Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
9456d8a
commit fbfaec4
Showing
14 changed files
with
427 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,251 @@ | ||
name: "DeepModel" | ||
|
||
layer { | ||
name: "data" | ||
type: "HDF5Data" | ||
top: "depth" | ||
top: "joint" | ||
hdf5_data_param { | ||
source:"train.txt" | ||
batch_size:512 | ||
} | ||
include { | ||
phase: TRAIN | ||
} | ||
} | ||
|
||
layer { | ||
name: "data" | ||
type: "HDF5Data" | ||
top: "depth" | ||
top: "joint" | ||
hdf5_data_param { | ||
source:"test.txt" | ||
batch_size: 64 | ||
} | ||
include { | ||
phase: TEST | ||
} | ||
} | ||
|
||
|
||
layer { | ||
name: "convL1" | ||
type: "Convolution" | ||
bottom: "depth" | ||
top: "convL1" | ||
convolution_param { | ||
num_output: 8 | ||
kernel_size: 5 | ||
weight_filler { | ||
type: "xavier" | ||
} | ||
bias_filler { | ||
type: "constant" | ||
} | ||
} | ||
} | ||
|
||
layer { | ||
name: "ReLUconvL1" | ||
type: "ReLU" | ||
bottom: "convL1" | ||
top: "convL1" | ||
} | ||
|
||
|
||
layer { | ||
name: "poolL1" | ||
type: "Pooling" | ||
bottom: "convL1" | ||
top: "poolL1" | ||
pooling_param { | ||
pool: MAX | ||
kernel_size: 4 | ||
stride: 4 | ||
} | ||
} | ||
|
||
layer { | ||
name: "convL2" | ||
type: "Convolution" | ||
bottom: "poolL1" | ||
top: "convL2" | ||
convolution_param { | ||
num_output: 8 | ||
kernel_size: 5 | ||
weight_filler { | ||
type: "xavier" | ||
} | ||
bias_filler { | ||
type: "constant" | ||
} | ||
} | ||
} | ||
|
||
layer { | ||
name: "ReLUconvL2" | ||
type: "ReLU" | ||
bottom: "convL2" | ||
top: "convL2" | ||
} | ||
|
||
layer { | ||
name: "poolL2" | ||
type: "Pooling" | ||
bottom: "convL2" | ||
top: "poolL2" | ||
pooling_param { | ||
pool: MAX | ||
kernel_size: 2 | ||
stride: 2 | ||
} | ||
} | ||
|
||
|
||
layer { | ||
name: "convL3" | ||
type: "Convolution" | ||
bottom: "poolL2" | ||
top: "convL3" | ||
convolution_param { | ||
num_output: 8 | ||
kernel_size: 3 | ||
weight_filler { | ||
type: "xavier" | ||
} | ||
bias_filler { | ||
type: "constant" | ||
} | ||
} | ||
} | ||
|
||
layer { | ||
name: "ReLUconvL3" | ||
type: "ReLU" | ||
bottom: "convL3" | ||
top: "convL3" | ||
} | ||
|
||
|
||
layer { | ||
name: "FC1" | ||
type: "InnerProduct" | ||
bottom: "convL3" | ||
top: "FC1" | ||
param { | ||
lr_mult: 1 | ||
} | ||
inner_product_param { | ||
num_output: 1024 | ||
weight_filler { | ||
type: "xavier" | ||
} | ||
bias_filler { | ||
type: "constant" | ||
} | ||
} | ||
} | ||
|
||
layer { | ||
name: "ReLUFC1" | ||
type: "ReLU" | ||
bottom: "FC1" | ||
top: "FC1" | ||
} | ||
|
||
layer { | ||
name: "DropoutFC1" | ||
type: "Dropout" | ||
bottom: "FC1" | ||
top: "FC1" | ||
dropout_param { | ||
dropout_ratio: 0.3 | ||
} | ||
} | ||
|
||
|
||
layer { | ||
name: "FC2" | ||
type: "InnerProduct" | ||
bottom: "FC1" | ||
