Source code for tensorlayer.files.dataset_loaders.voc_dataset

#! /usr/bin/python
# -*- coding: utf-8 -*-

import os

import tensorflow as tf
from tensorlayer import logging, utils
from tensorlayer.files.utils import (del_file, del_folder, folder_exists, load_file_list, maybe_download_and_extract)

__all__ = ['load_voc_dataset']


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