|
|
|
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 |
