In [ ]:
import os
import pandas as pd
from bs4 import BeautifulSoup
import voc_utils
from more_itertools import unique_everseen
In [ ]:
root_dir = '/Users/mprat/personal/VOCdevkit/VOC2012/'
img_dir = os.path.join(root_dir, 'JPEGImages')
ann_dir = os.path.join(root_dir, 'Annotations')
set_dir = os.path.join(root_dir, 'ImageSets', 'Main')
In [55]:
# list image sets
all_files = !ls {set_dir}
image_sets = sorted(list(set([filename.replace('.txt', '').strip().split('_')[0] for filename in all_files])))
print image_sets
['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'trainval', 'tvmonitor', 'val']
In [4]:
# category name is from above, dataset is either "train" or
# "val" or "train_val"
def imgs_from_category(cat_name, dataset):
filename = os.path.join(set_dir, cat_name + "_" + dataset + ".txt")
df = pd.read_csv(
filename,
delim_whitespace=True,
header=None,
names=['filename', 'true'])
return df
def imgs_from_category_as_list(cat_name, dataset):
df = imgs_from_category(cat_name, dataset)
df = df[df['true'] == 1]
return df['filename'].values
def annotation_file_from_img(img_name):
return os.path.join(ann_dir, img_name) + '.xml'
In [6]:
# annotation operations
def load_annotation(img_filename):
xml = ""
with open(annotation_file_from_img(img_filename)) as f:
xml = f.readlines()
xml = ''.join([line.strip('\t') for line in xml])
return BeautifulSoup(xml)
def get_all_obj_and_box(objname, img_set):
img_list = imgs_from_category_as_list(objname, img_set)
for img in img_list:
annotation = load_annotation(img)
In [7]:
# image operations
def load_img(img_filename):
return io.load_image(os.path.join(img_dir, img_filename + '.jpg'))
In [8]:
train_img_list = imgs_from_category_as_list('bicycle', 'train')
In [9]:
a = load_annotation(train_img_list[0])
In [61]:
def load_train_data(category):
to_find = category
train_filename = '/Users/mprat/personal/VOCdevkit/VOC2012/csvs/train_' + category + '.csv'
if os.path.isfile(train_filename):
return pd.read_csv(train_filename)
else:
train_img_list = imgs_from_category_as_list(to_find, 'train')
data = []
for item in train_img_list:
anno = load_annotation(item)
objs = anno.findAll('object')
for obj in objs:
obj_names = obj.findChildren('name')
for name_tag in obj_names:
if str(name_tag.contents[0]) == 'bicycle':
fname = anno.findChild('filename').contents[0]
bbox = obj.findChildren('bndbox')[0]
xmin = int(bbox.findChildren('xmin')[0].contents[0])
ymin = int(bbox.findChildren('ymin')[0].contents[0])
xmax = int(bbox.findChildren('xmax')[0].contents[0])
ymax = int(bbox.findChildren('ymax')[0].contents[0])
data.append([fname, xmin, ymin, xmax, ymax])
df = pd.DataFrame(data, columns=['fname', 'xmin', 'ymin', 'xmax', 'ymax'])
df.to_csv(train_filename, index=False)
return df
In [76]:
df = load_train_data('bicycle')
print list(unique_everseen(list(voc_utils.img_dir + df['fname'])))
['/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_000036.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_000191.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_000196.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_000531.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_000615.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_000764.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001336.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001375.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001402.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001523.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001566.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001626.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001791.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001813.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001860.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_001986.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002129.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002204.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002370.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002418.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002515.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002679.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002787.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002880.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002894.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_002948.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_003075.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2008_003140.jpg', 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'/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2011_003063.jpg', '/Users/mprat/personal/VOCdevkit/VOC2012/JPEGImages2011_003148.jpg']
In [63]:
for cat in image_sets:
if cat != 'train' and cat != 'val' and cat != 'trainval':
load_train_data(cat)
In [82]:
df = load_train_data('bicycle')
for row_num, entry in df.iterrows():
print entry
print df
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[393 rows x 5 columns]
Content source: zhangyuygss/WSL
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