def run(self):
while not self.kill_received:
# get a task
try:
job = self.work_queue.get_nowait()
except Queue.Empty:
break
start_time = time.time()
print("Creating a Scene")
boxm_batch.init_process("boxmCreateSceneProcess")
boxm_batch.set_input_string(0, job.input_scene_path)
boxm_batch.run_process()
(scene_id, scene_type) = boxm_batch.commit_output(0)
scene = dbvalue(scene_id, scene_type)
print("Save Scene")
boxm_batch.init_process("boxmSaveOccupancyRawProcess")
boxm_batch.set_input_from_db(0, scene)
boxm_batch.set_input_string(1, job.output_scene_path)
boxm_batch.set_input_unsigned(2, 0)
boxm_batch.set_input_unsigned(3, 1)
boxm_batch.run_process()
print("Runing time for worker:", self.name)
print(time.time() - start_time)
def runprobe(event):
posx = event.x
posy = event.y
array2d = list()
plt.figure(1)
plt.clf()
print ("Run ray tracing")
boxm_batch.init_process("boxmOclRunRenderProbeProcess")
boxm_batch.set_input_from_db(0, scene_mgr)
boxm_batch.set_input_from_db(1, cam)
boxm_batch.set_input_unsigned(2, posx)
boxm_batch.set_input_unsigned(3, posy)
boxm_batch.set_input_float(4, (image2.getpixel((posx, posy))) / 255.0)
boxm_batch.run_process()
for i in range(0, 10):
(scene_id, scene_type) = boxm_batch.commit_output(i)
array_1d = dbvalue(scene_id, scene_type)
vallist = boxm_batch.get_bbas_1d_array_float(scene_id)
array2d.append(vallist)
for i in [1, 2, 3, 5]:
plt.plot(array2d[0], array2d[i])
# plt.plot(array2d[0],array2d[7]);
plt.legend(("Omega", "Mean0", "Alpha", "Mean1"), loc="upper left")
print (image2.getpixel((posx, posy))) / 255.0
plt.show()
def neighborchange(event):
posx = event.x
posy = event.y
array2d = list()
vallist = list()
plt.figure(1)
plt.clf()
for i in (-1, 0):
for j in (-1, 0):
print ("Run ray tracing")
boxm_batch.init_process("boxmOclRunRenderProbeProcess")
boxm_batch.set_input_from_db(0, scene_mgr)
boxm_batch.set_input_from_db(1, cam)
boxm_batch.set_input_unsigned(2, posx + i)
boxm_batch.set_input_unsigned(3, posy + j)
boxm_batch.set_input_float(4, (image2.getpixel((posx, posy))) / 255.0)
boxm_batch.run_process()
(scene_id, scene_type) = boxm_batch.commit_output(10)
x = boxm_batch.get_input_float(scene_id)
vallist.append(x)
print vallist
def run(self):
while not self.kill_received:
# get a task
try:
job = self.work_queue.get_nowait()
except Queue.Empty:
break
start_time = time.time();
model_dir=job.model_dir;
model_name =job.model_name;
grey_offset = job.grey_offset;
print("Model dir:")
print model_dir
print("Model Name:")
print model_name
print("Creating a Scene");
boxm_batch.init_process("boxmCreateSceneProcess");
boxm_batch.set_input_string(0, model_dir + "/" + model_name + ".xml");
boxm_batch.run_process();
(scene_id, scene_type) = boxm_batch.commit_output(0);
scene= dbvalue(scene_id, scene_type);
print("Splitting the scene");
boxm_batch.init_process("boxmSplitSceneProcess");
boxm_batch.set_input_from_db(0, scene);
boxm_batch.run_process();
(scene_id, scene_type) = boxm_batch.commit_output(0);
apm_scene = dbvalue(scene_id, scene_type);
(scene_id, scene_type) = boxm_batch.commit_output(1);
alpha_scene = dbvalue(scene_id, scene_type);
print("Save Scene");
boxm_batch.init_process("boxmSaveScene RawProcess");
boxm_batch.set_input_from_db(0,alpha_scene);