top: "FC2" | ||
param { | ||
lr_mult: 1 | ||
} | ||
inner_product_param { | ||
num_output: 1024 | ||
weight_filler { | ||
type: "gaussian" | ||
std: 0.001 | ||
} | ||
bias_filler { | ||
type: "constant" | ||
} | ||
} | ||
} | ||
|
||
layer { | ||
name: "ReLUFC2" | ||
type: "ReLU" | ||
bottom: "FC2" | ||
top: "FC2" | ||
} | ||
|
||
layer { | ||
name: "DropoutFC2" | ||
type: "Dropout" | ||
bottom: "FC2" | ||
top: "FC2" | ||
dropout_param { | ||
dropout_ratio: 0.3 | ||
} | ||
} | ||
|
||
layer { | ||
name: "DoF" | ||
type: "InnerProduct" | ||
bottom: "FC2" | ||
top: "DoF" | ||
param { | ||
lr_mult: 1 | ||
} | ||
inner_product_param { | ||
num_output: 47 | ||
weight_filler { | ||
type: "gaussian" | ||
std: 0.001 | ||
} | ||
bias_filler { | ||
type: "constant" | ||
} | ||
} | ||
} | ||
|
||
|
||
layer { | ||
name: "DeepHandModel" | ||
type: "DeepHandModel" | ||
bottom: "DoF" | ||
top: "DeepHandModelxyz" | ||
} | ||
|
||
# uncomment to enable dof constraint | ||
#layer { | ||
# name: "DoFConstraintLoss" | ||
# type: "DeepHandModelDofConstraintLoss" | ||
# bottom: "DoF" | ||
# top: "DoFConstraintLoss" | ||
# loss_weight: 1 | ||
#} | ||
|
||
|
||
layer{ | ||
name:"DeepHandModelxyzloss" | ||
type:"EuclideanLoss" | ||
bottom:"DeepHandModelxyz" | ||
bottom:"joint" | ||
top:"DeepHandModelxyzloss" | ||
loss_weight: 1 | ||
} | ||
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,118 @@ | ||
import numpy as np | ||
import h5py | ||
import cv2 | ||
import scipy.io as sio | ||
import sys | ||
import os | ||
import math | ||
paths = {} | ||
with open('../path.config', 'r') as f: | ||
for line in f: | ||
name, path = line.split(': ') | ||
print name, path | ||
paths[name] = path | ||
|
||
## This part of code is modified from [DeepPrior](https://cvarlab.icg.tugraz.at/projects/hand_detection/) | ||
def CropImage(image, com): | ||
u, v, d = com | ||
zstart = d - cube_size / 2. | ||
zend = d + cube_size / 2. | ||
xstart = int(math.floor((u * d / fx - cube_size / 2.) / d * fx)) | ||
xend = int(math.floor((u * d / fx + cube_size / 2.) / d * fx)) | ||
ystart = int(math.floor((v * d / fy - cube_size / 2.) / d * fy)) | ||
yend = int(math.floor((v * d / fy + cube_size / 2.) / d * fy)) | ||
|
||
cropped = depth[max(ystart, 0):min(yend, depth.shape[0]), max(xstart, 0):min(xend, depth.shape[1])].copy() | ||
cropped = np.pad(cropped, ((abs(ystart)-max(ystart, 0), abs(yend)-min(yend, depth.shape[0])), | ||
(abs(xstart)-max(xstart, 0), abs(xend)-min(xend, depth.shape[1]))), mode='constant', constant_values=0) | ||
msk1 = np.bitwise_and(cropped < zstart, cropped != 0) | ||
msk2 = np.bitwise_and(cropped > zend, cropped != 0) | ||
cropped[msk1] = zstart | ||
cropped[msk2] = zend | ||
|
||
dsize = (img_size, img_size) | ||
wb = (xend - xstart) | ||
hb = (yend - ystart) | ||
if wb > hb: | ||
