def load_celebA_dataset(path='data'): """Load CelebA dataset Return a list of image path. Parameters ----------- path : str The path that the data is downloaded to, defaults is ``data/celebA/``. """ data_dir = 'celebA' filename, drive_id = "img_align_celeba.zip", "0B7EVK8r0v71pZjFTYXZWM3FlRnM" save_path = os.path.join(path, filename) image_path = os.path.join(path, data_dir) if os.path.exists(image_path): logging.info('[*] {} already exists'.format(save_path)) else: exists_or_mkdir(path) download_file_from_google_drive(drive_id, save_path) zip_dir = '' with zipfile.ZipFile(save_path) as zf: zip_dir = zf.namelist()[0] zf.extractall(path) os.remove(save_path) os.rename(os.path.join(path, zip_dir), image_path) data_files = load_file_list(path=image_path, regx='\\.jpg', printable=False) for i, _v in enumerate(data_files): data_files[i] = os.path.join(image_path, data_files[i]) return data_files
def load_celebA_dataset(path='data'): """Load CelebA dataset Return a list of image path. Parameters ----------- path : str The path that the data is downloaded to, defaults is ``data/celebA/``. """ data_dir = 'celebA' filename, drive_id = "img_align_celeba.zip", "0B7EVK8r0v71pZjFTYXZWM3FlRnM" save_path = os.path.join(path, filename) image_path = os.path.join(path, data_dir) if os.path.exists(image_path): logging.info('[*] {} already exists'.format(save_path)) else: exists_or_mkdir(path) download_file_from_google_drive(drive_id, save_path) zip_dir = '' with zipfile.ZipFile(save_path) as zf: zip_dir = zf.namelist()[0] zf.extractall(path) os.remove(save_path) os.rename(os.path.join(path, zip_dir), image_path) data_files = load_file_list(path=image_path, regx='\\.jpg', printable=False) for i, _v in enumerate(data_files): data_files[i] = os.path.join(image_path, data_files[i]) return data_files
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/>`__. """ 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.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
def load_flickr25k_dataset(tag='sky', path="data", n_threads=50, printable=False): """Load Flickr25K dataset. Returns a list of images by a given tag from Flick25k dataset, it will download Flickr25k from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__ at the first time you use it. Parameters ------------ tag : str or None What images to return. - If you want to get images with tag, use string like 'dog', 'red', see `Flickr Search <https://www.flickr.com/search/>`__. - If you want to get all images, set to ``None``. path : str The path that the data is downloaded to, defaults is ``data/flickr25k/``. n_threads : int The number of thread to read image. printable : boolean Whether to print infomation when reading images, default is ``False``. Examples ----------- Get images with tag of sky >>> images = tl.files.load_flickr25k_dataset(tag='sky') Get all images >>> images = tl.files.load_flickr25k_dataset(tag=None, n_threads=100, printable=True) """ path = os.path.join(path, 'flickr25k') filename = 'mirflickr25k.zip' url = 'http://press.liacs.nl/mirflickr/mirflickr25k/' # download dataset if folder_exists(os.path.join(path, "mirflickr")) is False: logging.info("[*] Flickr25k is nonexistent in {}".format(path)) maybe_download_and_extract(filename, path, url, extract=True) del_file(os.path.join(path, filename)) # return images by the given tag. # 1. image path list folder_imgs = os.path.join(path, "mirflickr") path_imgs = load_file_list(path=folder_imgs, regx='\\.jpg', printable=False) path_imgs.sort(key=natural_keys) # 2. tag path list folder_tags = os.path.join(path, "mirflickr", "meta", "tags") path_tags = load_file_list(path=folder_tags, regx='\\.txt', printable=False) path_tags.sort(key=natural_keys) # 3. select images if tag is None: logging.info("[Flickr25k] reading all images") else: logging.info("[Flickr25k] reading images with tag: {}".format(tag)) images_list = [] for idx, _v in enumerate(path_tags): tags = read_file(os.path.join(folder_tags, path_tags[idx])).split('\n') # logging.info(idx+1, tags) if tag is None or tag in tags: images_list.append(path_imgs[idx]) images = visualize.read_images(images_list, folder_imgs, n_threads=n_threads, printable=printable) return images
