def __init__(self,
content_img_addr,
style_img_addr,
img_width = 224,
img_height = 224):
self.img_width = img_width
self.img_height = img_height
### get content image, style image and initiate generated image
self.content_img = utils.get_image(content_img_addr, img_width, img_height)
self.style_img = utils.get_image(style_img_addr, img_width, img_height)
self.gen_img = utils.generate_noise_image(self.content_img, img_width, img_height, noise_ratio=0.6)
### identify weight used for calculating total loss
self.style_loss_weight = STYLE_LOSS_WEIGHT
self.style_weight_trend = STYLE_WEIGHT_TREND
self.content_loss_weight = CONTENT_LOSS_WEIGHT
### identify layers that represent style and content
self.content_layers = CONTENT_LAYERS
self.style_layers = STYLE_LAYERS
### identify image inputs
with tf.variable_scope('inputs', reuse=tf.AUTO_REUSE) as scope:
self.img_input = tf.get_variable(name='img_input',
shape=([1,self.img_width,self.img_height,3]),
dtype=tf.float32,
initializer=tf.zeros_initializer())
### setup learning parameters
self.learning_rate = LEARNING_RATE
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.training_epoch = TRAINING_EPOCH
def visualize(self, batch, return_image=False):
self.eval()
_, _, H_org, W_org = batch["meta"]["shape"]
batch_dict = self.get_input(batch)
targets_preds = self(batch_dict["imageList"])[0]
annList_gt = batch["annList"][0]
annList_pred = au.targets2annList(
targets_preds,
shape=(H_org, W_org),
image_id=batch["meta"]["image_id"][0])
# Resize
W_img, H_img = targets_preds.size
images_given = batch["images"].tensors[:, :, :H_img, :W_img]
images = F.interpolate(images_given,
size=(H_org, W_org),
mode="nearest")
pred_image = ut.get_image(images, annList=annList_pred, denorm="bgr")
pred_image_blacked = ut.get_image(images * 0, annList=annList_pred)
gt_image = ut.get_image(images, annList=annList_gt, denorm="bgr")
result = np.concatenate([pred_image, gt_image], axis=2)
return {
"gt_image": gt_image,
"pred_image": pred_image,
"pred_image_blacked": pred_image_blacked
}
def main():
# Load model
model = load_model(MODEL_PATH, custom_objects={'AdaIN': AdaIN})
# Get content image
content = get_image(CONTENT_PATH, resize=False)
content = preprocess(content)
content = np.expand_dims(content, axis=0)
# Get style image
style = get_image(STYLE_PATH, resize=False)
style = preprocess(style)
style = np.expand_dims(style, axis=0)
# Set alpha Value
alpha = tf.convert_to_tensor(ALPHA) # 0 < alpha <= 1
alpha = np.expand_dims(alpha, axis=0)
# Do inference
y = model.predict([content, style, alpha])[0]
# Convert output array to image
y = np.squeeze(y, axis=0)
y = deprocess(y)
img = array_to_img(y)
# Show image
img.show(command='fim')
def batch_gen(self, path, batch_size, crop_size):
content_path, mask_path = get_file_paths(path)
while True:
index = random.sample(range(1, len(content_path)), batch_size)
try:
offset_h = random.randint(0, (2448 - crop_size[0]))
offset_w = random.randint(0, (2448 - crop_size[1]))
offset = (offset_h, offset_w)
contents = [
vgg_sub_mean(
random_crop(get_image(content_path[i]), offset,
crop_size)) for i in index
]
masks = [
mask_preprocess(
random_crop(get_image(mask_path[i]), offset,
crop_size)) for i in index
]
contents = np.asarray(contents, dtype=np.float32)
masks = np.asarray(masks, dtype=np.uint8)
except Exception as err:
print("\nError: {}".format(err))
continue
yield contents, masks
def image():
uuid_val = request.cookies.get("uuid")
print("UUID Cookie: ", uuid_val)
# If the user has never been authenticated
if (uuid_val is None or uuid_val not in images):
# Assign user a uuid value
uuid_val = str(uuid.uuid4())
uuid_val = uuid_val.replace("-", "")
# Get image and description pair
images[uuid_val] = get_image()
print("uuid_val: ", uuid_val)