sz = (dsize[0], hb * dsize[0] / wb) | ||
else: | ||
sz = (wb * dsize[1] / hb, dsize[1]) | ||
|
||
roi = cropped | ||
rz = cv2.resize(cropped, sz) | ||
|
||
ret = np.ones(dsize, np.float32) * zend | ||
xstart = int(math.floor(dsize[0] / 2 - rz.shape[1] / 2)) | ||
xend = int(xstart + rz.shape[1]) | ||
ystart = int(math.floor(dsize[1] / 2 - rz.shape[0] / 2)) | ||
yend = int(ystart + rz.shape[0]) | ||
ret[ystart:yend, xstart:xend] = rz | ||
|
||
return ret | ||
|
||
dataset_path = paths['NYU_path'] | ||
J = 31 | ||
joint_id = np.array([0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 5, 11, 17, 23, 32, 30, 31, 28, 27, 25, 24]) | ||
img_size = 128 | ||
|
||
fx = 588.03 | ||
fy = 587.07 | ||
fu = 320. | ||
fv = 240. | ||
|
||
data_names = ['train', 'test_1', 'test_2'] | ||
cube_sizes = [300, 300, 300 * 0.87] | ||
id_starts = [0, 0, 2440] | ||
id_ends = [72756, 2440, 8252] | ||
num_packages = [3, 1, 1] | ||
|
||
for D in range(0, len(data_names)): | ||
data_name = data_names[D] | ||
cube_size = cube_sizes[D] | ||
id_start = id_starts[D] | ||
id_end = id_ends[D] | ||
chunck_size = (id_end - id_start) / num_packages[D] | ||
|
||
data_type = 'train' if data_name == 'train' else 'test' | ||
data_path = '{}/{}'.format(dataset_path, data_type) | ||
label_path = '{}/joint_data.mat'.format(data_path) | ||
|
||
labels = sio.loadmat(label_path) | ||
joint_uvd = labels['joint_uvd'][0] | ||
joint_xyz = labels['joint_xyz'][0] | ||
|
||
cnt = 0 | ||
chunck = 0 | ||
depth_h5, joint_h5, com_h5, inds_h5 = [], [], [], [] | ||
for id in range(id_start, id_end): | ||
img_path = '{}/depth_1_{:07d}.png'.format(data_path, id + 1) | ||
|
||
if not os.path.exists(img_path): | ||
print '{} Not Exists!'.format(img_path) | ||
continue | ||
print img_path | ||
img = cv2.imread(img_path) | ||
depth = np.asarray(img[:, :, 0] + img[:, :, 1] * 256) | ||
depth = CropImage(depth, joint_uvd[id, 34]) | ||
|
||
com3D = joint_xyz[id, 34] | ||
joint = joint_xyz[id][joint_id] - com3D | ||
depth = ((depth - com3D[2]) / (cube_size / 2)).reshape(1, img_size, img_size) | ||
|
||
joint = np.clip(joint / (cube_size / 2), -1, 1) | ||
depth_h5.append(depth.copy()) | ||
joint_h5.append(joint.copy().reshape(3 * J)) | ||
com_h5.append(com3D.copy()) | ||
inds_h5.append(id) | ||
cnt += 1 | ||
if cnt % chunck_size == 0 or id == id_end - 1: | ||
rng = np.arange(cnt) if data_type == 'test' else np.random.choice(np.arange(cnt), cnt, replace = False) | ||
dset = h5py.File('h5data/{}_{}.h5'.format(data_name, chunck), 'w') | ||
dset['depth'] = np.asarray(depth_h5)[rng] | ||
dset['joint'] = np.asarray(joint_h5)[rng] | ||
dset['com'] = np.asarray(com_h5)[rng] | ||
dset['inds'] =np.asarray(inds_h5)[rng] | ||
dset.close() | ||
depth_h5, joint_h5, com_h5, inds_h5 = [], [], [], [] | ||
chunck += 1 | ||
cnt = 0 |
Oops, something went wrong.