def run_training_pipeline(config, pos_file, neg_file, precision_thrsh,
recall_thrsh, det_name, target_label_id,
video_threshold):
"""Pipeline for training a new spatial scene detector.
Args:
config: Path to config file.
pos_file: Path to image file specifying positive images.
neg_file: Path to image file specifying negative images.
precision_thrsh: Cross-validation precision that must be achieved.
recall_thrsh: Cross-validation recall that must be achieved.
det_name: Name of detector to be created.
target_label_id: Detector's target label id.
video_threshold: Detector's video_threshold (the # images the detector
must fire on to create a VDR)
Raises:
AssertionError: Target label doesn't exist.
"""
logger.info("Running training pipeline...")
pos_images = read_image_file(pos_file)
neg_images = read_image_file(neg_file)
images = pos_images + neg_images
labels = [POS_LABEL] * len(pos_images) + [NEG_LABEL] * len(neg_images)
clf = SpatialSceneClassifier(config)
target_label = Label.get(target_label_id)
assert target_label
evaluate_and_inject(clf, images, labels, precision_thrsh, recall_thrsh,
det_name, target_label, video_threshold)
def save_boxes(l_id, video_id, boxes):
""" Save boxes to TextBox table and detector result to BoxDetectorResult
Args:
l_id: target Label id
video_id: Video id
boxes: dict with key: timestamp, value: list of rectangles
Returns:
List of newly created box ids
Raises/Assertions:
Asserts if video id does not exist
Asserts of label id does not exist
"""
vid = Video.get(video_id)
l = Label.get(l_id)
assert vid, "Video %s does not exist" % video_id
assert l, "Label %s does not exist" % l_id
clf_target = ClassifierTarget.query.filter_by(target_label_id=l.id)\
.join(TextDetectClassifier).one()
box_ids = []
for timestamp in boxes.keys():
for h, w, y, x in boxes[timestamp]:
box_id = Box.get_or_create(x=x,
y=y,
width=w,
height=h,
video=vid,
timestamp=timestamp,
box_type='Text')
box_ids.append(box_id)
BoxDetectorResult.log_result(box_id, clf_target.id)
return box_ids
def inject(self, clf_name, target_label_id_photo, target_label_id_sshow):
'''
Inject model to db.
Params:
clf_name: Name of classifier created.
target_label_id_photo: Classifier's target label for photo videos
target_label_id_sshow: target label for slideshow videos
Returns:
Classifier.
'''
target_labels_list = []
for target_label_id in [target_label_id_photo, target_label_id_sshow]:
l = Label.get(target_label_id)
assert l, 'Label id %s does not correspond to any Label!' % \
(target_label_id)
target_labels_list += [l]
assert not StaticVideoClassifier.by_name(clf_name), \
'StaticVideoClassifier with name %s already exists!' % clf_name
classifier = StaticVideoClassifier(name=clf_name)
session.flush()
classifier.add_targets(target_labels_list)
self.tar_and_upload(classifier)
return classifier
def inject_detector(self, detector_name, label_id, pred_thresh=None):
"""Create the detector in the table TextDetectClassifier, upload the tar
file with all the model files to s3
Args:
detector_name: string with detector name
(it has to be unique because this detector can't be replaced)
label_id: int, target label id of the detector
pred_thresh: set float threshold for word detection
Returns:
clf: the created TextDetectClassifier object
Raise/Assertions:
This function asserts if the label_id does not correspond to any
existing label and if the detector_name already exists in the db
"""
l = Label.get(label_id)
assert l, 'Label id %d does not correspond to any Label!' % (label_id)
clf = self.classifier_cls.by_name(detector_name)
assert not clf, '%s with name %s already exists!'\
% (self.classifier_cls.__name__, detector_name)
clf = self.classifier_cls(name=detector_name, pred_thresh=pred_thresh)
session.flush()
clf.add_targets([l])
self.tar_and_upload(clf)
logger.info('%s detector injected %s' %
(self.classifier_cls.__name__, clf))
return clf
def check_args(args):
assert os.path.exists(args.pos_file)
assert os.path.exists(args.neg_file)
assert os.path.exists(args.config)
assert not SpatialSceneDetector.by_name(args.det_name)
assert Label.get(args.target_label_id)
assert args.video_threshold > 0
assert 0 <= args.precision_thrsh <= 1
assert 0 <= args.recall_thrsh <= 1
def inject_detector(self,
detector_name,
list_label_ids,
true_vid_list=None):
"""
Create the detector in the table Cnn classifier, upload the tar
file with all the model files to s3, and save the video_ids used as
positive training data
Args:
detector_name: string with detector name
(it has to be unique because this detector can't be replaced)
label_id: int, target label id of the detector
true_vid_list: list of ints with ids of the videos used
as positive training data
Returns:
det: the created CnnClassifier object
Raise/Assertions:
This function asserts if the label_id does not correspond to any
existing label and if the detector_name already exists in the db
"""
target_label_list = {
Label.get(label_id)
for label_id in list_label_ids if Label.get(label_id)
}
target_label_list = list(target_label_list)
assert len(target_label_list),\
"Target label list needs at least one Label that exists in the DB"
det = self.detector_cls.by_name(detector_name)
assert not det, 'Cnn Classifier with name %s already exists!'\
% detector_name
det = self.detector_cls(name=detector_name)
session.flush()
det.add_targets(target_label_list)
self.tar_and_upload(det)
logger.info('CnnClassifier detector injected %s' % det)
if true_vid_list:
save_training_videos(det.id, true_vid_list)
return det
def main():
parser = argparse.ArgumentParser()
parser.add_argument('label_id', help='Label id')
parser.add_argument('true_pid_file',
help='File with one True page id per line')
parser.add_argument('false_pid_file',
help='File with one False page id per line')
parser.add_argument('op_dir', help='Directory to put trained models in')
args = parser.parse_args()
assert Label.get(args.label_id), 'Invalid label id'
trainer = NerTrainer(args.label_id)
trainer.run_pipeline(args)
def inject(self, clf_name, target_label_ids, training_box_ids):
"""'target_label_ids' and 'training_box_ids' should be sets"""
target_labels = [Label.get(l_id) for l_id in target_label_ids]
assert all(target_labels), "Bad target label id"
training_boxes = [Box.get(b_id) for b_id in training_box_ids]
assert all(training_boxes), "Bad training box id"
clf = FaceRecognizeClassifier.create(name=clf_name)
clf.add_targets(target_labels)
for box_id in training_box_ids:
TrainingBox(detector_id=clf.id, box_id=box_id)
session.flush()
self.tar_and_upload(clf)
return clf
def validate_config_file(cls, config_file):
config_obj = ConfigObj(config_file, configspec=cls.CFG_SPEC.split('\n'))
validator = Validator()
result = config_obj.validate(validator, copy=True, preserve_errors=True)
if result != True:
msg = 'Config file validation failed: %s'%result
raise Exception(msg)
ff = FreebaseFilter()
assert ff.is_domain(config_obj['domain']), "Invalid freebase domain: %s"%config_obj['domain']
assert ff.is_type(config_obj['fb_person']), "Invalid freebase type: %s"%config_obj['fb_person']
assert ff.is_type(config_obj['fb_org']), "Invalid freebase type: %s"%config_obj['fb_org']
assert Label.get(config_obj['label_id']), "Invalid label id: %s"%config_obj['label_id']
return config_obj
def load_training_config(configfile_name):
"""
Load the part of the config file relative to training of this classifier
"""
training_folders = []
cfg_obj = get_config(
configfile_name, spec=TRAINCNNCLF_CFG_SPEC.split('\n'))
if cfg_obj:
training_folders = cfg_obj['train_params']['training_folders']
target_labels_info = cfg_obj['train_params']['target_label_ids']
label_ids = target_labels_info[1::2]
for l_id in label_ids:
assert Label.get(l_id), \
" Target label id %d does not exist in the DB" % (l_id)
return training_folders
def inject(self, clf_name, target_label_ids):
'''
Injects classifier and classifier targets to DB.
Params:
clf_name: Classifier's name.
target_label_ids: The target label ids classifier's 'predict' gives
Returns:
Classifier.
'''
clf = self._clf_cls.create(name=clf_name)
target_labels = [Label.get(l_id) for l_id in target_label_ids]
assert all(target_labels), "Bad target label id"
clf.add_targets(target_labels)
self.tar_and_upload(clf)
return clf
def inject_detector(self,
detector_name,
label_id,
true_vid_list,
confidence_th=None,
acceptance_th=None):
"""
Create the detector in the table VideoMotionColorDetector, upload the tar
file with all the model files to s3, and save the video_ids used as
positive training data
Args:
detector_name: string with detector name
(it has to be unique because this detector can't be replaced)
label_id: int, target label id of the detector
true_vid_list: list of ints that correspond to the ids of the videos
used as positive training data
Returns:
det: the created VideoMotionColorDetector object
Raise/Assertions:
This function asserts if the label_id does not correspond to any
existing label and if the detector_name already exists in the db
"""
l = Label.get(label_id)
assert (l != None), 'Label id %d does not correspond to any Label!'\
% (label_id)
det = VideoMotionColorDetector.by_name(detector_name)
assert not det, 'VideoMotionColorDetector with name %s already exists!'\
% detector_name
det = VideoMotionColorDetector(name=detector_name)
if confidence_th and acceptance_th:
det.confidence_th = confidence_th
det.acceptance_th = acceptance_th
session.flush()
det.add_targets([l])
self.tar_and_upload(det)
logger.info('VideoMotionColorDetector detector injected %s' % det)
save_training_videos(det.id, true_vid_list)
return det
def inject_detector(self, detector_name, label_id, replace_old,
true_pid_file):
l = Label.get(label_id)
assert l is not None, "Label with id %s does not exist" % label_id
det = NerDetector.by_name(detector_name)
if replace_old:
assert det, 'NerDetector with name %s does not exist!'\
% detector_name
else:
assert not det, 'NerDetector with name %s already exists!'\
% detector_name
# create the new detector
det = NerDetector(name=detector_name)
session.flush()
det.add_targets([l])
self.tar_and_upload(det)
det.updated_at = datetime.utcnow()
session.flush()
logger.info('NER detector injected %s' % det)
save_training_pages(det.id, det.updated_at, true_pid_file)
def discover_scenes(config_file, folder, label_id):
""" Returns a set of clusters that contain similar scenes for a given label
Args:
config_file: configuration file where all the descriptor and video parameters are defined
folder: a directory for discovery's use
label_id: id of the label to retrieve videos for (optional)
Assertions:
ValueError if the label id is not present in the DB
"""
logger.info("starting scene discovery process")
discover = SceneDiscovery(config_file, folder)
pickle_folder = os.path.join(folder, 'pickles')
image_folder = os.path.join(folder, 'images')
feature_folder = os.path.join(folder, 'features')
for fold in [pickle_folder, image_folder, feature_folder]:
if not os.path.exists(fold):
os.makedirs(fold)
if label_id:
if not Label.get(label_id):
raise ValueError("Label id not found")
discover.get_videos_from_inventory(label_id)
else:
discover.ingest_videos_youtube()
discover.get_video_from_urls()
discover.download_data(image_folder)
discover.compute_descriptors(feature_folder)
discover_file = os.path.join(pickle_folder, 'scenediscovery.pickle')
SceneDiscovery.save_to_file(discover, discover_file)
distances = ['intersection', 'chisqr']
methods = ['single', 'ward', 'complete']
for dist in distances:
for meth in methods:
discover.cluster_scenes(dist, meth)
discover_file = os.path.join(
pickle_folder,
dist + '_' + meth + '_' + 'scenediscovery.pickle')
SceneDiscovery.save_to_file(discover, discover_file)
logger.info("saved clusters in %s " % discover_file)
logger.info("Finished running scene discovery")
def recognize_judge_video(clf_dir, video_id, imagedir):
model_name = FaceRecognizeClassifierInjector.get_model_name(clf_dir)
dp_client = DataProcessorClient(model_name)
results = {}
ra = ResultAggregator()
votes = defaultdict(int)
video = Video.get(video_id)
assert video
for box in video.face_boxes:
path = time_to_image_path(imagedir, box.timestamp)
fd, cropped_path = mkstemp(suffix='.jpg')
os.close(fd)
try:
rect = get_rect_to_recognize(box)
crop_image(path, cropped_path, *rect)
[bin_data] = convert_files_to_bin([cropped_path])
result = dp_client.predict(bin_data,
box.width,
box.height,
async=True)
results[box.id] = result
finally:
os.remove(cropped_path)
for box_id, result in results.iteritems():
label_id, conf, parts = result.wait(timeout=FACE_CELERY_TIMEOUT)
if conf is not None:
ra.add_face_info(box_id, conf, parts)
if conf > FACE_MIN_CONFIDENCE and label_id is not None:
assert Label.get(label_id)
ra.add_box_result(box_id, label_id)
votes[label_id] += 1
for label_id, occur in votes.iteritems():
if occur >= MIN_OCCURENCE_FOR_VIDEO:
ra.add_video_result(label_id)
return ra.result_dict
def is_ignored(label_id):
if label_id in LABELID_IGNORE_LIST or Label.get(
label_id).label_type == 'flip':
return True
return False
def get_list_of_videoids(target_label_id=None,
target_result=True,
excluded_label_list=[],
start_date=datetime(2013, 1, 1),
maxNumVideos=100):
"""
Returns a list of video ids that correspond to MTurk Hits done for target_label_id.