# Get a list of all prev words user has seen or empty list if none
prev_words = users.get(uuid_val, [])
word = get_word()[0]
while word in prev_words:
word = get_word()[0]
prev_words.append(word)
# Get prev img and desc for the user
img_list = images[uuid_val]
print("Prev words: ", prev_words)
print("len(prev_words): ", len(prev_words))
# Change image after 10 words
if len(prev_words) % 10 == 0:
print("Entered if")
prev_img = images[uuid_val][0]
img_list = get_image()
print("img_list: ", img_list)
print("prev_img: ", prev_img)
# import pdb; pdb.set_trace()
while prev_img == img_list[0]:
img_list = get_image()
images[uuid_val] = img_list
users[uuid_val] = prev_words
img_list = images[uuid_val]
to_speech(word=word, save_to="static/audio/", filename=word)
img_path = "images/" + img_list[0]
audio_path = "./audio/" + word + ".mp3"
img_desc = img_list[1]
print("Description: ", img_list[1])
response = make_response(url_for('static', filename=img_path))
response.headers["img_description"] = img_desc
response.headers["word"] = word
response.headers["translation"] = translation(word)["translatedText"]
response.headers["audio_file"] = url_for('static', filename=audio_path)
response.set_cookie("uuid", value=uuid_val)
return response
def find_the_emulator_game(self):
image = get_image("new-super-mario-bros-find-emu-game.png", self.emulator)
coordinates = pyautogui.locateCenterOnScreen(image)
if coordinates is None: # pixels not found
image = get_image("new-super-mario-bros-enabled.png", self.emulator)
# activate the emulator
x, y = coordinates[0], coordinates[1]
pyautogui.moveTo(x, y, self.duration)
pyautogui.click()
time.sleep(1)
def x_interpolate(path1, path2):
x0 = get_image(path1, 64, 64)
x1 = get_image(path2, 64, 64)
x_line = []
for idx in range(11):
x_tmp = x0*idx*0.1+x1*(10-idx)*0.1
x_line.append(x_tmp)
x_line = np.stack((x_line[0], x_line[1], x_line[2], x_line[3],
x_line[4], x_line[5], x_line[6], x_line[7],
x_line[8], x_line[9], x_line[10]))
return x_line
def main():
if args.mode == 'train':
content_path, mask_path = get_file_paths(args.image_path)
content_path.sort()
mask_path.sort()
for path in content_path:
split(get_image(path), path, mode='sat')
for path in mask_path:
split(get_image(path), path, mode='mask')
elif args.mode == 'validation':
content_path, _ = get_file_paths(args.image_path)
content_path.sort()
for path in content_path:
split(get_image(path), path, mode='sat')
def run(self):
iter_counter = 0
start_time = time.time()
for e in range(self.args.epochs):
shuffle(self.data_files)
sample_files = self.data_files[0:self.args.sample_size]
sample = [
get_image(file,
128,
is_crop=self.args.is_crop,
resize_w=self.args.image_size,
is_grayscale=0) for file in sample_files
]
sample_images = np.array(sample).astype(np.float32)
print("[*] Sample images updated! time per epoch: {}".format(
time.time() - start_time))
batch_idxs = min(len(self.data_files),
self.args.train_size) // self.args.batch_size
start_time = time.time()
for idx in range(0, batch_idxs):
batch_files = self.data_files[idx *
self.args.batch_size:(idx + 1) *
self.args.batch_size]
batch = [
get_image(file,
128,
is_crop=self.args.is_crop,
resize_w=self.args.image_size,
is_grayscale=0) for file in batch_files
]
batch_images = np.array(batch).astype(np.float32)
batch_z = np.random.normal(loc=0.0,
scale=1.0,
size=(self.args.sample_size,
self.args.z_dim)).astype(
np.float32)
g_err, d_err = self.gan.train(batch_images, batch_z)
print("Epoch: [%2d/%2d] [%4d/%4d], d_loss: %.8f, g_loss: %.8f" \
% (e, self.args.epochs, idx, batch_idxs, d_err, g_err))
iter_counter += 1
if np.mod(iter_counter, self.args.sample_step) == 0:
img = self.gan.generate(self.sample_seed)
tl.visualize.save_images(
img, [8, 8], './{}/train_{:02d}_{:04d}.png'.format(
self.args.sample_dir, e, idx))
def shoggoth(img_num):
image = get_image(img_num, in_dir)
