def _get_msg_group(self):
msg_group = []
dataset_list = ['eth', 'eth', 'ucy', 'ucy', 'ucy']
dataset_idx_list = [0, 1, 0, 1, 2]
#dataset_list = ['eth']
#dataset_idx_list = [0]
for i in range(len(dataset_list)):
dataset = dataset_list[i]
dataset_idx = dataset_idx_list[i]
msg = Message()
data = DataLoader(dataset, dataset_idx, self.fps)
msg = data.update_message(msg)
gp = Grouping(msg, self.history)
msg = gp.update_message(msg)
msg_group.append(msg)
return msg_group
print "Reading data file..."
df = pd.read_csv(file_path, sep=",", encoding='utf-8')
df['attr_val_pairs'] = df['attr_val_pairs'].apply(lambda x: json.loads(x))
df['variant_criterias'] = df['variant_criterias'].apply(
lambda x: json.loads(x))
print "Reading product data..."
product_data = list(df.itertuples(index=False))
print "Getting items..."
items = utils.get_unique_items_pt(product_data, item_type)
print "Creating groups..."
grp_instance = Grouping(items)
grp_instance.init_groups()
print "Cluster entropy score = ", grp_instance.get_clustering_scores()
groups1 = grp_instance.auto_groups if group_type == 'auto' else grp_instance.true_groups
print "Reading excluded attribute list..."
with open('excluded_attr_list.txt', 'rb') as x_attr:
excluded_attrs = x_attr.readlines()
excluded_attrs = [x.strip() for x in excluded_attrs]
excluded_attrs = set(excluded_attrs)
print "Getting variants..."
get_variants(items, groups1, excluded_attrs)
dataset_idx = 0 history = 16 frame_idx = 500 group_idx = 20 # Initialize a message msg = Message() # Initialize dataloader data = DataLoader(dataset, dataset_idx) # Update the message msg = data.update_message(msg) # Initialize grouping gp = Grouping(msg, 16) # Update the message msg = gp.update_message(msg) # This shows what group ids are in this frame print(msg.video_labels_matrix[frame_idx]) # Initialize group shape generation gs_gen = GroupShapeGeneration(msg) vertices, pedidx = gs_gen.generate_group_shape(frame_idx, group_idx) # The returned vertices for the group shape print(vertices) print(pedidx) # We can also draw it on an image (blank canvas in this case) canvas = np.zeros((msg.frame_height, msg.frame_width, 3), dtype=np.uint8)
