def __getitem__(self, index):
img = self.LoadImage(index)
pts2d, pts3d, emb, c, s = self.GetPartInfo(index)
s = min(s, max(img.shape[0], img.shape[1])) * 1.0
pts3d[:, 2] += s / 2
r = 0
if self.split == 'train':
s = s * (2**Rnd(ref.scale))
c[1] = c[1] + Rnd(ref.shiftY)
r = 0 if np.random.random() < 0.6 else Rnd(ref.rotate)
inp = Crop(img, c, s, r, ref.inputRes)
inp = inp.transpose(2, 0, 1).astype(np.float32) / 256.
starMap = np.zeros((1, ref.outputRes, ref.outputRes))
embMap = np.zeros((3, ref.outputRes, ref.outputRes))
depMap = np.zeros((1, ref.outputRes, ref.outputRes))
mask = np.concatenate([
np.ones((1, ref.outputRes, ref.outputRes)),
np.zeros((4, ref.outputRes, ref.outputRes))
])
for i in range(pts3d.shape[0]):
if self.annot['valid'][index][i] > ref.eps:
if (self.annot['vis'][index][i] > ref.eps):
pt3d = Transform3D(pts3d[i], c, s, r,
ref.outputRes).astype(np.int32)
pt2d = Transform(pts2d[i], c, s, r,
ref.outputRes).astype(np.int32)
if pt2d[0] >= 0 and pt2d[0] < ref.outputRes and pt2d[
1] >= 0 and pt2d[1] < ref.outputRes:
embMap[:, pt2d[1], pt2d[0]] = emb[i]
depMap[0, pt2d[1],
pt2d[0]] = 1.0 * pt3d[2] / ref.outputRes - 0.5
mask[1:, pt2d[1], pt2d[0]] = 1
starMap[0] = np.maximum(
starMap[0],
DrawGaussian(np.zeros((ref.outputRes, ref.outputRes)),
pt2d, ref.hmGauss).copy())
out = starMap
if 'emb' in self.opt.task:
out = np.concatenate([out, embMap])
if 'dep' in self.opt.task:
out = np.concatenate([out, depMap])
mask = mask[:out.shape[0]].copy()
if self.split == 'train':
if np.random.random() < 0.5:
inp = Flip(inp)
out = Flip(out)
mask = Flip(mask)
if 'emb' in self.opt.task:
out[1] = -out[1]
return inp, out, mask
def __getitem__(self, index):
if self.split == 'train':
index = np.random.randint(self.nSamples)
img = self.LoadImage(index)
pts, c, s, pts_3d, pts_3d_mono = self.GetPartInfo(index)
pts_3d[7] = (pts_3d[12] + pts_3d[13]) / 2
inp = Crop(img, c, s, 0, ref.inputRes) / 256.
outMap = np.zeros((ref.nJoints, ref.outputRes, ref.outputRes))
outReg = np.zeros((ref.nJoints, 3))
for i in range(ref.nJoints):
pt = Transform3D(pts_3d[i], c, s, 0, ref.outputRes)
if pts[i][0] > 1:
outMap[i] = DrawGaussian(outMap[i], pt[:2], ref.hmGauss)
outReg[i, 2] = pt[2] / ref.outputRes * 2 - 1
t_inp = inp.transpose(1, 2, 0)
out = cv2.resize(t_inp, dsize=(64, 64))
out = torch.from_numpy(out.transpose(2, 0, 1))
inp = torch.from_numpy(inp)
return inp, out, outMap
def __getitem__(self, index):
img = self.LoadImage(index)
class_id = self.annot['class_id'][index]
c, s, v = self.GetPartInfo(index)
s = min(s, max(img.shape[0], img.shape[1])) * 1.0
r = 0
if self.split == 'train':
s = s * (2 ** Rnd(ref.scale))
c[1] = c[1] + Rnd(ref.shiftY)
r = 0 if np.random.random() < 0.6 else Rnd(ref.rotate)
v[2] += r / 180.
v[2] += 2 if v[2] < -1 else (-2 if v[2] > 1 else 0)
inp = Crop(img, c, s, r, ref.inputRes)
inp = inp.transpose(2, 0, 1).astype(np.float32) / 256.
if self.split == 'train':
if np.random.random() < 0.5:
inp = Flip(inp)
v[0] = - v[0]
v[2] = - v[2]
v[2] += 2 if v[2] <= -1 else 0
#https://github.com/shubhtuls/ViewpointsAndKeypoints/blob/master/rcnnVp/rcnnBinnedJointTrainValTestCreate.m#L77
vv = v.copy()
if vv[0] < 0:
v[0] = self.opt.numBins - 1 - np.floor(-vv[0] * self.opt.numBins / 2.)
else:
v[0] = np.floor(vv[0] * self.opt.numBins / 2.)
v[1] = np.ceil(vv[1] * self.opt.numBins / 2. + self.opt.numBins / 2. - 1)
v[2] = np.ceil(vv[2] * self.opt.numBins / 2. + self.opt.numBins / 2. - 1)
v = v.astype(np.int32)
if self.opt.specificView:
vv = np.ones(3 * len(ref.pascalClassId), dtype = np.int32) * self.opt.numBins
vv[class_id * 3: class_id * 3 + 3] = v.copy()
v = vv.copy()
return inp, v
def poseEstimation(self, tracked_person):
person_id = tracked_person.person_id
try:
curImage = self.bridge.imgmsg_to_cv2(self.frameInfo.image_frame)
person_image = curImage[
int(tracked_person.bbox.top):int(tracked_person.bbox.top +
tracked_person.bbox.height),
int(tracked_person.bbox.left):int(tracked_person.bbox.left +
tracked_person.bbox.width)]
except CvBridgeError as e:
rospy.logerr(e)
# Resize input image
rospy.logdebug("person image shape: {}".format(person_image.shape))
if person_image.shape != self.image_shape:
h, w = person_image.shape[0], person_image.shape[1]
center = torch.FloatTensor((w / 2, h / 2))
scale = 1.0 * max(h, w)
res = 256
input_image = Crop(person_image, center, scale, 0, res)
else:
input_image = person_image
# Feed input image to model
rospy.loginfo("feeding image to model")
input = torch.from_numpy(input_image.transpose(2, 0, 1)).float() / 256.
