def generate_prechecked_pose(self, posture, stiffness, filename):
prechecked_pose_list = np.load(filename, allow_pickle=True).tolist()
print len(prechecked_pose_list)
shuffle(prechecked_pose_list)
pyRender = libRender.pyRenderMesh()
for shape_pose_vol in prechecked_pose_list[6:]:
#print shape_pose_vol
#print shape_pose_vol[0]
#print shape_pose_vol[1]
#print shape_pose_vol[2]
for idx in range(len(shape_pose_vol[0])):
#print shape_pose_vol[0][idx]
self.m.betas[idx] = shape_pose_vol[0][idx]
print 'init'
print shape_pose_vol[2][0], shape_pose_vol[2][1], shape_pose_vol[
2][2]
self.m.pose[:] = np.array(72 * [0.])
for idx in range(len(shape_pose_vol[1])):
#print shape_pose_vol[1][idx]
#print self.m.pose[shape_pose_vol[1][idx]]
#print shape_pose_vol[2][idx]
pose_index = shape_pose_vol[1][idx] * 1
self.m.pose[pose_index] = shape_pose_vol[2][idx] * 1.
#print idx, pose_index, self.m.pose[pose_index], shape_pose_vol[2][idx]
print self.m.pose[0:3]
init_root = np.array(self.m.pose[0:3]) + 0.000001
init_rootR = libKinematics.matrix_from_dir_cos_angles(init_root)
root_rot = libKinematics.eulerAnglesToRotationMatrix(
[np.pi, 0.0, np.pi / 2])
#print root_rot
trans_root = libKinematics.dir_cos_angles_from_matrix(
np.matmul(root_rot, init_rootR))
self.m.pose[0] = trans_root[0]
self.m.pose[1] = trans_root[1]
self.m.pose[2] = trans_root[2]
print root_rot
print init_rootR
print trans_root
print init_root, trans_root
#print self.m.J_transformed[1, :], self.m.J_transformed[4, :]
# self.m.pose[51] = selection_r
pyRender.mesh_render_pose_bed(self.m, self.point_cloud_array,
self.pc_isnew, self.pressure,
self.markers)
self.point_cloud_array = None
m.pose[48:51] = capsule_angles[41:44]
m.pose[51:54] = capsule_angles[44:47]
m.pose[55] = capsule_angles[47]
m.pose[58] = capsule_angles[48]
m.pose[60:63] = capsule_angles[49:52]
m.pose[63:66] = capsule_angles[52:55]
return m
if __name__ == '__main__':
from smpl.smpl_webuser.serialization import load_model
import time
import lib_pyrender as libPyRender
pyRender = libPyRender.pyRenderMesh(render=True)
gender = "f"
posture = "lay"
num_data = 2000
stiffness = "none"
resting_pose_data_list = np.load(
"/home/henry/data/resting_poses/resting_pose_roll0_plo_hbh_f_lay_set2_1769_of_2601_none_stiff_fix.npy",
allow_pickle=True)
model_path = '/home/henry/git/SMPL_python_v.1.0.0/smpl/models/basicModel_' + gender + '_lbs_10_207_0_v1.0.0.pkl'
m = load_model(model_path)
PERSON_SCALE = 50.0
# first get the offsets
def evaluate_data(self, filename1, filename2=None):
self.Render = libRender.pyRenderMesh()
self.pyRender = libPyRender.pyRenderMesh()
#model = torch.load(filename1, map_location={'cuda:5': 'cuda:0'})
if GPU == True:
for i in range(0, 8):
try:
model = torch.load(filename1, map_location={'cuda:'+str(i):'cuda:0'})
if self.CTRL_PNL['dropout'] == True:
model = model.cuda().train()
else:
model = model.cuda().eval()
break
except:
pass
if filename2 is not None:
for i in range(0, 8):
try:
model2 = torch.load(filename2, map_location={'cuda:'+str(i):'cuda:0'})
if self.CTRL_PNL['dropout'] == True:
model2 = model2.cuda().train()
else:
model2 = model2.cuda().eval()
break
except:
pass
else:
model2 = None
else:
model = torch.load(filename1, map_location='cpu')
if self.CTRL_PNL['dropout'] == True:
model = model.train()
else:
model = model.eval()
if filename2 is not None:
model2 = torch.load(filename2, map_location='cpu')
if self.CTRL_PNL['dropout'] == True:
model2 = model2.train()
else:
model2 = model2.eval()
else:
model2 = None
#function_input = np.array(function_input)*np.array([10, 10, 10, 10, 10, 10, 0.1, 0.1, 0.1, 0.1, 1])
#function_input += np.array([2.2, 32, -1, 1.2, 32, -5, 1.0, 1.0, 0.96, 0.95, 0.8])
function_input = np.array(self.calibration_optim_values)*np.array([10, 10, 10, 0.1, 0.1, 0.1, 0.1])
function_input += np.array([1.2, 32, -5, 1.0, 1.0, 0.96, 0.95])
kinect_rotate_angle = function_input[3-3]
kinect_shift_up = int(function_input[4-3])
kinect_shift_right = int(function_input[5-3])
camera_alpha_vert = function_input[6-3]
camera_alpha_horiz = function_input[7-3]
pressure_horiz_scale = function_input[8-3]
pressure_vert_scale = function_input[9-3]
#head_angle_multiplier = function_input[10-3]
#file_dir = "/media/henry/multimodal_data_1/all_hevans_data/0905_2_Evening/0255"
#file_dir_list = ["/media/henry/multimodal_data_2/test_data/data_072019_0001/"]
blah = True
#file_dir = "/media/henry/multimodal_data_2/test_data/data_072019_0007"
#file_dir = "/media/henry/multimodal_data_2/test_data/data_072019_0006"
