| import numpy as np |
| import os.path as osp |
| |
| class Imdb(object): |
| """ |
| Base class for dataset loading |
| |
| Parameters: |
| ---------- |
| name : str |
| name of dataset |
| """ |
| def __init__(self, name): |
| self.name = name |
| self.classes = [] |
| self.num_classes = 0 |
| self.image_set_index = [] |
| self.num_images = 0 |
| self.labels = None |
| self.padding = 0 |
| |
| def image_path_from_index(self, index): |
| """ |
| load image full path given specified index |
| |
| Parameters: |
| ---------- |
| index : int |
| index of image requested in dataset |
| |
| Returns: |
| ---------- |
| full path of specified image |
| """ |
| raise NotImplementedError |
| |
| def label_from_index(self, index): |
| """ |
| load ground-truth of image given specified index |
| |
| Parameters: |
| ---------- |
| index : int |
| index of image requested in dataset |
| |
| Returns: |
| ---------- |
| object ground-truths, in format |
| numpy.array([id, xmin, ymin, xmax, ymax]...) |
| """ |
| raise NotImplementedError |
| |
| def save_imglist(self, fname=None, root=None, shuffle=False): |
| """ |
| save imglist to disk |
| |
| Parameters: |
| ---------- |
| fname : str |
| saved filename |
| """ |
| str_list = [] |
| for index in range(self.num_images): |
| label = self.label_from_index(index) |
| path = self.image_path_from_index(index) |
| if root: |
| path = osp.relpath(path, root) |
| str_list.append('\t'.join([str(index), str(2), str(label.shape[1])] \ |
| + ["{0:.4f}".format(x) for x in label.ravel()] + [path,]) + '\n') |
| if str_list: |
| if shuffle: |
| import random |
| random.shuffle(str_list) |
| if not fname: |
| fname = self.name + '.lst' |
| with open(fname, 'w') as f: |
| for line in str_list: |
| f.write(line) |
| else: |
| raise RuntimeError("No image in imdb") |
