**2, 1))
# print skel_l2
skel_correct = np.nonzero(skel_l2 < 150)[0]
accuracy_measurements['per_joint'] += [skel_l2]
accuracy_measurements['overall'] += [
len(skel_correct) / float(len(skel_current_kinect)) * 100
]
print "{0:0.2f}% joints correct".format(
len(skel_correct) / float(len(skel_current_kinect)) * 100)
print "Overall accuracy: ", np.mean(
accuracy_measurements['overall'])
except:
pass
print "Visible:", visible_joints
# ----------------------Visualization-------------------------------------
# l = line(skel_img_box[joint][0], skel_img_box[joint][1], features[feat][0], features[feat][1])
# skimage.draw.set_color(cam.colorIm[box], l, (255,255,255))
# Add circles to image
cam.colorIm = np.ascontiguousarray(cam.colorIm)
if 0: #get_color:
cam.colorIm = display_skeletons(
cam.colorIm,
skel_img[:, [1, 0, 2]] * np.array(visible_joints)[:, None], (
0,
255,
),
skel_type='Other',
skel_contraints=constraint_links)
for i, s in enumerate(skel_img):
# if i not in skel_correct:
if i not in occlusion_set:
c = circle(s[0], s[1], 5)
cam.colorIm[c[0], c[1]] = (255, 0, 0)
# cam.colorIm = display_skeletons(cam.colorIm, world2rgb(skel_init+im_pos_mean, [240,320])[:,[1,0]], skel_type='Other', skel_contraints=constraint_links)
if 1:
if len(face_detector.face_position) > 0:
for (x, y) in face_detector.face_position:
pt1 = (int(y) + box[1].start - 15,
int(x) + box[0].start - 15)
pt2 = (pt1[0] + int(15), pt1[1] + int(15))
cv2.rectangle(cam.colorIm, pt1, pt2, (255, 0, 0), 3, 8, 0)
if len(cam_skels) > 0:
# cam.colorIm = display_skeletons(cam.colorIm[:,:,2], skel_msr_im, (255,), skel_type='Kinect')[:,:,None]
cam.colorIm[:, :, 1] = display_skeletons(cam.colorIm[:, :, 2],
skel_msr_im, (255, ),
skel_type='Kinect')
cam.visualize()
cam.depthIm = local_binary_pattern(
cam.depthIm * cam.foregroundMask, 50, 10)
cv2.imshow("Depth", cam.depthIm / float(cam.depthIm.max()))
# cam2.visualize()
# embed()
# 3D Visualization
if 0:
from mayavi import mlab
# figure = mlab.figure(1, bgcolor=(0,0,0), fgcolor=(1,1,1))
figure = mlab.figure(1, bgcolor=(1, 1, 1))
figure.scene.disable_render = True
mlab.clf()
mlab.view(azimuth=-45, elevation=45, roll=0, figure=figure)
mlab.points3d(-skel_current[:, 1],
-skel_current[:, 0],
skel_current[:, 2],
scale_factor=100.,
color=(.5, .5, .5))
for c in constraint_links:
x = np.array([skel_current[c[0]][0], skel_current[c[1]][0]])
y = np.array([skel_current[c[0]][1], skel_current[c[1]][1]])
z = np.array([skel_current[c[0]][2], skel_current[c[1]][2]])
mlab.plot3d(-y, -x, z, tube_radius=25., color=(1, 0, 0))
figure.scene.disable_render = False
# 3-panel view
if 0:
subplot(2, 2, 1)
scatter(skel_current[:, 1], skel_current[:, 2])
for i, c in enumerate(constraint_links):
plot([skel_current[c[0], 1], skel_current[c[1], 1]],
[skel_current[c[0], 2], skel_current[c[1], 2]])
axis('equal')
subplot(2, 2, 3)
scatter(skel_current[:, 1], -skel_current[:, 0])
for i, c in enumerate(constraint_links):
plot([skel_current[c[0], 1], skel_current[c[1], 1]],
[-skel_current[c[0], 0], -skel_current[c[1], 0]])
axis('equal')
subplot(2, 2, 4)
scatter(skel_current[:, 2], -skel_current[:, 0])
for i, c in enumerate(constraint_links):
plot([skel_current[c[0], 2], skel_current[c[1], 2]],
[-skel_current[c[0], 0], -skel_current[c[1], 0]])
axis('equal')
# show()
# ------------------------------------------------------------
video_writer.write(cam.colorIm[:, :, [2, 1, 0]])
frame_count += 1
print 'Done'
def main(device_id, record, base_dir, frame_difference_percent, get_skeleton, anonomize, viz, motion_lag_time=10, min_fps=0.5):
'''
---parameters---
device_id
record
base_dir
frame_difference_percent
get_skeleton
anonomize
viz
motion_lag_time
min_fps
'''
'''------------ Setup Kinect ------------'''
''' Physical Kinect '''
depthDevice = RealTimeDevice(device=device_id, get_depth=True, get_color=True, get_skeleton=get_skeleton)
depthDevice.start()
maxFramerate = 100
minFramerate = min_fps
recentMotionTime = time.time()
imgStoreCount = 100
''' ------------- Main -------------- '''
''' Setup time-based stuff '''
prevTime = 0
prevFrame = 0
prevFrameTime = 0
currentFrame = 0
ms = time.time()
prevFPSTime = time.time()
diff = 0
prevSec = 0;
secondCount = 0
prevSecondCountMax = 0
''' Ensure base folder is there '''
createDirectory(base_dir)
skel_dir = None
skel_filename = None
maskDir = None
maskName = None
depthOld = []
backgroundModel = None
while 1:
# try:
if 1:
depthDevice.update()
colorRaw = depthDevice.colorIm
depthRaw = depthDevice.depthIm
users = depthDevice.users
skel = None
if len(depthOld) == imgStoreCount:
depthOld.pop(0)
''' If framerate is too fast then skip '''
''' Keep this after update to ensure fast enough kinect refresh '''
if (time.time() - float(ms))*1000 < 1000.0/maxFramerate:
continue
''' Get new time info '''
time_ = time.localtime()
day = str(time_.tm_yday)
hour = str(time_.tm_hour)
minute = str(time_.tm_min)
second = str(time_.tm_sec)
ms = str(time.time())
ms_str = ms
''' Look at how much of the image has changed '''
if depthOld != []:
mask = (depthRaw != 0) * (depthOld[0] != 0)
diff = (((depthRaw - depthOld[0]).astype(np.int16) > 200)*mask).sum() / (float(mask.sum()) * 100.)
''' We want to watch all video for at least 5 seconds after we seen motion '''
''' This prevents problems where there is small motion that doesn't trigger the motion detector '''
if diff > frame_difference_percent:
recentMotionTime = time.time()
depthOld.append(depthRaw)
if anonomize:
'''Background model'''
if backgroundModel is None:
bgSubtraction = AdaptiveMixtureOfGaussians(depthRaw, maxGaussians=3, learningRate=0.2, decayRate=0.9, variance=100**2)
backgroundModel = bgSubtraction.getModel()
continue
else:
bgSubtraction.update(depthRaw)
backgroundModel = bgSubtraction.getModel()
cv2.imshow("BG Model", backgroundModel/backgroundModel.max())
foregroundMask = bgSubtraction.get_foreground(thresh=100)
''' Find people '''
foregroundMask, _, _ = extract_people(depthRaw, foregroundMask, minPersonPixThresh=5000, gradientFilter=True, gradThresh=15)
else:
foregroundMask = None
''' Write to file if there has been substantial change. '''
if diff > frame_difference_percent or time.time()-prevFrameTime > 1/minFramerate or time.time()-recentMotionTime < motion_lag_time:
currentFrame += 1
if depthRaw != []:
''' Logical time '''
if second != prevSec:
prevSecondCountMax = secondCount
secondCount = 0
prevSec = second
else:
secondCount = int(secondCount) + 1
secondCount = str(secondCount)
if len(ms_str) == 1:
ms_str = '0' + ms_str
if len(secondCount) == 1:
secondCount = '0' + secondCount
''' Keep track of framerate '''
if prevTime != second:
prevTime = second
print "FPS: {0:.1f} Diff: {1:.1f}%".format((currentFrame-prevFrame)/(time.time()-prevFPSTime), diff)
prevFrame = currentFrame
prevFPSTime = time.time()
''' Create folder/file names '''
if record:
if 1:
''' Version 1. 2012 '''
ms_str = str(ms)[str(ms).find(".")+1:]
depth_dir = base_dir+'depth/'+day+"/"+hour+"/"+minute+"/device_"+str(device_id)
depth_filename = depth_dir + "/depth_"+day+"_"+hour+"_"+minute+"_"+second+"_"+secondCount+"_"+ms_str+".png"
color_dir = base_dir+'color/'+day+"/"+hour+"/"+minute+"/device_"+str(device_id)
color_filename = color_dir + "/color_"+day+"_"+hour+"_"+minute+"_"+second+"_"+secondCount+"_"+ms_str+".jpg"
if get_skeleton:
skel_dir = base_dir+'skel/'+day+"/"+hour+"/"+minute+"/device_"+str(device_id)
skel_filename = skel_dir + "/skel_"+day+"_"+hour+"_"+minute+"_"+second+"_"+secondCount+"_"+ms_str+"_.dat"
if anonomize:
maskDir = base_dir+'mask/'+day+"/"+hour+"/"+minute+"/device_"+str(device_id)
maskName = maskDir + "/mask_"+day+"_"+hour+"_"+minute+"_"+second+"_"+secondCount+"_"+ms_str+".jpg"
else:
''' Version 2. April 2013 '''
base_sub_dir = "{:s}device_{:d}/{:s}/{:s}/{:s}".format(base_dir,device_id,day,hour,minute)
depth_dir = "{:s}/depth".format(base_sub_dir)
color_dir = "{:s}/color".format(base_sub_dir)
depth_filename = "{:s}/depth_{:s}_{:s}_{:s}_{:s}_{:s}_{:s}_.png".format(depth_dir,day,hour,minute,second,secondCount,ms_str)
color_filename = "{:s}/color_{:s}_{:s}_{:s}_{:s}_{:s}_{:s}_.jpg".format(color_dir,day,hour,minute,second,secondCount,ms_str)
if get_skeleton:
skel_dir = "{:s}/skel".format(base_sub_dir)
skel_filename = "{:s}/skel_{:s}_{:s}_{:s}_{:s}_{:s}_{:s}_.dat".format(skel_dir,day,hour,minute,second,secondCount,ms_str)
if anonomize:
maskDir = "{:s}/mask".format(base_sub_dir)
maskName = "{:s}/mask_{:s}_{:s}_{:s}_{:s}_{:s}_{:s}_.jpg".format(skel_dir,day,hour,minute,second,secondCount,ms_str)
''' Create folders if they doesn't exist '''
createDirectory(depth_dir)
createDirectory(color_dir)
if get_skeleton:
createDirectory(skel_dir)
if anonomize:
createDirectory(maskDir)
''' Save data '''
save_frame(depth_filename, depthRaw, color_filename, colorRaw, skel_filename, users, maskName=maskName, mask=foregroundMask)
prevFrameTime = time.time()
''' Display skeletons '''
if viz:
d = np.array(depthRaw)
d /= (np.nanmin([d.max(), 2**16])/256.0)
d = d.astype(np.uint8)
if get_skeleton:
for u_key in users.keys():
u = users[u_key]
pt = skel2depth(np.array([u.com]))[0]
w = 10
d[pt[1]-w:pt[1]+w, pt[0]-w:pt[0]+w] = 255
w = 3
if u.tracked:
pts = skel2depth(np.array(u.jointPositions.values()), d.shape)
d = display_skeletons(d, pts, (100,0,0), skel_type='Kinect')
if 1:
cv2.imshow("imageD", d)
if 0:
cv2.imshow("imageM", colorRaw + (255-colorRaw)*(foregroundMask>0)[:,:,np.newaxis] + 50*(((foregroundMask)[:,:,np.newaxis])))
if 1:
cv2.imshow("imageC", colorRaw)
ret = cv2.waitKey(10)
if ret >= 0:
break
def main(anonomization=False):
# Setup kinect data player
# cam = KinectPlayer(base_dir='./', bg_subtraction=False, get_depth=True, get_color=True, get_skeleton=False)
# actions = [2, 5, 7]
actions = [2]
actions_labels = ['JumpingJacks', 'Waving', 'Clapping']
action = 'JumpingJacks'
subjects = range(1,10)
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/', kinect=1, actions=actions, subjects=subjects, reps=[1], get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
if anonomization:
''' bg_type can be:
'box'[param=max_depth]
'static'[param=background]
'mean'
'median'
'adaptive_mog'
See BasePlayer for more details
'''
cam.set_bg_model(bg_type='box', param=2600)
# Test HOG pedestrian detector
# cv2.HOGDescriptor_getDefaultPeopleDetector()
# imshow(color[people])
body_data = [[]]
framerate = 1
prev_n = len(cam.kinect_folder_names)
action_idx = 0
# embed()
while cam.next(framerate):
if len(cam.kinect_folder_names) != prev_n:
# action_idx += 1
body_data += [[]]
prev_n = len(cam.kinect_folder_names)
body_data[-1] += [cam.users_uv[0]]
continue
if 0:
people_all = list(cv.HOGDetectMultiScale(cv.fromarray(cam.colorIm.mean(-1).astype(np.uint8)), cv.CreateMemStorage(0), hit_threshold=-1.5))
print len(people_all), " people detected"
for i,people in enumerate(people_all):
# embed()
try:
print people
# tmp = cam.colorIm[people[0][0]:people[0][0]+people[1][0], people[0][1]:people[0][1]+people[1][1]]
# tmp = cam.colorIm[people[0][1]:people[0][1]+people[1][1], people[0][0]:people[0][0]+people[1][0]]
cam.colorIm[people[0][1]:people[0][1]+people[1][1], people[0][0]:people[0][0]+people[1][0]] += 50
# cv2.imshow("Body2 "+str(i), tmp)
# subplot(4, len(people_all)/4, i + 1)
# imshow(tmp)
# tmp = cam.colorIm[people[0][0]:people[0][0]+people[0][1], people[1][0]:people[1][0]+people[1][1]]
# cv2.imshow("Body1 "+str(i), tmp)
# tmp = cam.colorIm[people[1][0]:people[1][0]+people[1][1], people[0][0]:people[0][0]+people[0][1]]
# print tmp
# cv2.imshow("Body "+str(i), tmp)
except:
pass
# show()
if anonomization and cam.mask is not None:
mask = (sm.imresize(cam.mask, [480,640]) > 0).copy()
mask[40:170, 80:140] = True # person in background
px = draw.circle(145,285, 40)
mask[px] = True
# Version 1 - Blur + Circle
color = cam.colorIm.copy()
color[mask] = cv2.GaussianBlur(color, (29,29), 50)[mask]
subplot(1,3,1)
imshow(color)
# Version 2 - Noise on mask
color = cam.colorIm.copy()
noise_key = np.random.randint(0, 255, cam.colorIm.shape).astype(np.uint8)
color[mask] = color[mask]+noise_key[mask]
subplot(1,3,2)
imshow(color)
# Version 3 - All noise
color = cam.colorIm.copy()
color = color+noise_key
subplot(1,3,3)
imshow(color)
show()
# cam.colorIm = cam.colorIm[:,:,[2,1,0]]
# cam.colorIm *= mask[:,:,None]
# cam.visualize(color=True, depth=True, text=True, colorize=True, depth_bounds=[0,4000])
# cam.visualize()
cam.colorIm = display_skeletons(cam.colorIm, cam.users_uv[0], skel_type='Kinect', color=(0,255,0))
cam.visualize()
body_data[actions_labels[action_idx]] += [cam.users_uv[0]]
# cam.visualize(color=True, depth=True, text=False, colorize=False, depth_bounds=None)
print 'Done'
# Pause at the end
# embed()
import scipy.io as si
si.matlab.savemat(action+".mat", {'data':body_data})
def main(visualize=False, learn=False, actions=None, subjects=None, n_frames=220):
# learn = True
# learn = False
if actions is []:
actions = [2]
if subjects is []:
subjects = [2]
# actions = [1]
# actions = [1, 2, 3, 4, 5]
# subjects = [1]
if 1:
MHAD = True
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/', kinect=1, actions=actions, subjects=subjects, reps=[1], get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
else:
MHAD = False
cam = KinectPlayer(base_dir='./', device=1, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_A.tif')
# cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_B.tif')
cam.bgSubtraction.backgroundModel = np.array(bg.getdata()).reshape([240,320]).clip(0, 4500)
height, width = cam.depthIm.shape
skel_previous = None
face_detector = FaceDetector()
hand_detector = HandDetector(cam.depthIm.shape)
# curve_detector = CurveDetector(cam.depthIm.shape)
# Video writer
# video_writer = cv2.VideoWriter("/Users/colin/Desktop/test.avi", cv2.cv.CV_FOURCC('M','J','P','G'), 15, (320,240))
# Save Background model
# im = Image.fromarray(cam.depthIm.astype(np.int32), 'I')
# im.save("/Users/Colin/Desktop/k2.png")
# Setup pose database
append = True