#file_dir = "/home/henry/ivy_test_data/data_102019_kneeup_0000"
#file_dir = "/media/henry/multimodal_data_1/CVPR2020_study/P000/data_102019_kneeup_0000"
if PARTICIPANT == "P106":
file_dir = "/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"_00"
#file_dir = "/home/henry/Desktop/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"_000"
else:
file_dir = "/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"-2_00"
#file_dir = "/home/henry/Desktop/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"-2_00"
file_dir_nums = ["00","01","02","03","04","05","06","07","08","09"]#,"10"]#,"11","12"]
overall_counter = 1
overall_counter_disp = 1
bedstatenpy = []
colornpy = []
config_codenpy = []
date_stampnpy = []
depth_rnpy = []
markersnpy = []
point_cloudnpy = []
pressurenpy = []
time_stampnpy = []
SAVE = True
for file_dir_num in file_dir_nums:
file_dir_curr = file_dir + file_dir_num
print "LOADING", file_dir_curr
V3D.load_next_file(file_dir_curr)
start_num = 0
print self.color_all.shape
#for im_num in range(29, 100):
for im_num in range(start_num, self.color_all.shape[0]):
if PARTICIPANT == "S103" and overall_counter in [26, 27, 28, 45, 53, 54, 55]:#, 52, 53]:
overall_counter += 1
pass
elif PARTICIPANT == "S104" and overall_counter in [49, 50]: #S104 is everything but the last two
overall_counter += 1
pass
elif PARTICIPANT == "S107" and overall_counter in [25, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S114" and overall_counter in [42, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S118" and overall_counter in [11, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S121" and overall_counter in [7, 47]:
overall_counter += 1
pass
elif PARTICIPANT == "S130" and overall_counter in [30, 31, 34, 52, 53, 54, 55]:
overall_counter += 1
pass
elif PARTICIPANT == "S134" and overall_counter in [49, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S140" and overall_counter in [49, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S141" and overall_counter in [49, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S145" and overall_counter in [23, 49, 50, 51]:
overall_counter += 1
pass
elif PARTICIPANT == "S151" and overall_counter in [9, 48]:
overall_counter += 1
pass
elif PARTICIPANT == "S163" and overall_counter in [46, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S165" and overall_counter in [19, 45]:
overall_counter += 1
pass
elif PARTICIPANT == "S170" and overall_counter in [49, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S179" and overall_counter in [42, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S184" and overall_counter in [49, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S187" and overall_counter in [39, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S188" and overall_counter in [47, 50]:
overall_counter += 1
pass
elif PARTICIPANT == "S196" and overall_counter in [20, 36]:
overall_counter += 1
pass
#elif overall_counter < 41:# and im_num > 0:
# overall_counter += 1
# overall_counter_disp += 1
# pass
else:
print file_dir_curr, " subset count: ", im_num, " overall ct: ", overall_counter_disp, overall_counter
overall_counter += 1
overall_counter_disp += 1
self.overall_image_scale_amount = 0.85
half_w_half_l = [0.4, 0.4, 1.1, 1.1]
all_image_list = []
self.label_single_image = []
self.label_index = 0
self.color = self.color_all[im_num]
self.depth_r = self.depth_r_all[im_num]
self.pressure = self.pressure_all[im_num]
self.bed_state = self.bedstate_all[im_num]
self.point_cloud_autofil = self.point_cloud_autofil_all[im_num] + [0.0, 0.0, 0.1]
print self.point_cloud_autofil.shape
self.bed_state[0] = self.bed_state[0]#*head_angle_multiplier
self.bed_state *= 0
#self.bed_state += 60.
print self.bed_state, np.shape(self.pressure)
bedstatenpy.append(self.bedstate_all[im_num])
colornpy.append(self.color_all[im_num])
config_codenpy.append(self.config_code_all[im_num])
date_stampnpy.append(self.date_stamp_all[im_num])
depth_rnpy.append(self.depth_r_all[im_num])
markersnpy.append(list(self.markers_all[im_num]))
point_cloudnpy.append(self.point_cloud_autofil_all[im_num])
pressurenpy.append(self.pressure_all[im_num])
time_stampnpy.append(self.time_stamp_all[im_num])
if im_num == start_num and blah == True:
markers_c = []
markers_c.append(self.markers_all[im_num][0])
markers_c.append(self.markers_all[im_num][1])
markers_c.append(self.markers_all[im_num][2])
markers_c.append(self.markers_all[im_num][3])
for idx in range(4):
if markers_c[idx] is not None:
markers_c[idx] = np.array(markers_c[idx])*213./228.
