Nirvan101 commented 6 years ago

I don't understand how retrain_yolo.py expects the training data. How do I feed my images and my annotation files into retrain_yolo ? Should the images and annotation xml files be in 2 different folders and should corresponding images and xml files have the same name? Also retrain_yolo.py expects a data_path , classes_path and anchors_path. How do these correspond to my images and my annotation xmls?
I wasn't able to find an explanation about this. Any help would be appreciated.

sc12408 commented 6 years ago

See this https://github.com/shadySource/DATA. The npz file is what you need to creat. One array of vectorised images and one array with class label + box boundaries

2017TJM commented 5 years ago

import os import numpy as np import xml.etree.ElementTree as ElementTree import h5py classes = ["plate"] voc_path=u'./data/PascalVOC/VOCdevkit' train_set = [('2012', 'train')] def get_boxes_for_id(voc_path, year, image_id): """Get object bounding boxes annotations for given image.

Parameters
----------
voc_path : str
    Path to VOCdevkit directory.
year : str
    Year of dataset containing image. Either '2007' or '2012'.
image_id : str
    Pascal VOC identifier for given image.

Returns
-------
boxes : array of int
    bounding box annotations of class label, xmin, ymin, xmax, ymax as a
    5xN array.
"""
fname = os.path.join(voc_path, 'VOC{}/Annotations/{}.xml'.format(year,
                                                                 image_id))
with open(fname) as in_file:
    xml_tree = ElementTree.parse(in_file)
root = xml_tree.getroot()
boxes = []
for obj in root.iter('object'):
    difficult = obj.find('difficult').text
    label = obj.find('name').text
    if label not in classes or int(
            difficult) == 1:  # exclude difficult or unlisted classes
        continue
    xml_box = obj.find('bndbox')
    bbox = (classes.index(label), int(xml_box.find('xmin').text),
            int(xml_box.find('ymin').text), int(xml_box.find('xmax').text),
            int(xml_box.find('ymax').text))
    boxes.extend(bbox)
return np.array(
    boxes)  # .T  # return transpose so last dimension is variable length

def get_image_for_id(voc_path, year, image_id): """Get image data as uint8 array for given image.

Parameters
----------
voc_path : str
    Path to VOCdevkit directory.
year : str
    Year of dataset containing image. Either '2007' or '2012'.
image_id : str
    Pascal VOC identifier for given image.

Returns
-------
image_data : array of uint8
    Compressed JPEG byte string represented as array of uint8.
"""
fname = os.path.join(voc_path, 'VOC{}/JPEGImages/{}.jpg'.format(year,
                                                                image_id))
with open(fname, 'rb') as in_file:
    data = in_file.read()
# Use of encoding based on: https://github.com/h5py/h5py/issues/745
return np.fromstring(data, dtype='uint8')

def add_to_dataset(voc_path, year, ids, images, boxes,start=0): """Process all given ids and adds them to given datasets.""" for i, voc_id in enumerate(ids):

print(i)

    #print(voc_id)
    image_data = get_image_for_id(voc_path, year, voc_id)
    image_boxes = get_boxes_for_id(voc_path, year, voc_id)
   # images.append(image_data)
   # boxes.append(image_boxes)
    images[i+start]=image_data
    boxes[i+start]=image_boxes
    # images[i]=image_data
    # boxes[i]= image_boxes
return i

def get_ids(voc_path, datasets): """Get image identifiers for corresponding list of dataset identifies.

Parameters
----------
voc_path : str
    Path to VOCdevkit directory.
datasets : list of str tuples
    List of dataset identifiers in the form of (year, dataset) pairs.

Returns
-------
ids : list of str
    List of all image identifiers for given datasets.
"""
ids = []
#for year, image_set in datasets:
id_file_train= './data/PascalVOC/VOCdevkit/VOC2012/ImageSets/Main/train.txt'

id_file_test='./data/PascalVOC/VOCdevkit/VOC2012/ImageSets/Main/test.txt'

list_file=[id_file_train ,id_file_test]

#for i in id_file_train:
   # print("打印 ---------------------------------------------------i",i)
   #  print(i[0])
    # id_file = os.path.join(voc_path, 'V0C{}/ImageSets/Main/{}.txt'.format(
    #     year, image_set))
    # id_file =voc_path + '/'+'V0C{}/ImageSets/Main/{}.txt'.format(
    #     year, image_set)
    #print(str(id_file))
# print(i)
#print(list_file[i],'\n')
with open(id_file_train, 'r',encoding='utf-8') as image_ids:
    ids.extend(map(str.strip, image_ids.readlines()))
      #print(map(str.strip, image_ids.readlines()))
    # print(i)
  #  print(ids)
return ids

train_ids = get_ids(voc_path, train_set)

print(train_ids)

images=[]

boxes=[]

for train_ids in train_ids:

get_image_for_id(voc_path, 2012, train_ids)

i =add_to_dataset(voc_path, 2012, train_ids, images, boxes)

print(images)

print(i+1)

print(images) #uint8 array的图像数据

print(boxes) #array的数据

打印数据有多少个

print(len(train_ids)) total_train_ids = len(train_ids) fname = os.path.join(voc_path, 'pascal_voc_07_12.hdf5') voc_h5file = h5py.File(fname, 'w') uint8_dt = h5py.special_dtype( vlen=np.dtype('uint8')) # variable length uint8 vlen_int_dt = h5py.special_dtype( vlen=np.dtype(int)) # variable length default int train_group = voc_h5file.create_group('train')

store class list for reference class ids as csv fixed-length numpy string

voch5file.attrs['classes'] = np.string(str.join(',', classes))

store images as variable length uint8 arrays

train_images = train_group.create_dataset( 'images', shape=(total_train_ids,), dtype=uint8_dt)

store boxes as class_id, xmin, ymin, xmax, ymax

train_boxes = train_group.create_dataset( 'boxes', shape=(total_train_ids,), dtype=vlen_int_dt)

process all ids and add to datasets

print('Processing Pascal V0C 2012 datasets for training set.') last_2007 = add_to_dataset(voc_path, 2012, train_ids, train_images, train_boxes) print("last_2007",last_2007)

print('Processing Pascal VOC 2012 training set.')

print('Closing HDF5 file.') voc_h5file.close() print('Done.')