append = False
# pose_database = PoseDatabase("PoseDatabase.pkl", learn=learn, search_joints=[0,4,7,10,13], append=append)
pose_database = PoseDatabase("PoseDatabase.pkl", learn=learn, search_joints=[0,2,4,5,7,10,13], append=append)
# Setup Tracking
skel_init, joint_size, constraint_links, features_joints,skel_parts, convert_to_kinect = get_14_joint_properties()
constraint_values = []
for c in constraint_links:
constraint_values += [np.linalg.norm(skel_init[c[0]]-skel_init[c[1]], 2)]
constraint_values = np.array(constraint_values)
skel_current = None#skel_init.copy()
skel_previous = None#skel_current.copy()
# Evaluation
accuracy_all_db = []
accuracy_all_track = []
joint_accuracy_db = []
joint_accuracy_track = []
# geo_accuracy = []
# color_accuracy = []
# lbp_accuracy = []
frame_count = 0
frame_rate = 1
if not MHAD:
cam.next(350)
frame_prev = 0
# try:
if 1:
while cam.next(frame_rate):# and frame_count < n_frames:
if frame_count - frame_prev > 100:
print ""
print "Frame #{0:d}".format(frame_count)
frame_prev = frame_count
if not MHAD:
if len(cam.users) == 0:
continue
else:
# cam.users = [np.array(cam.users[0]['jointPositions'].values())]
if np.any(cam.users[0][0] == -1):
continue
cam.users[0][:,1] *= -1
cam.users_uv_msr = [cam.camera_model.world2im(cam.users[0], [240,320])]
# Apply mask to image
if MHAD:
mask = cam.get_person(2) > 0
else:
mask = cam.get_person() > 0
if np.all(mask==False):
continue
im_depth = cam.depthIm
cam.depthIm[cam.depthIm>3000] = 0
im_color = cam.colorIm*mask[:,:,None]
cam.colorIm *= mask[:,:,None]
pose_truth = cam.users[0]
pose_truth_uv = cam.users_uv_msr[0]
# Get bounding box around person
box = nd.find_objects(mask)[0]
d = 20
# Widen box
box = (slice(np.maximum(box[0].start-d, 0), \
np.minimum(box[0].stop+d, height-1)), \
slice(np.maximum(box[1].start-d, 0), \
np.minimum(box[1].stop+d, width-1)))
box_corner = [box[0].start,box[1].start]
''' ---------- ----------------------------------- --------'''
''' ----------- Feature Detector centric approach ---------'''
''' ---------- ----------------------------------- --------'''
''' ---- Calculate Detectors ---- '''
# Face detection
face_detector.run(im_color[box])
# Skin detection
hand_markers = hand_detector.run(im_color[box], n_peaks=3)
# Calculate Geodesic Extrema
im_pos = cam.camera_model.im2PosIm(cam.depthIm*mask)[box] * mask[box][:,:,None]
geodesic_markers = geodesic_extrema_MPI(im_pos, iterations=5, visualize=False)
_, geo_map = geodesic_extrema_MPI(im_pos, iterations=1, visualize=True)
geodesic_markers_pos = im_pos[geodesic_markers[:,0], geodesic_markers[:,1]]
markers = list(geodesic_markers) + list(hand_markers) #+ list(lop_markers) + curve_markers
markers = np.array([list(x) for x in markers])
''' ---- Database lookup ---- '''
if 1:
pts_mean = im_pos[(im_pos!=0)[:,:,2]].mean(0)
if learn:
# Normalize pose
pose_uv = cam.users_uv[0]
if np.any(pose_uv==0):
print "skip"
frame_count += frame_rate
continue
pose_database.update(pose_truth - pts_mean)
else:
# Concatenate markers
markers = list(geodesic_markers) + hand_markers
markers = np.array([list(x) for x in markers])
# Normalize pose
pts = im_pos[markers[:,0], markers[:,1]]
pts = np.array([x for x in pts if x[0] != 0])
pts -= pts_mean
# Get closest pose
pose = pose_database.query(pts, knn=5)
# embed()
for i in range(5):
pose_tmp = cam.camera_model.world2im(pose[i]+pts_mean, cam.depthIm.shape)
cam.colorIm = display_skeletons(cam.colorIm, pose_tmp, skel_type='Kinect', color=(0,i*40+50,0))
pose = pose[0]
# im_pos -= pts_mean
# R,t = IterativeClosestPoint(pose, im_pos.reshape([-1,3])-pts_mean, max_iters=5, min_change=.001, pt_tolerance=10000)
# pose = np.dot(R.T, pose.T).T - t
# pose = np.dot(R, pose.T).T + t
pose += pts_mean
pose_uv = cam.camera_model.world2im(pose, cam.depthIm.shape)
# print pose
surface_map = nd.distance_transform_edt(-nd.binary_erosion(mask[box]), return_distances=False, return_indices=True)
try:
pose_uv[:,:2] = surface_map[:, pose_uv[:,0]-box_corner[0], pose_uv[:,1]-box_corner[1]].T + [box_corner[0], box_corner[1]]
except:
pass
pose = cam.camera_model.im2world(pose_uv, cam.depthIm.shape)
# print pose
''' ---- Tracker ---- '''
# surface_map = nd.distance_transform_edt(-mask[box], return_distances=False, return_indices=True)
# surface_map = nd.distance_transform_edt(im_pos[:,:,2]==0, return_distances=False, return_indices=True)
if skel_previous is None:
# if 1:
skel_previous = pose.copy()
skel_current = pose.copy()
skel_previous_uv = pose_uv.copy()
skel_current_uv = pose_uv.copy()
for _ in range(1):
# ---- (Step 1A) Find feature coordespondences ----
try:
skel_previous_uv[:,:2] = surface_map[:, skel_previous_uv[:,0]-box_corner[0], skel_previous_uv[:,1]-box_corner[1]].T + [box_corner[0], box_corner[1]]
except:
pass
skel_current = cam.camera_model.im2world(skel_previous_uv, cam.depthIm.shape)
# Alternative method: use kdtree
## Calc euclidian distance between each pixel and all joints
px_corr = np.zeros([im_pos.shape[0], im_pos.shape[1], len(skel_current)])
# for i,s in enumerate(pose):
# for i,s in enumerate(skel_current):
# px_corr[:,:,i] = np.sqrt(np.sum((im_pos - s)**2, -1))# / joint_size[i]**2
# for i,s in enumerate(pose_uv):
# Geodesics
for i,s in enumerate(skel_previous_uv):
''' Problem: need to constrain pose_uv to mask '''
_, geo_map = geodesic_extrema_MPI(im_pos, [s[0]-box_corner[0],s[1]-box_corner[1]], iterations=1, visualize=True)
px_corr[:,:,i] = geo_map
subplot(2,7,i+1)
# imshow(geo_map, vmin=0, vmax=2000)
# axis('off')
px_corr[geo_map==0,i] = 9999
cv2.imshow('gMap', (px_corr.argmin(-1)+1)/15.*mask[box])
## Handle occlusions by argmax'ing over set of skel parts
# visible_configurations = list(it.product([0,1], repeat=5))[1:]
visible_configurations = [
# [0,1,1,1,1],
# [1,0,0,0,0],
[1,1,1,1,1]
]
px_visibility_label = np.zeros([im_pos.shape[0], im_pos.shape[1], len(visible_configurations)], dtype=np.uint8)
visible_scores = np.ones(len(visible_configurations))*np.inf
# Try each occlusion configuration set
for i,v in enumerate(visible_configurations):
visible_joints = list(it.chain.from_iterable(skel_parts[np.array(v)>0]))
px_visibility_label[:,:,i] = np.argmin(px_corr[:,:,visible_joints], -1)#.reshape([im_pos.shape[0], im_pos.shape[1]])
visible_scores[i] = np.min(px_corr[:,:,visible_joints], -1).sum()
# Choose best occlusion configuration
occlusion_index = np.argmin(visible_scores)
occlusion_configuration = visible_configurations[occlusion_index]
occlusion_set = list(it.chain.from_iterable(skel_parts[np.array(visible_configurations[occlusion_index])>0]))
# Choose label for pixels based on occlusion configuration
px_label = px_visibility_label[:,:,occlusion_index]*mask[box]
px_label_flat = px_visibility_label[:,:,occlusion_index][mask[box]].flatten()
visible_joints = [1 if x in occlusion_set else 0 for x in range(len(pose))]
# print visible_joints
# Project distance to joint's radius
px_joint_displacement = im_pos[mask[box]] - skel_current[px_label_flat]
px_joint_magnitude = np.sqrt(np.sum(px_joint_displacement**2,-1))
joint_mesh_pos = skel_current[px_label_flat] + px_joint_displacement*(joint_size[px_label_flat]/px_joint_magnitude)[:,None]
px_joint_displacement = joint_mesh_pos - im_pos[mask[box]]
# Ensure pts aren't too far away
px_joint_displacement[np.abs(px_joint_displacement) > 500] = 0
# embed()
if 0:
x = im_pos.copy()*0
x[mask[box]] = joint_mesh_pos
for i in range(3):
subplot(1,4,i+1)
imshow(x[:,:,i])
axis('off')
subplot(1,4,4)
imshow((px_label+1)*mask[box])
# Calc the correspondance change in position for each joint
correspondence_displacement = np.zeros([len(skel_current), 3])
ii = 0
for i,_ in enumerate(skel_current):
if i in occlusion_set:
labels = px_label_flat==i
correspondence_displacement[i] = np.sum(px_joint_displacement[px_label_flat==ii], 0) / np.sum(px_joint_displacement[px_label_flat==ii]!=0)
ii+=1
correspondence_displacement = np.nan_to_num(correspondence_displacement)
# print correspondence_displacement
# Viz correspondences
if 0:
x = im_pos.copy()*0
x[mask[box]] = px_joint_displacement
for i in range(3):
subplot(1,4,i+1)
imshow(x[:,:,i])
axis('off')
subplot(1,4,4)
imshow((px_label+1)*mask[box])
# embed()
# for j in range(3):
# for i in range(14):
# subplot(3,14,j*14+i+1)
# imshow(x[:,:,j]*((px_label==i)*mask[box]))
# axis('off')
show()
# ---- (Step 2) Update pose state, x ----
lambda_p = .0
lambda_c = 1.
skel_prev_difference = (skel_current - skel_previous)
# print skel_prev_difference
skel_current = skel_previous \
+ lambda_p * skel_prev_difference \
- lambda_c * correspondence_displacement#\
# ---- (Step 3) Add constraints ----
if 1:
# A: Link lengths / geometry
# skel_current = link_length_constraints(skel_current, constraint_links, constraint_values, alpha=.5)
skel_current = geometry_constraints(skel_current, joint_size, alpha=0.5)
skel_current = collision_constraints(skel_current, constraint_links)
skel_img_box = (cam.camera_model.world2im(skel_current, cam.depthIm.shape) - [box[0].start, box[1].start, 0])#/mask_interval
skel_img_box = skel_img_box.clip([0,0,0], [box[0].stop-box[0].start-1, box[1].stop-box[1].start-1, 9999])
# skel_img_box = skel_img_box.clip([0,0,0], [cam.depthIm.shape[0]-1, cam.depthIm.shape[1]-1, 9999])
# B: Ray-cast constraints
# embed()
skel_current, skel_current_uv = ray_cast_constraints(skel_current, skel_img_box, im_pos, surface_map, joint_size)
# skel_img_box -= [box[0].start, box[1].start, 0]
# # Map back from mask to image
# try:
# skel_current_uv[:,:2] = surface_map[:, skel_img_box[:,0], skel_img_box[:,1]].T# + [box_corner[0], box_corner[1]]
# except:
# pass
prob = link_length_probability(skel_current, constraint_links, constraint_values, 100)
# print "Prob:", np.mean(prob), np.min(prob), prob
print frame_count
thresh = .05
if np.min(prob) < thresh:# and frame_count > 1:
print 'Resetting pose'
for c in constraint_links[prob<thresh]:
for cc in c:
skel_current_uv[c] = pose_uv[c] - [box[0].start, box[1].start, 0]
skel_current[c] = pose[c]
# skel_current_uv = pose_uv.copy() - [box[0].start, box[1].start, 0]
# skel_current = pose.copy()
skel_current_uv = skel_current_uv + [box[0].start, box[1].start, 0]
skel_current = cam.camera_model.im2world(skel_current_uv, cam.depthIm.shape)
else:
skel_current_uv = (cam.camera_model.world2im(skel_current, cam.depthIm.shape))
# skel_img_box = skel_img_box.clip([0,0,0], [cam.depthIm.shape[0]-1, cam.depthIm.shape[1]-1, 9999])
# Update for next round
skel_previous = skel_current.copy()
skel_previous_uv = skel_current_uv.copy()
''' ---- Accuracy ---- '''
# embed()
if 1 and not learn:
# pose_truth = cam.users[0]
error_db = pose_truth - pose
error_track = pose_truth - skel_current
# print "Error", error
error_l2_db = np.sqrt(np.sum(error_db**2, 1))
error_l2_track = np.sqrt(np.sum(error_track**2, 1))
joint_accuracy_db += [error_l2_db]
joint_accuracy_track += [error_l2_track]
accuracy_db = np.sum(error_l2_db < 150) / 14.
accuracy_track = np.sum(error_l2_track < 150) / 14.
print "Current db:", accuracy_db, error_l2_db.mean()
print "Current track:", accuracy_track, error_l2_track.mean()
print ""
accuracy_all_db += [accuracy_db]
accuracy_all_track += [accuracy_track]
# print "Running avg:", np.mean(accuracy_all)
# print "Joint avg (per-joint):", np.mean(joint_accuracy_all, -1)
# print "Joint avg (overall):", np.mean(joint_accuracy_all)
''' --- Visualization --- '''
display_markers(cam.colorIm, hand_markers[:2], box, color=(0,250,0))
if len(hand_markers) > 2:
display_markers(cam.colorIm, [hand_markers[2]], box, color=(0,200,0))
display_markers(cam.colorIm, geodesic_markers, box, color=(200,0,0))
# display_markers(cam.colorIm, curve_markers, box, color=(0,100,100))
# display_markers(cam.colorIm, lop_markers, box, color=(0,0,200))
cam.colorIm = display_skeletons(cam.colorIm, pose_truth_uv, skel_type='Kinect', color=(0,255,0))
cam.colorIm = display_skeletons(cam.colorIm, pose_uv, skel_type='Kinect')
cam.colorIm = display_skeletons(cam.colorIm, skel_current_uv, skel_type='Kinect', color=(0,0,255))
# cam.visualize(color=True, depth=False)
cam.visualize(color=True, depth=True)
# embed()
# ------------------------------------------------------------
# video_writer.write((geo_clf_map/float(geo_clf_map.max())*255.).astype(np.uint8))
# video_writer.write(cam.colorIm[:,:,[2,1,0]])
frame_count += frame_rate
# except:
# pass
print "-- Results for subject {:d} action {:d}".format(subjects[0],actions[0])
print "Running avg (db):", np.mean(accuracy_all_db)
print "Running avg (track):", np.mean(accuracy_all_track)
print "Joint avg (overall db):", np.mean(joint_accuracy_db)
print "Joint avg (overall track):", np.mean(joint_accuracy_track)
# print 'Done'
embed()
return
def main(visualize=False, learn=False, actions=[1], subjects=[1], n_frames=220):
# search_joints=[0,2,4,5,7,10,13]
search_joints=range(14)
# interactive = True
interactive = False
save_results = False
if 0:
learn = False
# learn = learn
else:
learn = True
actions = [1]
subjects = [1]
# actions = range(1,10)
# subjects = range(1,9)
if 1:
dataset = 'MHAD'
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/', kinect=1, actions=actions, subjects=subjects, reps=[1], get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
elif 0:
dataset = 'JHU'
cam = KinectPlayer(base_dir='./', device=1, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_A.tif')
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/Wall_Background_A.tif')
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/Office_Background_A.tif')
# bg = Image.open('/Users/colin/Data/WICU_May2013_C2/WICU_C2_Background.tif')
# cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_B.tif')
cam.bgSubtraction.backgroundModel = np.array(bg.getdata()).reshape([240,320]).clip(0, 4500) - 000.