def load_image_from_folder(path): path_imgs = load_file_list(path=path, regx='\\.jpg', printable=False) return visualize.read_images(path_imgs, path=path, n_threads=10, printable=False)
def load_flickr1M_dataset(tag='sky', size=10, path="data", n_threads=50, printable=False): """Load Flick1M dataset. Returns a list of images by a given tag from Flickr1M dataset, it will download Flickr1M from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__ at the first time you use it. Parameters ------------ tag : str or None What images to return. - If you want to get images with tag, use string like 'dog', 'red', see `Flickr Search <https://www.flickr.com/search/>`__. - If you want to get all images, set to ``None``. size : int integer between 1 to 10. 1 means 100k images ... 5 means 500k images, 10 means all 1 million images. Default is 10. path : str The path that the data is downloaded to, defaults is ``data/flickr25k/``. n_threads : int The number of thread to read image. printable : boolean Whether to print infomation when reading images, default is ``False``. Examples ---------- Use 200k images >>> images = tl.files.load_flickr1M_dataset(tag='zebra', size=2) Use 1 Million images >>> images = tl.files.load_flickr1M_dataset(tag='zebra') """ import shutil path = os.path.join(path, 'flickr1M') logging.info("[Flickr1M] using {}% of images = {}".format(size * 10, size * 100000)) images_zip = [ 'images0.zip', 'images1.zip', 'images2.zip', 'images3.zip', 'images4.zip', 'images5.zip', 'images6.zip', 'images7.zip', 'images8.zip', 'images9.zip' ] tag_zip = 'tags.zip' url = 'http://press.liacs.nl/mirflickr/mirflickr1m/' # download dataset for image_zip in images_zip[0:size]: image_folder = image_zip.split(".")[0] # logging.info(path+"/"+image_folder) if folder_exists(os.path.join(path, image_folder)) is False: # logging.info(image_zip) logging.info("[Flickr1M] {} is missing in {}".format(image_folder, path)) maybe_download_and_extract(image_zip, path, url, extract=True) del_file(os.path.join(path, image_zip)) # os.system("mv {} {}".format(os.path.join(path, 'images'), os.path.join(path, image_folder))) shutil.move(os.path.join(path, 'images'), os.path.join(path, image_folder)) else: logging.info("[Flickr1M] {} exists in {}".format(image_folder, path)) # download tag if folder_exists(os.path.join(path, "tags")) is False: logging.info("[Flickr1M] tag files is nonexistent in {}".format(path)) maybe_download_and_extract(tag_zip, path, url, extract=True) del_file(os.path.join(path, tag_zip)) else: logging.info("[Flickr1M] tags exists in {}".format(path)) # 1. image path list images_list = [] images_folder_list = [] for i in range(0, size): images_folder_list += load_folder_list(path=os.path.join(path, 'images%d' % i)) images_folder_list.sort(key=lambda s: int(s.split('/')[-1])) # folder/images/ddd for folder in images_folder_list[0:size * 10]: tmp = load_file_list(path=folder, regx='\\.jpg', printable=False) tmp.sort(key=lambda s: int(s.split('.')[-2])) # ddd.jpg images_list.extend([os.path.join(folder, x) for x in tmp]) # 2. tag path list tag_list = [] tag_folder_list = load_folder_list(os.path.join(path, "tags")) # tag_folder_list.sort(key=lambda s: int(s.split("/")[-1])) # folder/images/ddd tag_folder_list.sort(key=lambda s: int(os.path.basename(s))) for folder in tag_folder_list[0:size * 10]: tmp = load_file_list(path=folder, regx='\\.txt', printable=False) tmp.sort(key=lambda s: int(s.split('.')[-2])) # ddd.txt tmp = [os.path.join(folder, s) for s in tmp] tag_list += tmp # 3. select images logging.info("[Flickr1M] searching tag: {}".format(tag)) select_images_list = [] for idx, _val in enumerate(tag_list): tags = read_file(tag_list[idx]).split('\n') if tag in tags: select_images_list.append(images_list[idx]) logging.info("[Flickr1M] reading images with tag: {}".format(tag)) images = visualize.read_images(select_images_list, '', n_threads=n_threads, printable=printable) return images
