def _render(self, renderer, no_of_samples):
height = self._kw['H']
width = self._kw['W']
clip_near = self._kw['clip_near']
clip_far = self._kw['clip_far']
K = np.array(eval(self._kw['K']))
i = 0
while i < no_of_samples:
R = transform.random_rotation_matrix()[:3, :3]
t = np.array([0, 0, random.randint(50, 400)], dtype=np.float32)
color, depth_x = renderer.render(0, int(width), int(height), K, R,
t, clip_near, clip_far)
ys, xs = np.nonzero(depth_x > 0)
ys = np.array(ys, dtype=np.int16)
xs = np.array(xs, dtype=np.int16)
tx, ty = random.randint(-height,
height), random.randint(-width, width)
M = np.array(((1, 0, tx), (0, 1, ty)), dtype=np.float)
color = cv.warpAffine(color, M, (color.shape[1], color.shape[0]))
depth_x = cv.warpAffine(depth_x, M,
(depth_x.shape[1], depth_x.shape[0]))
try:
x, y, w, h = view_sampler.calc_2d_bbox(xs + tx, ys + ty,
(width, height))
self.obj_bb[i] = x, y, w, h
if x <= 0 or y <= 0 or x >= width or y >= height or x + tx >= width or y + ty >= height or w < 0 or h < 0:
#print('Object translated out of bounds. Regenerating ...')
continue
self.train_x[i] = color
self.mask_x[i] = depth_x == 0
i += 1
except ValueError as e:
#print('Object in Rendering not visible. Regenerating ...')
continue
def render(height, width, s):
height = int(height*s)
width = int(width*s)
clip_near = 10
clip_far = 5000
K = np.array([[572.4114, 0.0, 325.2611], [0.0, 573.57043, 242.04899], [0.0, 0.0, 1.0]])
renderer = meshrenderer_phong.Renderer('/home/sid/thesis/ply/models_cad/obj_05_red.ply',
samples=1, vertex_tmp_store_folder='.', clamp=False, vertex_scale=1.0)
# R = np.array([[1, 0, 0], [0, -1, 0], [0, 0, -1]], dtype=np.float32)
R = transform.random_rotation_matrix()[:3, :3]
t = np.array([0, 0, random.random()], dtype=np.float32) * 1000
start = time.time()
num_runs = 3000
# for i in tqdm(range(num_runs)):
color, depth_x = renderer.render(
0, width, height, K
, R, t, clip_near, clip_far)
# cv2.imshow("win", color)
# cv2.waitKey(1)
mask_x = depth_x == 0
ys, xs = np.nonzero(depth_x > 0)
try:
obj_bb = view_sampler.calc_2d_bbox(xs, ys, (width, height))
print obj_bb
except ValueError as e:
print('Object in Rendering not visible. Have you scaled the vertices to mm?')
x, y, w, h = obj_bb
return color, mask_x, obj_bb
def render_embedding_image_batch(self, start, end):
kw = self._kw
h, w = self.shape[:2]
azimuth_range = (0, 2 * np.pi)
elev_range = (-0.5 * np.pi, 0.5 * np.pi)
radius = float(kw['radius'])
render_dims = eval(kw['render_dims'])
K = eval(kw['k'])
K = np.array(K).reshape(3, 3)
clip_near = float(kw['clip_near'])
clip_far = float(kw['clip_far'])
pad_factor = float(kw['pad_factor'])
t = np.array([0, 0, float(kw['radius'])])
batch = np.empty((end - start, ) + self.shape)
obj_bbs = np.empty((end - start, ) + (4, ))
for i, R in enumerate(self.viewsphere_for_embedding[start:end]):
bgr_y, depth_y = self.renderer.render(obj_id=0,
W=render_dims[0],
H=render_dims[1],
K=K.copy(),
R=R,
t=t,
near=clip_near,
far=clip_far,
random_light=False)
# cv2.imshow('depth',depth_y)
# cv2.imshow('bgr',bgr_y)
# print depth_y.max()
# cv2.waitKey(0)
ys, xs = np.nonzero(depth_y > 0)
obj_bb = view_sampler.calc_2d_bbox(xs, ys, render_dims)
obj_bbs[i] = obj_bb
resized_bgr_y = self.extract_square_patch(
bgr_y,
obj_bb,
pad_factor,
resize=self.shape[:2],
interpolation=cv2.INTER_NEAREST)
if self.shape[2] == 1:
resized_bgr_y = cv2.cvtColor(resized_bgr_y,
cv2.COLOR_BGR2GRAY)[:, :,
np.newaxis]
batch[i] = resized_bgr_y / 255.