blah = False
# Get the marker points in 2D on the color image
u_c, v_c = ArTagLib().color_2D_markers(markers_c, self.new_K_kin)
# Get the marker points dropped to the height of the pressure mat
u_c_drop, v_c_drop, markers_c_drop = ArTagLib().color_2D_markers_drop(markers_c, self.new_K_kin)
# Get the geometry for sizing the pressure mat
pmat_ArTagLib = ArTagLib()
self.pressure_im_size_required, u_c_pmat, v_c_pmat, u_p_bend, v_p_bend, half_w_half_l = \
pmat_ArTagLib.p_mat_geom(markers_c_drop, self.new_K_kin, self.pressure_im_size_required, self.bed_state, half_w_half_l)
tf_corners = np.zeros((8, 2))
tf_corners[0:8,:] = np.copy(self.tf_corners)
#COLOR
#if self.color is not 0:
color_reshaped, color_size = VizLib().color_image(self.color, self.kcam, self.new_K_kin,
u_c, v_c, u_c_drop, v_c_drop, u_c_pmat, v_c_pmat, camera_alpha_vert, camera_alpha_horiz)
color_reshaped = imutils.rotate(color_reshaped, kinect_rotate_angle)
color_reshaped = color_reshaped[pre_VERT_CUT+kinect_shift_up:-pre_VERT_CUT+kinect_shift_up, HORIZ_CUT+kinect_shift_right : 540 - HORIZ_CUT+kinect_shift_right, :]
tf_corners[0:4, :], color_reshaped = self.transform_selected_points(color_reshaped,
camera_alpha_vert,
camera_alpha_horiz,
kinect_rotate_angle,
kinect_shift_right,
kinect_shift_up, [1.0, 0],
[1.0, 0],
np.copy(self.tf_corners[0:4][:]))
all_image_list.append(color_reshaped)
#DEPTH
h = VizLib().get_new_K_kin_homography(camera_alpha_vert, camera_alpha_horiz, self.new_K_kin)
depth_r_orig = cv2.warpPerspective(self.depth_r, h, (self.depth_r.shape[1], self.depth_r.shape[0]))
depth_r_orig = imutils.rotate(depth_r_orig, kinect_rotate_angle)
depth_r_orig = depth_r_orig[HORIZ_CUT + kinect_shift_right: 540 - HORIZ_CUT + kinect_shift_right, pre_VERT_CUT - kinect_shift_up:-pre_VERT_CUT - kinect_shift_up]
depth_r_reshaped, depth_r_size, depth_r_orig = VizLib().depth_image(depth_r_orig, u_c, v_c)
self.depth_r_orig = depth_r_orig
self.depthcam_midpixel = [self.new_K_kin[1, 2] - HORIZ_CUT - kinect_shift_right, (960-self.new_K_kin[0, 2]) - pre_VERT_CUT - kinect_shift_up]
all_image_list.append(depth_r_reshaped)
self.get_pc_from_depthmap(self.bed_state[0], tf_corners[2, :])
#PRESSURE
self.pressure = np.clip(self.pressure*4, 0, 100)
pressure_reshaped, pressure_size, coords_from_top_left = VizLib().pressure_image(self.pressure, self.pressure_im_size,
self.pressure_im_size_required, color_size,
u_c_drop, v_c_drop, u_c_pmat, v_c_pmat,
u_p_bend, v_p_bend)
pressure_shape = pressure_reshaped.shape
pressure_reshaped = cv2.resize(pressure_reshaped, None, fx=pressure_horiz_scale,
fy=pressure_vert_scale)[0:pressure_shape[0],
0:pressure_shape[1], :]
if pressure_horiz_scale < 1.0 or pressure_vert_scale < 1.0:
pressure_reshaped_padded = np.zeros(pressure_shape).astype(np.uint8)
pressure_reshaped_padded[0:pressure_reshaped.shape[0], 0:pressure_reshaped.shape[1], :] += pressure_reshaped
pressure_reshaped = np.copy(pressure_reshaped_padded)
coords_from_top_left[0] -= coords_from_top_left[0]*(1-pressure_horiz_scale)
coords_from_top_left[1] += (960 - coords_from_top_left[1])*(1-pressure_vert_scale)
pressure_reshaped = pressure_reshaped[pre_VERT_CUT:-pre_VERT_CUT, HORIZ_CUT : 540 - HORIZ_CUT, :]
all_image_list.append(pressure_reshaped)
self.all_images = np.zeros((960-np.abs(pre_VERT_CUT)*2, 1, 3)).astype(np.uint8)
for image in all_image_list:
print image.shape
self.all_images = np.concatenate((self.all_images, image), axis = 1)
self.all_images = self.all_images[VERT_CUT : 960 - VERT_CUT, :, :]
is_not_mult_4 = True
while is_not_mult_4 == True:
is_not_mult_4 = cv2.resize(self.all_images, (0, 0), fx=self.overall_image_scale_amount, fy=self.overall_image_scale_amount).shape[1]%4
self.overall_image_scale_amount+= 0.001
coords_from_top_left[0] -= (HORIZ_CUT)
coords_from_top_left[1] = 960 - pre_VERT_CUT - coords_from_top_left[1]
self.coords_from_top_left = (np.array(coords_from_top_left) * self.overall_image_scale_amount)
#print self.coords_from_top_left
self.all_images = cv2.resize(self.all_images, (0, 0), fx=self.overall_image_scale_amount, fy=self.overall_image_scale_amount)
self.cursor_shift = self.all_images.shape[1]/4
self.all_images_clone = self.all_images.copy()
cv2.imshow('all_images', self.all_images)
k = cv2.waitKey(1)
if SAVE == False:
time.sleep(5)
#cv2.waitKey(0)
#self.estimate_pose(self.pressure, self.bed_state[0], markers_c, model, model2)
if SAVE == True:
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/color.npy", colornpy)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/depth_r.npy", depth_rnpy)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/pressure.npy", pressurenpy)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/bedstate.npy", bedstatenpy)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/markers.npy", np.array(markersnpy), allow_pickle=True)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/time_stamp.npy", time_stampnpy)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/point_cloud.npy", point_cloudnpy)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/config_code.npy", config_codenpy)