boxm_batch.set_input_string(1,model_dir + "/drishti/alpha_scene");
boxm_batch.set_input_unsigned(2,0);
boxm_batch.set_input_unsigned(3,1);
boxm_batch.run_process();
#free memory
boxm_batch.clear();
print ("Runing time for worker:", self.name)
print(time.time() - start_time);
#output exit code in this case
#important: having a result queue makes the execute_jobs wait for all jobs in the queue before exiting
self.result_queue.put(0);
def run(self):
while not self.kill_received:
# get a task
try:
job = self.work_queue.get_nowait()
except Queue.Empty:
break
start_time = time.time();
model_dir=job.model_dir;
ply_file =job.ply_file;
grey_offset = job.grey_offset;
boxm_batch.set_stdout('logs/log_' + str(os.getpid())+ ".txt");
boxm_batch.init_process("boxmCreateSceneProcess");
boxm_batch.set_input_string(0, model_dir +"/pmvs_scene.xml");
boxm_batch.run_process();
(scene_id, scene_type) = boxm_batch.commit_output(0);
scene= dbvalue(scene_id, scene_type);
boxm_batch.init_process("boxm_create_scene_from_ply_process");
boxm_batch.set_input_string(0,ply_file);
boxm_batch.set_input_from_db(1,scene);
boxm_batch.set_input_float(2,grey_offset);
boxm_batch.run_process();
(scene_id, scene_type) = boxm_batch.commit_output(0);
scene = dbvalue(scene_id, scene_type);
print("Save Scene");
boxm_batch.init_process("boxmSaveSceneRawProcess");
boxm_batch.set_input_from_db(0,scene);
boxm_batch.set_input_string(1,model_dir + "/drishti/ply_scene");
boxm_batch.set_input_unsigned(2,0);
boxm_batch.set_input_unsigned(3,1);
boxm_batch.run_process();
#free memory
boxm_batch.reset_stdout();
boxm_batch.clear();
print ("Runing time for worker:", self.name)
print(time.time() - start_time);
#output exit code in this case
#important: having a result queue makes the execute_jobs wait for all jobs in the queue before exiting
self.result_queue.put(0);
boxm_batch.register_datatypes()
class dbvalue:
def __init__(self, index, type):
self.id = index # unsigned integer
self.type = type # string
dir = "/Users/isa/Experiments/super3d/scene/expectedImgs_2"
test_frames = [8, 112, 96, 208]
for frame in test_frames:
boxm_batch.init_process("vilLoadImageViewProcess")
boxm_batch.set_input_string(0, dir + "/predicted_img_mask_%(#)05d.tiff" % {"#": frame})
boxm_batch.run_process()
(id, type) = boxm_batch.commit_output(0)
vis_img = dbvalue(id, type)
boxm_batch.init_process("vilThresholdImageProcess")
boxm_batch.set_input_from_db(0, vis_img)
boxm_batch.set_input_float(1, 0.99)
boxm_batch.set_input_bool(2, True)
boxm_batch.run_process()
(id, type) = boxm_batch.commit_output(0)
mask_img = dbvalue(id, type)
boxm_batch.init_process("vilSaveImageViewProcess")
boxm_batch.set_input_from_db(0, mask_img)
boxm_batch.set_input_string(1, dir + "/binary_mask_%(#)05d.tiff" % {"#": frame})
boxm_batch.run_process()
if not os.path.isdir( model_imgs_dir + "/"):
os.mkdir( model_imgs_dir + "/");
image_fnames = "/Volumes/vision/video/dec/Downtown/video/frame_%05d.png";
camera_fnames = "/Volumes/vision/video/dec/Downtown/cameras_KRT/camera_%05d.txt";
expected_fname = model_imgs_dir + "/expected_%05d.tiff";
image_id_fname = model_imgs_dir + "/schedule_refined.txt";
expected_fname_no_dir = "/expected_%05d.tiff"
print("Creating a Scene");
boxm_batch.init_process("boxmCreateSceneProcess");