def load_mpii_pose_dataset(path='data', is_16_pos_only=False): """Load MPII Human Pose Dataset. Parameters ----------- path : str The path that the data is downloaded to. is_16_pos_only : boolean If True, only return the peoples contain 16 pose keypoints. (Usually be used for single person pose estimation) Returns ---------- img_train_list : list of str The image directories of training data. ann_train_list : list of dict The annotations of training data. img_test_list : list of str The image directories of testing data. ann_test_list : list of dict The annotations of testing data. Examples -------- >>> import pprint >>> import tensorlayer as tl >>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset() >>> image = tl.vis.read_image(img_train_list[0]) >>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png') >>> pprint.pprint(ann_train_list[0]) References ----------- - `MPII Human Pose Dataset. CVPR 14 <http://human-pose.mpi-inf.mpg.de>`__ - `MPII Human Pose Models. CVPR 16 <http://pose.mpi-inf.mpg.de>`__ - `MPII Human Shape, Poselet Conditioned Pictorial Structures and etc <http://pose.mpi-inf.mpg.de/#related>`__ - `MPII Keyponts and ID <http://human-pose.mpi-inf.mpg.de/#download>`__ """ path = os.path.join(path, 'mpii_human_pose') logging.info("Load or Download MPII Human Pose > {}".format(path)) # annotation url = "http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/" tar_filename = "mpii_human_pose_v1_u12_2.zip" extracted_filename = "mpii_human_pose_v1_u12_2" if folder_exists(os.path.join(path, extracted_filename)) is False: logging.info("[MPII] (annotation) {} 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)) # images url = "http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/" tar_filename = "mpii_human_pose_v1.tar.gz" extracted_filename2 = "images" if folder_exists(os.path.join(path, extracted_filename2)) is False: logging.info("[MPII] (images) {} 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)) # parse annotation, format see http://human-pose.mpi-inf.mpg.de/#download import scipy.io as sio logging.info("reading annotations from mat file ...") # mat = sio.loadmat(os.path.join(path, extracted_filename, "mpii_human_pose_v1_u12_1.mat")) # def fix_wrong_joints(joint): # https://github.com/mitmul/deeppose/blob/master/datasets/mpii_dataset.py # if '12' in joint and '13' in joint and '2' in joint and '3' in joint: # if ((joint['12'][0] < joint['13'][0]) and # (joint['3'][0] < joint['2'][0])): # joint['2'], joint['3'] = joint['3'], joint['2'] # if ((joint['12'][0] > joint['13'][0]) and # (joint['3'][0] > joint['2'][0])): # joint['2'], joint['3'] = joint['3'], joint['2'] # return joint ann_train_list = [] ann_test_list = [] img_train_list = [] img_test_list = [] def save_joints(): # joint_data_fn = os.path.join(path, 'data.json') # fp = open(joint_data_fn, 'w') mat = sio.loadmat( os.path.join(path, extracted_filename, "mpii_human_pose_v1_u12_1.mat")) for _, (anno, train_flag) in enumerate( # all images zip(mat['RELEASE']['annolist'][0, 0][0], mat['RELEASE']['img_train'][0, 0][0])): img_fn = anno['image']['name'][0, 0][0] train_flag = int(train_flag) # print(i, img_fn, train_flag) # DEBUG print all images if train_flag: img_train_list.append(img_fn) ann_train_list.append([]) else: img_test_list.append(img_fn) ann_test_list.append([]) head_rect = [] if 'x1' in str(anno['annorect'].dtype): head_rect = zip([x1[0, 0] for x1 in anno['annorect']['x1'][0]], [y1[0, 0] for y1 in anno['annorect']['y1'][0]], [x2[0, 0] for x2 in anno['annorect']['x2'][0]], [y2[0, 0] for y2 in anno['annorect']['y2'][0]]) else: head_rect = [] # TODO if 'annopoints' in str(anno['annorect'].dtype): annopoints = anno['annorect']['annopoints'][0] head_x1s = anno['annorect']['x1'][0] head_y1s = anno['annorect']['y1'][0] head_x2s = anno['annorect']['x2'][0] head_y2s = anno['annorect']['y2'][0] for annopoint, head_x1, head_y1, head_x2, head_y2 in zip( annopoints, head_x1s, head_y1s, head_x2s, head_y2s): # if annopoint != []: # if len(annopoint) != 0: if annopoint.size: head_rect = [ float(head_x1[0, 0]), float(head_y1[0, 0]), float(head_x2[0, 0]), float(head_y2[0, 0]) ] # joint coordinates annopoint = annopoint['point'][0, 0] j_id = [str(j_i[0, 0]) for j_i in annopoint['id'][0]] x = [x[0, 0] for x in annopoint['x'][0]] y = [y[0, 0] for y in annopoint['y'][0]] joint_pos = {} for _j_id, (_x, _y) in zip(j_id, zip(x, y)): joint_pos[int(_j_id)] = [float(_x), float(_y)] # joint_pos = fix_wrong_joints(joint_pos) # visibility list if 'is_visible' in str(annopoint.dtype): vis = [ v[0] if v.size > 0 else [0] for v in annopoint['is_visible'][0] ] vis = dict([(k, int(v[0])) if len(v) > 0 else v for k, v in zip(j_id, vis)]) else: vis = None # if len(joint_pos) == 16: if ((is_16_pos_only == True) and (len(joint_pos) == 16)) or (is_16_pos_only == False): # only use image with 16 key points / or use all data = { 'filename': img_fn, 'train': train_flag, 'head_rect': head_rect, 'is_visible': vis, 'joint_pos': joint_pos } # print(json.dumps(data), file=fp) # py3 if train_flag: ann_train_list[-1].append(data) else: ann_test_list[-1].append(data) # def write_line(datum, fp): # joints = sorted([[int(k), v] for k, v in datum['joint_pos'].items()]) # joints = np.array([j for i, j in joints]).flatten() # # out = [datum['filename']] # out.extend(joints) # out = [str(o) for o in out] # out = ','.join(out) # # print(out, file=fp) # def split_train_test(): # # fp_test = open('data/mpii/test_joints.csv', 'w') # fp_test = open(os.path.join(path, 'test_joints.csv'), 'w') # # fp_train = open('data/mpii/train_joints.csv', 'w') # fp_train = open(os.path.join(path, 'train_joints.csv'), 'w') # # all_data = open('data/mpii/data.json').readlines() # all_data = open(os.path.join(path, 'data.json')).readlines() # N = len(all_data) # N_test = int(N * 0.1) # N_train = N - N_test # # print('N:{}'.format(N)) # print('N_train:{}'.format(N_train)) # print('N_test:{}'.format(N_test)) # # np.random.seed(1701) # perm = np.random.permutation(N) # test_indices = perm[:N_test] # train_indices = perm[N_test:] # # print('train_indices:{}'.format(len(train_indices))) # print('test_indices:{}'.format(len(test_indices))) # # for i in train_indices: # datum = json.loads(all_data[i].strip()) # write_line(datum, fp_train) # # for i in test_indices: # datum = json.loads(all_data[i].strip()) # write_line(datum, fp_test) save_joints() # split_train_test() # ## read images dir logging.info("reading images list ...") img_dir = os.path.join(path, extracted_filename2) _img_list = load_file_list(path=os.path.join(path, extracted_filename2), regx='\\.jpg', printable=False) # ann_list = json.load(open(os.path.join(path, 'data.json'))) for i, im in enumerate(img_train_list): if im not in _img_list: print( 'missing training image {} in {} (remove from img(ann)_train_list)' .format(im, img_dir)) # img_train_list.remove(im) del img_train_list[i] del ann_train_list[i] for i, im in enumerate(img_test_list): if im not in _img_list: print( 'missing testing image {} in {} (remove from img(ann)_test_list)' .format(im, img_dir)) # img_test_list.remove(im) del img_train_list[i] del ann_train_list[i] ## check annotation and images n_train_images = len(img_train_list) n_test_images = len(img_test_list) n_images = n_train_images + n_test_images logging.info("n_images: {} n_train_images: {} n_test_images: {}".format( n_images, n_train_images, n_test_images)) n_train_ann = len(ann_train_list) n_test_ann = len(ann_test_list) n_ann = n_train_ann + n_test_ann logging.info("n_ann: {} n_train_ann: {} n_test_ann: {}".format( n_ann, n_train_ann, n_test_ann)) n_train_people = len(sum(ann_train_list, [])) n_test_people = len(sum(ann_test_list, [])) n_people = n_train_people + n_test_people logging.info("n_people: {} n_train_people: {} n_test_people: {}".format( n_people, n_train_people, n_test_people)) # add path to all image file name for i, value in enumerate(img_train_list): img_train_list[i] = os.path.join(img_dir, value) for i, value in enumerate(img_test_list): img_test_list[i] = os.path.join(img_dir, value) return img_train_list, ann_train_list, img_test_list, ann_test_list
def load_mpii_pose_dataset(path='data', is_16_pos_only=False): """Load MPII Human Pose Dataset. Parameters ----------- path : str The path that the data is downloaded to. is_16_pos_only : boolean If True, only return the peoples contain 16 pose keypoints. (Usually be used for single person pose estimation) Returns ---------- img_train_list : list of str The image directories of training data. ann_train_list : list of dict The annotations of training data. img_test_list : list of str The image directories of testing data. ann_test_list : list of dict The annotations of testing data. Examples -------- >>> import pprint >>> import tensorlayer as tl >>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset() >>> image = tl.vis.read_image(img_train_list[0]) >>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png') >>> pprint.pprint(ann_train_list[0]) References ----------- - `MPII Human Pose Dataset. CVPR 14 <http://human-pose.mpi-inf.mpg.de>`__ - `MPII Human Pose Models. CVPR 16 <http://pose.mpi-inf.mpg.de>`__ - `MPII Human Shape, Poselet Conditioned Pictorial Structures and etc <http://pose.mpi-inf.mpg.de/#related>`__ - `MPII Keyponts and ID <http://human-pose.mpi-inf.mpg.de/#download>`__ """ path = os.path.join(path, 'mpii_human_pose') logging.info("Load or Download MPII Human Pose > {}".format(path)) # annotation url = "http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/" tar_filename = "mpii_human_pose_v1_u12_2.zip" extracted_filename = "mpii_human_pose_v1_u12_2" if folder_exists(os.path.join(path, extracted_filename)) is False: logging.info("[MPII] (annotation) {} 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)) # images url = "http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/" tar_filename = "mpii_human_pose_v1.tar.gz" extracted_filename2 = "images" if folder_exists(os.path.join(path, extracted_filename2)) is False: logging.info("[MPII] (images) {} 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)) # parse annotation, format see http://human-pose.mpi-inf.mpg.de/#download import scipy.io as sio logging.info("reading annotations from mat file ...") # mat = sio.loadmat(os.path.join(path, extracted_filename, "mpii_human_pose_v1_u12_1.mat")) # def fix_wrong_joints(joint): # https://github.com/mitmul/deeppose/blob/master/datasets/mpii_dataset.py # if '12' in joint and '13' in joint and '2' in joint and '3' in joint: # if ((joint['12'][0] < joint['13'][0]) and # (joint['3'][0] < joint['2'][0])): # joint['2'], joint['3'] = joint['3'], joint['2'] # if ((joint['12'][0] > joint['13'][0]) and # (joint['3'][0] > joint['2'][0])): # joint['2'], joint['3'] = joint['3'], joint['2'] # return joint ann_train_list = [] ann_test_list = [] img_train_list = [] img_test_list = [] def save_joints(): # joint_data_fn = os.path.join(path, 'data.json') # fp = open(joint_data_fn, 'w') mat = sio.loadmat(os.path.join(path, extracted_filename, "mpii_human_pose_v1_u12_1.mat")) for _, (anno, train_flag) in enumerate( # all images zip(mat['RELEASE']['annolist'][0, 0][0], mat['RELEASE']['img_train'][0, 0][0])): img_fn = anno['image']['name'][0, 0][0] train_flag = int(train_flag) # print(i, img_fn, train_flag) # DEBUG print all images if train_flag: img_train_list.append(img_fn) ann_train_list.append([]) else: img_test_list.append(img_fn) ann_test_list.append([]) head_rect = [] if 'x1' in str(anno['annorect'].dtype): head_rect = zip( [x1[0, 0] for x1 in anno['annorect']['x1'][0]], [y1[0, 0] for y1 in anno['annorect']['y1'][0]], [x2[0, 0] for x2 in anno['annorect']['x2'][0]], [y2[0, 0] for y2 in anno['annorect']['y2'][0]] ) else: head_rect = [] # TODO if 'annopoints' in str(anno['annorect'].dtype): annopoints = anno['annorect']['annopoints'][0] head_x1s = anno['annorect']['x1'][0] head_y1s = anno['annorect']['y1'][0] head_x2s = anno['annorect']['x2'][0] head_y2s = anno['annorect']['y2'][0] for annopoint, head_x1, head_y1, head_x2, head_y2 in zip(annopoints, head_x1s, head_y1s, head_x2s, head_y2s): # if annopoint != []: # if len(annopoint) != 0: if annopoint.size: head_rect = [ float(head_x1[0, 0]), float(head_y1[0, 0]), float(head_x2[0, 0]), float(head_y2[0, 0]) ] # joint coordinates annopoint = annopoint['point'][0, 0] j_id = [str(j_i[0, 0]) for j_i in annopoint['id'][0]] x = [x[0, 0] for x in annopoint['x'][0]] y = [y[0, 0] for y in annopoint['y'][0]] joint_pos = {} for _j_id, (_x, _y) in zip(j_id, zip(x, y)): joint_pos[int(_j_id)] = [float(_x), float(_y)] # joint_pos = fix_wrong_joints(joint_pos) # visibility list if 'is_visible' in str(annopoint.dtype): vis = [v[0] if v.size > 0 else [0] for v in annopoint['is_visible'][0]] vis = dict([(k, int(v[0])) if len(v) > 0 else v for k, v in zip(j_id, vis)]) else: vis = None # if len(joint_pos) == 16: if ((is_16_pos_only ==True) and (len(joint_pos) == 16)) or (is_16_pos_only == False): # only use image with 16 key points / or use all data = { 'filename': img_fn, 'train': train_flag, 'head_rect': head_rect, 'is_visible': vis, 'joint_pos': joint_pos } # print(json.dumps(data), file=fp) # py3 if train_flag: ann_train_list[-1].append(data) else: ann_test_list[-1].append(data) # def write_line(datum, fp): # joints = sorted([[int(k), v] for k, v in datum['joint_pos'].items()]) # joints = np.array([j for i, j in joints]).flatten() # # out = [datum['filename']] # out.extend(joints) # out = [str(o) for o in out] # out = ','.join(out) # # print(out, file=fp) # def split_train_test(): # # fp_test = open('data/mpii/test_joints.csv', 'w') # fp_test = open(os.path.join(path, 'test_joints.csv'), 'w') # # fp_train = open('data/mpii/train_joints.csv', 'w') # fp_train = open(os.path.join(path, 'train_joints.csv'), 'w') # # all_data = open('data/mpii/data.json').readlines() # all_data = open(os.path.join(path, 'data.json')).readlines() # N = len(all_data) # N_test = int(N * 0.1) # N_train = N - N_test # # print('N:{}'.format(N)) # print('N_train:{}'.format(N_train)) # print('N_test:{}'.format(N_test)) # # np.random.seed(1701) # perm = np.random.permutation(N) # test_indices = perm[:N_test] # train_indices = perm[N_test:] # # print('train_indices:{}'.format(len(train_indices))) # print('test_indices:{}'.format(len(test_indices))) # # for i in train_indices: # datum = json.loads(all_data[i].strip()) # write_line(datum, fp_train) # # for i in test_indices: # datum = json.loads(all_data[i].strip()) # write_line(datum, fp_test) save_joints() # split_train_test() # ## read images dir logging.info("reading images list ...") img_dir = os.path.join(path, extracted_filename2) _img_list = load_file_list(path=os.path.join(path, extracted_filename2), regx='\\.jpg', printable=False) # ann_list = json.load(open(os.path.join(path, 'data.json'))) for i, im in enumerate(img_train_list): if im not in _img_list: print('missing training