np.save("/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_checked_"+PARTICIPANT+"-2/date_stamp.npy", date_stampnpy)
def evaluate_data(self, filename1):
#self.Render = libRender.pyRenderMesh(render = False)
self.pyRender = libPyRender.pyRenderMesh(render=False)
#function_input = np.array(function_input)*np.array([10, 10, 10, 10, 10, 10, 0.1, 0.1, 0.1, 0.1, 1])
#function_input += np.array([2.2, 32, -1, 1.2, 32, -5, 1.0, 1.0, 0.96, 0.95, 0.8])
function_input = np.array(self.calibration_optim_values) * np.array(
[10, 10, 10, 0.1, 0.1, 0.1, 0.1])
function_input += np.array([1.2, 32, -5, 1.0, 1.0, 0.96, 0.95])
kinect_rotate_angle = function_input[3 - 3]
kinect_shift_up = int(function_input[4 - 3]) # - 40
kinect_shift_right = int(function_input[5 - 3]) # - 20
camera_alpha_vert = function_input[6 - 3]
camera_alpha_horiz = function_input[7 - 3]
pressure_horiz_scale = function_input[8 - 3]
pressure_vert_scale = function_input[9 - 3]
#head_angle_multiplier = function_input[10-3]
#print kinect_shift_up, kinect_shift_right, "SHIFT UP RIGHT"
#print pressure_horiz_scale, pressure_vert_scale, "PRESSURE SCALES" #1.04 for one too far to left
#file_dir = "/media/henry/multimodal_data_1/all_hevans_data/0905_2_Evening/0255"
#file_dir_list = ["/media/henry/multimodal_data_2/test_data/data_072019_0001/"]
blah = True
#file_dir = "/media/henry/multimodal_data_2/test_data/data_072019_0007"
#file_dir = "/media/henry/multimodal_data_2/test_data/data_072019_0006"
#file_dir = "/home/henry/ivy_test_data/data_102019_kneeup_0000"
#file_dir = "/media/henry/multimodal_data_1/CVPR2020_study/P000/data_102019_kneeup_0000"
if PARTICIPANT == "P106":
#file_dir = "/media/henry/multimodal_data_1/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"_000"
file_dir = "/home/henry/Desktop/CVPR2020_study/" + PARTICIPANT + "/data_" + PARTICIPANT + "_000"
file_dirs = [ #file_dir+str(0),
file_dir + str(1), file_dir + str(2), file_dir + str(3),
file_dir + str(4), file_dir + str(5)
]
else:
#file_dir = "/media/henry/multimodal_data_1/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"-2_000"
file_dir = "/media/henry/multimodal_data_2/CVPR2020_study/" + PARTICIPANT + "/data_checked_" + PARTICIPANT + "-" + POSE_TYPE
file_dirs = [file_dir]
#file_dir = "/media/henry/multimodal_data_1/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"-2_000"
#file_dir = "/media/henry/multimodal_data_2/CVPR2020_study/"+PARTICIPANT+"/data_"+PARTICIPANT+"-C_0000"
#file_dirs = [file_dir]
self.RESULTS_DICT = {}
self.RESULTS_DICT['body_roll_rad'] = []
self.RESULTS_DICT['v_to_gt_err'] = []
self.RESULTS_DICT['v_limb_to_gt_err'] = []
self.RESULTS_DICT['gt_to_v_err'] = []
self.RESULTS_DICT['precision'] = []
self.RESULTS_DICT['recall'] = []
self.RESULTS_DICT['overlap_d_err'] = []
self.RESULTS_DICT['all_d_err'] = []
self.RESULTS_DICT['betas'] = []
init_time = time.time()
for file_dir in file_dirs:
V3D.load_next_file(file_dir)
start_num = 0
#print self.color_all.shape
#for im_num in range(29, 100):
for im_num in range(start_num, self.color_all.shape[0]):
#For P188: skip 5. 13 good cross legs
print "NEXT IM!", im_num, " ", time.time() - init_time
if PARTICIPANT == "S114" and POSE_TYPE == "2" and im_num in [
26, 29
]:
continue #these don't have point clouds
if PARTICIPANT == "S165" and POSE_TYPE == "2" and im_num in [
1, 3, 15
]:
continue #these don't have point clouds
if PARTICIPANT == "S188" and POSE_TYPE == "2" and im_num in [
5, 17, 21
]:
continue
#good picks: 103 - 6 good for what info is there
#151 11 is good
#179 - 7 is great
#187 natural poses very good
#196 - 11 has great smile :)
self.overall_image_scale_amount = 0.85
half_w_half_l = [0.4, 0.4, 1.1, 1.1]
all_image_list = []
self.label_single_image = []
self.label_index = 0
self.color = self.color_all[im_num]
self.depth_r = self.depth_r_all[im_num]
self.pressure = self.pressure_all[im_num]
self.bed_state = self.bedstate_all[im_num]
if self.point_cloud_autofil_all[im_num].shape[0] == 0:
self.point_cloud_autofil_all[im_num] = np.array(
[[0.0, 0.0, 0.0]])
self.point_cloud_autofil = self.point_cloud_autofil_all[
im_num] + self.markers_all[im_num][
2] #[0.0, 0.0, 0.0]#0.1]
#print self.markers_all[im_num]
#print self.point_cloud_autofil.shape, 'PC AUTOFIL ORIG'
self.bed_state[
0] = self.bed_state[0] * 0.0 #*head_angle_multiplier
self.bed_state *= 0
#self.bed_state += 60.
#print self.bed_state, np.shape(self.pressure)
if im_num == start_num and blah == True:
markers_c = []
markers_c.append(self.markers_all[im_num][0])
markers_c.append(self.markers_all[im_num][1])
markers_c.append(self.markers_all[im_num][2])
markers_c.append(self.markers_all[im_num][3])
#for idx in range(4):
#if markers_c[idx] is not None:
#markers_c[idx] = np.array(markers_c[idx])*213./228.
