def renderer(self):
return meshrenderer.Renderer(
self.ply_model_paths,
samples=1,
vertex_tmp_store_folder='.',
vertex_scale=float(
self.vertex_scale[0])) # 1000 for models in meters
def __init__(self,
models_cad_files,
vertex_tmp_store_folder,
vertex_scale,
width,
height,
K,
augmenters,
vocdevkit_path,
min_num_objects_per_scene,
max_num_objects_per_scene,
near_plane=10,
far_plane=2000,
min_n_views=1000,
radius=650,
obj_ids=None,
model_type='reconst'):
self._models_cad_files = models_cad_files
self._width = width
self._height = height
self._radius = radius
self._K = K
self._augmenters = augmenters
self._min_num_objects_per_scene = min_num_objects_per_scene
self._max_num_objects_per_scene = max_num_objects_per_scene
self._near_plane = near_plane
self._far_plane = far_plane
self.obj_ids = np.array(obj_ids)
# pascal_imgs_path = os.path.join(vocdevkit_path, 'VOC2012/JPEGImages')
self._voc_imgs = glob.glob(os.path.join(
vocdevkit_path, '*.jpg')) + glob.glob(
os.path.join(vocdevkit_path, '*.png'))
print len(self._voc_imgs)
if model_type == 'reconst':
self._renderer = mr_phong.Renderer(
self._models_cad_files,
1,
vertex_tmp_store_folder=vertex_tmp_store_folder,
vertex_scale=vertex_scale)
elif model_type == 'cad':
self._renderer = mr.Renderer(
self._models_cad_files,
1,
vertex_tmp_store_folder=vertex_tmp_store_folder,
vertex_scale=vertex_scale)
else:
print 'unknown model_type, ', model_type
exit()
azimuth_range = (0, 2 * math.pi)
elev_range = (-0.5 * math.pi, 0.5 * math.pi)
self.all_views, _ = view_sampler.sample_views(min_n_views, radius,
azimuth_range,
elev_range)
def renderer(self):
from auto_pose.meshrenderer import meshrenderer, meshrenderer_phong
if self._kw['model'] == 'cad':
renderer = meshrenderer.Renderer([self._kw['model_path']],
int(self._kw['antialiasing']),
self.dataset_path,
float(self._kw['vertex_scale']))
elif self._kw['model'] == 'reconst':
renderer = meshrenderer_phong.Renderer(
[self._kw['model_path']], int(self._kw['antialiasing']),
self.dataset_path, float(self._kw['vertex_scale']))
else:
'Error: neither cad nor reconst in model path!'
exit()
return renderer
def renderer(self):
from auto_pose.meshrenderer import meshrenderer, meshrenderer_phong
if self._kw['model'] == 'cad':
renderer = meshrenderer.Renderer(
eval(str(self._kw['model_path'])),
int(self._kw['antialiasing']),
self.dataset_path,
float(self._kw['vertex_scale'])
)
elif self._kw['model'] == 'reconst':
# print(meshrenderer)
renderer = meshrenderer_phong.Renderer(
eval(str(self._kw['model_path'])),
int(self._kw['antialiasing']),
vertex_tmp_store_folder = self.dataset_path,
vertex_scale = float(self._kw['vertex_scale'])
)
else:
print('Error: neither cad nor reconst in model path!')
exit()
return renderer
def renderer(self):
from auto_pose.meshrenderer import meshrenderer, meshrenderer_phong
render_dims = list(map(int, eval(self._kw['render_dims'])))
if self._kw['model'] == 'cad':
renderer = meshrenderer.Renderer(
[self._kw['model_path']],
int(self._kw['antialiasing']),
self.dataset_path,
float(self._kw['vertex_scale']),
max_fbo_width=render_dims[0], max_fbo_height=render_dims[1]
)
elif self._kw['model'] == 'reconst':
renderer = meshrenderer_phong.Renderer(
[self._kw['model_path']],
int(self._kw['antialiasing']),
self.dataset_path,
float(self._kw['vertex_scale']),
max_fbo_width=render_dims[0], max_fbo_height=render_dims[1]
)
else:
'Error: neither cad nor reconst in model path!'
