forked from TensorLayer/tensorlayer3
336 lines
16 KiB
Python
336 lines
16 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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import os
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import tensorflow as tf
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from tensorlayer import logging, utils
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from tensorlayer.files.utils import (del_file, del_folder, folder_exists, load_file_list, maybe_download_and_extract)
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__all__ = ['load_voc_dataset']
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def load_voc_dataset(path='data', dataset='2012', contain_classes_in_person=False):
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"""Pascal VOC 2007/2012 Dataset.
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It has 20 objects:
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aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor
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and additional 3 classes : head, hand, foot for person.
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Parameters
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-----------
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path : str
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The path that the data is downloaded to, defaults is ``data/VOC``.
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dataset : str
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The VOC dataset version, `2012`, `2007`, `2007test` or `2012test`. We usually train model on `2007+2012` and test it on `2007test`.
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contain_classes_in_person : boolean
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Whether include head, hand and foot annotation, default is False.
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Returns
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---------
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imgs_file_list : list of str
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Full paths of all images.
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imgs_semseg_file_list : list of str
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Full paths of all maps for semantic segmentation. Note that not all images have this map!
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imgs_insseg_file_list : list of str
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Full paths of all maps for instance segmentation. Note that not all images have this map!
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imgs_ann_file_list : list of str
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Full paths of all annotations for bounding box and object class, all images have this annotations.
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classes : list of str
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Classes in order.
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classes_in_person : list of str
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Classes in person.
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classes_dict : dictionary
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Class label to integer.
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n_objs_list : list of int
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Number of objects in all images in ``imgs_file_list`` in order.
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objs_info_list : list of str
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Darknet format for the annotation of all images in ``imgs_file_list`` in order. ``[class_id x_centre y_centre width height]`` in ratio format.
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objs_info_dicts : dictionary
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The annotation of all images in ``imgs_file_list``, ``{imgs_file_list : dictionary for annotation}``,
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format from `TensorFlow/Models/object-detection <https://github.com/tensorflow/models/blob/master/object_detection/create_pascal_tf_record.py>`__.
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Examples
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----------
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>>> imgs_file_list, imgs_semseg_file_list, imgs_insseg_file_list, imgs_ann_file_list,
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>>> classes, classes_in_person, classes_dict,
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>>> n_objs_list, objs_info_list, objs_info_dicts = tl.files.load_voc_dataset(dataset="2012", contain_classes_in_person=False)
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>>> idx = 26
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>>> print(classes)
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['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
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>>> print(classes_dict)
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{'sheep': 16, 'horse': 12, 'bicycle': 1, 'bottle': 4, 'cow': 9, 'sofa': 17, 'car': 6, 'dog': 11, 'cat': 7, 'person': 14, 'train': 18, 'diningtable': 10, 'aeroplane': 0, 'bus': 5, 'pottedplant': 15, 'tvmonitor': 19, 'chair': 8, 'bird': 2, 'boat': 3, 'motorbike': 13}
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>>> print(imgs_file_list[idx])
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data/VOC/VOC2012/JPEGImages/2007_000423.jpg
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>>> print(n_objs_list[idx])
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2
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>>> print(imgs_ann_file_list[idx])
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data/VOC/VOC2012/Annotations/2007_000423.xml
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>>> print(objs_info_list[idx])
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14 0.173 0.461333333333 0.142 0.496
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14 0.828 0.542666666667 0.188 0.594666666667
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>>> ann = tl.prepro.parse_darknet_ann_str_to_list(objs_info_list[idx])
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>>> print(ann)
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[[14, 0.173, 0.461333333333, 0.142, 0.496], [14, 0.828, 0.542666666667, 0.188, 0.594666666667]]
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>>> c, b = tl.prepro.parse_darknet_ann_list_to_cls_box(ann)
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>>> print(c, b)
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[14, 14] [[0.173, 0.461333333333, 0.142, 0.496], [0.828, 0.542666666667, 0.188, 0.594666666667]]
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References
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-------------
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- `Pascal VOC2012 Website <https://pjreddie.com/projects/pascal-voc-dataset-mirror/>`__.
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- `Pascal VOC2007 Website <https://pjreddie.com/projects/pascal-voc-dataset-mirror/>`__.
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"""
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try:
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import lxml.etree as etree
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except ImportError as e:
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print(e)
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raise ImportError("Module lxml not found. Please install lxml via pip or other package managers.")
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path = os.path.join(path, 'VOC')
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def _recursive_parse_xml_to_dict(xml):
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"""Recursively parses XML contents to python dict.
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We assume that `object` tags are the only ones that can appear
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multiple times at the same level of a tree.
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Args:
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xml: xml tree obtained by parsing XML file contents using lxml.etree
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Returns:
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Python dictionary holding XML contents.