return (batch, obj_bbs)
def render_rot(self, R, t=None ,downSample = 1):
kw = self._kw
h, w = self.shape[:2]
radius = float(kw['radius'])
render_dims = eval(kw['render_dims'])
K = eval(kw['k'])
K = np.array(K).reshape(3,3)
K[:2,:] = K[:2,:] / downSample
clip_near = float(kw['clip_near'])
clip_far = float(kw['clip_far'])
pad_factor = float(kw['pad_factor'])
t = np.array([0, 0, float(kw['radius'])])
bgr_y, depth_y = self.renderer.render(
obj_id=0,
W=render_dims[0]/downSample,
H=render_dims[1]/downSample,
K=K.copy(),
R=R,
t=t,
near=clip_near,
far=clip_far,
random_light=False
)
ys, xs = np.nonzero(depth_y > 0)
obj_bb = view_sampler.calc_2d_bbox(xs, ys, render_dims)
x, y, w, h = np.array(obj_bb).astype(np.int32)
size = int(np.maximum(h, w) * pad_factor)
left = np.maximum(x+w/2-size/2, 0)
right = np.minimum(x+w/2+size/2, bgr_y.shape[1])
top = np.maximum(y+h/2-size/2, 0)
bottom = np.minimum(y+h/2+size/2, bgr_y.shape[0])
bgr_y = bgr_y[top:bottom, left:right]
return cv2.resize(bgr_y, self.shape[:2])
def _render(self, renderer, no_of_samples):
train_x = np.empty((no_of_samples, ) + self.shape, dtype=np.uint8)
mask_x = np.empty((no_of_samples, ) + self.shape[:2], dtype=bool)
obj_bb = np.empty((no_of_samples, 4), dtype=np.uint16)
height = self._kw['H']
width = self._kw['W']
clip_near = self._kw['clip_near']
clip_far = self._kw['clip_far']
i = 0
while i < no_of_samples:
R = transform.random_rotation_matrix()[:3, :3]
t = np.array([0, 0, random.randint(50, 400)], dtype=np.float32)
color, depth_x = renderer.render(0, int(width), int(height), K, R,
t, clip_near, clip_far)
ys, xs = np.nonzero(depth_x > 0)
ys = np.array(ys, dtype=np.int16)
xs = np.array(xs, dtype=np.int16)
tx, ty = random.randint(-height,
height), random.randint(-width, width)
M = np.array(((1, 0, tx), (0, 1, ty)), dtype=np.float)
color = cv.warpAffine(color, M, (color.shape[1], color.shape[0]))
depth_x = cv.warpAffine(depth_x, M,
(depth_x.shape[1], depth_x.shape[0]))
try:
x, y, w, h = view_sampler.calc_2d_bbox(xs + tx, ys + ty,
(width, height))
if x <= 0 or y <= 0 or x >= width or y >= height or x + tx >= width or y + ty >= height or w < 0 or h < 0:
#print('Object translated out of bounds. Regenerating ...')
continue
train_x[i] = np.copy(color)
obj_bb[i] = x, y, w, h
#self.show_image("train_x[i]", train_x[i])
mask_x[i] = np.copy(depth_x == 0)
cv2.waitKey(0)
i += 1
except ValueError as e:
#print('Object in Rendering not visible. Regenerating ...')
continue
return train_x, mask_x, obj_bb
def render_training_images(self,
dataset_path=None,
experiment_name=None,
augment_data=False):
'''
3 params added by lxc: dataset_path = None, experiment_name=None, augment_data = False
'''
kw = self._kw
H, W = int(kw['h']), int(kw['w'])
render_dims = eval(kw['render_dims'])
K = eval(kw['k'])
K = np.array(K).reshape(3, 3)
clip_near = float(kw['clip_near'])
clip_far = float(kw['clip_far'])
pad_factor = float(kw['pad_factor'])
max_rel_offset = float(kw['max_rel_offset'])
t = np.array([0, 0, float(kw['radius'])])
widgets = [
'Training: ',
progressbar.Percentage(), ' ',
progressbar.Bar(), ' ',
progressbar.Counter(),
' / %s' % self.noof_training_imgs, ' ',
progressbar.ETA(), ' '
]
bar = progressbar.ProgressBar(maxval=self.noof_training_imgs,
widgets=widgets)
bar.start()
for i in np.arange(self.noof_training_imgs):
bar.update(i)
# print '%s/%s' % (i,self.noof_training_imgs)
# start_time = time.time()
R = transform.random_rotation_matrix()[:3, :3]
bgr_x, depth_x = self.renderer.render(obj_id=0,
W=render_dims[0],
H=render_dims[1],
K=K.copy(),
R=R,
t=t,
near=clip_near,
far=clip_far,
random_light=True)
bgr_y, depth_y = self.renderer.render(obj_id=0,
W=render_dims[0],
H=render_dims[1],
K=K.copy(),
R=R,
t=t,
near=clip_near,
far=clip_far,
random_light=False)
# render_time = time.time() - start_time
# cv2.imshow('bgr_x',bgr_x)
# cv2.imshow('bgr_y',bgr_y)
# cv2.waitKey(0)
ys, xs = np.nonzero(depth_x > 0)
try:
obj_bb = view_sampler.calc_2d_bbox(xs, ys, render_dims)
except ValueError as e:
print 'Object in Rendering not visible. Have you scaled the vertices to mm?'
break
x, y, w, h = obj_bb
rand_trans_x = np.random.uniform(-max_rel_offset,
max_rel_offset) * w
rand_trans_y = np.random.uniform(-max_rel_offset,
max_rel_offset) * h
obj_bb_off = obj_bb + np.array([rand_trans_x, rand_trans_y, 0, 0])
bgr_x = self.extract_square_patch(bgr_x,
obj_bb_off,
pad_factor,
resize=(W, H),
interpolation=cv2.INTER_NEAREST)
depth_x = self.extract_square_patch(
depth_x,
obj_bb_off,
pad_factor,
resize=(W, H),
interpolation=cv2.INTER_NEAREST)
mask_x = depth_x == 0.
ys, xs = np.nonzero(depth_y > 0)
obj_bb = view_sampler.calc_2d_bbox(xs, ys, render_dims)
bgr_y = self.extract_square_patch(bgr_y,
obj_bb,
pad_factor,
resize=(W, H),
interpolation=cv2.INTER_NEAREST)
if self.shape[2] == 1:
bgr_x = cv2.cvtColor(np.uint8(bgr_x),
cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
bgr_y = cv2.cvtColor(np.uint8(bgr_y),
cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
self.train_x[i] = bgr_x.astype(np.uint8)
self.mask_x[i] = mask_x
self.train_y[i] = bgr_y.astype(np.uint8)
#print 'rendertime ', render_time, 'processing ', time.time() - start_time
# lxc
self.Rs[i] = R
self.obj_bbs_x[i] = obj_bb_off
self.obj_bbs_y[i] = obj_bb
if augment_data:
print("render augmented data.")
'''written by lxc:
add background image to original training image by replacing black background with PASCAL VOC image.'''
self.load_bg_images(dataset_path, experiment_name)
self.train_x, self.train_y = self.batch(self.noof_training_imgs)
self.Rs = self.Rs[self.rand_idcs]
self.obj_bbs_x = self.obj_bbs_x[self.rand_idcs]
self.obj_bbs_y = self.obj_bbs_y[self.rand_idcs]
self.mask_x = self.mask_x[self.rand_idcs]
bar.finish()
def render_training_images(self):
kw = self._kw
H, W = int(kw['h']), int(kw['w'])
render_dims = eval(kw['render_dims'])
K = eval(kw['k'])
K = np.array(K).reshape(3,3)
clip_near = float(kw['clip_near'])
clip_far = float(kw['clip_far'])
pad_factor = float(kw['pad_factor'])
crop_offset_sigma = float(kw['crop_offset_sigma'])
t = np.array([0, 0, float(kw['radius'])])
bar = progressbar.ProgressBar(
maxval=self.noof_training_imgs,
widgets=[' [', progressbar.Timer(), ' | ',
progressbar.Counter('%0{}d / {}'.format(len(str(self.noof_training_imgs)),
self.noof_training_imgs)), ' ] ', progressbar.Bar(), ' (', progressbar.ETA(), ') ']
)
bar.start()
for i in np.arange(self.noof_training_imgs):
bar.update(i)
# print '%s/%s' % (i,self.noof_training_imgs)
# start_time = time.time()
R = transform.random_rotation_matrix()[:3,:3]
bgr_x, depth_x = self.renderer.render(
obj_id=0,
W=render_dims[0],
H=render_dims[1],
K=K.copy(),
R=R,
t=t,
near=clip_near,
far=clip_far,
random_light=True
)
bgr_y, depth_y = self.renderer.render(
obj_id=0,
W=render_dims[0],
H=render_dims[1],
K=K.copy(),
R=R,
t=t,
near=clip_near,
far=clip_far,
random_light=False
)
# render_time = time.time() - start_time
# cv2.imshow('bgr_x',bgr_x)
# cv2.imshow('bgr_y',bgr_y)
# cv2.waitKey(0)
ys, xs = np.nonzero(depth_x > 0)
try:
obj_bb = view_sampler.calc_2d_bbox(xs, ys, render_dims)
except ValueError as e:
print 'Object in Rendering not visible. Have you scaled the vertices to mm?'
break
x, y, w, h = obj_bb
rand_trans_x = np.random.uniform(-crop_offset_sigma, crop_offset_sigma)
rand_trans_y = np.random.uniform(-crop_offset_sigma, crop_offset_sigma)
size = int(np.maximum(h, w) * pad_factor)
left = int(x+w/2-size/2 + rand_trans_x)
right = int(x+w/2+size/2 + rand_trans_x)
top = int(y+h/2-size/2 + rand_trans_y)
bottom = int(y+h/2+size/2 + rand_trans_y)
bgr_x = bgr_x[top:bottom, left:right]
depth_x = depth_x[top:bottom, left:right]
bgr_x = cv2.resize(bgr_x, (W, H), interpolation = cv2.INTER_NEAREST)
depth_x = cv2.resize(depth_x, (W, H), interpolation = cv2.INTER_NEAREST)
mask_x = depth_x == 0.
ys, xs = np.nonzero(depth_y > 0)
obj_bb = view_sampler.calc_2d_bbox(xs, ys, render_dims)
bgr_y = self.extract_square_patch(bgr_y, obj_bb, pad_factor,resize=(W,H),interpolation = cv2.INTER_NEAREST)
if self.shape[2] == 1:
bgr_x = cv2.cvtColor(np.uint8(bgr_x), cv2.COLOR_BGR2GRAY)[:,:,np.newaxis]
bgr_y = cv2.cvtColor(np.uint8(bgr_y), cv2.COLOR_BGR2GRAY)[:,:,np.newaxis]
self.train_x[i] = bgr_x.astype(np.uint8)
self.mask_x[i] = mask_x
self.train_y[i] = bgr_y.astype(np.uint8)
#print 'rendertime ', render_time, 'processing ', time.time() - start_time
bar.finish()