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 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);
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 __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()
boxm_batch.remove_data(vis_img.id)
boxm_batch.remove_data(mask_img.id)
(scene_id, scene_type) = boxm_batch.commit_output(0);
scene= dbvalue(scene_id, scene_type);
#print("*************************************");
#print("Save Scene");
#boxm_batch.init_process("boxmSaveOccupancyRawProcess");
#boxm_batch.set_input_from_db(0,scene);
#boxm_batch.set_input_string(1,model_dir + "/" + model_name);
#boxm_batch.set_input_unsigned(2,0);
#boxm_batch.set_input_unsigned(3,1);
#boxm_batch.run_process();
print("*************************************");
print("Computing Excpected Color Scene");
boxm_batch.init_process("boxmComputeExpectedColorSceneProcess");
boxm_batch.set_input_from_db(0, scene);
boxm_batch.set_input_float(1, grey_offset);
boxm_batch.run_process();
(scene_id, scene_type) = boxm_batch.commit_output(0);
expected_color_scene = dbvalue(scene_id, scene_type);
print("*************************************");
print("Save Scene");
boxm_batch.init_process("boxmSaveOccupancyRawProcess");
boxm_batch.set_input_from_db(0,expected_color_scene);
boxm_batch.set_input_string(1, model_dir + "/mean_color_scene");
boxm_batch.set_input_unsigned(2,0);
boxm_batch.set_input_unsigned(3,1);
boxm_batch.run_process();
(id, type) = boxm_batch.commit_output(1)
out_img2 = dbvalue(id, type)
boxm_batch.init_process("vilSaveImageViewProcess")
boxm_batch.set_input_from_db(0, out_img1)
boxm_batch.set_input_string(1, out_match_img_name % (i, j))
boxm_batch.run_process()
boxm_batch.init_process("vilSaveImageViewProcess")
boxm_batch.set_input_from_db(0, out_img2)
boxm_batch.set_input_string(1, out_match_img_name % (j, i))
boxm_batch.run_process()
boxm_batch.init_process("baplRefineMatchProcess")
boxm_batch.set_input_from_db(0, match_set)
boxm_batch.set_input_float(1, outlier_thresholds[ii])
boxm_batch.set_input_int(2, min_number_of_matches)
boxm_batch.run_process()
(id, type) = boxm_batch.commit_output(0)
refined_match_set = dbvalue(id, type)
boxm_batch.init_process("baplMatchDisplayProcess")
boxm_batch.set_input_from_db(0, imgs[i])
boxm_batch.set_input_from_db(1, imgs[j])
boxm_batch.set_input_from_db(2, refined_match_set)
boxm_batch.run_process()
(id, type) = boxm_batch.commit_output(0)
out_img1 = dbvalue(id, type)
(id, type) = boxm_batch.commit_output(1)
out_img2 = dbvalue(id, type)
(id, type) = boxm_batch.commit_output(1)
out_img2 = dbvalue(id, type)
boxm_batch.init_process("vilSaveImageViewProcess")
boxm_batch.set_input_from_db(0, out_img1)
boxm_batch.set_input_string(1, out_match_img_name % (i, j))
boxm_batch.run_process()
boxm_batch.init_process("vilSaveImageViewProcess")
boxm_batch.set_input_from_db(0, out_img2)
boxm_batch.set_input_string(1, out_match_img_name % (j, i))
boxm_batch.run_process()
boxm_batch.init_process("baplRefineMatchProcess")
boxm_batch.set_input_from_db(0, match_set)
boxm_batch.set_input_float(1, outlier_thresholds[ii])
boxm_batch.set_input_int(2, min_number_of_matches)
boxm_batch.run_process()
(id, type) = boxm_batch.commit_output(0)
refined_match_set = dbvalue(id, type)
boxm_batch.init_process("baplMatchDisplayProcess")
boxm_batch.set_input_from_db(0, imgs[i])
boxm_batch.set_input_from_db(1, imgs[j])
boxm_batch.set_input_from_db(2, refined_match_set)
boxm_batch.run_process()
(id, type) = boxm_batch.commit_output(0)
out_img1 = dbvalue(id, type)
(id, type) = boxm_batch.commit_output(1)
out_img2 = dbvalue(id, type)
image = dbvalue(id,type);
if(status):
print("Updating Scene");
boxm_batch.init_process("boxmUpdateRTProcess");
boxm_batch.set_input_from_db(0,image);
boxm_batch.set_input_from_db(1,cam);
boxm_batch.set_input_from_db(2,scene);