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"""
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if xml is not None:
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return {xml.tag: xml.text}
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result = {}
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for child in xml:
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child_result = _recursive_parse_xml_to_dict(child)
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if child.tag != 'object':
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result[child.tag] = child_result[child.tag]
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else:
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if child.tag not in result:
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result[child.tag] = []
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result[child.tag].append(child_result[child.tag])
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return {xml.tag: result}
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import xml.etree.ElementTree as ET
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if dataset == "2012":
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url = "http://pjreddie.com/media/files/"
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tar_filename = "VOCtrainval_11-May-2012.tar"
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extracted_filename = "VOC2012" #"VOCdevkit/VOC2012"
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logging.info(" [============= VOC 2012 =============]")
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elif dataset == "2012test":
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extracted_filename = "VOC2012test" #"VOCdevkit/VOC2012"
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logging.info(" [============= VOC 2012 Test Set =============]")
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logging.info(
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" \nAuthor: 2012test only have person annotation, so 2007test is highly recommended for testing !\n"
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)
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import time
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time.sleep(3)
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if os.path.isdir(os.path.join(path, extracted_filename)) is False:
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logging.info("For VOC 2012 Test data - online registration required")
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logging.info(
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" Please download VOC2012test.tar from: \n register: http://host.robots.ox.ac.uk:8080 \n voc2012 : http://host.robots.ox.ac.uk:8080/eval/challenges/voc2012/ \ndownload: http://host.robots.ox.ac.uk:8080/eval/downloads/VOC2012test.tar"
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)
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logging.info(" unzip VOC2012test.tar,rename the folder to VOC2012test and put it into %s" % path)
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exit()
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# # http://host.robots.ox.ac.uk:8080/eval/downloads/VOC2012test.tar
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# url = "http://host.robots.ox.ac.uk:8080/eval/downloads/"
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# tar_filename = "VOC2012test.tar"
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elif dataset == "2007":
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url = "http://pjreddie.com/media/files/"
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tar_filename = "VOCtrainval_06-Nov-2007.tar"
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extracted_filename = "VOC2007"
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logging.info(" [============= VOC 2007 =============]")
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elif dataset == "2007test":
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# http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html#testdata
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# http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
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url = "http://pjreddie.com/media/files/"
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tar_filename = "VOCtest_06-Nov-2007.tar"
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extracted_filename = "VOC2007test"
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logging.info(" [============= VOC 2007 Test Set =============]")
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else:
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raise Exception("Please set the dataset aug to 2012, 2012test or 2007.")
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# download dataset
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if dataset != "2012test":
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from sys import platform as _platform
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if folder_exists(os.path.join(path, extracted_filename)) is False:
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logging.info("[VOC] {} is nonexistent in {}".format(extracted_filename, path))
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maybe_download_and_extract(tar_filename, path, url, extract=True)
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del_file(os.path.join(path, tar_filename))
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if dataset == "2012":
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if _platform == "win32":
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os.system("move {}\VOCdevkit\VOC2012 {}\VOC2012".format(path, path))
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else:
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os.system("mv {}/VOCdevkit/VOC2012 {}/VOC2012".format(path, path))
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elif dataset == "2007":
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if _platform == "win32":
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os.system("move {}\VOCdevkit\VOC2007 {}\VOC2007".format(path, path))
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else:
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os.system("mv {}/VOCdevkit/VOC2007 {}/VOC2007".format(path, path))
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elif dataset == "2007test":
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if _platform == "win32":
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os.system("move {}\VOCdevkit\VOC2007 {}\VOC2007test".format(path, path))
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else:
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os.system("mv {}/VOCdevkit/VOC2007 {}/VOC2007test".format(path, path))
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del_folder(os.path.join(path, 'VOCdevkit'))
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# object classes(labels) NOTE: YOU CAN CUSTOMIZE THIS LIST
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classes = [
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"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
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"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"
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]
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if contain_classes_in_person:
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classes_in_person = ["head", "hand", "foot"]
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else:
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classes_in_person = []
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classes += classes_in_person # use extra 3 classes for person
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classes_dict = utils.list_string_to_dict(classes)
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logging.info("[VOC] object classes {}".format(classes_dict))
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# 1. image path list
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# folder_imgs = path+"/"+extracted_filename+"/JPEGImages/"
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folder_imgs = os.path.join(path, extracted_filename, "JPEGImages")
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imgs_file_list = load_file_list(path=folder_imgs, regx='\\.jpg', printable=False)
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logging.info("[VOC] {} images found".format(len(imgs_file_list)))
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imgs_file_list.sort(
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key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
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) # 2007_000027.jpg --> 2007000027
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imgs_file_list = [os.path.join(folder_imgs, s) for s in imgs_file_list]
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# logging.info('IM',imgs_file_list[0::3333], imgs_file_list[-1])
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if dataset != "2012test":
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##======== 2. semantic segmentation maps path list
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# folder_semseg = path+"/"+extracted_filename+"/SegmentationClass/"
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folder_semseg = os.path.join(path, extracted_filename, "SegmentationClass")
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imgs_semseg_file_list = load_file_list(path=folder_semseg, regx='\\.png', printable=False)
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logging.info("[VOC] {} maps for semantic segmentation found".format(len(imgs_semseg_file_list)))
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imgs_semseg_file_list.sort(
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key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
