def weight_set(self, measurement, particle_set):
pos_mat = ut.list_mat_to_mat(particle_set, axis=1)
gauss = pr.Gaussian(m = measurement, v = self.cov)
f = gauss.pdf_mat()
weight_mat = f(pos_mat)
def pair_weights(tup):
part, idx = tup
return (part, weight_mat[idx])
return map(pair_weights, it.izip(particle_set, xrange(len(particle_set))))
def learn_run(exposure = LaserPointerDetector.SUN_EXPOSURE, num_examples_to_collect=200, display_during_run = True):
hg.cvNamedWindow("video", 1)
hg.cvNamedWindow("thresholded", 1)
hg.cvNamedWindow('motion', 1)
hg.cvNamedWindow('intensity', 1)
hg.cvMoveWindow("video", 0, 0)
hg.cvMoveWindow("thresholded", 800, 0)
hg.cvMoveWindow("intensity", 0, 600)
hg.cvMoveWindow("motion", 800, 600)
video = cam.VidereStereo(0, gain=96, exposure=exposure)
frames = video.next()
detector = LaserPointerDetector(frames[0], exposure=exposure,
dataset=PatchClassifier.DEFAULT_DATASET_FILE,
use_color=False, use_learning=False)
detector2 = LaserPointerDetector(frames[1], exposure=exposure,
dataset=PatchClassifier.DEFAULT_DATASET_FILE,
use_color=False, use_learning=False)
def append_examples_to_file(dataset, file = PatchClassifier.DEFAULT_DATASET_FILE):
try:
loaded_set = load_pickle(file)
dataset.append(loaded_set)
except IOError:
pass
dump_pickle(dataset, file)
print 'Saved examples!'
#Gather positive examples from laser detector
if confirmation_prompt('gather positive examples?'):
print 'Lets gather some positive examples... we need', num_examples_to_collect, 'examples'
positive_examples_for_classifier = []
count_down(0)
for i in xrange(10):
frames = video.next()
detector.detect(frames[0])
detector2.detect(frames[1])
for img in video:
image = None
combined = None
motion = None
intensity = None
laser_blob = None
intensity_motion_blob = None
for raw_image, detect in zip(img, [detector, detector2]):
before = time.time()
image, combined, laser_blob, intensity_motion_blob = detect.detect(raw_image)
diff = time.time() - before
#print 'took %.2f seconds to run or %.2f fps' % (diff, 1.0/diff)
if laser_blob != None:
instance = blob_to_input_instance(image, laser_blob)
if instance is not None:
positive_examples_for_classifier.append(instance)
print 'got', len(positive_examples_for_classifier), 'instances'
motion, intensity = detect.get_motion_intensity_images()
show_processed(image, [combined, motion, intensity], laser_blob, intensity_motion_blob, detector2)
if len(positive_examples_for_classifier) > num_examples_to_collect:
break
positive_instances_dataset = matrix_to_dataset(ut.list_mat_to_mat(positive_examples_for_classifier, axis=1))
append_examples_to_file(positive_instances_dataset)
if confirmation_prompt('gather negative examples?'):
#Gather negative examples from laser detector
print 'lets gather some negative examples... we need', num_examples_to_collect,' examples'
negative_examples_for_classifier = []
count_down(10)
for i in xrange(10):
frames = video.next()
detector.detect(frames[0])
detector2.detect(frames[1])
for img in video:
image = None
combined = None
motion = None
intensity = None
laser_blob = None
intensity_motion_blob = None
for raw_image, detect in zip(img, [detector, detector2]):
image, combined, laser_blob, intensity_motion_blob = detect.detect(raw_image)
if laser_blob != None:
instance = blob_to_input_instance(image, laser_blob)
if instance is not None:
negative_examples_for_classifier.append(instance)
print 'got', len(negative_examples_for_classifier), 'instances'
motion, intensity = detect.get_motion_intensity_images()
show_processed(image, [combined, motion, intensity], laser_blob, intensity_motion_blob, detector2)
if len(negative_examples_for_classifier) > (num_examples_to_collect*2):
break
negative_instances_dataset = matrix_to_dataset(ut.list_mat_to_mat(negative_examples_for_classifier, axis=1))
append_examples_to_file(negative_instances_dataset)
if confirmation_prompt('run classifier?'):
run(exposure, video = video, display=display_during_run)