boxm_batch.set_input_unsigned(3,0);
boxm_batch.set_input_bool(4, 0);
boxm_batch.run_process();
print("Refine Scene");
boxm_batch.init_process("boxmRefineSceneProcess");
boxm_batch.set_input_from_db(0,scene);
boxm_batch.set_input_float(1,0.2);
boxm_batch.set_input_bool(2,1);
boxm_batch.run_process();
# Generate Expected Image
print("Generating Expected Image");
boxm_batch.init_process("boxmRenderExpectedRTProcess");
boxm_batch.set_input_from_db(0,scene);
boxm_batch.set_input_from_db(1,vcam);
boxm_batch.set_input_unsigned(2,1280);
boxm_batch.set_input_unsigned(3,720);
boxm_batch.set_input_bool(4,0);
boxm_batch.run_process();
(id,type) = boxm_batch.commit_output(0);
expected = dbvalue(id,type);
(id,type) = boxm_batch.commit_output(1);
print("Loading Camera");
boxm_batch.init_process("vpglLoadPerspectiveCameraProcess");
boxm_batch.set_input_string(0,camera_fnames % i);
boxm_batch.run_process();
(id,type) = boxm_batch.commit_output(0);
cam = dbvalue(id,type);
print("Loading Image");
boxm_batch.init_process("vilLoadImageViewProcess");
boxm_batch.set_input_string(0,image_fnames % i);
boxm_batch.run_process();
(id,type) = boxm_batch.commit_output(0);
image = dbvalue(id,type);
print("Updating Scene");
boxm_batch.init_process("boxmUpdateProcess");
boxm_batch.set_input_from_db(0,image);
boxm_batch.set_input_from_db(1,cam);
boxm_batch.set_input_from_db(2,scene);
boxm_batch.set_input_unsigned(3,0);
boxm_batch.run_process();
print("Refine Scene");
boxm_batch.init_process("boxmRefineSceneProcess");
boxm_batch.set_input_from_db(0,scene);
boxm_batch.set_input_float(1,0.2);
boxm_batch.set_input_bool(2,1);
boxm_batch.run_process();
(id,type) = boxm_batch.commit_output(0);
image = dbvalue(id,type);
print "Generating opt_samples for camera ", camera_idx[c];
boxm_batch.init_process("boxmGenerateOptSamplesProcess");
boxm_batch.set_input_from_db(0,image);
boxm_batch.set_input_from_db(1,cam);
boxm_batch.set_input_from_db(2,scene);
boxm_batch.set_input_string(3,image_ids[c]);
boxm_batch.set_input_bool(4,0);
boxm_batch.run_process();
# Do the optimization
boxm_batch.init_process("boxmOptBayesianUpdateProcess");
boxm_batch.set_input_from_db(0,scene);
boxm_batch.set_input_float(1,damping_factor);
boxm_batch.set_input_string(2,image_id_fname);
boxm_batch.run_process();
# Generate Expected Image
print("Generating Expected Image");
boxm_batch.init_process("boxmRenderExpectedRTProcess");
boxm_batch.set_input_from_db(0,scene);
boxm_batch.set_input_from_db(1,vcam);
boxm_batch.set_input_unsigned(2,1280);
boxm_batch.set_input_unsigned(3,720);
boxm_batch.set_input_bool(4,0);
boxm_batch.run_process();
(id,type) = boxm_batch.commit_output(0);
expected = dbvalue(id,type);
(id,type) = boxm_batch.commit_output(1);
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("*************************************");
print("Save Occupancy Scene");
boxm_batch.init_process("boxmSaveOccupancyRawProcess");
boxm_batch.set_input_from_db(0,scene);
boxm_batch.set_input_string(1, model_dir + "/drishti/occupancy");
boxm_batch.set_input_unsigned(2,0);
boxm_batch.set_input_unsigned(3,1);
boxm_batch.run_process();
print("*************************************");
print("Computing Excpected Color Scene");
boxm_batch.init_process("boxmComputeExpectedColorSceneProcess");
boxm_batch.set_input_from_db(0, scene);
boxm_batch.set_input_float(1, grey_offset);
boxm_batch.run_process();
(scene_id, scene_type) = boxm_batch.commit_output(0);
expected_color_scene = dbvalue(scene_id, scene_type);
print("*************************************");
print("Save Mean Scene");
boxm_batch.init_process("boxmSaveSceneRawProcess");
boxm_batch.set_input_from_db(0,expected_color_scene);
boxm_batch.set_input_string(1, model_dir + "/drishti/mean_color_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);