blah = False
#print markers_c, 'Markers C'
# Get the marker points in 2D on the color image
u_c, v_c = ArTagLib().color_2D_markers(markers_c,
self.new_K_kin)
# Get the marker points dropped to the height of the pressure mat
u_c_drop, v_c_drop, markers_c_drop = ArTagLib(
).color_2D_markers_drop(markers_c, self.new_K_kin)
#print markers_c_drop, self.new_K_kin, self.pressure_im_size_required, self.bed_state, half_w_half_l
# Get the geometry for sizing the pressure mat
pmat_ArTagLib = ArTagLib()
self.pressure_im_size_required, u_c_pmat, v_c_pmat, u_p_bend, v_p_bend, half_w_half_l = \
pmat_ArTagLib.p_mat_geom(markers_c_drop, self.new_K_kin, self.pressure_im_size_required, self.bed_state, half_w_half_l)
tf_corners = np.zeros((8, 2))
tf_corners[0:8, :] = np.copy(self.tf_corners)
#COLOR
#if self.color is not 0:
color_reshaped, color_size = VizLib().color_image(
self.color, self.kcam, self.new_K_kin, u_c, v_c, u_c_drop,
v_c_drop, u_c_pmat, v_c_pmat, camera_alpha_vert,
camera_alpha_horiz)
color_reshaped = imutils.rotate(color_reshaped,
kinect_rotate_angle)
color_reshaped = color_reshaped[pre_VERT_CUT +
kinect_shift_up:-pre_VERT_CUT +
kinect_shift_up, HORIZ_CUT +
kinect_shift_right:540 -
HORIZ_CUT +
kinect_shift_right, :]
tf_corners[
0:4, :], color_reshaped = self.transform_selected_points(
color_reshaped, camera_alpha_vert, camera_alpha_horiz,
kinect_rotate_angle, kinect_shift_right,
kinect_shift_up, [1.0, 0], [1.0, 0],
np.copy(self.tf_corners[0:4][:]))
all_image_list.append(color_reshaped)
#DEPTH
h = VizLib().get_new_K_kin_homography(camera_alpha_vert,
camera_alpha_horiz,
self.new_K_kin)
depth_r_orig = cv2.warpPerspective(
self.depth_r, h,
(self.depth_r.shape[1], self.depth_r.shape[0]))
depth_r_orig = imutils.rotate(depth_r_orig,
kinect_rotate_angle)
depth_r_orig = depth_r_orig[HORIZ_CUT +
kinect_shift_right:540 -
HORIZ_CUT + kinect_shift_right,
pre_VERT_CUT -
kinect_shift_up:-pre_VERT_CUT -
kinect_shift_up]
depth_r_reshaped, depth_r_size, depth_r_orig = VizLib(
).depth_image(depth_r_orig, u_c, v_c)
self.depth_r_orig = depth_r_orig
self.depthcam_midpixel = [
self.new_K_kin[1, 2] - HORIZ_CUT - kinect_shift_right,
(960 - self.new_K_kin[0, 2]) - pre_VERT_CUT -
kinect_shift_up
]
self.depthcam_midpixel2 = [
self.new_K_kin[1, 2] - HORIZ_CUT,
(960 - self.new_K_kin[0, 2]) - pre_VERT_CUT
]
#print h, "H" #warping perspective
#print kinect_rotate_angle #the amount to rotate counterclockwise about normal vector to the bed
#print kinect_shift_right, kinect_shift_up #pixel shift of depth im. convert this to meters based on depth of
depth_r_orig_nowarp = imutils.rotate(self.depth_r, 0)
depth_r_orig_nowarp = depth_r_orig_nowarp[HORIZ_CUT + 0:540 -
HORIZ_CUT + 0,
pre_VERT_CUT -
0:-pre_VERT_CUT - 0]
depth_r_reshaped_nowarp, depth_r_size, depth_r_orig_nowarp = VizLib(
).depth_image(depth_r_orig_nowarp, u_c,
v_c) #this just does two rotations
all_image_list.append(depth_r_reshaped)
all_image_list.append(depth_r_reshaped_nowarp)
X, Y, Z = self.get_pc_from_depthmap(self.bed_state[0],
tf_corners[2, :])
#print self.pressure_im_size_required, color_size, u_c_drop, v_c_drop, u_c_pmat, v_c_pmat, u_p_bend, v_p_bend
#PRESSURE
#pressure_vert_scale = 1.0
#pressure_horiz_scale = 1.0
self.pressure = np.clip(self.pressure * 4, 0, 100)
pressure_reshaped, pressure_size, coords_from_top_left = VizLib(
).pressure_image(self.pressure, self.pressure_im_size,
self.pressure_im_size_required, color_size,
u_c_drop, v_c_drop, u_c_pmat, v_c_pmat,
u_p_bend, v_p_bend)
pressure_shape = pressure_reshaped.shape
pressure_reshaped = cv2.resize(
pressure_reshaped,
None,
fx=pressure_horiz_scale,
fy=pressure_vert_scale)[0:pressure_shape[0],
0:pressure_shape[1], :]
if pressure_horiz_scale < 1.0 or pressure_vert_scale < 1.0:
pressure_reshaped_padded = np.zeros(pressure_shape).astype(
np.uint8)
pressure_reshaped_padded[
0:pressure_reshaped.shape[0],
0:pressure_reshaped.shape[1], :] += pressure_reshaped
pressure_reshaped = np.copy(pressure_reshaped_padded)
coords_from_top_left[0] -= coords_from_top_left[0] * (
1 - pressure_horiz_scale)
coords_from_top_left[1] += (960 - coords_from_top_left[1]) * (
1 - pressure_vert_scale)
pressure_reshaped = pressure_reshaped[
pre_VERT_CUT:-pre_VERT_CUT, HORIZ_CUT:540 - HORIZ_CUT, :]
all_image_list.append(pressure_reshaped)
self.all_images = np.zeros(
(960 - np.abs(pre_VERT_CUT) * 2, 1, 3)).astype(np.uint8)
for image in all_image_list:
#print image.shape
self.all_images = np.concatenate((self.all_images, image),
axis=1)
self.all_images = self.all_images[VERT_CUT:960 -
VERT_CUT, :, :]
is_not_mult_4 = True