else:
# Realtime
dataset = 'RT'
cam = RealtimePlayer(device=0, edit=True, get_depth=True, get_color=True, get_skeleton=True)
# cam.set_bg_model('box', 2500)
tmp = cam.depthIm
tmp[tmp>4000] = 4000
cam.set_bg_model(bg_type='static', param=tmp)
# embed()
height, width = cam.depthIm.shape
skel_previous = None
face_detector = FaceDetector()
hand_detector = HandDetector(cam.depthIm.shape)
n_joints = 14
# gmm = GMM(n_components=n_joints)
kmeans = KMeans(n_clusters=n_joints, n_init=4, max_iter=100)
# Video writer
# video_writer = cv2.VideoWriter("/Users/colin/Desktop/test.avi", cv2.cv.CV_FOURCC('M','J','P','G'), 15, (640,480))
# Save Background model
# im = Image.fromarray(cam.depthIm.astype(np.int32), 'I')
# im.save("/Users/Colin/Desktop/k2.png")
# Setup pose database
append = True
# append = False
# pose_database = PoseDatabase("PoseDatabase.pkl", learn=learn, search_joints=[0,4,7,10,13], append=append)
# pose_database = PoseDatabase("PoseDatabase.pkl", learn=learn, search_joints=search_joints,
# append=append, scale=1.1, n_clusters=-1)#1000
pose_database = PoseDatabase("PoseDatabase.pkl", learn=learn, search_joints=search_joints,
append=append, scale=1.0, n_clusters=1500)
pose_prob = np.ones(len(pose_database.database), dtype=np.float)/len(pose_database.database)
# embed()
# Setup Tracking
skel_init, joint_size, constraint_links, features_joints,skel_parts, convert_to_kinect = get_14_joint_properties()
constraint_values = []
for c in constraint_links:
constraint_values += [np.linalg.norm(skel_init[c[0]]-skel_init[c[1]], 2)]
constraint_values = np.array(constraint_values)
skel_current = None#skel_init.copy()
skel_previous = None#skel_current.copy()
skel_previous_uv = None
# Evaluation
accuracy_all_db = []
accuracy_all_track = []
joint_accuracy_db = []
joint_accuracy_track = []
if not learn:
try:
results = pickle.load(open('Accuracy_Results.pkl'))
except:
results = { 'subject':[], 'action':[], 'accuracy_all':[],
'accuracy_mean':[], 'joints_all':[],
'joint_mean':[], 'joint_median':[]}
frame_count = 0
frame_rate = 10
if dataset == 'JHU':
cam.next(350)
# cam.next(700)
pass
frame_prev = 0
try:
# if 1:
while cam.next(frame_rate):# and frame_count < n_frames:
# Print every once in a while
if frame_count - frame_prev > 99:
print ""
print "Frame #{0:d}".format(frame_count)
frame_prev = frame_count
if dataset in ['MHAD', 'JHU']:
users = deepcopy(cam.users)
else:
users = deepcopy(cam.user_skels)
ground_truth = False
if dataset in ['RT','JHU']:
if len(users) > 0:
if not np.any(users[0][0] == -1):
ground_truth = True
users[0][:,1] *= -1
cam.users_uv_msr = [cam.camera_model.world2im(users[0], cam.depthIm.shape)]
else:
ground_truth = True
# Apply mask to image
mask = cam.get_person(200) == 1 # > 0
# cv2.imshow('bg',(mask*255).astype(np.uint8))
# cv2.imshow('bg',cam.colorIm)
# cv2.waitKey(1)
if type(mask)==bool or np.all(mask==False):
# print "No mask"
continue
# cv2.imshow('bg',cam.bgSubtraction.backgroundModel)
# cv2.imshow('bg',(mask*255).astype(np.uint8))
im_depth = cam.depthIm
# if dataset in ['RT']:
# cam.depthIm[cam.depthIm>2500] = 0
if cam.colorIm is not None:
im_color = cam.colorIm*mask[:,:,None]
cam.colorIm *= mask[:,:,None]
if ground_truth:
pose_truth = users[0]
pose_truth_uv = cam.users_uv_msr[0]
# Get bounding box around person
box = nd.find_objects(mask)[0]
d = 20
# Widen box
box = (slice(np.maximum(box[0].start-d, 0), \
np.minimum(box[0].stop+d, height-1)), \
slice(np.maximum(box[1].start-d, 0), \
np.minimum(box[1].stop+d, width-1)))
box_corner = [box[0].start,box[1].start]
mask_box = mask[box]
''' ---------- ----------------------------------- --------'''
''' ---------- ----------------------------------- --------'''
''' ---- Calculate Detectors ---- '''
# Face detection
# face_detector.run(im_color[box])
# Skin detection
# hand_markers = hand_detector.run(im_color[box], n_peaks=3)
hand_markers = []
# Calculate Geodesic Extrema
im_pos = cam.camera_model.im2PosIm(cam.depthIm*mask)[box]
# geodesic_markers = geodesic_extrema_MPI(im_pos, iterations=5, visualize=False)
if 1:
''' Find pts using kmeans or gmm '''
pts = im_pos[np.nonzero(im_pos)].reshape([-1,3])
# gmm.fit(pts)
kmeans.fit(pts)
# pts = cam.camera_model.world2im(gmm.means_)
pts = cam.camera_model.world2im(kmeans.cluster_centers_)
geodesic_markers = pts[:,:2] - box_corner
else:
''' Find pts using geodesic extrema '''
geodesic_markers = geodesic_extrema_MPI(im_pos, iterations=10, visualize=False)
if len(geodesic_markers) == 0:
print "No markers"
continue
# Concatenate markers
markers = list(geodesic_markers) + list(hand_markers) #+ list(lop_markers) + curve_markers
markers = np.array([list(x) for x in markers])
if np.any(markers==0):
print "Bad markers"
continue
''' ---- Database lookup ---- '''
time_t0 = time.time()
pts_mean = im_pos[(im_pos!=0)[:,:,2]].mean(0)
if learn and ground_truth:
# pose_uv = pose_truth_uv
if np.any(pose_truth_uv==0):
frame_count += frame_rate
if not interactive:
continue
# Markers can be just outside of bounds
markers = list(geodesic_markers) + hand_markers
markers = np.array([list(x) for x in markers])
# pose_database.update(pose_truth-pts_mean, keys=im_pos[markers[:,0],markers[:,1]]-pts_mean)
pose_database.update(pose_truth-pts_mean)
if not interactive:
continue
# else:
if 1:
# Normalize pose
pts = im_pos[markers[:,0], markers[:,1]]
pts = np.array([x for x in pts if x[0] != 0])
pts -= pts_mean
# Get closest pose
# Based on markers/raw positions
# poses_obs, pose_error = pose_database.query(pts, knn=1, return_error=True)
pose_error = pose_query(pts, np.array(pose_database.database), search_joints=search_joints)
# pose_error = query_error(pts, pose_database.trees, search_joints=search_joints)
# Based on markers/keys:
# pts = im_pos[markers[:,0], markers[:,1]] - pts_mean
# # poses, pose_error = pose_database.query_tree(pts, knn=len(pose_database.database), return_error=True)
# # poses, pose_error = pose_database.query_flann(pts, knn=len(pose_database.database), return_error=True)
# pose_error = np.sqrt(np.sum((pose_database.keys - pts.reshape([27]))**2, 1))
observation_variance = 100.
prob_obervation = np.exp(-pose_error / observation_variance) / np.sum(np.exp(-pose_error/observation_variance))
# subplot(2,2,1)
# plot(prob_obervation)
# subplot(2,2,2)
# plot(prob_motion)
# subplot(2,2,3)
# plot(pose_prob_new)
# subplot(2,2,4)
# plot(pose_prob)
# show()
# inference = 'NN'
inference = 'Bayes'
# inference = 'PF'
if inference=='NN': # Nearest neighbor
poses_obs, _ = pose_database.query(pts, knn=1, return_error=True)
poses = [poses_obs[0]]
elif inference=='Bayes': # Bayes
if frame_count is 0:
poses_obs, _ = pose_database.query(pts, knn=1, return_error=True)
skel_previous = poses_obs[0].copy()
# poses_m, pose_m_error = pose_database.query(skel_previous-pts_mean, knn=1, return_error=True)
pose_m_error = pose_query(skel_previous-pts_mean, np.array(pose_database.database), search_joints=search_joints)
# poses_m, pose_m_error = pose_database.query(skel_previous-pts_mean+(np.random.random([3,14])-.5).T*30, knn=5, return_error=True)
motion_variance = 10000.
prob_motion = np.exp(-pose_m_error / motion_variance) / np.sum(np.exp(-pose_m_error/motion_variance))
pose_prob_new = prob_obervation*prob_motion
if pose_prob_new.shape == pose_prob.shape:
pose_prob = (pose_prob_new+pose_prob).T/2.
else:
pose_prob = pose_prob_new.T
prob_sorted = np.argsort(pose_prob)
poses = [pose_database.database[np.argmax(pose_prob)]]
# poses = pose_database.database[prob_sorted[-1:]]
# Particle Filter
elif inference=='PF':
prob_sorted = np.argsort(pose_prob)
poses = pose_database.database[prob_sorted[-5:]]
## ICP
# im_pos -= pts_mean
# R,t = IterativeClosestPoint(pose, im_pos.reshape([-1,3])-pts_mean, max_iters=5, min_change=.001, pt_tolerance=10000)
# pose = np.dot(R.T, pose.T).T - t
# pose = np.dot(R, pose.T).T + t
# scale = 1.
# poses *= scale
poses += pts_mean
# print "DB time:", time.time() - time_t0
''' ---- Tracker ---- '''
surface_map = nd.distance_transform_edt(-nd.binary_erosion(mask_box), return_distances=False, return_indices=True)
if skel_previous_uv is None:
skel_previous = poses[0].copy()
skel_current = poses[0].copy()
pose_tmp = cam.camera_model.world2im(poses[0], cam.depthIm.shape)
skel_previous_uv = pose_tmp.copy()
skel_current_uv = pose_tmp.copy()
pose_weights = np.zeros(len(poses), dtype=np.float)
pose_updates = []
pose_updates_uv = []
time_t0 = time.time()
# 2) Sample poses
if inference in ['PF', 'Bayes']:
for pose_i, pose in enumerate(poses):
skel_current = skel_previous.copy()
skel_current_uv = skel_previous_uv.copy()
pose_uv = cam.camera_model.world2im(pose, cam.depthIm.shape)
try:
pose_uv[:,:2] = surface_map[:, pose_uv[:,0]-box_corner[0], pose_uv[:,1]-box_corner[1]].T + [box_corner[0], box_corner[1]]
except:
pass
pose = cam.camera_model.im2world(pose_uv, cam.depthIm.shape)
# ---- (Step 2) Update pose state, x ----
correspondence_displacement = skel_previous - pose
lambda_p = .0
lambda_c = 1.
skel_prev_difference = (skel_current - skel_previous)
# print skel_prev_difference
skel_current = skel_previous \
+ lambda_p * skel_prev_difference \
- lambda_c * correspondence_displacement#\
# ---- (Step 3) Add constraints ----
# A: Link lengths / geometry
# skel_current = link_length_constraints(skel_current, constraint_links, constraint_values, alpha=.5)
# skel_current = geometry_constraints(skel_current, joint_size, alpha=0.5)
# skel_current = collision_constraints(skel_current, constraint_links)
skel_current_uv = (cam.camera_model.world2im(skel_current, cam.depthIm.shape) - [box[0].start, box[1].start, 0])#/mask_interval
skel_current_uv = skel_current_uv.clip([0,0,0], [box[0].stop-box[0].start-1, box[1].stop-box[1].start-1, 9999])
# B: Ray-cast constraints
skel_current, skel_current_uv = ray_cast_constraints(skel_current, skel_current_uv, im_pos, surface_map, joint_size)
# Map back from mask to image
# try:
# skel_current_uv[:,:2] = surface_map[:, skel_current_uv[:,0], skel_current_uv[:,1]].T# + [box_corner[0], box_corner[1]]
# except:
# pass
# ---- (Step 4) Update the confidence ----
if inference=='PF':
time_t1 = time.time()
## Calc distance between each pixel and all joints
px_corr = np.zeros([im_pos.shape[0], im_pos.shape[1], 14])
for i,s in enumerate(skel_current):
px_corr[:,:,i] = np.sqrt(np.sum((im_pos - s)**2, -1))# / joint_size[i]**2
# for i,s in enumerate(pose_uv):
# for i,s in enumerate(skel_current_uv):
# ''' Problem: need to constrain pose_uv to mask '''
# _, geo_map = geodesic_extrema_MPI(im_pos, [s[0],s[1]], iterations=1, visualize=True)
# px_corr[:,:,i] = geo_map
# subplot(2,7,i+1)
# imshow(geo_map, vmin=0, vmax=2000)
# axis('off')
# px_corr[geo_map==0,i] = 9999
px_label = np.argmin(px_corr, -1)*mask_box
px_label_flat = px_label[mask_box].flatten()
# cv2.imshow('gMap', (px_corr.argmin(-1)+1)/15.*mask_box)
# cv2.waitKey(1)
# Project distance to joint's radius
px_joint_displacement = im_pos[mask_box] - skel_current[px_label_flat]
px_joint_magnitude = np.sqrt(np.sum(px_joint_displacement**2,-1))
joint_mesh_pos = skel_current[px_label_flat] + px_joint_displacement*(joint_size[px_label_flat]/px_joint_magnitude)[:,None]
px_joint_displacement = joint_mesh_pos - im_pos[mask_box]
