def is_scene_separable(diff,fps,program_mode,check_mode,img1,img2):
if diff>1: return True
DIST_TRESH = None
TIMEDIFF_TRESH = None
DISTANCE_TRESH_CHECK_SIFT = None
if program_mode == 'scenes':
DIST_TRESH = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_SCENECUT
TIMEDIFF_TRESH = settings.TIMEDIFF_TRESH_MODE_SCENECUT
DISTANCE_TRESH_CHECK_SIFT = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_SCENECUT_CHECK_SIFT
elif program_mode == 'joined':
DIST_TRESH = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_JOINCUT
TIMEDIFF_TRESH = settings.TIMEDIFF_TRESH_MODE_JOINCUT
DISTANCE_TRESH_CHECK_SIFT = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_JOINCUT_CHECK_SIFT
results = []
if 'time_diff' in check_mode:
time_diff = frame_diff_to_milisecs(diff,fps)
if time_diff>(float(TIMEDIFF_TRESH)*1000.0):
results.append(True)
else:
results.append(False)
was_separated_by_dist = False
if 'dist' in check_mode:
dist = img1.get_distance(img2,from_object=True)
# print 'img1',img1.get_only_num(),'img2',img2.get_only_num(),'dist',dist,'DIST_TRESH',DIST_TRESH
if dist > DIST_TRESH:
was_separated_by_dist = True
results.append(True)
else:
results.append(False)
if 'sift' in check_mode and img1 and img2:
if not was_separated_by_dist:
dist = img1.get_distance(img2,from_object=True)
if dist > DISTANCE_TRESH_CHECK_SIFT:
img1 = prepare_img_for_sift(img1)
img2 = prepare_img_for_sift(img2)
loc_desc1 = (img1.loc, img1.desc)
loc_desc2 = (img2.loc, img2.desc)
im1, im2, mscores,qom,mpercent = get_kpm(loc_desc1, loc_desc2, mode='only_match')
# print 'img1',img1.get_only_num(),'img2',img2.get_only_num(),'qom',qom,'QOM_MINIMUM_SCENECUT',settings.QOM_MINIMUM_SCENECUT
if qom < settings.QOM_MINIMUM_SCENECUT:
results.append(True)
else:
results.append(False)
if 'AND' in check_mode:
if False in results: return False
else: return True
if 'OR' in check_mode:
if True in results: return True
else: return False
def are_scenes_joinable(diff,fps,dist,program_mode,check_mode,img1=None,img2=None, length_diff=None,similar_check=False):
# h nem-e lognak egymasba
if diff > 0:
DIST_TRESH = None
TIMEDIFF_TRESH = None
DISTANCE_TRESH_CHECK_SIFT = None
if program_mode == 'scenes':
DIST_TRESH = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_SCENECUT
if similar_check:
DIST_TRESH = settings.CLUSTER_DISTANCE_TRESHOLD_SIMILAR_CHECK
TIMEDIFF_TRESH = settings.TIMEDIFF_TRESH_MODE_SCENECUT
LENGTH_TRESH = settings.LENGTH_TRESH_MODE_SCENECUT
DISTANCE_TRESH_CHECK_SIFT = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_SCENECUT_CHECK_SIFT
elif program_mode == 'joined':
DIST_TRESH = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_JOINCUT
if similar_check:
DIST_TRESH = settings.CLUSTER_DISTANCE_TRESHOLD_SIMILAR_CHECK
TIMEDIFF_TRESH = settings.TIMEDIFF_TRESH_MODE_JOINCUT
LENGTH_TRESH = settings.LENGTH_TRESH_MODE_JOINCUT
DISTANCE_TRESH_CHECK_SIFT = settings.CLUSTER_DISTANCE_TRESHOLD_MODE_JOINCUT_CHECK_SIFT
results = []
was_joined_by_dist = False
if 'dist' in check_mode:
if dist < DIST_TRESH:
results.append(True)
was_joined_by_dist = True
else:
results.append(False)
if 'sift' in check_mode and img1 and img2:
if not was_joined_by_dist:
dist = img1.get_distance(img2,from_object=True)
if dist > DISTANCE_TRESH_CHECK_SIFT:
img1 = prepare_img_for_sift(img1)
img2 = prepare_img_for_sift(img2)
loc_desc1 = (img1.loc, img1.desc)
loc_desc2 = (img2.loc, img2.desc)
im1, im2, mscores,qom,mpercent = get_kpm(loc_desc1, loc_desc2, mode='only_match')
if qom > settings.QOM_MINIMUM_SCENEJOIN:
results.append(True)
else:
results.append(False)
if 'length' in check_mode:
length_time = frame_diff_to_milisecs(length_diff,fps)
if length_time <= float(LENGTH_TRESH)*1000.0:
results.append(True)
else:
results.append(False)
if 'time_diff' in check_mode:
time_diff = frame_diff_to_milisecs(diff,fps)
if time_diff<(float(TIMEDIFF_TRESH)*1000.0):
results.append(True)
else:
results.append(False)
if 'AND' in check_mode:
if False in results: return False
else: return True
if 'OR' in check_mode:
if True in results: return True
else: return False
else:
return False
if not num_of_clusters:
if video: num_of_clusters = 1
else:
# default number...modified by plus 2
num_of_clusters = int(math.sqrt(len(objects)/2)) + 2
if len(objects)<int(num_of_clusters): num_of_clusters = len(objects)
# objectek nullazas, elokeszites
if verbose:print '\nPreparing images...\n'
for img in objects:
img.is_result = False
img.was_matched_with = []
# minden kepre: lemeretezes, pgm keszites, keypointok megtalalasa ha meg nem volt
if is_sift:
img = prepare_img_for_sift(img)
if verbose:print 'Preparing images done\n'
best_clustering=None
best_clustering_error=Decimal('Infinity')
for i in range(num_of_repeats):
if verbose:print '-----------------------------------'
if verbose:print 'Repeat %s of %s' % (str(i+1),str(num_of_repeats))
if verbose:print '-----------------------------------'
#kmeans++
initial_clusters=do_kmeans_plus_plus(objects,int(num_of_clusters))
#random
#initial_clusters=[[o] for o in random.sample(objects,num_of_clusters)]
if video and fps:
