def __init__(self, sync_directory, classifier_name, mode):
### Step 1: filename management ###
print_inner_status("Initialization",
"Looking at file " + sync_directory)
self.jvid_filename_raw = os.path.join(sync_directory, 'Raw/video.jvid')
self.jvid_filename_pops_marked = os.path.join(sync_directory,
'Marked/video.jvid')
self.jvid_filename_synchronized = os.path.join(sync_directory,
'Synced/video.jvid')
### Step 2: get the original skeletons ###
print_inner_status("Initialization", "Getting input skeletons")
self.original_skeletons = read_in_skeletons(self.jvid_filename_raw)
### Step 3: add derivatives to them ###
print_inner_status("Initialization", "Adding derivatives to skeletons")
self.original_skeletons = add_derivatives_to_skeletons(
self.original_skeletons, 5, 10, 5, 10)
### Step 4: set up the classifier filename ###
self.classifier_filename = os.path.join(
os.getcwd(), 'python_backend/classifiers/' + classifier_name)
### Step 5.0: if synchronize mode, load the classifier and mark probabilities ###
if mode == 'synchronize':
print_inner_status("Initialization", "Loading the classifier")
self.load_classifier(
'/Users/jhack/Programming/NI/ni_template/python_backend/classifiers/toprock_front_training.obj'
)
print_inner_status("Initialization",
"Adding pop probabilities to skeletons")
self.original_skeletons = mark_pop_probabilities(
self.original_skeletons, self.classifier)
### Step 5.1: if train mode, train the classifier and save it ###
elif mode == 'train':
print_inner_status("Initialization", "Training the classifier")
self.train_classifier()
print_inner_status("Initialization", "Saving the classifier")
self.save_classifier()
return
def __init__ (self, sync_directory, classifier_name, mode):
### Step 1: filename management ###
print_inner_status ("Initialization", "Looking at file " + sync_directory)
self.jvid_filename_raw = os.path.join(sync_directory, 'Raw/video.jvid')
self.jvid_filename_pops_marked = os.path.join(sync_directory, 'Marked/video.jvid')
self.jvid_filename_synchronized = os.path.join(sync_directory, 'Synced/video.jvid')
### Step 2: get the original skeletons ###
print_inner_status ("Initialization", "Getting input skeletons")
self.original_skeletons = read_in_skeletons (self.jvid_filename_raw)
### Step 3: add derivatives to them ###
print_inner_status ("Initialization", "Adding derivatives to skeletons")
self.original_skeletons = add_derivatives_to_skeletons(self.original_skeletons, 5, 10, 5, 10)
### Step 4: set up the classifier filename ###
self.classifier_filename = os.path.join (os.getcwd(), 'python_backend/classifiers/' + classifier_name)
### Step 5.0: if synchronize mode, load the classifier and mark probabilities ###
if mode == 'synchronize':
print_inner_status ("Initialization", "Loading the classifier")
self.load_classifier ('/Users/jhack/Programming/NI/ni_template/python_backend/classifiers/toprock_front_training.obj')
print_inner_status ("Initialization", "Adding pop probabilities to skeletons")
self.original_skeletons = mark_pop_probabilities (self.original_skeletons, self.classifier)
### Step 5.1: if train mode, train the classifier and save it ###
elif mode == 'train':
print_inner_status ("Initialization", "Training the classifier")
self.train_classifier ()
print_inner_status ("Initialization", "Saving the classifier")
self.save_classifier ()
return
def __init__ (self, sync_directory, classifier_name):
### Step 1: filename management ###
print_inner_status ("Initialization", "Looking at file " + sync_directory)
self.jvid_filename_raw = os.path.join(sync_directory, 'Raw/video.jvid')
self.jvid_filename_pops_marked = os.path.join(sync_directory, 'Marked/video.jvid')
self.jvid_filename_synchronized = os.path.join(sync_directory, 'Synced/video.jvid')
### Step 2: load the classifier ###
print_inner_status ("Initialization", "Unpickling the classifier")
self.classifier = pickle.load (open('/Users/jhack/Programming/NI/ni_template/python_backend/classifiers/toprock_front_training.obj', 'r'))
### Step 2: get the original skeletons ###
print_inner_status ("Initialization", "Getting input skeletons")
self.original_skeletons = read_in_skeletons (self.jvid_filename_raw)
### Step 3: add derivatives to them ###
print_inner_status ("Initialization", "Adding derivatives to skeletons")
self.original_skeletons = add_derivatives_to_skeletons(self.original_skeletons, 5, 10, 5, 10)
### Step 4: add pop probabilities to them ###
print_inner_status ("Initialization", "Adding pop probabilities to skeletons")
self.original_skeletons = mark_pop_probabilities (self.original_skeletons, self.classifier)