# Ensure pts aren't too far away (these are noise!)
px_joint_displacement[np.abs(px_joint_displacement) > 500] = 0
if 0:
x = im_pos.copy()*0
x[mask_box] = joint_mesh_pos
for i in range(3):
subplot(1,4,i+1)
imshow(x[:,:,i])
axis('off')
subplot(1,4,4)
imshow((px_label+1)*mask_box)
# Calc the correspondance change in position for each joint
correspondence_displacement = np.zeros([len(skel_current), 3])
ii = 0
for i,_ in enumerate(skel_current):
labels = px_label_flat==i
correspondence_displacement[i] = np.sum(px_joint_displacement[px_label_flat==ii], 0) / np.sum(px_joint_displacement[px_label_flat==ii]!=0)
ii+=1
correspondence_displacement = np.nan_to_num(correspondence_displacement)
# print "time:", time.time() - time_t1
# Likelihood
motion_variance = 500
prob_motion = np.exp(-np.mean(np.sum((pose-skel_previous)**2,1)/motion_variance**2))
if inference == 'PF':
correspondence_variance = 40
prob_coor = np.exp(-np.mean(np.sum(correspondence_displacement**2,1)/correspondence_variance**2))
prob = prob_motion * prob_coor
prob = prob_motion
# Viz correspondences
# x = im_pos.copy()*0
# x[mask_box] = px_joint_displacement
# for i in range(3):
# subplot(1,4,i+1)
# imshow(x[:,:,i])
# axis('off')
# subplot(1,4,4)
# imshow((px_label+1)*mask_box)
# # embed()
# # for j in range(3):
# # for i in range(14):
# # subplot(3,14,j*14+i+1)
# # imshow(x[:,:,j]*((px_label==i)*mask_box))
# # axis('off')
# show()
# prob = link_length_probability(skel_current, constraint_links, constraint_values, 100)
# print frame_count
# print "Prob:", np.mean(prob)#, np.min(prob), prob
# thresh = .05
# if np.min(prob) < thresh:
# # print 'Resetting pose'
# for c in constraint_links[prob<thresh]:
# for cc in c:
# skel_current_uv[c] = pose_uv[c] - [box[0].start, box[1].start, 0]
# skel_current[c] = pose[c]
# skel_current_uv = pose_uv.copy() - [box[0].start, box[1].start, 0]
# skel_current = pose.copy()
skel_current_uv = skel_current_uv + [box[0].start, box[1].start, 0]
skel_current = cam.camera_model.im2world(skel_current_uv, cam.depthIm.shape)
# print 'Error:', np.sqrt(np.sum((pose_truth-skel_current)**2, 0))
pose_weights[pose_i] = prob
# pose_updates += [skel_current.copy()]
# pose_updates_uv += [skel_current_uv.copy()]
pose_updates += [pose.copy()]
pose_updates_uv += [pose_uv.copy()]
if cam.colorIm is not None:
cam.colorIm = display_skeletons(cam.colorIm, skel_current_uv, skel_type='Kinect', color=(0,0,pose_i*40+50))
else:
cam.depthIm = display_skeletons(cam.depthIm, skel_current_uv, skel_type='Kinect', color=(0,0,pose_i*40+50))
# cam.colorIm = display_skeletons(cam.colorIm, pose_uv, skel_type='Kinect', color=(0,pose_i*40+50,pose_i*40+50))
# print "Tracking time:", time.time() - time_t0
# Update for next round
pose_ind = np.argmax(pose_weights)
# print "Pickled:", pose_ind
skel_previous = pose_updates[pose_ind].copy()
skel_previous_uv = pose_updates_uv[pose_ind].copy()
# print pose_weights
else:
pose = poses[0]
skel_previous = pose.copy()
pose_uv = cam.camera_model.world2im(skel_previous, cam.depthIm.shape)
skel_current_uv = pose_uv.copy()
skel_previous_uv = pose_uv.copy()
''' ---- Accuracy ---- '''
if ground_truth:
error_track = pose_truth - skel_previous
error_track *= np.any(pose_truth!=0, 1)[:,None]
error_l2_track = np.sqrt(np.sum(error_track**2, 1))
joint_accuracy_track += [error_l2_track]
accuracy_track = np.sum(error_l2_track < 150) / n_joints
accuracy_all_track += [accuracy_track]
print "Current track: {}% {} mm".format(accuracy_track, error_l2_track.mean())
print "Running avg (track):", np.mean(accuracy_all_track)
# print "Joint avg (overall track):", np.mean(joint_accuracy_track)
print ""
''' --- Visualization --- '''
if visualize:
display_markers(cam.colorIm, hand_markers[:2], box, color=(0,250,0))
if len(hand_markers) > 2:
display_markers(cam.colorIm, [hand_markers[2]], box, color=(0,200,0))
display_markers(cam.colorIm, geodesic_markers, box, color=(200,0,0))
# display_markers(cam.colorIm, curve_markers, box, color=(0,100,100))
# display_markers(cam.colorIm, lop_markers, box, color=(0,0,200))
if ground_truth:
cam.colorIm = display_skeletons(cam.colorIm, pose_truth_uv, skel_type='Kinect', color=(0,255,0))
cam.colorIm = display_skeletons(cam.colorIm, skel_current_uv, skel_type='Kinect', color=(255,0,0))
cam.visualize(color=True, depth=False)
# ------------------------------------------------------------
# video_writer.write((geo_clf_map/float(geo_clf_map.max())*255.).astype(np.uint8))
# video_writer.write(cam.colorIm[:,:,[2,1,0]])
frame_count += frame_rate
print "Frame:", frame_count
except:
traceback.print_exc(file=sys.stdout)
pass
try:
print "-- Results for subject {:d} action {:d}".format(subjects[0],actions[0])
except:
pass
# print "Running avg (db):", np.mean(accuracy_all_db)
print "Running mean (track):", np.mean(accuracy_all_track)
# print "Joint avg (overall db):", np.mean(joint_accuracy_db)
print "Joint mean (overall track):", np.mean(joint_accuracy_track)
print "Joint median (overall track):", np.median(joint_accuracy_track)
# print 'Done'
embed()
if learn:
pose_database.save()
elif save_results:
# Save results:
results['subject'] += [subjects[0]]
results['action'] += [actions[0]]
results['accuracy_all'] += [accuracy_all_track]
results['accuracy_mean'] += [np.mean(accuracy_all_track)]
results['joints_all'] += [joint_accuracy_track]
results['joint_mean'] += [np.mean(joint_accuracy_track)]
results['joint_median'] += [np.median(joint_accuracy_track)]
pickle.dump(results, open('/Users/colin/Data/BerkeleyMHAD/Accuracy_Results.pkl', 'w'))
def main(get_depth, get_color, get_skeleton, visualize):
# fill = True
fill = False
get_color = True
# VP = KinectPlayer(base_dir='./', device=device, get_depth=get_depth, get_color=get_color, get_skeleton=get_skeleton, get_mask=get_mask)
# VP = KinectPlayer(base_dir='./', device=1, bg_subtraction=True, get_depth=get_depth, get_color=get_color, get_skeleton=get_skeleton, fill_images=fill)
VP = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=False, fill_images=fill)
cam_count = 1
if cam_count == 2:
VP2 = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=False, fill_images=fill)
# Transformation matrix from first to second camera
# data = pickle.load(open("./Registration.dat", 'r'))
# transform_c1_to_c2 = data['transform']
# print 'aaaaaaaaaaaaaaaa'
# embed()
while VP.next():
# VP.update_background()
# Transform skels from cam1 to cam2
if get_skeleton:
VP_skels = [np.array(VP.users[s]['jointPositions'].values()) for s in VP.users.keys()]
if cam_count == 2:
VP2.sync_cameras(VP)
if get_skeleton:
VP2_skels = transform_skels(VP_skels, transform_c1_to_c2)
VP.colorIm = VP.colorIm[:,:,[2,1,0]]
# im_tmp = np.dstack([VP.depthIm/float(VP.depthIm.max()),\
# VP.depthIm/float(VP.depthIm.max()),\
# VP.colorIm.mean(-1)/255])
# im = felzenszwalb(im_tmp, scale=255)
# im = felzenszwalb(VP.depthIm/float(VP.depthIm.max()), scale=255)
# im = VP.depthIm/float(VP.depthIm.max()) * VP.
# im = felzenszwalb(im, scale=255)
# cv2.imshow("seg", im/float(im.max()))
# cv2.waitKey(10)
# embed()
# Geodesic extrema
if 0:
if VP.foregroundMask is not None:
im = VP.depthIm * (VP.foregroundMask>0)
# cv2.imshow("im--", im/float(im.max()))
if 1:#(im>0).sum() > 1000:
# Extract person
labelMask, boxes, labels, px_counts= extract_people(im, VP.foregroundMask, minPersonPixThresh=3000)
# print labels, px_counts
if len(labels) > 0:
max_ind = np.argmax(px_counts)
mask = labelMask==max_ind+1
im[-mask] = 0
edge_thresh = 200#10
gradients = np.gradient(im)
mag = np.sqrt(gradients[0]**2+gradients[1]**2)
im[mag>edge_thresh] = 0
# Segmentation experiment
# im_s = VP.depthIm/float(VP.depthIm.max())
# im_s = felzenszwalb(im, scale=255)
# cv2.imshow("seg", im_s/float(im_s.max()))
x,y = nd.center_of_mass(im)
if im[x,y] == 0:
tmp = np.nonzero(im>0)
x,y = [tmp[0][0], tmp[1][0]]
# min_map = geodesic_extrema_MPI(im, centroid=[x,y], iterations=15)
extrema = geodesic_extrema_MPI(im, centroid=[x,y], iterations=15)
# embed()
# for e, i in zip(extrema, range(20)):
# if 0 < i < 6:
# box_color = [255, 0, 0]
# elif i==0:
# box_color = [0, 0, 255]
# else:
# box_color = [255,255,255]
# VP.colorIm[e[0]-4:e[0]+5, e[1]-4:e[1]+5] = box_color
# cv2.imshow("ExtremaB", min_map/float(min_map.max()))
# cv2.waitKey(500)
cv2.waitKey(20)
# cv2.imshow("bg model", VP.backgroundModel/float(VP.backgroundModel.max()))
# cv2.imshow("foregroundMask", ((VP.foregroundMask>0)*255).astype(np.uint8))
if visualize:
if cam_count == 2:
for s in VP2_skels:
VP2.depthIm = display_skeletons(VP2.depthIm, s, (VP2.depthIm.max(),), skel_type='Kinect')
VP2.visualize()
VP2.playback_control()
VP.visualize()
def get_per_pixel_joints(im_depth, skel, alg='geodesic', radius=5):
'''
Find the closest joint to each pixel using geodesic distances.
---Paramaters---
im_depth : should be masked depth image
skel : in image coords
alg : 'geodesic' or 'circular'
radius : [only for circular]
'''
height, width = im_depth.shape
distance_ims = np.empty([height, width, N_SKEL_JOINTS])
MAX_VALUE = 32000
# Get rid of edges around joints
edge_thresh = 200 #10
gradients = np.gradient(im_depth)
mag = np.sqrt(gradients[0]**2 + gradients[1]**2)
im_depth[mag > edge_thresh] = 0
joints_out_of_bounds = np.abs(im_depth[skel[:, 0], skel[:, 1]] -
skel[:, 2]) > 100
# Only look at major joints
for i, j in zip(SKEL_JOINTS, range(N_SKEL_JOINTS)):
if skel[i] in joints_out_of_bounds:
distance_ims[:, :, j] = MAX_VALUE
continue
pos = skel[i]
if alg == 'geodesic':
x = np.maximum(np.minimum(pos[1], height - 1), 0)
y = np.maximum(np.minimum(pos[0], width - 1), 0)
# if j == 0:
# pts, min_map = generateKeypoints(np.ascontiguousarray(im_depth), centroid=[x,y], iterations=10, scale=6)
im_dist = distance_map(np.ascontiguousarray(im_depth),
centroid=[x, y],
scale=6)
distance_ims[:, :, j] = im_dist
if 0:
radius = 5
x = np.maximum(np.minimum(pos[1], height - 1 - radius), radius)
y = np.maximum(np.minimum(pos[0], width - 1 - radius), radius)
# print x,y
pts = circle(x, y, radius)
# print pts[0]
max_ = distance_ims[distance_ims[:, :, j] < MAX_VALUE, j].max()
distance_ims[distance_ims[:, :, j] >= MAX_VALUE,
j] = max_ + 100
distance_ims[pts[0], pts[1], j] = max_ + 100
figure(1)
subplot(3, 5, j + 1)
imshow(distance_ims[:, :, j])
axis('off')
# subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.1, hspace=.1)
tight_layout()
subplots_adjust(left=.001,
bottom=.001,
wspace=.003,
hspace=.003)
elif alg == 'circular':
x = np.maximum(np.minimum(pos[1], height - 1 - radius), radius)
y = np.maximum(np.minimum(pos[0], width - 1 - radius), radius)
pts = draw.circle(x, y, radius)
closest_pos[pts] = i
else:
print "No algorithm type in 'get_per_pixel_joints'"
if alg == 'geodesic':
closest_pos = np.argmin(distance_ims, -1).astype(np.uint8)
closest_pos_all = np.argsort(distance_ims, -1).astype(np.uint8)
elif alg == 'circular':
closest_pos = np.zeros_like(im_depth)
#Change all background pixels
# mask = im_depth!=0
# mask = (distance_ims.max(-1)<32000)
mask = im_depth != 0
closest_pos[-mask] = N_MSR_JOINTS + 1
if 0:
# Reorder for occlusions
skel_depths = np.array([skel[i, 2] for i in SKEL_JOINTS])
skel_XYZ = np.array([skel[i] for i in SKEL_JOINTS])
pos_order = np.argsort(skel_depths).tolist()
pos_order.reverse()
residual = im_depth[mask] - skel_depths[closest_pos[mask]]
im_tmp = np.zeros_like(im_depth)
im_tmp[mask] = residual
closest_arg = np.argmin(distance_ims + skel_depths, -1)
closest_arg[-mask] = -1
# If geo dist < euc distance then kill
# Go from back to front. Only care about things ahead
from scipy.spatial import distance
# euclid_dists = distance.squareform(distance.pdist(depth2world(skel_XYZ, [240,320])))
euclid_dists = distance.squareform(distance.pdist(skel_XYZ))
geo_dists = np.zeros_like(euclid_dists)
for i in xrange(len(SKEL_JOINTS)):
p = pos_order[i]
# If a joint is behind it, don't care -- set to infinity
geo_dists[p, :] = [
distance_ims[skel[j][0], skel[j][1],
p] if j in pos_order[i:] else np.inf
for j in SKEL_JOINTS
]
# Check if it's incorrect at least 2x
removed_joints = []
err = (geo_dists * 2. < euclid_dists)
for i in xrange(len(err)):
if err[i].sum(0) >= 1:
# removed_joints += [i]
distance_ims[:, :, i] = MAX_VALUE
closest_pos_e = np.argmin(distance_ims, -1).astype(np.uint8)
closest_pos_e[-mask] = N_MSR_JOINTS + 1
subplot(2, 2, 1)
closest_pos = display_skeletons(closest_pos,
skel,
color=(23, ),
skel_type='Low')
imshow(closest_pos)
axis('off')
subplot(2, 2, 2)
imshow(closest_pos_e)
axis('off')
subplot(2, 2, 3)
imshow(closest_pos_e == closest_pos)
axis('off')
# blank = np.ones(300,300)
# removed_text = ''
# for i in removed_joints:
# removed_text += str(i)+" "
# cv2.putText()
tight_layout()
subplots_adjust(left=.001, bottom=.001, wspace=.003, hspace=.003)
show()
# embed()
# closest_pos_e[im_depth==0] = N_MSR_JOINTS+1
# closest_pos2[-mask] = N_MSR_JOINTS+1
# closest_pos2[closest_pos>=32000] = N_MSR_JOINTS+1
# closest_arg[im_depth==0] = N_MSR_JOINTS+1
# figure(1); imshow(closest_arg);
# figure(3); imshow(closest_pos2)
# # Reorder
# im_order = np.zeros_like(im_depth)-1
# for p in range(len(pos_order)):
# mask_tmp = closest_pos==p
# dists_tmp = distance_ims[mask_tmp, p] + skel[pos_order[p],2]
# im_order[mask_tmp] = np.maximum(im_order[mask_tmp], dists_tmp)
# figure(100)
# imshow(closest_pos)
# figure(101)
# imshow(im_depth)
# show()
# embed()
return closest_pos
def main(visualize=False,
learn=False,
actions=None,
subjects=None,
n_frames=220):
# learn = True
# learn = False
if actions is []:
actions = [2]
if subjects is []:
subjects = [2]
# actions = [1]
# actions = [1, 2, 3, 4, 5]
# subjects = [1]
if 1:
MHAD = True
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/',
kinect=1,
actions=actions,
subjects=subjects,
reps=[1],
get_depth=True,
get_color=True,
get_skeleton=True,
fill_images=False)
else:
MHAD = False
cam = KinectPlayer(base_dir='./',
device=2,
bg_subtraction=True,
get_depth=True,
get_color=True,
get_skeleton=True,
fill_images=False)
bg = Image.open(
'/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_A.tif')
bg = Image.open(
'/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_B.tif')
cam.bgSubtraction.backgroundModel = np.array(bg.getdata()).reshape(
[240, 320]).clip(0, 4500)
height, width = cam.depthIm.shape
skel_previous = None
# clf_geo = pickle.load(open('geodesic_svm_sorted_scaled_5class.pkl'))
# clf_color,color_approx = pickle.load(open('color_histogram_approx_svm_5class.pkl'))
# clf_lbp,lbp_approx = pickle.load(open('lbp_histogram_approx_svm_5class.pkl'))
face_detector = FaceDetector()
hand_detector = HandDetector(cam.depthIm.shape)
curve_detector = CurveDetector(cam.depthIm.shape)
# Video writer
# video_writer = cv2.VideoWriter("/Users/colin/Desktop/test.avi", cv2.cv.CV_FOURCC('M','J','P','G'), 15, (320,240))
# Save Background model
# im = Image.fromarray(cam.depthIm.astype(np.int32), 'I')
# im.save("/Users/Colin/Desktop/k2.png")
# Setup pose database
append = True
append = False
pose_database = PoseDatabase("PoseDatabase.pkl",
learn=learn,
search_joints=[0, 4, 7, 10, 13],
append=append)
# Per-joint classification
head_features = []
hand_features = []
feet_features = []
joint_features = {
'geodesic': [None] * 14,
'color_histograms': [None] * 14,
'lbp': [None] * 14
}
# Evaluation
accuracy_all = []
joint_accuracy_all = []
geo_accuracy = []
color_accuracy = []
lbp_accuracy = []
frame_count = 0
frame_rate = 2
if not MHAD:
cam.next(350)
frame_prev = 0
try:
# if 1:
while cam.next(frame_rate): # and frame_count < n_frames:
if frame_count - frame_prev > 100:
print ""
print "Frame #{0:d}".format(frame_count)
frame_prev = frame_count
if not MHAD:
if len(cam.users) == 0:
continue
else:
# cam.users = [np.array(cam.users[0]['jointPositions'].values())]
if np.any(cam.users[0][0] == -1):
continue
cam.users[0][:, 1] *= -1
cam.users_uv_msr = [
cam.camera_model.world2im(cam.users[0], [240, 320])
]
# Apply mask to image
if MHAD:
mask = cam.get_person(2) > 0
else:
mask = cam.get_person() > 0
if np.all(mask == False):
continue
im_depth = cam.depthIm
cam.depthIm[cam.depthIm > 3000] = 0
im_color = cam.colorIm * mask[:, :, None]
cam.colorIm *= mask[:, :, None]
pose_truth = cam.users[0]
pose_truth_uv = cam.users_uv_msr[0]
# Get bounding box around person
box = nd.find_objects(mask)[0]
d = 20
# Widen box
box = (slice(np.maximum(box[0].start-d, 0), \
np.minimum(box[0].stop+d, height-1)), \
slice(np.maximum(box[1].start-d, 0), \
np.minimum(box[1].stop+d, width-1)))
box_corner = [box[0].start, box[1].start]
''' ---------- ----------------------------------- --------'''
''' ----------- Feature Detector centric approach ---------'''
''' ---------- ----------------------------------- --------'''
''' ---- Calculate Detectors ---- '''
# Face detection
face_detector.run(im_color[box])
# Skin detection
hand_markers = hand_detector.run(im_color[box], n_peaks=3)
# curve detection
# curve_markers = curve_detector.run((im_depth*mask)[box], n_peaks=3)
# Calculate LBPs ##Max P=31 for LBPs becuase of datatype
# x = local_occupancy_pattern(cam.depthIm[box]*mask[box], [5,5,5],[3,3,3])
# lop_texture = local_binary_pattern_depth(cam.depthIm[box]*mask[box], 10, 20, px_diff_thresh=100)*mask[box]
# lop_markers = []#peak_local_max(lop_texture, min_distance=20, num_peaks=5, exclude_border=False)
# lbp_texture = local_binary_pattern(cam.depthIm[box]*mask[box], 6, 20)*mask[box]
# Calculate Geodesic Extrema
im_pos = cam.camera_model.im2PosIm(
cam.depthIm * mask)[box] * mask[box][:, :, None]
geodesic_markers = geodesic_extrema_MPI(im_pos,
iterations=5,
visualize=False)
# geodesic_markers, geo_map = geodesic_extrema_MPI(im_pos, iterations=5, visualize=True)
geodesic_markers_pos = im_pos[geodesic_markers[:, 0],
geodesic_markers[:, 1]]
markers = list(geodesic_markers) + list(
hand_markers) #+ list(lop_markers) + curve_markers
markers = np.array([list(x) for x in markers])
if 1:
''' ---- Database lookup ---- '''
pts_mean = im_pos[(im_pos != 0)[:, :, 2]].mean(0)
if learn:
# Normalize pose
pose_uv = cam.users_uv[0]
if np.any(pose_uv == 0):
print "skip"
frame_count += frame_rate
continue
# print pose_truth[2], pts_mean
pose_database.update(pose_truth - pts_mean)
else:
# Concatenate markers
markers = list(geodesic_markers) + hand_markers
# markers = list(geodesic_markers) + list(lop_markers) + curve_markers + hand_markers
markers = np.array([list(x) for x in markers])
# Normalize pose
pts = im_pos[markers[:, 0], markers[:, 1]]
pts = np.array([x for x in pts if x[0] != 0])
pts -= pts_mean
# Get closest pose
pose = pose_database.query(pts, knn=1)
# pose = pose_database.weighted_query(pts, knn=1)
# pose = pose_database.reverse_query(pts[:,[1,0,2]])
# im_pos -= pts_mean
# R,t = IterativeClosestPoint(pose, im_pos.reshape([-1,3])-pts_mean, max_iters=5, min_change=.001, pt_tolerance=10000)
# pose = np.dot(R.T, pose.T).T - t
# pose = np.dot(R, pose.T).T + t
pose += pts_mean
pose_uv = cam.camera_model.world2im(
pose, cam.depthIm.shape)
# Constrain
if 0:
try:
''' This does worse because the joint may fall to a different part of the body (e.g. hand to torso) which throws the error upward '''
surface_map = nd.distance_transform_edt(
im_pos[:, :, 2] == 0,
return_distances=False,
return_indices=True)
pose_uv[:, :2] = surface_map[:, pose_uv[:, 0] -
box_corner[0],
pose_uv[:, 1] -
box_corner[1]].T + [
box_corner[0],
box_corner[1]
]
pose = cam.camera_model.im2world(pose_uv)
# skel_current = link_length_constraints(skel_current, constraint_links, constraint_values, alpha=.5)
# skel_current = geometry_constraints(skel_current, joint_size, alpha=0.5)
# skel_current = collision_constraints(skel_current, constraint_links)
# embed()
# pose_uv_box = pose_uv - [box_corner[0], box_corner[1], 0]
# pose_uv_box = pose_uv_box.clip([0,0,0], [cam.depthIm.shape[0]-1, cam.depthIm.shape[1]-1, 9999])
# joint_size = np.array([75]*14)
# pose_n, pose_uv_n = ray_cast_constraints(pose, pose_uv_box, im_pos, surface_map, joint_size)
# print 'Pose',pose,pose_n
# pose = pose_n
# pose_uv = pose_uv_n + [box_corner[0], box_corner[1], 0]
except:
print 'error constraining'
# skel_previous = np.array(pose, copy=True)
display_markers(cam.colorIm,
hand_markers[:2],
box,
color=(0, 250, 0))
if len(hand_markers) > 2:
display_markers(cam.colorIm, [hand_markers[2]],
box,
color=(0, 200, 0))
display_markers(cam.colorIm,
geodesic_markers,
box,
color=(200, 0, 0))
# display_markers(cam.colorIm, curve_markers, box, color=(0,100,100))
# display_markers(cam.colorIm, lop_markers, box, color=(0,0,200))
if 0:
''' ---------- ----------------------------------- --------'''
''' ---------- Feature Descriptor centric approach --------'''
''' ---------- ----------------------------------- --------'''
''' ---- Calculate Descriptors ---- '''
hand_markers = np.array(hand_markers)
# Geodesics
geodesic_features = relative_marker_positions(
im_pos, geodesic_markers_pos[:, [1, 0, 2]])
geodesic_features = np.sort(geodesic_features)
# Color Histogram
skin = skimage.exposure.rescale_intensity(
hand_detector.im_skin, out_range=[0, 255]).astype(np.uint8)
color_histograms = local_histograms(
skin, n_bins=5, max_bound=255,
patch_size=11) * mask[box][:, :, None]
# LBP Histogram
lbp_texture = local_binary_pattern(
cam.depthIm[box] * mask[box], 6, 5) * mask[box]
lbp_histograms = local_histograms(
lbp_texture.astype(np.uint8),
n_bins=10,
max_bound=2**6,
patch_size=11) * mask[box][:, :, None]
# for i in range(10):
# subplot(2,5,i+1)
# imshow(lbp_histograms[:,:,i])
''' ---- Per Joint Learning ---- '''
if learn:
for ii, i in enumerate(pose_truth_uv):
if i[0] != 0:
try:
if joint_features['geodesic'][ii] is None:
joint_features['geodesic'][
ii] = geodesic_features[i[1] -
box_corner[0],
i[0] -
box_corner[1]]
else:
joint_features['geodesic'][ii] = np.vstack(
[
joint_features['geodesic'][ii],
(geodesic_features[i[1] -
box_corner[0],
i[0] -
box_corner[1]])
])
if joint_features['color_histograms'][
ii] is None:
joint_features['color_histograms'][
ii] = color_histograms[i[1] -
box_corner[0],
i[0] -
box_corner[1]]
else:
joint_features['color_histograms'][
ii] = np.vstack([
joint_features['color_histograms']
[ii],
deepcopy(color_histograms[
i[1] - box_corner[0],
i[0] - box_corner[1]])
])
if joint_features['lbp'][ii] is None:
joint_features['lbp'][ii] = lbp_histograms[
i[1] - box_corner[0],
i[0] - box_corner[1]]
else:
joint_features['lbp'][ii] = np.vstack([
joint_features['lbp'][ii],
deepcopy(lbp_histograms[i[1] -
box_corner[0],
i[0] -
box_corner[1]])
])
except:
print "error"
''' ---- Per Joint Classification ---- '''
if not learn:
try:
# Geodesic clasification
tmp = geodesic_features.reshape([-1, 6])
tmp = np.array([x / x[-1] for x in tmp])
tmp = np.nan_to_num(tmp)
geo_clf_map = clf_geo.predict(tmp).reshape(
im_pos.shape[:2]) * mask[box]
geo_clf_labels = geo_clf_map[
pose_truth_uv[[0, 1, 4, 7, 10, 13], 1] -
box_corner[0],
pose_truth_uv[[0, 1, 4, 7, 10, 13], 0] -
box_corner[1]]
geo_accuracy += [
geo_clf_labels == [0, 1, 4, 7, 10, 13]
]
print 'G', np.mean(
geo_accuracy,
0), geo_clf_labels == [0, 1, 4, 7, 10, 13]
cv2.imshow('Geo',
geo_clf_map / float(geo_clf_map.max()))
except:
pass
try:
# Color histogram classification
color_test = color_approx.transform(
color_histograms.reshape([-1, 5]))
color_clf_map = clf_color.predict(color_test).reshape(
im_pos.shape[:2]) * mask[box]
color_clf_labels = color_clf_map[
pose_truth_uv[[0, 1, 4, 7, 10, 13], 1] -
box_corner[0],
pose_truth_uv[[0, 1, 4, 7, 10, 13], 0] -
box_corner[1]]
color_accuracy += [
color_clf_labels == [0, 1, 4, 7, 10, 13]
]
print 'C', np.mean(
color_accuracy,
0), color_clf_labels == [0, 1, 4, 7, 10, 13]
cv2.imshow('Col',
color_clf_map / float(color_clf_map.max()))
except:
pass
try:
# lbp histogram classification
lbp_test = color_approx.transform(
lbp_histograms.reshape([-1, 10]))
lbp_clf_map = clf_lbp.predict(lbp_test).reshape(
im_pos.shape[:2]) * mask[box]
lbp_clf_labels = lbp_clf_map[
pose_truth_uv[[0, 1, 4, 7, 10, 13], 1] -
box_corner[0],
pose_truth_uv[[0, 1, 4, 7, 10, 13], 0] -
box_corner[1]]
lbp_accuracy += [
lbp_clf_labels == [0, 1, 4, 7, 10, 13]
]
print 'L', np.mean(
lbp_accuracy,
0), lbp_clf_labels == [0, 1, 4, 7, 10, 13]
cv2.imshow('LBP',
lbp_clf_map / float(lbp_clf_map.max()))
except:
pass
pose_uv = pose_truth_uv
pose = pose_truth
# ''' ---- Accuracy ---- '''
if 1 and not learn:
# pose_truth = cam.users[0]
error = pose_truth - pose
# print "Error", error
error_l2 = np.sqrt(np.sum(error**2, 1))
# error_l2 = np.sqrt(np.sum(error[:,:2]**2, 1))
joint_accuracy_all += [error_l2]
accuracy = np.sum(error_l2 < 150) / 14.
accuracy_all += [accuracy]
print "Current", accuracy
# print "Running avg:", np.mean(accuracy_all)
# print "Joint avg (per-joint):", np.mean(joint_accuracy_all, -1)
# print "Joint avg (overall):", np.mean(joint_accuracy_all)
''' --- Visualization --- '''
cam.colorIm = display_skeletons(cam.colorIm,
pose_truth_uv,
skel_type='Kinect',
color=(0, 255, 0))
cam.colorIm = display_skeletons(cam.colorIm,
pose_uv,
skel_type='Kinect')
cam.visualize()
# print "Extrema:", geo_clf_map[geodesic_markers[:,0], geodesic_markers[:,1]]
# print "Skin:", geo_clf_map[hand_markers[:,0], hand_markers[:,1]]
# print "Skin val:", hand_detector.skin_match[hand_markers[:,0], hand_markers[:,1]]
# hand_data += [[x[0] for x in hand_markers],
# [x[1] for x in hand_markers],
# list(hand_detector.skin_match[hand_markers[:,0], hand_markers[:,1]])]
# ------------------------------------------------------------
# video_writer.write((geo_clf_map/float(geo_clf_map.max())*255.).astype(np.uint8))
# video_writer.write(cam.colorIm[:,:,[2,1,0]])
frame_count += frame_rate
except:
pass
print "-- Results for subject {:d} action {:d}".format(
subjects[0], actions[0])
print "Running avg:", np.mean(accuracy_all)
print "Joint avg (overall):", np.mean(joint_accuracy_all)
# print 'Done'
if learn:
pose_database.save()
print 'Pose database saved'
embed()
return
''' --- Format Geodesic features ---'''
geodesics_train = []
geodesics_labels = []
for i in xrange(len(joint_features['geodesic'])):
# joint_features['geodesic'][i] = np.array([np.sort(x) for x in joint_features['geodesic'][i] if x[0] != 0])
joint_features['geodesic'][i] = np.array(
[x / x.max() for x in joint_features['geodesic'][i] if x[0] != 0])
ii = i
if i not in [0, 1, 4, 7, 10, 13]:
ii = 1
else:
geodesics_labels += [
i * np.ones(len(joint_features['geodesic'][i]))
]
geodesics_train = np.vstack(
[joint_features['geodesic'][x] for x in [0, 1, 4, 7, 10, 13]])
# geodesics_train = np.vstack(joint_features['geodesic'])
geodesics_labels = np.hstack(geodesics_labels)
figure(1)
title('Distances of each joint to first 6 geodesic extrema')
for i in range(14):
subplot(4, 4, i + 1)
ylabel('Distance')
xlabel('Sample')
plot(joint_features['geodesic'][i])
axis([0, 400, 0, 1600])
# Learn geodesic classifier
clf_geo = SGDClassifier(n_iter=10000,
alpha=.01,
n_jobs=-1,
class_weight='auto')
clf_geo.fit(geodesics_train, geodesics_labels)
print clf_geo.score(geodesics_train, geodesics_labels)
geodesic_features = np.sort(geodesic_features)
sgd_map = clf_geo.predict(geodesic_features.reshape([-1, 6])).reshape(
im_pos.shape[:2])
pickle.dump(clf_geo, open('geodesic_svm_sorted_scaled_5class.pkl', 'w'),
pickle.HIGHEST_PROTOCOL)
# clf_geo = pickle.load(open('geodesic_svm_sorted_scaled_5class.pkl'))
''' --- Color Histogram features ---'''
color_train = []
color_labels = []
for i in xrange(len(joint_features['color_histograms'])):
ii = i
if i not in [0, 1, 4, 7, 10, 13]:
ii = 1
else:
color_labels += [
i * np.ones(len(joint_features['color_histograms'][i]))
]
# color_labels += [i*np.ones(len(joint_features['color_histograms'][i]))]
# color_train = np.vstack(joint_features['color_histograms'])
color_train = np.vstack(
[joint_features['color_histograms'][x] for x in [0, 1, 4, 7, 10, 13]])
color_labels = np.hstack(color_labels)
color_approx = AdditiveChi2Sampler()
color_approx_train = color_approx.fit_transform(color_train)
clf = SGDClassifier(n_iter=10000,
alpha=.01,
n_jobs=-1,
class_weight='auto')
clf.fit(color_approx_train, color_labels)
print clf.score(color_approx_train, color_labels)
color_test = color_approx.transform(color_histograms.reshape([-1, 5]))
sgd_map = clf.predict(color_test).reshape(im_pos.shape[:2]) * mask[box]
figure(1)
title('Color Histograms per Joint')
for i in range(14):
subplot(4, 4, i + 1)
ylabel('Count')
xlabel('Sample')
plot(joint_features['color_histograms'][i])
axis([0, 10, 0, 30])
for i in range(5):
subplot(1, 5, i + 1)
imshow(color_histograms[:, :, i])
pickle.dump([clf, color_approx],
open('color_histogram_approx_svm_5class.pkl', 'w'),
pickle.HIGHEST_PROTOCOL)
# clf_color,color_approx = pickle.load(open('color_histogram_approx_svm_5class.pkl'))
''' --- LBP Histogram features ---'''
color_train = []
color_labels = []
for i in xrange(len(joint_features['lbp'])):
ii = i
if i not in [0, 1, 4, 7, 10, 13]:
ii = 1
else:
color_labels += [i * np.ones(len(joint_features['lbp'][i]))]
# color_labels += [i*np.ones(len(joint_features['color_histograms'][i]))]
# color_train = np.vstack(joint_features['color_histograms'])
color_train = np.vstack(
[joint_features['lbp'][x] for x in [0, 1, 4, 7, 10, 13]])
color_labels = np.hstack(color_labels)
color_approx = AdditiveChi2Sampler()
color_approx_train = color_approx.fit_transform(color_train)
clf = SGDClassifier(n_iter=10000,
alpha=.01,
n_jobs=-1,
class_weight='auto')
clf.fit(color_approx_train, color_labels)
print clf.score(color_approx_train, color_labels)
color_test = color_approx.transform(lbp_histograms.reshape([-1, 10]))
sgd_map = clf.predict(color_test).reshape(im_pos.shape[:2]) * mask[box]
figure(1)
title('LBP Histograms per Joint')
for i in range(14):
subplot(4, 4, i + 1)
ylabel('Count')
xlabel('Sample')
plot(joint_features['lbp'][i])
axis([0, 10, 0, 30])
for i in range(5):
subplot(1, 5, i + 1)
imshow(color_histograms[:, :, i])
pickle.dump([clf, color_approx],
open('lbp_histogram_approx_svm_5class.pkl', 'w'),
pickle.HIGHEST_PROTOCOL)
def main_infer(rf_name=None):
'''
'''
if rf_name is None:
import os
files = os.listdir('Saved_Params/')
rf_name = 'Saved_Params/' + files[-1]
print "Classifier file:", rf_name
# Load classifier data
data = pickle.load(open(rf_name))
rf = data['rf']
offsets_1, offsets_2 = data['offsets']
''' Read data from each video/sequence '''
depthIms = []
skels_world = []
if 1:
# VP = KinectPlayer(base_dir='/Users/colin/Data/Office_25Feb2013/', device=2, get_depth=True, get_color=False, get_skeleton=True, get_mask=False)
VP = KinectPlayer(base_dir='/Users/colin/Data/Room_close_26Feb13/',
device=1,
get_depth=True,
get_color=False,
get_skeleton=True,
get_mask=False)
_, _ = VP.get_n_skeletons(50)
depthIms, skels_world = VP.get_n_skeletons(100)
# depthIms = np.array(depthIms)[:,:,::-1]
else:
# name = 'a01_s01_e02_'
name = 'a01_s10_e02_'
# name = 'a02_s06_e02_'
# name = 'a05_s02_e02_'
depth_file = name + "depth.bin"
color_file = name + "rgb.avi"
skeleton_file = name + "skeleton.txt"
try:
depthIms, maskIms = read_MSR_depth_ims(depth_file)
depthIms *= maskIms
depthIms /= 10
# colorIms = read_MSR_color_ims(color_file)
skels_world, skels_im = read_MSR_skeletons(skeleton_file)
skels_world[:, 2] /= 10
except:
print "Error reading data"
''' Process data '''
all_pred_ims = []
for i in xrange(1, len(depthIms), 1):
# try:
if 1:
print i
''' Get frame data '''
im_depth = depthIms[i]
# im_depth[160:, :] = 0
skel_pos = skel2depth(skels_world[i], rez=[240, 320])
# ''' --- NOTE THIS 10 PX OFFSET IN THE MSR DATASET !!! --- '''
# skel_pos[:,0] -= 10
skel_pos_pred, im_predict = infer_pose(im_depth, rf, offsets_1,
offsets_2)
# Overlay skeletons
if 1:
# colorIm = colorIms[i]
# im_predict = colorIm
cv2.imshow("forest_prediction",
im_predict / float(im_predict.max()))
im_predict = np.repeat(im_depth[:, :, None].astype(np.float),
3, -1)
# embed()
im_predict[im_depth > 0] -= im_depth[im_depth > 0].min()
im_predict /= float(im_predict.max() / 255.)
im_predict = im_predict.astype(np.uint8)
# im_predict = np.repeat(im_predict[:,:,None], 3, -1)
# im_predict /= float(im_predict.max())*255
# im_predict = im_predict.astype(np.uint8)
im_predict = display_skeletons(im_predict, skel_pos,
(255, 0, 0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos_pred,
(0, 255, 0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos,
(255, 0, 0), SKEL_DISPLAY_MODE)
im_predict = display_skeletons(im_predict, skel_pos_pred,
(0, 255, 0), SKEL_DISPLAY_MODE)
# embed()
# max_ = (im_predict * (im_predict < 255)).max()
all_pred_ims += [im_predict]
''' Visualize '''
if 1:
cv2.putText(im_predict, "Blue=Truth", (10, 210),
cv2.FONT_HERSHEY_DUPLEX, .5,
(int(im_predict.max() / 2), 0, 0))
cv2.putText(im_predict, "Green=Predict", (10, 230),
cv2.FONT_HERSHEY_DUPLEX, .5,
(0, int(im_predict.max() / 2), 0))
cv2.imshow("prediction", im_predict)
ret = cv2.waitKey(10)
if ret > 0: break
time.sleep(.5)
# except:
# print "Frame failed:", i
# break
embed()
def main(visualize=False, learn=False, actions=None, subjects=None, n_frames=220):
# learn = True
# learn = False
if actions is []:
actions = [2]
if subjects is []:
subjects = [2]
# actions = [1]
# actions = [1, 2, 3, 4, 5]
# subjects = [1]
if 1:
MHAD = True
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/', kinect=1, actions=actions, subjects=subjects, reps=[1], get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
else:
MHAD = False
cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_A.tif')
bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_B.tif')
cam.bgSubtraction.backgroundModel = np.array(bg.getdata()).reshape([240,320]).clip(0, 4500)
height, width = cam.depthIm.shape
skel_previous = None
# clf_geo = pickle.load(open('geodesic_svm_sorted_scaled_5class.pkl'))
# clf_color,color_approx = pickle.load(open('color_histogram_approx_svm_5class.pkl'))
# clf_lbp,lbp_approx = pickle.load(open('lbp_histogram_approx_svm_5class.pkl'))
face_detector = FaceDetector()
hand_detector = HandDetector(cam.depthIm.shape)
curve_detector = CurveDetector(cam.depthIm.shape)
# Video writer
# video_writer = cv2.VideoWriter("/Users/colin/Desktop/test.avi", cv2.cv.CV_FOURCC('M','J','P','G'), 15, (320,240))
# Save Background model
# im = Image.fromarray(cam.depthIm.astype(np.int32), 'I')
# im.save("/Users/Colin/Desktop/k2.png")
# Setup pose database
append = True
append = False
pose_database = PoseDatabase("PoseDatabase.pkl", learn=learn, search_joints=[0,4,7,10,13], append=append)
# Per-joint classification
head_features = []
hand_features = []
feet_features = []
joint_features = {'geodesic':[None]*14,
'color_histograms':[None]*14,
'lbp':[None]*14}
# Evaluation
accuracy_all = []
joint_accuracy_all = []
geo_accuracy = []
color_accuracy = []
lbp_accuracy = []
frame_count = 0
frame_rate = 2
if not MHAD:
cam.next(350)
frame_prev = 0
try:
# if 1:
while cam.next(frame_rate):# and frame_count < n_frames:
if frame_count - frame_prev > 100:
print ""
print "Frame #{0:d}".format(frame_count)
frame_prev = frame_count
if not MHAD:
if len(cam.users) == 0:
continue
else:
# cam.users = [np.array(cam.users[0]['jointPositions'].values())]
if np.any(cam.users[0][0] == -1):
continue
cam.users[0][:,1] *= -1
cam.users_uv_msr = [cam.camera_model.world2im(cam.users[0], [240,320])]
# Apply mask to image
if MHAD:
mask = cam.get_person(2) > 0
else:
mask = cam.get_person() > 0
if np.all(mask==False):
continue
im_depth = cam.depthIm
cam.depthIm[cam.depthIm>3000] = 0
im_color = cam.colorIm*mask[:,:,None]
cam.colorIm *= mask[:,:,None]
pose_truth = cam.users[0]
pose_truth_uv = cam.users_uv_msr[0]
# Get bounding box around person
box = nd.find_objects(mask)[0]
d = 20
# Widen box
box = (slice(np.maximum(box[0].start-d, 0), \
np.minimum(box[0].stop+d, height-1)), \
slice(np.maximum(box[1].start-d, 0), \
np.minimum(box[1].stop+d, width-1)))
box_corner = [box[0].start,box[1].start]
''' ---------- ----------------------------------- --------'''
''' ----------- Feature Detector centric approach ---------'''
''' ---------- ----------------------------------- --------'''
''' ---- Calculate Detectors ---- '''
# Face detection
face_detector.run(im_color[box])
# Skin detection
hand_markers = hand_detector.run(im_color[box], n_peaks=3)
# curve detection
# curve_markers = curve_detector.run((im_depth*mask)[box], n_peaks=3)
# Calculate LBPs ##Max P=31 for LBPs becuase of datatype
# x = local_occupancy_pattern(cam.depthIm[box]*mask[box], [5,5,5],[3,3,3])
# lop_texture = local_binary_pattern_depth(cam.depthIm[box]*mask[box], 10, 20, px_diff_thresh=100)*mask[box]
# lop_markers = []#peak_local_max(lop_texture, min_distance=20, num_peaks=5, exclude_border=False)
# lbp_texture = local_binary_pattern(cam.depthIm[box]*mask[box], 6, 20)*mask[box]
# Calculate Geodesic Extrema
im_pos = cam.camera_model.im2PosIm(cam.depthIm*mask)[box] * mask[box][:,:,None]
geodesic_markers = geodesic_extrema_MPI(im_pos, iterations=5, visualize=False)
# geodesic_markers, geo_map = geodesic_extrema_MPI(im_pos, iterations=5, visualize=True)
geodesic_markers_pos = im_pos[geodesic_markers[:,0], geodesic_markers[:,1]]
markers = list(geodesic_markers) + list(hand_markers) #+ list(lop_markers) + curve_markers
markers = np.array([list(x) for x in markers])
if 1:
''' ---- Database lookup ---- '''
pts_mean = im_pos[(im_pos!=0)[:,:,2]].mean(0)
if learn:
# Normalize pose
pose_uv = cam.users_uv[0]
if np.any(pose_uv==0):
print "skip"
frame_count += frame_rate
continue
# print pose_truth[2], pts_mean
pose_database.update(pose_truth - pts_mean)
else:
# Concatenate markers
markers = list(geodesic_markers) + hand_markers
# markers = list(geodesic_markers) + list(lop_markers) + curve_markers + hand_markers
markers = np.array([list(x) for x in markers])
# Normalize pose
pts = im_pos[markers[:,0], markers[:,1]]
pts = np.array([x for x in pts if x[0] != 0])
pts -= pts_mean
# Get closest pose
pose = pose_database.query(pts, knn=1)
# pose = pose_database.weighted_query(pts, knn=1)
# pose = pose_database.reverse_query(pts[:,[1,0,2]])
# im_pos -= pts_mean
# R,t = IterativeClosestPoint(pose, im_pos.reshape([-1,3])-pts_mean, max_iters=5, min_change=.001, pt_tolerance=10000)
# pose = np.dot(R.T, pose.T).T - t
# pose = np.dot(R, pose.T).T + t
pose += pts_mean
pose_uv = cam.camera_model.world2im(pose, cam.depthIm.shape)
# Constrain
if 0:
try:
''' This does worse because the joint may fall to a different part of the body (e.g. hand to torso) which throws the error upward '''
surface_map = nd.distance_transform_edt(im_pos[:,:,2]==0, return_distances=False, return_indices=True)
pose_uv[:,:2] = surface_map[:, pose_uv[:,0]-box_corner[0], pose_uv[:,1]-box_corner[1]].T + [box_corner[0], box_corner[1]]
pose = cam.camera_model.im2world(pose_uv)
# skel_current = link_length_constraints(skel_current, constraint_links, constraint_values, alpha=.5)
# skel_current = geometry_constraints(skel_current, joint_size, alpha=0.5)
# skel_current = collision_constraints(skel_current, constraint_links)
# embed()
# pose_uv_box = pose_uv - [box_corner[0], box_corner[1], 0]
# pose_uv_box = pose_uv_box.clip([0,0,0], [cam.depthIm.shape[0]-1, cam.depthIm.shape[1]-1, 9999])
# joint_size = np.array([75]*14)
# pose_n, pose_uv_n = ray_cast_constraints(pose, pose_uv_box, im_pos, surface_map, joint_size)
# print 'Pose',pose,pose_n
# pose = pose_n
# pose_uv = pose_uv_n + [box_corner[0], box_corner[1], 0]
except:
print 'error constraining'
# skel_previous = np.array(pose, copy=True)
display_markers(cam.colorIm, hand_markers[:2], box, color=(0,250,0))
if len(hand_markers) > 2:
display_markers(cam.colorIm, [hand_markers[2]], box, color=(0,200,0))
display_markers(cam.colorIm, geodesic_markers, box, color=(200,0,0))
# display_markers(cam.colorIm, curve_markers, box, color=(0,100,100))
# display_markers(cam.colorIm, lop_markers, box, color=(0,0,200))
if 0:
''' ---------- ----------------------------------- --------'''
''' ---------- Feature Descriptor centric approach --------'''
''' ---------- ----------------------------------- --------'''
''' ---- Calculate Descriptors ---- '''
hand_markers = np.array(hand_markers)
# Geodesics
geodesic_features = relative_marker_positions(im_pos, geodesic_markers_pos[:,[1,0,2]])
geodesic_features = np.sort(geodesic_features)
# Color Histogram
skin = skimage.exposure.rescale_intensity(hand_detector.im_skin, out_range=[0,255]).astype(np.uint8)
color_histograms = local_histograms(skin, n_bins=5, max_bound=255, patch_size=11)*mask[box][:,:,None]
# LBP Histogram
lbp_texture = local_binary_pattern(cam.depthIm[box]*mask[box], 6, 5)*mask[box]
lbp_histograms = local_histograms(lbp_texture.astype(np.uint8), n_bins=10, max_bound=2**6, patch_size=11)*mask[box][:,:,None]
# for i in range(10):
# subplot(2,5,i+1)
# imshow(lbp_histograms[:,:,i])
''' ---- Per Joint Learning ---- '''
if learn:
for ii,i in enumerate(pose_truth_uv):
if i[0] != 0:
try:
if joint_features['geodesic'][ii] is None:
joint_features['geodesic'][ii] = geodesic_features[i[1]-box_corner[0], i[0]-box_corner[1]]
else:
joint_features['geodesic'][ii] = np.vstack([joint_features['geodesic'][ii], (geodesic_features[i[1]-box_corner[0], i[0]-box_corner[1]])])
if joint_features['color_histograms'][ii] is None:
joint_features['color_histograms'][ii] = color_histograms[i[1]-box_corner[0], i[0]-box_corner[1]]
else:
joint_features['color_histograms'][ii] = np.vstack([joint_features['color_histograms'][ii], deepcopy(color_histograms[i[1]-box_corner[0], i[0]-box_corner[1]])])
if joint_features['lbp'][ii] is None:
joint_features['lbp'][ii] = lbp_histograms[i[1]-box_corner[0], i[0]-box_corner[1]]
else:
joint_features['lbp'][ii] = np.vstack([joint_features['lbp'][ii], deepcopy(lbp_histograms[i[1]-box_corner[0], i[0]-box_corner[1]])])
except:
print "error"
''' ---- Per Joint Classification ---- '''
if not learn:
try:
# Geodesic clasification
tmp = geodesic_features.reshape([-1, 6])
tmp = np.array([x/x[-1] for x in tmp])
tmp = np.nan_to_num(tmp)
geo_clf_map = clf_geo.predict(tmp).reshape(im_pos.shape[:2])*mask[box]
geo_clf_labels = geo_clf_map[pose_truth_uv[[0,1,4,7,10,13],1]-box_corner[0], pose_truth_uv[[0,1,4,7,10,13],0]-box_corner[1]]
geo_accuracy += [geo_clf_labels == [0,1,4,7,10,13]]
print 'G',np.mean(geo_accuracy,0), geo_clf_labels==[0,1,4,7,10,13]
cv2.imshow('Geo', geo_clf_map/float(geo_clf_map.max()))
except:
pass
try:
# Color histogram classification
color_test = color_approx.transform(color_histograms.reshape([-1, 5]))
color_clf_map = clf_color.predict(color_test).reshape(im_pos.shape[:2])*mask[box]
color_clf_labels = color_clf_map[pose_truth_uv[[0,1,4,7,10,13],1]-box_corner[0], pose_truth_uv[[0,1,4,7,10,13],0]-box_corner[1]]
color_accuracy += [color_clf_labels == [0,1,4,7,10,13]]
print 'C',np.mean(color_accuracy,0), color_clf_labels==[0,1,4,7,10,13]
cv2.imshow('Col', color_clf_map/float(color_clf_map.max()))
except:
pass
try:
# lbp histogram classification
lbp_test = color_approx.transform(lbp_histograms.reshape([-1, 10]))
lbp_clf_map = clf_lbp.predict(lbp_test).reshape(im_pos.shape[:2])*mask[box]
lbp_clf_labels = lbp_clf_map[pose_truth_uv[[0,1,4,7,10,13],1]-box_corner[0], pose_truth_uv[[0,1,4,7,10,13],0]-box_corner[1]]
lbp_accuracy += [lbp_clf_labels == [0,1,4,7,10,13]]
print 'L',np.mean(lbp_accuracy,0), lbp_clf_labels==[0,1,4,7,10,13]
cv2.imshow('LBP', lbp_clf_map/float(lbp_clf_map.max()))
except:
pass
pose_uv = pose_truth_uv
pose = pose_truth
# ''' ---- Accuracy ---- '''
if 1 and not learn:
# pose_truth = cam.users[0]
error = pose_truth - pose
# print "Error", error
error_l2 = np.sqrt(np.sum(error**2, 1))
# error_l2 = np.sqrt(np.sum(error[:,:2]**2, 1))
joint_accuracy_all += [error_l2]
accuracy = np.sum(error_l2 < 150) / 14.
