|
|
| import os |
| import numpy as np |
| from .func_feats import boxes_to_locfeats |
|
|
| import pdb |
|
|
| def read_lines(path): |
| with open(path, 'r') as fid: |
| lines = [line.strip() for line in fid] |
| return lines |
| |
| def read_lines_set(path): |
| lines = read_lines(path) |
| lines = set(lines) |
| return lines |
|
|
| |
| def read_np(path, preload=None): |
| if preload: |
| content = preload[path] |
| else: |
| content = np.load(path, allow_pickle=True) |
| if isinstance(content, np.ndarray): |
| return { "features": content } |
|
|
| keys = content.keys() |
| if len(keys) == 1: |
| return { "features": content[list(keys)[0]] } |
| return content |
|
|
| def read_np_bbox(path, max_feat_num, use_global_v=True, preload=None): |
| content = read_np(path, preload) |
| features = content['features'][0:max_feat_num - 1] |
| boxes = content['boxes'][0:max_feat_num - 1] |
| image_h = content['image_h'][0] |
| image_w = content['image_w'][0] |
| num_boxes = len(boxes) |
|
|
| if use_global_v: |
| g_feat = np.sum(features, axis=0) / num_boxes |
| features = np.concatenate([np.expand_dims(g_feat, axis=0), features], axis=0) |
|
|
| image_locations = boxes_to_locfeats(boxes, image_w, image_h) |
| if use_global_v: |
| g_location = np.array([0, 0, 1, 1, 1]) |
| image_locations = np.concatenate([np.expand_dims(g_location, axis=0), image_locations], axis=0) |
| return features, image_locations |
|
|
|
|
|
|
| def load_vocab(path): |
| if len(path) == 0: |
| return None |
| vocab = ['.'] |
| with open(path, 'r') as fid: |
| for line in fid: |
| vocab.append(line.strip()) |
| return vocab |