If target_result is True they correspond to True Positives; if False, correspond to False Positives.
If target_label_id is None, then we get True positives for any random label (except those indicated in the excluded label list)
Note: maxNumVideos is an upper bound, we may get less video ids, either because they do not exist
or because there were a lot of duplicated ids in the query result
Args:
target_label_id: label id that we want to get.
IF it's == -1, we'll get random videos EXCEPT those with label id 'negative_label'
maxNumVideos: maximum number of video ids that we want to get
excluded_label_list:
if label_id == None, this function will return videos with any
label id except the label ids in this excluded_label_list
(e.g., if we want negative training for soccer,
excluded_label_list will have soccer and sport labels ids)
start_date: datetime object.
date that specifies the start date of the period when mturk hits where obtained
target_result: target result we want to get from MTurk
(either True: True Positives, or False: False Positives)
By default we'll get True Positives
Returns:
list of video ids matching the input parameters requirements
Raises/Assertions:
"""
assert (type(start_date) == datetime and start_date <= datetime.today()), \
"start_date should be a datetime object and can't be in the future"
assert (not target_label_id or Label.get(target_label_id)), \
"target_label_id should correspond to a Label id in the DB or be None"
if target_label_id:
logger.info("obtaining %d '%s positive' video ids for label %s" %
(maxNumVideos, str(target_result),
Label.get(target_label_id).name))
queryVideos = VideoHit.query.\
filter(VideoHit.result == target_result,
VideoHit.label_id == target_label_id,
VideoHit.timestamp >= start_date).\
limit(maxNumVideos * EXTRA_RATIO)
else:
logger.info(
'obtaining %d recent video ids from any label except label id in %s'
% (maxNumVideos, str(excluded_label_list)))
queryVideos = VideoHit.query.\
filter(VideoHit.result == True,
VideoHit.timestamp >= start_date)
if len(excluded_label_list) > 0:
queryVideos = queryVideos.filter(
~VideoHit.label_id.in_(excluded_label_list))
else:
queryVideos = queryVideos.limit(maxNumVideos * EXTRA_RATIO)
video_ids = np.unique([v.video_id for v in queryVideos])
video_ids = list(video_ids[0:maxNumVideos])
return video_ids
def run_get_training_data(target_label_id,
npy_training_info_file=None,
old_detector_id=None,
max_num_pos_videos=500,
ratio_neg_pos=5,
excluded_labels_file=None):
"""
Append given training data (if any) with new available video ids from
MTurk hits
If no npy is provided, positive are obtained from TrainingVideo and
negative from random labels
Args:
target_label_id: label id that we want to get training data for
npy_training_info_file: file with already labeled video ids
(same format as output data in this function. column 0: video ids; column 1: label)
old_detector_id: detector id from previous version of a similar detector,
if we want to use that detector training data to train this new one,
i.e., we will add as positive training data all the video_id entries
in TrainingVideo with this detector_id
max_num_pos_videos: upper limit on the number of positive training data
(this function will try to get at least 6 times more negative data than positive, if it exists)
RATIO_NEG_POS: ratio between negative and positive data
(e.g., ratio 10 means #neg = 10 * #pos)
excluded_labels_file: npy file with a list of ids from labels
that we dont want to use as negative training data
(e.g., specific types of videogames for the videogame classifier)
Returns:
numpy array with:
- first column containing video ids to be used for training
- second column containing the corresponding label: 0 - target_label according to MTurk results
1 - any other label
Raises/Assertions:
asserts if label_id given as target label does not exist in the DB
"""
assert Label.get(target_label_id), "Target label does not exist"
video_id_label_list = []
positive_vids = []
negative_vids = []
new_positive_vids_from_tp = []
new_negative_vids_from_fp = []