accuracy_all += [accuracy]
print "Current", accuracy
# print "Running avg:", np.mean(accuracy_all)
# print "Joint avg (per-joint):", np.mean(joint_accuracy_all, -1)
# print "Joint avg (overall):", np.mean(joint_accuracy_all)
''' --- Visualization --- '''
cam.colorIm = display_skeletons(cam.colorIm, pose_truth_uv, skel_type='Kinect', color=(0,255,0))
cam.colorIm = display_skeletons(cam.colorIm, pose_uv, skel_type='Kinect')
cam.visualize()
# print "Extrema:", geo_clf_map[geodesic_markers[:,0], geodesic_markers[:,1]]
# print "Skin:", geo_clf_map[hand_markers[:,0], hand_markers[:,1]]
# print "Skin val:", hand_detector.skin_match[hand_markers[:,0], hand_markers[:,1]]
# hand_data += [[x[0] for x in hand_markers],
# [x[1] for x in hand_markers],
# list(hand_detector.skin_match[hand_markers[:,0], hand_markers[:,1]])]
# ------------------------------------------------------------
# video_writer.write((geo_clf_map/float(geo_clf_map.max())*255.).astype(np.uint8))
# video_writer.write(cam.colorIm[:,:,[2,1,0]])
frame_count += frame_rate
except:
pass
print "-- Results for subject {:d} action {:d}".format(subjects[0],actions[0])
print "Running avg:", np.mean(accuracy_all)
print "Joint avg (overall):", np.mean(joint_accuracy_all)
# print 'Done'
if learn:
pose_database.save()
print 'Pose database saved'
embed()
return
''' --- Format Geodesic features ---'''
geodesics_train = []
geodesics_labels = []
for i in xrange(len(joint_features['geodesic'])):
# joint_features['geodesic'][i] = np.array([np.sort(x) for x in joint_features['geodesic'][i] if x[0] != 0])
joint_features['geodesic'][i] = np.array([x/x.max() for x in joint_features['geodesic'][i] if x[0] != 0])
ii = i
if i not in [0,1,4,7,10,13]:
ii=1
else:
geodesics_labels += [i*np.ones(len(joint_features['geodesic'][i]))]
geodesics_train = np.vstack([joint_features['geodesic'][x] for x in [0,1,4,7,10,13]])
# geodesics_train = np.vstack(joint_features['geodesic'])
geodesics_labels = np.hstack(geodesics_labels)
figure(1)
title('Distances of each joint to first 6 geodesic extrema')
for i in range(14):
subplot(4,4,i+1)
ylabel('Distance')
xlabel('Sample')
plot(joint_features['geodesic'][i])
axis([0,400,0,1600])
# Learn geodesic classifier
clf_geo = SGDClassifier(n_iter=10000, alpha=.01, n_jobs=-1, class_weight='auto')
clf_geo.fit(geodesics_train, geodesics_labels)
print clf_geo.score(geodesics_train, geodesics_labels)
geodesic_features = np.sort(geodesic_features)
sgd_map = clf_geo.predict(geodesic_features.reshape([-1, 6])).reshape(im_pos.shape[:2])
pickle.dump(clf_geo, open('geodesic_svm_sorted_scaled_5class.pkl','w'), pickle.HIGHEST_PROTOCOL)
# clf_geo = pickle.load(open('geodesic_svm_sorted_scaled_5class.pkl'))
''' --- Color Histogram features ---'''
color_train = []
color_labels = []
for i in xrange(len(joint_features['color_histograms'])):
ii = i
if i not in [0,1,4,7,10,13]:
ii=1
else:
color_labels += [i*np.ones(len(joint_features['color_histograms'][i]))]
# color_labels += [i*np.ones(len(joint_features['color_histograms'][i]))]
# color_train = np.vstack(joint_features['color_histograms'])
color_train = np.vstack([joint_features['color_histograms'][x] for x in [0,1,4,7,10,13]])
color_labels = np.hstack(color_labels)
color_approx = AdditiveChi2Sampler()
color_approx_train = color_approx.fit_transform(color_train)
clf = SGDClassifier(n_iter=10000, alpha=.01, n_jobs=-1, class_weight='auto')
clf.fit(color_approx_train, color_labels)
print clf.score(color_approx_train, color_labels)
color_test = color_approx.transform(color_histograms.reshape([-1, 5]))
sgd_map = clf.predict(color_test).reshape(im_pos.shape[:2])*mask[box]
figure(1)
title('Color Histograms per Joint')
for i in range(14):
subplot(4,4,i+1)
ylabel('Count')
xlabel('Sample')
plot(joint_features['color_histograms'][i])
axis([0,10,0,30])
for i in range(5):
subplot(1,5,i+1)
imshow(color_histograms[:,:,i])
pickle.dump([clf,color_approx], open('color_histogram_approx_svm_5class.pkl','w'), pickle.HIGHEST_PROTOCOL)
# clf_color,color_approx = pickle.load(open('color_histogram_approx_svm_5class.pkl'))
''' --- LBP Histogram features ---'''
color_train = []
color_labels = []
for i in xrange(len(joint_features['lbp'])):
ii = i
if i not in [0,1,4,7,10,13]:
ii=1
else:
color_labels += [i*np.ones(len(joint_features['lbp'][i]))]
# color_labels += [i*np.ones(len(joint_features['color_histograms'][i]))]
# color_train = np.vstack(joint_features['color_histograms'])
color_train = np.vstack([joint_features['lbp'][x] for x in [0,1,4,7,10,13]])
color_labels = np.hstack(color_labels)
color_approx = AdditiveChi2Sampler()
color_approx_train = color_approx.fit_transform(color_train)
clf = SGDClassifier(n_iter=10000, alpha=.01, n_jobs=-1, class_weight='auto')
clf.fit(color_approx_train, color_labels)
print clf.score(color_approx_train, color_labels)
color_test = color_approx.transform(lbp_histograms.reshape([-1, 10]))
sgd_map = clf.predict(color_test).reshape(im_pos.shape[:2])*mask[box]
figure(1)
title('LBP Histograms per Joint')
for i in range(14):
subplot(4,4,i+1)
ylabel('Count')
xlabel('Sample')
plot(joint_features['lbp'][i])
axis([0,10,0,30])
for i in range(5):
subplot(1,5,i+1)
imshow(color_histograms[:,:,i])
pickle.dump([clf,color_approx], open('lbp_histogram_approx_svm_5class.pkl','w'), pickle.HIGHEST_PROTOCOL)
def main(visualize=False, learn=False, patch_size=32, n_frames=2500):
if 1:
get_color = True
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/', kinects=[1], actions=[1], subjects=[1], get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
# cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
# cam.bgSubtraction.backgroundModel = sm.imread('/Users/colin/Data/CIRL_28Feb2013/depth/59/13/47/device_1/depth_59_13_47_4_13_35507.png').clip(0, 4500)
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/Office_Background_B.tif')
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/CIRL_Background_B.tif')
# bg = Image.open('/Users/colin/Data/JHU_RGBD_Pose/Wall_Background_B.tif')
# cam.bgSubtraction.backgroundModel = np.array(bg.getdata()).reshape([240,320]).clip(0, 4500)
# embed()
# cam = KinectPlayer(base_dir='./', device=2, bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
elif 0:
get_color = False
cam = EVALPlayer(base_dir='/Users/colin/Data/EVAL/', bg_subtraction=True, get_depth=True, get_skeleton=True, fill_images=False)
elif 0:
get_color = False
cam = MSRPlayer(base_dir='/Users/colin/Data/MSR_DailyActivities/Data/', actions=[1], subjects=[1,2,3,4,5], bg_subtraction=True, get_depth=True, get_color=True, get_skeleton=True, fill_images=False)
embed()
height, width = cam.depthIm.shape
skel_names = np.array(['head', 'neck', 'torso', 'l_shoulder', 'l_elbow', 'l_hand', \
'r_shoulder', 'r_elbow', 'r_hand', 'l_hip', 'l_knee', 'l_foot',\
'r_hip', 'r_knee', 'r_foot'])
# skel_init, joint_size, constraint_links, features_joints,convert_to_kinect = get_11_joint_properties()
skel_init, joint_size, constraint_links, features_joints,skel_parts, convert_to_kinect = get_13_joint_properties()
# skel_init, joint_size, constraint_links, features_joints,skel_parts, convert_to_kinect = get_14_joint_properties()
# skel_init, joint_size, constraint_links, features_joints,convert_to_kinect = get_15_joint_properties()
constraint_values = []
for c in constraint_links:
constraint_values += [np.linalg.norm(skel_init[c[0]]-skel_init[c[1]], 2)]
constraint_values = np.array(constraint_values)
skel_current = skel_init.copy()
skel_previous = skel_current.copy()
face_detector = FaceDetector()
hand_template = sm.imread('/Users/colin/Desktop/fist.png')[:,:,2]
hand_template = (255 - hand_template)/255.
if height == 240:
hand_template = cv2.resize(hand_template, (10,10))
else:
hand_template = cv2.resize(hand_template, (20,20))
frame_count = 0
if get_color and height==240:
cam.next(220)
accuracy_measurements = {'overall':[], 'per_joint':[]}
# Video writer
# print '1'
video_writer = cv2.VideoWriter("/Users/colin/Desktop/test.avi", cv2.cv.CV_FOURCC('M','J','P','G'), 15, (320,240))
# print '1'
# embed()
while cam.next(1) and frame_count < n_frames:
print ""
print "Frame #{0:d}".format(frame_count)
# Get rid of bad skeletons
if type(cam.users)==dict:
cam_skels = [np.array(cam.users[s]['jointPositions'].values()) for s in cam.users]
else:
cam_skels = [np.array(s) for s in cam.users]
cam_skels = [s for s in cam_skels if np.all(s[0] != -1)]
# Check for skeletons
# if len(cam_skels) == 0:
# continue
# Apply mask to image
mask = cam.get_person() > 0
# if mask is False:
if 1:
if len(cam_skels) > 0:
# cam.colorIm = display_skeletons(cam.colorIm[:,:,2], skel_msr_im, (255,), skel_type='Kinect')[:,:,None]
cam.colorIm[:,:,1] = display_skeletons(cam.colorIm[:,:,2], skel2depth(cam_skels[0], cam.depthIm.shape), (255,), skel_type='Kinect')
## Max P=31 for LBPs becuase of datatype
# tmp = local_binary_pattern(-cam.depthIm, 1, 10)#*(cam.foregroundMask>0)
# embed()
# tmp = local_occupancy_pattern(cam.depthIm, 31, 20, px_diff_thresh=100)*(cam.foregroundMask>0)
# cv2.imshow("LBP", np.abs(tmp.astype(np.float))/float(tmp.max()))
cam.colorIm = cam.colorIm[:,:,[0,2,1]]
cam.visualize()
continue
# Anonomize
# c_masked = cam.colorIm*mask[:,:,None]
# d_masked = cam.depthIm*mask
# c_masked_neg = cam.colorIm*(-mask[:,:,None])
im_depth = cam.depthIm
if get_color:
im_color = cam.colorIm
im_color *= mask[:,:,None]
im_color = np.ascontiguousarray(im_color)
im_color = im_color[:,:,[2,1,0]]
if len(cam_skels) > 0:
skel_msr_xyz = cam_skels[0]
skel_msr_im = skel2depth(cam_skels[0], cam.depthIm.shape)
box = nd.find_objects(mask)[0]
d = 20
box = (slice(np.maximum(box[0].start-d, 0), \
np.minimum(box[0].stop+d, height-1)), \
slice(np.maximum(box[1].start-d, 0), \
np.minimum(box[1].stop+d, width-1)))
# Face and skin detection
if get_color:
face_detector.run(im_color[box])
im_skin = rgb2lab(cam.colorIm[box].astype(np.int16))[:,:,1]
# im_skin = skimage.exposure.equalize_hist(im_skin)
# im_skin = skimage.exposure.rescale_intensity(im_skin, out_range=[0,1])
im_skin *= im_skin > face_detector.min_threshold
im_skin *= im_skin < face_detector.max_threshold
# im_skin *= face_detector>.068
skin_match_c = nd.correlate(im_skin, hand_template)
# Display Predictions - Color Based matching
optima = peak_local_max(skin_match_c, min_distance=20, num_peaks=3, exclude_border=False)
# Visualize
if len(optima) > 0:
optima_values = skin_match_c[optima[:,0], optima[:,1]]
optima_thresh = np.max(optima_values) / 2
optima = optima.tolist()
for i,o in enumerate(optima):
if optima_values[i] < optima_thresh:
optima.pop(i)
break
joint = np.array(o) + [box[0].start, box[1].start]
circ = np.array(circle(joint[0],joint[1], 5)).T
circ = circ.clip([0,0], [height-1, width-1])
cam.colorIm[circ[:,0], circ[:,1]] = (0,120 - 30*i,0)#(255*(i==0),255*(i==1),255*(i==2))
markers = optima
# ---------------- Tracking Algorithm ----------------
# ---- Preprocessing ----
if get_color:
im_pos = rgbIm2PosIm(cam.depthIm*mask)[box] * mask[box][:,:,None]
else:
im_pos = cam.posIm[box]
cam.depthIm[cam.depthIm==4500] = 0
im_pos_mean = np.array([
im_pos[:,:,0][im_pos[:,:,2]!=0].mean(),
im_pos[:,:,1][im_pos[:,:,2]!=0].mean(),
im_pos[:,:,2][im_pos[:,:,2]!=0].mean()
], dtype=np.int16)
# Zero-center
if skel_current[0,2] == 0:
skel_current += im_pos_mean
skel_previous += im_pos_mean
# Calculate Geodesic Extrema
extrema = geodesic_extrema_MPI(im_pos, iterations=15, visualize=False)
if len(extrema) > 0:
for i,o in enumerate(extrema):
joint = np.array(o) + [box[0].start, box[1].start]
circ = np.array(circle(joint[0],joint[1], 5)).T
circ = circ.clip([0,0], [height-1, width-1])
cam.colorIm[circ[:,0], circ[:,1]] = (0,0,200-10*i)
# Calculate Z-surface
surface_map = nd.distance_transform_edt(-mask[box], return_distances=False, return_indices=True)
# Only sample some of the points
if 1:
mask_interval = 1
feature_radius = 10
else:
mask_interval = 3
feature_radius = 2
# Modify the box wrt the sampling
box = (slice(box[0].start, box[0].stop, mask_interval),slice(box[1].start, box[1].stop, mask_interval))
im_pos_full = im_pos.copy()
im_pos = im_pos[::mask_interval,::mask_interval]
box_height, box_width,_ = im_pos.shape
skel_img_box = world2rgb(skel_current, cam.depthIm.shape) - [box[0].start, box[1].start, 0]
skel_img_box = skel_img_box.clip([0,0,0], [im_pos.shape[0]-1, im_pos.shape[1]-1, 9999])
feature_width = feature_radius*2+1
all_features = [face_detector.face_position, optima, extrema]
total_feature_count = np.sum([len(f) for f in all_features])
# Loop through the rest of the constraints
for _ in range(1):
# ---- (Step 1A) Find feature coordespondences ----
color_feature_displacement = feature_joint_displacements(skel_current, im_pos, all_features[1], features_joints[1], distance_thresh=500)
depth_feature_displacement = feature_joint_displacements(skel_current, im_pos, all_features[2], features_joints[2], distance_thresh=500)
