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)