image {} in {} (remove from img(ann)_train_list)'.format(im, img_dir)) # img_train_list.remove(im) del img_train_list[i] del ann_train_list[i] for i, im in enumerate(img_test_list): if im not in _img_list: print('missing testing image {} in {} (remove from img(ann)_test_list)'.format(im, img_dir)) # img_test_list.remove(im) del img_train_list[i] del ann_train_list[i] ## check annotation and images n_train_images = len(img_train_list) n_test_images = len(img_test_list) n_images = n_train_images + n_test_images logging.info("n_images: {} n_train_images: {} n_test_images: {}".format(n_images, n_train_images, n_test_images)) n_train_ann = len(ann_train_list) n_test_ann = len(ann_test_list) n_ann = n_train_ann + n_test_ann logging.info("n_ann: {} n_train_ann: {} n_test_ann: {}".format(n_ann, n_train_ann, n_test_ann)) n_train_people = len(sum(ann_train_list, [])) n_test_people = len(sum(ann_test_list, [])) n_people = n_train_people + n_test_people logging.info("n_people: {} n_train_people: {} n_test_people: {}".format(n_people, n_train_people, n_test_people)) # add path to all image file name for i, value in enumerate(img_train_list): img_train_list[i] = os.path.join(img_dir, value) for i, value in enumerate(img_test_list): img_test_list[i] = os.path.join(img_dir, value) return img_train_list, ann_train_list, img_test_list, ann_test_list
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
def load_image_from_folder(path): path_imgs = load_file_list(path=path, regx='\\.jpg', printable=False) return visualize.read_images(path_imgs, path=path, n_threads=10, printable=False)
def load_flickr25k_dataset(tag='sky', path="data", n_threads=50, printable=False): """Load Flickr25K dataset. Returns a list of images by a given tag from Flick25k dataset, it will download Flickr25k from `the official website <http://press.liacs.nl/mirflickr/mirdownload.html>`__ at the first time you use it. Parameters ------------ tag : str or None What images to return. - If you want to get images with tag, use string like 'dog', 'red', see `Flickr Search <https://www.flickr.com/search/>`__. - If you want to get all images, set to ``None``. path : str The path that the data is downloaded to, defaults is ``data/flickr25k/``. n_threads : int The number of thread to read image. printable : boolean Whether to print infomation when reading images, default is ``False``. Examples ----------- Get images with tag of sky >>> images = tl.files.load_flickr25k_dataset(tag='sky') Get all images >>> images = tl.files.load_flickr25k_dataset(tag=None, n_threads=100, printable=True) """ path = os.path.join(path, 'flickr25k') filename = 'mirflickr25k.zip' url = 'http://press.liacs.nl/mirflickr/mirflickr25k/' # download dataset if folder_exists(os.path.join(path, "mirflickr")) is False: logging.info("[*] Flickr25k is nonexistent in {}".format(path)) maybe_download_and_extract(filename, path, url, extract=True) del_file(os.path.join(path, filename)) # return images by the given tag. # 1. image path list folder_imgs = os.path.join(path, "mirflickr") path_imgs = load_file_list(path=folder_imgs, regx='\\.jpg', printable=False) path_imgs.sort(key=natural_keys) # 2. tag path list folder_tags = os.path.join(path, "mirflickr", "meta", "tags") path_tags = load_file_list(path=folder_tags, regx='\\.txt', printable=False) path_tags.sort(key=natural_keys) # 3. select images if tag is None: logging.info("[Flickr25k] reading all images") else: logging.info("[Flickr25k] reading images with tag: {}".format(tag)) images_list = [] for idx, _v in enumerate(path_tags): tags = read_file(os.path.join(folder_tags, path_tags[idx])).split('\n') # logging.info(idx+1, tags) if tag is None or tag in tags: images_list.append(path_imgs[idx]) images = visualize.read_images(images_list, folder_imgs, n_threads=n_threads, printable=printable) return images