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) # 2007_000032.png --> 2007000032
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imgs_semseg_file_list = [os.path.join(folder_semseg, s) for s in imgs_semseg_file_list]
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# logging.info('Semantic Seg IM',imgs_semseg_file_list[0::333], imgs_semseg_file_list[-1])
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##======== 3. instance segmentation maps path list
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# folder_insseg = path+"/"+extracted_filename+"/SegmentationObject/"
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folder_insseg = os.path.join(path, extracted_filename, "SegmentationObject")
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imgs_insseg_file_list = load_file_list(path=folder_insseg, regx='\\.png', printable=False)
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logging.info("[VOC] {} maps for instance segmentation found".format(len(imgs_semseg_file_list)))
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imgs_insseg_file_list.sort(
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key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
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) # 2007_000032.png --> 2007000032
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imgs_insseg_file_list = [os.path.join(folder_insseg, s) for s in imgs_insseg_file_list]
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# logging.info('Instance Seg IM',imgs_insseg_file_list[0::333], imgs_insseg_file_list[-1])
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else:
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imgs_semseg_file_list = []
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imgs_insseg_file_list = []
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# 4. annotations for bounding box and object class
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# folder_ann = path+"/"+extracted_filename+"/Annotations/"
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folder_ann = os.path.join(path, extracted_filename, "Annotations")
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imgs_ann_file_list = load_file_list(path=folder_ann, regx='\\.xml', printable=False)
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logging.info(
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"[VOC] {} XML annotation files for bounding box and object class found".format(len(imgs_ann_file_list))
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)
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imgs_ann_file_list.sort(
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key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
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) # 2007_000027.xml --> 2007000027
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imgs_ann_file_list = [os.path.join(folder_ann, s) for s in imgs_ann_file_list]
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# logging.info('ANN',imgs_ann_file_list[0::3333], imgs_ann_file_list[-1])
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if dataset == "2012test": # remove unused images in JPEG folder
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imgs_file_list_new = []
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for ann in imgs_ann_file_list:
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ann = os.path.split(ann)[-1].split('.')[0]
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for im in imgs_file_list:
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if ann in im:
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imgs_file_list_new.append(im)
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break
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imgs_file_list = imgs_file_list_new
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logging.info("[VOC] keep %d images" % len(imgs_file_list_new))
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# parse XML annotations
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def convert(size, box):
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dw = 1. / size[0]
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dh = 1. / size[1]
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x = (box[0] + box[1]) / 2.0
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y = (box[2] + box[3]) / 2.0
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w = box[1] - box[0]
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h = box[3] - box[2]
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x = x * dw
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w = w * dw
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y = y * dh
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h = h * dh
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return x, y, w, h
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def convert_annotation(file_name):
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"""Given VOC2012 XML Annotations, returns number of objects and info."""
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in_file = open(file_name)
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out_file = ""
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tree = ET.parse(in_file)
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root = tree.getroot()
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size = root.find('size')
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w = int(size.find('width').text)
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h = int(size.find('height').text)
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n_objs = 0
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for obj in root.iter('object'):
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if dataset != "2012test":
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difficult = obj.find('difficult').text
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cls = obj.find('name').text
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if cls not in classes or int(difficult) == 1:
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continue
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else:
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cls = obj.find('name').text
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if cls not in classes:
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continue
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cls_id = classes.index(cls)
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xmlbox = obj.find('bndbox')
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b = (
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float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
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float(xmlbox.find('ymax').text)
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)
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bb = convert((w, h), b)
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out_file += str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n'
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n_objs += 1
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if cls in "person":
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for part in obj.iter('part'):
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cls = part.find('name').text
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if cls not in classes_in_person:
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continue
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cls_id = classes.index(cls)
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xmlbox = part.find('bndbox')
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b = (
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float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text),
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float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)
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)
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bb = convert((w, h), b)
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# out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
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out_file += str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n'
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n_objs += 1
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in_file.close()
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return n_objs, out_file
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logging.info("[VOC] Parsing xml annotations files")
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n_objs_list = []
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objs_info_list = [] # Darknet Format list of string
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objs_info_dicts = {}
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for idx, ann_file in enumerate(imgs_ann_file_list):
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n_objs, objs_info = convert_annotation(ann_file)
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n_objs_list.append(n_objs)
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objs_info_list.append(objs_info)
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with tf.io.gfile.GFile(ann_file, 'r') as fid:
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xml_str = fid.read()
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xml = etree.fromstring(xml_str)
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data = _recursive_parse_xml_to_dict(xml)['annotation']
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objs_info_dicts.update({imgs_file_list[idx]: data})
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return imgs_file_list, imgs_semseg_file_list, imgs_insseg_file_list, imgs_ann_file_list, classes, classes_in_person, classes_dict, n_objs_list, objs_info_list, objs_info_dicts
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