def voc_savefiletopickle(root_dir, pickfile, category, annotatefile): pathlist = [] cat_all = voc_utils.list_image_sets() with open(pickfile, 'ab') as handle: for cat in cat_all: imlist = voc_utils.imgs_from_category_as_list(category,cat,annotatefile) #print(cat) #print(root_dir) #print(imlist) for im in imlist: imgpath = os.path.join(root_dir, im+".jpg") pathlist.append(imgpath) pickle.dump(pathlist, handle, protocol=pickle.HIGHEST_PROTOCOL) handle.close()
from MajorityImageObject import Image
from cnn import Architectures
import pickle
import random
random.seed(10)
import numpy as np
import sys
import matplotlib.pylab as plt
import skimage
import pylab
from crf import CRF
import theano.tensor.nnet.abstract_conv as absconv
import keras.backend as K
import cv2
img_categories = list_image_sets()
imgsize = (227, 227)
train_images = []
val_images = []
df = load_data_multilabel('train')
data = df.as_matrix()
for row in data:
imgobj = Image(row[0], imgsize[0], imgsize[1], row[1:].tolist())
train_images.append(imgobj)
df = load_data_multilabel('val')
data = df.as_matrix()
tf.get_variable(
init_layers[i][0],
init_layers[i][1],
initializer=tf.contrib.layers.xavier_initializer())
name_dict.append(init_layers[i][0])
scope_dict[scope_name] = name_dict
return scope_dict
img_root = '/media/ubuntu/65db2e03-ffde-4f3d-8f33-55d73836211a/dataset/VOCdevkit/VOC2007/Test/JPEGImages'
labelfiles = '/media/ubuntu/65db2e03-ffde-4f3d-8f33-55d73836211a/dataset/VOCdevkit/VOC2007/Test/ImageSets/Main'
checkpoint_dir = '../../model/yolol2sum_epoch_SGD'
classes = voc.list_image_sets()
val_list = voc.imgs_from_category_as_list('', 'test', labelfiles)
yolo_old = YOLO_tiny_tf.YOLO_TF()
with tf.device('/gpu:0'):
#Vanilla YOLO_tiny Weight
x = tf.placeholder(tf.float32, (None, 448, 448, 3))
label = tf.placeholder(tf.float32, (None, 1470), name='labels')
keep_prob = tf.placeholder(tf.float32)
modelTicket_G = {'root': 'yolo_tiny', 'branch': 'vanilla'}
init_layers = mu.model_zoo(modelTicket_G)
var_dict = recursive_create_var('recursive', 1, 0.2, init_layers)
yolo_ds = nf.glosso_train("recursive_0", 'test', x, var_dict, keep_prob,
False)
from args import arg_parser, arch_resume_names
# TODO
# import make_graph as mk_grf
try:
from tensorboard_logger import configure, log_value
except BaseException:
configure = None
# Dataset
root_dir = '/home/wenboz/ProJEX/data_root/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')
img_set_cat = vutil.list_image_sets()
num_cat = len(img_set_cat)
CLASS = img_set_cat[15]
print('Object to detect: ', CLASS)
# Load data
# TODO: load less val
# TODO: multithreading
# TODO: use DataLoader Class
def dataloader(batch_size):
# data list
trn_img_fn = [
vutil.imgs_from_category_as_list(c, 'train') for c in img_set_cat
]
## -*- coding: utf-8 -*-
import sys
sys.path.append("..")
import voc_utils
from skimage.io import imread
from skimage.io import imshow
import matplotlib.pyplot as plt
# imgPath = r"D:\dataset\VOCtrainval_11-May-2012\VOCdevkit\VOC2012"
# imgSegIndex = imgPath + r"\ImageSets\Segmentation"
# imgSetPath = imgPath + r"\JPEGImages"
#
# f = open(imgSegIndex + r"\train.txt", 'r')
# tmp = f.read()
# imgSegIndexList = tmp.split('\n')
# # print imgSegIndexList
#
# img=imread(imgSetPath+'\\'+imgSegIndexList[0]+'.jpg')
# imshow(img)
# plt.show()
#
print voc_utils.list_image_sets()