# Alternative method: use kdtree
## Calc euclidian distance between each pixel and all joints
px_corr = np.zeros([im_pos.shape[0], im_pos.shape[1], len(skel_current)])
for i,s in enumerate(skel_current):
px_corr[:,:,i] = np.sqrt(np.sum((im_pos - s)**2, -1))# / joint_size[i]**2
## Handle occlusions by argmax'ing over set of skel parts
# visible_configurations = list(it.product([0,1], repeat=5))[1:]
visible_configurations = [
#[0,1,1,1,1],
#[1,0,0,0,0],
[1,1,1,1,1]
]
px_visibility_label = np.zeros([im_pos.shape[0], im_pos.shape[1], len(visible_configurations)], dtype=np.uint8)
visible_scores = np.ones(len(visible_configurations))*np.inf
# Try each occlusion configuration set
for i,v in enumerate(visible_configurations):
visible_joints = list(it.chain.from_iterable(skel_parts[np.array(v)>0]))
px_visibility_label[:,:,i] = np.argmin(px_corr[:,:,visible_joints], -1)#.reshape([im_pos.shape[0], im_pos.shape[1]])
visible_scores[i] = np.min(px_corr[:,:,visible_joints], -1).sum()
# Choose best occlusion configuration
occlusion_index = np.argmin(visible_scores)
occlusion_configuration = visible_configurations[occlusion_index]
occlusion_set = list(it.chain.from_iterable(skel_parts[np.array(visible_configurations[occlusion_index])>0]))
# Choose label for pixels based on occlusion configuration
px_label = px_visibility_label[:,:,occlusion_index]*mask[box]
px_label_flat = px_visibility_label[:,:,occlusion_index][mask[box]].flatten()
visible_joints = [1 if x in occlusion_set else 0 for x in range(len(skel_current))]
# Project distance to joint's radius
px_joint_displacement = skel_current[px_label_flat] - im_pos[mask[box]]
px_joint_magnitude = np.sqrt(np.sum(px_joint_displacement**2,-1))
joint_mesh_pos = skel_current[px_label_flat] + px_joint_displacement*(joint_size[px_label_flat]/px_joint_magnitude)[:,None]
px_joint_displacement = joint_mesh_pos - im_pos[mask[box]]
# Calc the correspondance change in position for each joint
correspondence_displacement = np.zeros([len(skel_current), 3])
ii = 0
for i,_ in enumerate(skel_current):
if i in occlusion_set:
labels = px_label_flat==i
correspondence_displacement[i] = np.mean(px_joint_displacement[px_label_flat==ii], 0)
# correspondence_displacement[ii] = np.sum(px_joint_displacement[px_label_flat==ii], 0)
ii+=1
correspondence_displacement = np.nan_to_num(correspondence_displacement)
# ---- (Step 2) Update pose state, x ----
lambda_p = .0
lambda_c = .3
lambda_cf = .3
lambda_df = .0
skel_prev_difference = (skel_current - skel_previous)
# embed()
skel_current = skel_previous \
+ lambda_p * skel_prev_difference \
- lambda_c * correspondence_displacement\
- lambda_cf * color_feature_displacement\
- lambda_df * depth_feature_displacement
# ---- (Step 3) Add constraints ----
# A: Link lengths / geometry
skel_current = link_length_constraints(skel_current, constraint_links, constraint_values, alpha=.5)
skel_current = geometry_constraints(skel_current, joint_size, alpha=0.5)
# skel_current = collision_constraints(skel_current, constraint_links)
skel_img_box = (world2rgb(skel_current, cam.depthIm.shape) - [box[0].start, box[1].start, 0])/mask_interval
skel_img_box = skel_img_box.clip([0,0,0], [cam.depthIm.shape[0]-1, cam.depthIm.shape[1]-1, 9999])
# B: Ray-cast constraints
skel_current, skel_img_box = ray_cast_constraints(skel_current, skel_img_box, im_pos_full, surface_map, joint_size)
# # Map back from mask to image
skel_img = skel_img_box + [box[0].start, box[1].start, 0]
# Update for next round
skel_previous = skel_current.copy()
# skel_previous = skel_init.copy()
# ---------------------Accuracy --------------------------------------
# Compute accuracy wrt standard Kinect data
# skel_im_error = skel_msr_im[:,[1,0]] - skel_img[[0,2,3,4,5,6,7,8,9,10,11,12,13,14],:2]
skel_current_kinect = convert_to_kinect(skel_current)
try:
skel_msr_im_box = np.array([skel_msr_im[:,1]-box[0].start,skel_msr_im[:,0]-box[1].start]).T.clip([0,0],[box_height-1, box_width-1])
skel_xyz_error = im_pos[skel_msr_im_box[:,0],skel_msr_im_box[:,1]] - skel_current_kinect#skel_current[[0,2,3,4,5,6,7,8,9,10,11,12],:]
skel_l2 = np.sqrt(np.sum(skel_xyz_error**2, 1))
# print skel_l2
skel_correct = np.nonzero(skel_l2 < 150)[0]
accuracy_measurements['per_joint'] += [skel_l2]
accuracy_measurements['overall'] += [len(skel_correct)/float(len(skel_current_kinect))*100]
print "{0:0.2f}% joints correct".format(len(skel_correct)/float(len(skel_current_kinect))*100)
print "Overall accuracy: ", np.mean(accuracy_measurements['overall'])
except:
pass
print "Visible:", visible_joints
# ----------------------Visualization-------------------------------------
# l = line(skel_img_box[joint][0], skel_img_box[joint][1], features[feat][0], features[feat][1])
# skimage.draw.set_color(cam.colorIm[box], l, (255,255,255))
# Add circles to image
cam.colorIm = np.ascontiguousarray(cam.colorIm)
if 0:#get_color:
cam.colorIm = display_skeletons(cam.colorIm, skel_img[:,[1,0,2]]*np.array(visible_joints)[:,None], (0,255,), skel_type='Other', skel_contraints=constraint_links)
for i,s in enumerate(skel_img):
# if i not in skel_correct:
if i not in occlusion_set:
c = circle(s[0], s[1], 5)
cam.colorIm[c[0], c[1]] = (255,0,0)
# cam.colorIm = display_skeletons(cam.colorIm, world2rgb(skel_init+im_pos_mean, [240,320])[:,[1,0]], skel_type='Other', skel_contraints=constraint_links)
if 1:
if len(face_detector.face_position) > 0:
for (x, y) in face_detector.face_position:
pt1 = (int(y)+box[1].start-15, int(x)+box[0].start-15)
pt2 = (pt1[0]+int(15), pt1[1]+int(15))
cv2.rectangle(cam.colorIm, pt1, pt2, (255, 0, 0), 3, 8, 0)
if len(cam_skels) > 0:
# cam.colorIm = display_skeletons(cam.colorIm[:,:,2], skel_msr_im, (255,), skel_type='Kinect')[:,:,None]
cam.colorIm[:,:,1] = display_skeletons(cam.colorIm[:,:,2], skel_msr_im, (255,), skel_type='Kinect')
cam.visualize()
cam.depthIm = local_binary_pattern(cam.depthIm*cam.foregroundMask, 50, 10)
cv2.imshow("Depth", cam.depthIm/float(cam.depthIm.max()))
# cam2.visualize()
# embed()
# 3D Visualization
if 0:
from mayavi import mlab
# figure = mlab.figure(1, bgcolor=(0,0,0), fgcolor=(1,1,1))
figure = mlab.figure(1, bgcolor=(1,1,1))
figure.scene.disable_render = True
mlab.clf()
mlab.view(azimuth=-45, elevation=45, roll=0, figure=figure)
mlab.points3d(-skel_current[:,1], -skel_current[:,0], skel_current[:,2], scale_factor=100., color=(.5,.5,.5))
for c in constraint_links:
x = np.array([skel_current[c[0]][0], skel_current[c[1]][0]])
y = np.array([skel_current[c[0]][1], skel_current[c[1]][1]])
z = np.array([skel_current[c[0]][2], skel_current[c[1]][2]])
mlab.plot3d(-y,-x,z, tube_radius=25., color=(1,0,0))
figure.scene.disable_render = False
# 3-panel view
if 0:
subplot(2,2,1)
scatter(skel_current[:,1], skel_current[:,2]);
for i,c in enumerate(constraint_links):
plot([skel_current[c[0],1], skel_current[c[1],1]],[skel_current[c[0],2], skel_current[c[1],2]])
axis('equal')
subplot(2,2,3)
scatter(skel_current[:,1], -skel_current[:,0]);
for i,c in enumerate(constraint_links):
plot([skel_current[c[0],1], skel_current[c[1],1]],[-skel_current[c[0],0], -skel_current[c[1],0]])
axis('equal')
subplot(2,2,4)
scatter(skel_current[:,2], -skel_current[:,0]);
for i,c in enumerate(constraint_links):
plot([skel_current[c[0],2], skel_current[c[1],2]],[-skel_current[c[0],0], -skel_current[c[1],0]])
axis('equal')
# show()
# ------------------------------------------------------------
video_writer.write(cam.colorIm[:,:,[2,1,0]])
frame_count+=1
print 'Done'
def main(anonomization=False):
# Setup kinect data player
# cam = KinectPlayer(base_dir='./', bg_subtraction=False, get_depth=True, get_color=True, get_skeleton=False)
# actions = [2, 5, 7]
actions = [2]
actions_labels = ['JumpingJacks', 'Waving', 'Clapping']
action = 'JumpingJacks'
subjects = range(1, 10)
cam = MHADPlayer(base_dir='/Users/colin/Data/BerkeleyMHAD/',
kinect=1,
actions=actions,
subjects=subjects,
reps=[1],
get_depth=True,
get_color=True,
get_skeleton=True,
fill_images=False)
if anonomization:
''' bg_type can be:
'box'[param=max_depth]
'static'[param=background]
'mean'
'median'
'adaptive_mog'
See BasePlayer for more details
'''
cam.set_bg_model(bg_type='box', param=2600)
# Test HOG pedestrian detector
# cv2.HOGDescriptor_getDefaultPeopleDetector()
# imshow(color[people])
body_data = [[]]
framerate = 1
prev_n = len(cam.kinect_folder_names)
action_idx = 0
# embed()
while cam.next(framerate):
if len(cam.kinect_folder_names) != prev_n:
# action_idx += 1
body_data += [[]]
prev_n = len(cam.kinect_folder_names)
body_data[-1] += [cam.users_uv[0]]
continue
if 0:
people_all = list(
cv.HOGDetectMultiScale(cv.fromarray(
cam.colorIm.mean(-1).astype(np.uint8)),
cv.CreateMemStorage(0),
hit_threshold=-1.5))
print len(people_all), " people detected"
for i, people in enumerate(people_all):
# embed()
try:
print people
# tmp = cam.colorIm[people[0][0]:people[0][0]+people[1][0], people[0][1]:people[0][1]+people[1][1]]
# tmp = cam.colorIm[people[0][1]:people[0][1]+people[1][1], people[0][0]:people[0][0]+people[1][0]]
cam.colorIm[people[0][1]:people[0][1] + people[1][1],
people[0][0]:people[0][0] + people[1][0]] += 50
# cv2.imshow("Body2 "+str(i), tmp)
# subplot(4, len(people_all)/4, i + 1)
# imshow(tmp)
# tmp = cam.colorIm[people[0][0]:people[0][0]+people[0][1], people[1][0]:people[1][0]+people[1][1]]
# cv2.imshow("Body1 "+str(i), tmp)
# tmp = cam.colorIm[people[1][0]:people[1][0]+people[1][1], people[0][0]:people[0][0]+people[0][1]]
# print tmp
# cv2.imshow("Body "+str(i), tmp)
except:
pass
# show()
if anonomization and cam.mask is not None:
mask = (sm.imresize(cam.mask, [480, 640]) > 0).copy()
mask[40:170, 80:140] = True # person in background
px = draw.circle(145, 285, 40)
mask[px] = True
# Version 1 - Blur + Circle
color = cam.colorIm.copy()
color[mask] = cv2.GaussianBlur(color, (29, 29), 50)[mask]
subplot(1, 3, 1)
imshow(color)
# Version 2 - Noise on mask
color = cam.colorIm.copy()
noise_key = np.random.randint(0, 255,
cam.colorIm.shape).astype(np.uint8)
color[mask] = color[mask] + noise_key[mask]
subplot(1, 3, 2)
imshow(color)
# Version 3 - All noise
color = cam.colorIm.copy()
color = color + noise_key
subplot(1, 3, 3)
imshow(color)
show()
# cam.colorIm = cam.colorIm[:,:,[2,1,0]]
# cam.colorIm *= mask[:,:,None]
# cam.visualize(color=True, depth=True, text=True, colorize=True, depth_bounds=[0,4000])
# cam.visualize()
cam.colorIm = display_skeletons(cam.colorIm,
cam.users_uv[0],
skel_type='Kinect',
color=(0, 255, 0))
cam.visualize()
body_data[actions_labels[action_idx]] += [cam.users_uv[0]]
# cam.visualize(color=True, depth=True, text=False, colorize=False, depth_bounds=None)
print 'Done'
# Pause at the end
# embed()
import scipy.io as si
si.matlab.savemat(action + ".mat", {'data': body_data})
def main(visualize=False, learn=False):
# Init both cameras
# fill = True
fill = False
get_color = True
cam = KinectPlayer(base_dir='./',
device=1,
bg_subtraction=True,
get_depth=True,
get_color=get_color,
get_skeleton=True,
fill_images=fill)
cam2 = KinectPlayer(base_dir='./',
device=2,
bg_subtraction=True,
get_depth=True,
get_color=get_color,
get_skeleton=True,
fill_images=fill)
# Transformation matrix from first to second camera
data = pickle.load(open("Registration.dat", 'r'))
transform_c1_to_c2 = data['transform']
# Get random forest parameters
if learn:
rf = RFPose(offset_max=100, feature_count=300)
else:
rf = RFPose()
rf.load_forest()
ii = 0
# cam.next(200)
all_joint_ims_z = []
all_joint_ims_c = []
while cam.next() and ii < 200:
# Update frames
cam2.sync_cameras(cam)
if ii % 10 != 0:
ii += 1
continue
# Transform skels from cam1 to cam2
cam_skels = [
np.array(cam.users[s]['jointPositions'].values())
for s in cam.users.keys()
]
# Get rid of bad skels
cam_skels = [s for s in cam_skels if np.all(s[0] != -1)]
if len(cam_skels) == 0:
continue
ii += 1
# Save images
if 1:
joint_ims_z = []
joint_ims_c = []
dx = 10
skel_tmp = skel2depth(cam_skels[0], [240, 320])
for j_pos in skel_tmp:
# embed()
joint_ims_z += [
cam.depthIm[j_pos[0] - dx:j_pos[0] + dx,
j_pos[1] - dx:j_pos[1] + dx]
]
joint_ims_c += [
cam.colorIm[j_pos[0] - dx:j_pos[0] + dx,
j_pos[1] - dx:j_pos[1] + dx]
]
if len(joint_ims_z) > 0:
all_joint_ims_z += [joint_ims_z]
all_joint_ims_c += [joint_ims_c]
if 0:
cam2_skels = transform_skels(cam_skels, transform_c1_to_c2,
'image')
try:
depth = cam2.get_person()
if learn:
rf.add_frame(depth, cam2_skels[0])
else:
rf.infer_pose(depth)
if visualize:
cam2.depthIm = display_skeletons(cam2.depthIm,
cam2_skels[0], (5000, ),
skel_type='Low')
skel1 = kinect_to_msr_skel(
skel2depth(cam_skels[0], [240, 320]))
cam.depthIm = display_skeletons(cam.depthIm,
skel1, (5000, ),
skel_type='Low')
cam.visualize()
cam2.visualize()
except:
pass
embed()
if learn:
print "Starting forest"
rf.learn_forest()
print 'Done'