boxm_batch.set_input_string(0, model_dir +"/downtown_scene.xml");
boxm_batch.run_process();
(scene_id, scene_type) = boxm_batch.commit_output(0);
scene = dbvalue(scene_id, scene_type);
print("Loading Virtual Camera");
boxm_batch.init_process("vpglLoadPerspectiveCameraProcess");
boxm_batch.set_input_string(0,camera_fnames % 40);
boxm_batch.run_process();
(id,type) = boxm_batch.commit_output(0);
vcam = dbvalue(id,type);
import random;
schedule = [i for i in range(0,180,9)];
nframes =len(schedule);
keys_available = 0 # if keys have already been extracted, just load them
# after finding F between a pair, all matches that are off by 0.6% of
# max(image_width, image_height) pixels are considered outliers
outlier_threshold_percentage = 0.6
# for an image pair to be connected in the image connectivity graph
min_number_of_matches = 16
imgs = []
sizes = []
for i in range(0, img_cnt, 1):
print("Loading Image")
boxm_batch.init_process("vilLoadImageViewProcess")
boxm_batch.set_input_string(0, img_path + img_name % i)
boxm_batch.run_process()
(id, type) = boxm_batch.commit_output(0)
image = dbvalue(id, type)
imgs.append(image)
boxm_batch.init_process("vilImageSizeProcess")
boxm_batch.set_input_from_db(0, image)
boxm_batch.run_process()
(ni_id, type) = boxm_batch.commit_output(0)
(nj_id, type) = boxm_batch.commit_output(1)
ni = boxm_batch.get_input_unsigned(ni_id)
nj = boxm_batch.get_input_unsigned(nj_id)
if ni > nj:
sizes.append(ni)
else:
sizes.append(nj)
keys_available = 0 # if keys have already been extracted, just load them
# after finding F between a pair, all matches that are off by 0.6% of
# max(image_width, image_height) pixels are considered outliers
outlier_threshold_percentage = 0.6
# for an image pair to be connected in the image connectivity graph
min_number_of_matches = 16
imgs = []
sizes = []
for i in range(0, img_cnt, 1):
print("Loading Image")
batch.init_process("vilLoadImageViewProcess")
batch.set_input_string(0, img_path + img_name % i)
batch.run_process()
(id, type) = batch.commit_output(0)
image = dbvalue(id, type)
imgs.append(image)
batch.init_process("vilImageSizeProcess")
batch.set_input_from_db(0, image)
batch.run_process()
(ni_id, type) = batch.commit_output(0)
(nj_id, type) = batch.commit_output(1)
ni = batch.get_input_unsigned(ni_id)
nj = batch.get_input_unsigned(nj_id)
if ni > nj:
sizes.append(ni)
else:
sizes.append(nj)
keys_available = 0 # if keys have already been extracted, just load them
# after finding F between a pair, all matches that are off by 0.6% of
# max(image_width, image_height) pixels are considered outliers
outlier_threshold_percentage = 0.6
# for an image pair to be connected in the image connectivity graph
min_number_of_matches = 16
imgs = []
sizes = []
for i in range(0, img_cnt, 1):
print("Loading Image")
batch.init_process("vilLoadImageViewProcess")
batch.set_input_string(0, img_path + img_name % i)
batch.run_process()
(id, type) = batch.commit_output(0)
image = dbvalue(id, type)
imgs.append(image)
batch.init_process("vilImageSizeProcess")
batch.set_input_from_db(0, image)
batch.run_process()
(ni_id, type) = batch.commit_output(0)
(nj_id, type) = batch.commit_output(1)
ni = batch.get_input_unsigned(ni_id)
nj = batch.get_input_unsigned(nj_id)
if ni > nj:
sizes.append(ni)
else:
sizes.append(nj)