while is_not_mult_4 == True:
is_not_mult_4 = cv2.resize(
self.all_images, (0, 0),
fx=self.overall_image_scale_amount,
fy=self.overall_image_scale_amount).shape[1] % 4
self.overall_image_scale_amount += 0.001
coords_from_top_left[0] -= (HORIZ_CUT)
coords_from_top_left[
1] = 960 - pre_VERT_CUT - coords_from_top_left[1]
self.coords_from_top_left = (np.array(coords_from_top_left) *
self.overall_image_scale_amount)
#print self.coords_from_top_left
self.all_images = cv2.resize(
self.all_images, (0, 0),
fx=self.overall_image_scale_amount,
fy=self.overall_image_scale_amount)
self.cursor_shift = self.all_images.shape[1] / 4
self.all_images_clone = self.all_images.copy()
cv2.imshow('all_images', self.all_images)
k = cv2.waitKey(1)
#cv2.waitKey(0)
self.pc_all = [Y, X, -Z]
#print np.shape(self.pc_all), "PC ALL SHAPE"
self.estimate_pose(self.pressure, self.bed_state[0], markers_c,
tf_corners, camera_alpha_vert,
camera_alpha_horiz, h, kinect_rotate_angle)
pkl.dump(
self.RESULTS_DICT,
open(
'/media/henry/multimodal_data_2/data/final_results/results_real_'
+ PARTICIPANT + '_' + POSE_TYPE + '_' + NETWORK_2 + '.p',
'wb'))
def estimate_real_time(self, filename1, filename2=None):
pyRender = libRender.pyRenderMesh()
mat_size = (64, 27)
from unpack_batch_lib import UnpackBatchLib
if torch.cuda.is_available():
# Use for GPU
GPU = True
dtype = torch.cuda.FloatTensor
print '######################### CUDA is available! #############################'
else:
# Use for CPU
GPU = False
dtype = torch.FloatTensor
print '############################## USING CPU #################################'
import convnet as convnet
from torch.autograd import Variable
if GPU == True:
for i in range(0, 8):
try:
model = torch.load(
filename1, map_location={'cuda:' + str(i): 'cuda:0'})
model = model.cuda().eval()
break
except:
pass
if filename2 is not None:
for i in range(0, 8):
try:
model2 = torch.load(
filename2,
map_location={'cuda:' + str(i): 'cuda:0'})
model2 = model2.cuda().eval()
break
except:
pass
else:
model2 = None
else:
model = torch.load(filename1, map_location='cpu').eval()
if filename2 is not None:
model2 = torch.load(filename2, map_location='cpu').eval()
else:
model2 = None
pub = rospy.Publisher('meshTopic', MeshAttr)
#rospy.init_node('talker', anonymous=False)
while not rospy.is_shutdown():
pmat = np.fliplr(
np.flipud(
np.clip(self.pressure.reshape(mat_size) *
float(self.CTRL_PNL['pmat_mult']),
a_min=0,
a_max=100)))
if self.CTRL_PNL['cal_noise'] == False:
pmat = gaussian_filter(pmat, sigma=0.5)
pmat_stack = PreprocessingLib(
).preprocessing_create_pressure_angle_stack_realtime(
pmat, self.bedangle, mat_size)
if self.CTRL_PNL['cal_noise'] == False:
pmat_stack = np.clip(pmat_stack, a_min=0, a_max=100)
pmat_stack = np.array(pmat_stack)
if self.CTRL_PNL['incl_pmat_cntct_input'] == True:
pmat_contact = np.copy(pmat_stack[:, 0:1, :, :])
pmat_contact[pmat_contact > 0] = 100
pmat_stack = np.concatenate((pmat_contact, pmat_stack), axis=1)
weight_input = WEIGHT_LBS / 2.20462
height_input = (HEIGHT_IN * 0.0254 - 1) * 100
batch1 = np.zeros((1, 162))
if GENDER == 'f':
batch1[:, 157] += 1
elif GENDER == 'm':
batch1[:, 158] += 1
batch1[:, 160] += weight_input
batch1[:, 161] += height_input
if self.CTRL_PNL['normalize_input'] == True:
self.CTRL_PNL['depth_map_input_est'] = False
pmat_stack = self.TPL.normalize_network_input(
pmat_stack, self.CTRL_PNL)
batch1 = self.TPL.normalize_wt_ht(batch1, self.CTRL_PNL)
pmat_stack = torch.Tensor(pmat_stack)
batch1 = torch.Tensor(batch1)
batch = []
batch.append(pmat_stack)
batch.append(batch1)
self.CTRL_PNL['adjust_ang_from_est'] = False
scores, INPUT_DICT, OUTPUT_DICT = UnpackBatchLib(
).unpackage_batch_kin_pass(batch, False, model, self.CTRL_PNL)
mdm_est_pos = OUTPUT_DICT['batch_mdm_est'].clone().unsqueeze(
1) / 16.69545796387731
mdm_est_neg = OUTPUT_DICT['batch_mdm_est'].clone().unsqueeze(
1) / 45.08513083167194
mdm_est_pos[mdm_est_pos < 0] = 0
mdm_est_neg[mdm_est_neg > 0] = 0
mdm_est_neg *= -1
cm_est = OUTPUT_DICT['batch_cm_est'].clone().unsqueeze(
1) * 100 / 43.55800622930469
#1. / 16.69545796387731, # pos est depth
#1. / 45.08513083167194, # neg est depth
#1. / 43.55800622930469, # cm est
if model2 is not None:
batch_cor = []
batch_cor.append(
torch.cat(
(pmat_stack[:, 0:1, :, :],
mdm_est_pos.type(torch.FloatTensor),
mdm_est_neg.type(
torch.FloatTensor), cm_est.type(
torch.FloatTensor), pmat_stack[:, 1:, :, :]),
dim=1))
if self.CTRL_PNL['full_body_rot'] == False:
batch_cor.append(
torch.cat(
(batch1, OUTPUT_DICT['batch_betas_est'].cpu(),
OUTPUT_DICT['batch_angles_est'].cpu(),