exit()
return renderer
def renderer(self):
return meshrenderer.Renderer([self.model_path], 1, '.', 1)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-test_config",
type=str,
required=False,
default='test_config_webcam.cfg')
parser.add_argument("-vis", action='store_true', default=False)
args = parser.parse_args()
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
if workspace_path == None:
print('Please define a workspace path:')
print('export AE_WORKSPACE_PATH=/path/to/workspace')
exit(-1)
test_configpath = os.path.join(workspace_path, 'cfg_eval',
args.test_config)
test_args = configparser.ConfigParser()
test_args.read(test_configpath)
ae_pose_est = AePoseEstimator(test_configpath)
videoStream = WebcamVideoStream(0, ae_pose_est._width,
ae_pose_est._height).start()
if args.vis:
from auto_pose.meshrenderer import meshrenderer
ply_model_paths = [
str(train_args.get('Paths', 'MODEL_PATH'))
for train_args in ae_pose_est.all_train_args
]
cad_reconst = [
str(train_args.get('Dataset', 'MODEL'))
for train_args in ae_pose_est.all_train_args
]
renderer = meshrenderer.Renderer(
ply_model_paths,
samples=1,
vertex_tmp_store_folder=get_dataset_path(workspace_path),
vertex_scale=float(1)) # float(1) for some models
color_dict = [(0, 255, 0), (0, 0, 255), (255, 0, 0), (255, 255, 0)] * 10
while videoStream.isActive():
image = videoStream.read()
if image is None or not image.any():
print("Failed to capture webcam image")
exit(-1)
boxes, scores, labels = ae_pose_est.process_detection(image)
all_pose_estimates, all_class_idcs = ae_pose_est.process_pose(
boxes, labels, image)
if args.vis:
bgr, depth, _ = renderer.render_many(
obj_ids=[clas_idx for clas_idx in all_class_idcs],
W=ae_pose_est._width,
H=ae_pose_est._height,
K=ae_pose_est._camK,
# R = transform.random_rotation_matrix()[:3,:3],
Rs=[pose_est[:3, :3] for pose_est in all_pose_estimates],
ts=[pose_est[:3, 3] for pose_est in all_pose_estimates],
near=10,
far=10000,
random_light=False,
phong={
'ambient': 0.4,
'diffuse': 0.8,
'specular': 0.3
})
bgr = cv2.resize(bgr, (ae_pose_est._width, ae_pose_est._height))
g_y = np.zeros_like(bgr)
g_y[:, :, 1] = bgr[:, :, 1]
im_bg = cv2.bitwise_and(image,
image,
mask=(g_y[:, :, 1] == 0).astype(np.uint8))
image_show = cv2.addWeighted(im_bg, 1, g_y, 1, 0)
#cv2.imshow('pred view rendered', pred_view)
for label, box, score in zip(labels, boxes, scores):
box = box.astype(np.int32)
xmin, ymin, xmax, ymax = box[0], box[
1], box[0] + box[2], box[1] + box[3]
print(label)
cv2.putText(image_show, '%s : %1.3f' % (label, score),
(xmin, ymax + 20), cv2.FONT_ITALIC, .5,
color_dict[int(label)], 2)
cv2.rectangle(image_show, (xmin, ymin), (xmax, ymax),
(255, 0, 0), 2)
#cv2.imshow('', bgr)
cv2.imshow('real', image_show)
cv2.waitKey(1)
if args.vis:
from auto_pose.meshrenderer import meshrenderer
ply_model_paths = [
str(train_args.get('Paths', 'MODEL_PATH'))
for train_args in ae_pose_est.all_train_args
]
cad_reconst = [
str(train_args.get('Dataset', 'MODEL'))
for train_args in ae_pose_est.all_train_args
]
renderer = meshrenderer.Renderer(
ply_model_paths,
samples=1,
vertex_tmp_store_folder=get_dataset_path(workspace_path),
vertex_scale=float(1)) # float(1) for some models
color_dict = [(0, 255, 0), (0, 0, 255), (255, 0, 0), (255, 255, 0)] * 10
while camera.IsGrabbing(): #videoStream.isActive():
grabResult = camera.RetrieveResult(5000,
pylon.TimeoutHandling_ThrowException)
if grabResult.GrabSucceeded():
# Access the image data
image = converter.Convert(grabResult)
image = image.GetArray()
boxes, scores, labels = ae_pose_est.process_detection(image)