if npy_training_info_file and os.path.exists(npy_training_info_file):
list_vids_label = np.load(npy_training_info_file)
all_vids = list_vids_label[:, 0]
all_labels = list_vids_label[:, 1]
positive_vids = list(all_vids[all_labels == 0])
negative_vids = list(all_vids[all_labels > 0])
elif old_detector_id:
res = TrainingVideo.query.filter_by(detector_id=old_detector_id)
positive_vids = [t.video_id for t in res]
if max_num_pos_videos > len(positive_vids):
new_positive_vids_from_tp = gtd.get_list_of_videoids(
target_label_id=target_label_id,
target_result=True,
excluded_label_list=[],
maxNumVideos=max_num_pos_videos - len(positive_vids))
new_negative_vids_from_fp = gtd.get_list_of_videoids(
target_label_id=target_label_id,
target_result=False,
excluded_label_list=[],
maxNumVideos=max_num_pos_videos)
positive_vids = np.unique(positive_vids + new_positive_vids_from_tp)
logger.info('Number of positive ids: %d' % len(positive_vids))
negative_vids = np.unique(negative_vids + new_negative_vids_from_fp)
exclusion_label_ids = []
if len(negative_vids) < ratio_neg_pos * len(positive_vids):
if excluded_labels_file:
exclusion_label_ids = list(np.load(excluded_labels_file))
random_negative_vids = gtd.get_list_of_videoids(
target_label_id=None,
excluded_label_list=[target_label_id] + exclusion_label_ids,
maxNumVideos=len(positive_vids) * ratio_neg_pos -
len(negative_vids))
negative_vids = np.unique(
np.append(negative_vids, random_negative_vids))
logger.info('Number of negative ids: %d' % len(negative_vids))
video_id_label_list = np.zeros(
(len(positive_vids) + len(negative_vids), 2))
video_id_label_list[:, 0] = np.append(positive_vids, negative_vids)
zeros_arr = np.zeros((len(positive_vids), 1))
ones_arr = np.ones((len(negative_vids), 1))
video_id_label_list[:, 1] = np.append(zeros_arr, ones_arr)
return video_id_label_list
def find_labels_statistics(list_vids):
"""
Gets a list of videos and prints the following statistics about it:
-Most frequent label
-Most frequent QAed label
-Labels for video results sorted based on their frequency:
-Labels for QAed video results sorted based on their frequency
Args:
List of video ids
Returns:
A tuple of webpage label ids and webpage label ids that have been QAed
"""
pages_q = VideoOnPage.query.filter(VideoOnPage.video_id.in_(list_vids))
page_ids = [p.page_id for p in pages_q]
page_ids = list(set(page_ids))
LABELID_IGNORE_LIST = [
2853, 2854, 3077, 3078, 3079, 3080, 5639, 7840, 7972, 1054, 3076
]
def is_ignored(label_id):
if label_id in LABELID_IGNORE_LIST or Label.get(
label_id).label_type == 'flip':
return True
return False
def clean_up_list(label_ids_list):
return filter(lambda item: not is_ignored(item[0]), label_ids_list)
label_ids_from_wpr = []
if page_ids:
label_ids_from_wpr = session.query(WebPageLabelResult.label_id,
func.count(WebPageLabelResult.label_id)).\
filter(WebPageLabelResult.page_id.in_(page_ids)).group_by(
WebPageLabelResult.label_id).all()
label_ids_from_wpr = clean_up_list(label_ids_from_wpr)
label_ids_from_qawpr = []
if page_ids:
label_ids_from_qawpr = session.\
query(VideoHit.label_id,
func.count(VideoHit.label_id)).\
filter(VideoHit.page_id.in_(page_ids)).\
filter(VideoHit.result == True).group_by(
VideoHit.label_id).all()
label_ids_from_qawpr = clean_up_list(label_ids_from_qawpr)
label_ids_from_wpr.sort(key=lambda item: item[1])
label_ids_from_qawpr.sort(key=lambda item: item[1])
max_label = 'None'
if label_ids_from_wpr:
max_label = Label.get(label_ids_from_wpr[-1][0])
max_label_qa = 'None'
if label_ids_from_qawpr:
max_label_qa = Label.get(label_ids_from_qawpr[-1][0])
print 'Most frequent label = %s' % max_label
print 'Most frequent QAed label = %s' % max_label_qa
print 'Labels for video results sorted based on their frequency:'
label_list = [Label.get(items[0]).name for items in label_ids_from_wpr]
label_list.reverse()
print '\n'.join(label_list)
print 'labels for QAed video results sorted based on their frequency:'
label_list_qa = [
Label.get(items[0]).name for items in label_ids_from_qawpr
]
label_list_qa.reverse()
print '\n'.join(label_list_qa)
return (label_ids_from_wpr, label_ids_from_qawpr)