OUTPUT_DICT['batch_root_xyz_est'].cpu()),
dim=1))
elif self.CTRL_PNL['full_body_rot'] == True:
batch_cor.append(
torch.cat(
(batch1, OUTPUT_DICT['batch_betas_est'].cpu(),
OUTPUT_DICT['batch_angles_est'].cpu(),
OUTPUT_DICT['batch_root_xyz_est'].cpu(),
OUTPUT_DICT['batch_root_atan2_est'].cpu()),
dim=1))
self.CTRL_PNL['adjust_ang_from_est'] = True
scores, INPUT_DICT, OUTPUT_DICT = UnpackBatchLib(
).unpackage_batch_kin_pass(batch_cor, False, model2,
self.CTRL_PNL)
betas_est = np.squeeze(
OUTPUT_DICT['batch_betas_est_post_clip'].cpu().numpy())
angles_est = np.squeeze(OUTPUT_DICT['batch_angles_est_post_clip'])
root_shift_est = np.squeeze(
OUTPUT_DICT['batch_root_xyz_est_post_clip'].cpu().numpy())
#print betas_est.shape, root_shift_est.shape, angles_est.shape
#print betas_est, root_shift_est, angles_est
angles_est = angles_est.reshape(72)
for idx in range(10):
#print shape_pose_vol[0][idx]
self.m.betas[idx] = betas_est[idx]
for idx in range(72):
self.m.pose[idx] = angles_est[idx]
init_root = np.array(self.m.pose[0:3]) + 0.000001
init_rootR = libKinematics.matrix_from_dir_cos_angles(init_root)
root_rot = libKinematics.eulerAnglesToRotationMatrix(
[np.pi, 0.0, np.pi / 2])
#print root_rot
trans_root = libKinematics.dir_cos_angles_from_matrix(
np.matmul(root_rot, init_rootR))
self.m.pose[0] = trans_root[0]
self.m.pose[1] = trans_root[1]
self.m.pose[2] = trans_root[2]
#print self.m.J_transformed[1, :], self.m.J_transformed[4, :]
# self.m.pose[51] = selection_r
print self.m.r
#print OUTPUT_DICT['verts']
pyRender.mesh_render_pose_bed_orig(
self.m, root_shift_est, self.point_cloud_array, self.pc_isnew,
pmat * 5. / float(self.CTRL_PNL['pmat_mult']), self.markers,
self.bedangle)
self.point_cloud_array = None
def val_convnet_general(self, epoch):
self.gender = "f"
if self.gender == "m":
model_path = '/home/henry/git/SMPL_python_v.1.0.0/smpl/models/basicModel_m_lbs_10_207_0_v1.0.0.pkl'
else:
model_path = '/home/henry/git/SMPL_python_v.1.0.0/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl'
self.m = load_model(model_path)
self.pyRender = libPyRender.pyRenderMesh(render = True)
'''
Train the model for one epoch.
'''
# Some models use slightly different forward passes and train and test
# time (e.g., any model with Dropout). This puts the model in train mode
# (as opposed to eval mode) so it knows which one to use.
self.model.eval()#train()
self.model2.eval()#train()
RESULTS_DICT = {}
RESULTS_DICT['j_err'] = []
RESULTS_DICT['betas'] = []
RESULTS_DICT['dir_v_err'] = []
RESULTS_DICT['dir_v_limb_err'] = []
RESULTS_DICT['v_to_gt_err'] = []
RESULTS_DICT['v_limb_to_gt_err'] = []
RESULTS_DICT['gt_to_v_err'] = []
RESULTS_DICT['precision'] = []
RESULTS_DICT['recall'] = []
RESULTS_DICT['overlap_d_err'] = []
RESULTS_DICT['all_d_err'] = []
init_time = time.time()
with torch.autograd.set_detect_anomaly(True):
# This will loop a total = training_images/batch_size times
for batch_idx, batch in enumerate(self.train_loader):
batch1 = batch[1].clone()
betas_gt = torch.mean(batch[1][:, 72:82], dim = 0).numpy()
angles_gt = torch.mean(batch[1][:, 82:154], dim = 0).numpy()
root_shift_est_gt = torch.mean(batch[1][:, 154:157], dim = 0).numpy()
NUMOFOUTPUTDIMS = 3
NUMOFOUTPUTNODES_TRAIN = 24
self.output_size_train = (NUMOFOUTPUTNODES_TRAIN, NUMOFOUTPUTDIMS)
self.CTRL_PNL['adjust_ang_from_est'] = False
self.CTRL_PNL['depth_map_labels'] = True
print batch[0].size(), "batch 0 shape"
scores, INPUT_DICT, OUTPUT_DICT = UnpackBatchLib().unpackage_batch_kin_pass(batch, False, self.model,
self.CTRL_PNL)
print OUTPUT_DICT['batch_betas_est_post_clip'].cpu().numpy()[0], 'betas init'
mdm_est_pos = OUTPUT_DICT['batch_mdm_est'].clone().unsqueeze(1) / 16.69545796387731
mdm_est_neg = OUTPUT_DICT['batch_mdm_est'].clone().unsqueeze(1) / 45.08513083167194
mdm_est_pos[mdm_est_pos < 0] = 0
mdm_est_neg[mdm_est_neg > 0] = 0
mdm_est_neg *= -1
cm_est = OUTPUT_DICT['batch_cm_est'].clone().unsqueeze(1) * 100 / 43.55800622930469
# 1. / 16.69545796387731, # pos est depth
# 1. / 45.08513083167194, # neg est depth
# 1. / 43.55800622930469, # cm est
sc_sample1 = OUTPUT_DICT['batch_targets_est'].clone()
sc_sample1 = sc_sample1[0, :].squeeze() / 1000
sc_sample1 = sc_sample1.view(self.output_size_train)
# print sc_sample1
if self.model2 is not None:
print "Using model 2"
batch_cor = []
batch_cor.append(torch.cat((batch[0][:, 0:1, :, :],
mdm_est_pos.type(torch.FloatTensor),
mdm_est_neg.type(torch.FloatTensor),
cm_est.type(torch.FloatTensor),
batch[0][:, 1:, :, :]), dim=1))
if self.CTRL_PNL['full_body_rot'] == False:
batch_cor.append(torch.cat((batch1,