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float().cuda()
# lock when using model to estimate pose
self.lock.acquire()
try:
output = self.model(input_var)
finally:
self.lock.release()
rospy.logdebug("got output from model")
# Get 2D pose
rospy.logdebug("Rendering 2D pose")
pose2D = getPreds((output[-2].data).cpu().numpy())[0] * 4
# Get 3D pose
rospy.logdebug("Rendering 3D pose")
reg = (output[-1].data).cpu().numpy().reshape(pose2D.shape[0], 1)
pose3D = np.concatenate([pose2D, (reg + 1) / 2. * 256], axis=1)
rospy.logdebug("pose 3d shape: {}".format(pose3D.shape))
for pose in pose3D:
joint = Point()
joint.x = pose[0]
joint.y = pose[1]
joint.z = pose[2]
tracked_person.person_pose.append(joint)
# publish person
if self.publish_person:
self.person_pub.publish(tracked_person)
self.lock.acquire()
try:
self.frameInfo.persons.append(tracked_person)
finally:
self.lock.release()
rospy.logdebug("pose3D: \n {}".format(pose3D))
# Save pose image
if self.save_pose_image:
cv2.imwrite(
pkg_path + '/scripts/debug/original/ogImg_' +
str(self.frame_id) + '.png', self.cv_image)
cv2.imwrite(
pkg_path + '/scripts/debug/input/inputImg_' +
str(self.frame_id) + '.png', input_image)
self.debugger.addImg(input_image, imgId=self.frame_id)
self.debugger.addPoint2D(pose2D, (255, 0, 0), imgId=self.frame_id)
self.debugger.saveImg(pkg_path + '/scripts/debug/pose/poseImg_' +
str(self.frame_id) + '.png',
imgId=self.frame_id)
if self.save_pose_file:
file_name = pkg_path + '/pose_file/pose_{:04d}.txt'.format(
self.frame_id)
with file(file_name, 'w') as outfile:
np.savetxt(outfile, pose3D, fmt='%-7.2f')
rospy.loginfo("Person {} processing finished".format(person_id))
def __getitem__(self, index):
seqIdx = self.getSeq(index)
"""
input: predSeqLen x 3 x inputRes x inputRes Input image After Crop and transform
hmap: predSeqLen x numJoints x outputRes x outputRes
gtpts: predSeqLen x numJoints x 2 Joints Positions BEFORE crop and transform
proj: predSeqLen x numJoints x 2 Joints Positions AFTER crop and transform
"""
input = np.zeros((self.nPhase, 3, self.inputRes, self.inputRes))
hmap = np.zeros(
(self.nPhase, self.nJoints, self.outputRes, self.outputRes))
gtpts = np.zeros((self.nPhase, self.nJoints, 2))
repos, trans, focal, proj = {}, {}, {}, {}
for i in range(len(seqIdx)):
sid = seqIdx[i]
im = self.LoadImage(int(sid))
if i == 0:
center, scale = self.getCenterScale(im)
inp = Crop(im, center, scale, 0, self.inputRes)
pts = self.part[int(sid)]
pj = np.zeros(np.shape(pts))
for j in range(len(pts)):
if pts[j][0] != 0 and pts[j][1] != 0:
pj[j] = Transform(pts[j], center, scale, 0, self.outputRes,
False)
hm = np.zeros((np.shape(pts)[0], self.outputRes, self.outputRes))
for j in range(len(pts)):
if pts[j][0] != 0 and pts[j][1] != 0:
DrawGaussian(hm[j], np.round(pj[j]), 2)
inp = inp.transpose(2, 1, 0)
input[i] = inp
repos[i] = np.zeros((np.size(1), 3))
trans[i] = np.zeros(3)
focal[i] = np.zeros(1)
hmap[i] = hm
proj[i] = pj
gtpts[i] = pts
if self.split == 'train':
m1 = np.random.uniform(0.8, 1.2)
m2 = np.random.uniform(0.8, 1.2)
m3 = np.random.uniform(0.8, 1.2)
for i in range(len(input)):
input[i][:, :, 0] = input[i][:, :, 0] * m1
np.clip(input[i][:, :, 0], 0, 1, out=input[i][:, :, 0])
input[i][:, :, 1] = input[i][:, :, 1] * m2
np.clip(input[i][:, :, 1], 0, 1, out=input[i][:, :, 1])
input[i][:, :, 2] = input[i][:, :, 2] * m3
np.clip(input[i][:, :, 2], 0, 1, out=input[i][:, :, 2])
if np.random.uniform() <= 0.5:
for i in range(len(input)):
input[i] = cv2.flip(input[i], 1)
hmap[i] = Flip(ShuffleLR(hmap[i]))
proj[i] = ShuffleLR(proj[i])
ind = np.where(proj[i] == 0)
proj[i][:, 0] = self.outputRes - proj[i][:, 0] + 1
if len(ind[0]) != 0:
proj[i][ind[0][0]] = 0
return {
'input': input,
'label': hmap,
'gtpts': gtpts,
'center': center,
'scale': scale,
'proj': proj
}