OUTPUT_DICT['batch_betas_est'].cpu(),
OUTPUT_DICT['batch_angles_est'].cpu(),
OUTPUT_DICT['batch_root_xyz_est'].cpu()), dim=1))
elif self.CTRL_PNL['full_body_rot'] == True:
batch_cor.append(torch.cat((batch1,
OUTPUT_DICT['batch_betas_est'].cpu(),
OUTPUT_DICT['batch_angles_est'].cpu(),
OUTPUT_DICT['batch_root_xyz_est'].cpu(),
OUTPUT_DICT['batch_root_atan2_est'].cpu()), dim=1))
self.CTRL_PNL['adjust_ang_from_est'] = True
self.CTRL_PNL['depth_map_labels'] = False
scores, INPUT_DICT, OUTPUT_DICT = UnpackBatchLib().unpackage_batch_kin_pass(batch_cor, False,
self.model2,
self.CTRL_PNL)
# print betas_est, root_shift_est, angles_est
if self.CTRL_PNL['dropout'] == True:
#print OUTPUT_DICT['verts'].shape
smpl_verts = np.mean(OUTPUT_DICT['verts'], axis=0)
dropout_variance = np.std(OUTPUT_DICT['verts'], axis=0)
dropout_variance = np.linalg.norm(dropout_variance, axis=1)
else:
smpl_verts = OUTPUT_DICT['verts'][0, :, :]
dropout_variance = None
smpl_verts = np.concatenate((smpl_verts[:, 1:2] - 0.286 + 0.0143, smpl_verts[:, 0:1] - 0.286 + 0.0143,
- smpl_verts[:, 2:3]), axis=1)
smpl_faces = np.array(self.m.f)
q = OUTPUT_DICT['batch_mdm_est'].data.numpy().reshape(OUTPUT_DICT['batch_mdm_est'].size()[0], 64, 27) * -1
q = np.mean(q, axis=0)
camera_point = [1.09898028, 0.46441343, -CAM_BED_DIST]
bedangle = 0.0
# print smpl_verts
RESULTS_DICT['betas'].append(OUTPUT_DICT['batch_betas_est_post_clip'].cpu().numpy()[0])
print RESULTS_DICT['betas'][-1], "BETAS"
viz_type = "3D"
if viz_type == "2D":
from visualization_lib import VisualizationLib
if self.model2 is not None:
self.im_sample = INPUT_DICT['batch_images'][0, 4:,:].squeeze() * 20. # normalizing_std_constants[4]*5. #pmat
else:
self.im_sample = INPUT_DICT['batch_images'][0, 1:,:].squeeze() * 20. # normalizing_std_constants[4]*5. #pmat
self.im_sample_ext = INPUT_DICT['batch_images'][0, 0:,:].squeeze() * 20. # normalizing_std_constants[0] #pmat contact
# self.im_sample_ext2 = INPUT_DICT['batch_images'][im_display_idx, 2:, :].squeeze()*20.#normalizing_std_constants[4] #sobel
self.im_sample_ext3 = OUTPUT_DICT['batch_mdm_est'][0, :, :].squeeze().unsqueeze(0) * -1 # est depth output
# print scores[0, 10:16], 'scores of body rot'
# print self.im_sample.size(), self.im_sample_ext.size(), self.im_sample_ext2.size(), self.im_sample_ext3.size()
# self.publish_depth_marker_array(self.im_sample_ext3)
self.tar_sample = INPUT_DICT['batch_targets']
self.tar_sample = self.tar_sample[0, :].squeeze() / 1000
sc_sample = OUTPUT_DICT['batch_targets_est'].clone()
sc_sample = sc_sample[0, :].squeeze() / 1000
sc_sample = sc_sample.view(self.output_size_train)
VisualizationLib().visualize_pressure_map(self.im_sample, sc_sample1, sc_sample,
# self.im_sample_ext, None, None,
self.im_sample_ext3, None, None,
# , self.tar_sample_val, self.sc_sample_val,
block=False)
elif viz_type == "3D":
pmat = batch[0][0, 1, :, :].clone().numpy()*25.50538629767412
#print pmat.shape
for beta in range(betas_gt.shape[0]):
self.m.betas[beta] = betas_gt[beta]
for angle in range(angles_gt.shape[0]):
self.m.pose[angle] = angles_gt[angle]
smpl_verts_gt = np.array(self.m.r)
for s in range(root_shift_est_gt.shape[0]):
smpl_verts_gt[:, s] += (root_shift_est_gt[s] - float(self.m.J_transformed[0, s]))
smpl_verts_gt = np.concatenate(
(smpl_verts_gt[:, 1:2] - 0.286 + 0.0143, smpl_verts_gt[:, 0:1] - 0.286 + 0.0143,
0.0 - smpl_verts_gt[:, 2:3]), axis=1)
joint_cart_gt = np.array(self.m.J_transformed).reshape(24, 3)
for s in range(root_shift_est_gt.shape[0]):
joint_cart_gt[:, s] += (root_shift_est_gt[s] - float(self.m.J_transformed[0, s]))
#print joint_cart_gt, 'gt'
sc_sample = OUTPUT_DICT['batch_targets_est'].clone()
sc_sample = (sc_sample[0, :].squeeze().numpy() / 1000).reshape(24, 3)
#print sc_sample, 'estimate'
joint_error = np.linalg.norm(joint_cart_gt-sc_sample, axis = 1)
#print joint_error
RESULTS_DICT['j_err'].append(joint_error)
camera_point = [1.09898028, 0.46441343, -CAM_BED_DIST]
# render everything
RESULTS_DICT = self.pyRender.render_mesh_pc_bed_pyrender_everything_synth(smpl_verts, smpl_faces,
camera_point, bedangle, RESULTS_DICT,
smpl_verts_gt=smpl_verts_gt, pmat=pmat,
markers=None,
dropout_variance=dropout_variance)
#time.sleep(300)
#print RESULTS_DICT['j_err']
print np.mean(np.array(RESULTS_DICT['j_err']), axis = 0)
#print RESULTS_DICT['precision']
print np.mean(RESULTS_DICT['precision'])
print time.time() - init_time
#break
#save here
pkl.dump(RESULTS_DICT, open('/media/henry/multimodal_data_2/data/final_results/results_synth_'+TESTING_FILENAME+'_'+NETWORK_2+'.p', 'wb'))