# Wykrycie narożników kartki, skorygowanie perspektywy oraz wycięcie kartki.
image_perspective_operations, sPoints, tPoints = perspective_operations(
image)
# Usunięcie kratek z kartki tak, żeby zostały widoczne tylko słowa
image_remove_lines_operations = removeLines(
image_perspective_operations)
image_checkered_operations = checkered_operations(
image_remove_lines_operations)
# Wykrycie słów
image_detect_words = detect_words_operations(
image_checkered_operations)
# Wykrycie indeksów
image_crop_numbers, cropped_numbers = crop_numbers(
image_detect_words, image_perspective_operations)
# Podział na cyfry
image_crop_digits = crop_digits(cropped_numbers, image)
cropped_rectangles = crop_small_rectangles(image_detect_words)
mask = paint_lines(cropped_rectangles)
mask = prepare_mask(mask)
new_img = move_to_original_coords(mask, sPoints, tPoints,
image.shape[:2])
save_image(img_num, new_img, "png", out_dir)
return image_crop_digits
def train_products_keywords(products):
"""
Поиск с помощью OpenVINO слов на каждом изображении в базе
Добавление найденных слов в столбец keywords в датафрейм с продуктами
:param products: датарфейм или часть датафрейма с продуктами, для которых необходимо получить слова
:return: датафрейм с найденными словами для каждого изображения в столбце keywords
"""
logging.info('Поиск и добавление в датафрейм слов по каждому изображению')
products['keywords'] = products['vendor'] + ' ' + products['model']
products['keywords'] = products['keywords'].str.replace(
'мл', '').str.lower().str.split()
http = requests.Session()
http.mount("https://", utils.adapter)
# загрузка модели OpenVINO
text_spotting_net = TextSpotting().train_network()
i = 0
for index, row in products.iterrows():
image = utils.get_image(row['picture'], http)
image = image.convert('RGB')
row['keywords'] = row['keywords'] + text_spotting_net.search_text(
image)
i += 1
if i % 10 == 0:
print(i)
logging.info(f'Обработано строк: {i}')
return products
def __getitem__(self, idx):
picture_url = self.products.iloc[idx]['picture']
image = utils.get_image(picture_url, self.http)
image = image.convert('RGB')
if self.transform:
image = self.transform(image)
return image
def extract_to(path, cursor, limitportipo=100, crop=True):
tipo_counter = Counter()
try:
os.mkdir(path)
except FileExistsError:
pass
for linha in cursor:
_id = linha['_id']
ncms = linha.get('metadata').get('carga').get('ncm')
ncms_encontrados = set()
for ncm in ncms:
posicao = ncm.get('ncm')[:4]
ncms_encontrados.add(posicao)
if len(ncms_encontrados) == 1: # Achou 1 e somente 1 posição ncm
posicao = list(ncms_encontrados)[0]
if tipo_counter[posicao] < limitportipo:
image = get_image(linha, crop=crop)
if image:
sub_path = os.path.join(path, posicao)
try:
os.mkdir(sub_path)
except FileExistsError:
pass
filename = str(_id) + '.jpg'
image.save(os.path.join(sub_path, filename))
del image
tipo_counter[posicao] += 1
def save_data_list(inpath, outpath, filenames):
for size in IMG_SIZES:
print('Processing images of size %d' % size)
cnt = 0
images = np.ndarray(shape=(len(filenames), size, size, 3), dtype=np.uint8)
for idx, key in enumerate(filenames):
f_name = '%s/%s.jpg' % (inpath, key)
img = get_image(f_name, LOAD_SIZE, is_crop=False)
img = img.astype('uint8')
img = img.astype('uint8')
if size != LOAD_SIZE:
img = scipy.misc.imresize(img, [size, size], 'bicubic')
images[idx, :, :, :] = np.array(img)
cnt += 1
if cnt % 100 == 0:
print('\rLoad %d......' % cnt, end="", flush=True)
print('Images processed: %d', len(filenames))
outfile = outpath + str(size) + 'images.pickle'
joblib.dump(images, outfile)
print('save to: ', outfile)
def save_data_list(inpath, outpath, filenames):
hr_images = []
lr_images = []
lr_size = int(LOAD_SIZE / LR_HR_RETIO)
cnt = 0
for key in filenames:
f_name = '%s/%s.jpg' % (inpath, key)
img = get_image(f_name, LOAD_SIZE, is_crop=False)
img = img.astype('uint8')
hr_images.append(img)
lr_img = resize(img, [lr_size, lr_size], order=3)
lr_images.append(lr_img)
cnt += 1
if cnt % 100 == 0:
print('Load %d......' % cnt)
#
print('images', len(hr_images), hr_images[0].shape, lr_images[0].shape)
#
outfile = outpath + str(LOAD_SIZE) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(hr_images, f_out)
print('save to: ', outfile)
#
outfile = outpath + str(lr_size) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_images, f_out)
print('save to: ', outfile)
def extractRealFeatrue():
f_data = glob(os.path.join(conf.data_dir, conf.dataset, "*"))
n_iters = int(len(f_data) / conf.n_batch)
for idx in range(0, n_iters):
f_batch = f_data[idx * conf.n_batch:(idx + 1) * conf.n_batch]
data_batch = [
get_image(f,
conf.n_img_pix,
is_crop=conf.is_crop,
resize_w=conf.n_img_out_pix,
is_grayscale=conf.is_gray) for f in f_batch
]
img_batch = np.array(data_batch).astype(np.float32)
if conf.is_gray:
s, h, w = img_batch.shape
img_batch = img_batch.reshape(s, h, w, n_channel)
l_featrue = sess.run(e_feature, feed_dict={g_net: img_batch})
for i in range(len(l_featrue)):
n_idx = 0
f_df_real = open(
checkpoint_dir + '/' + str(i) + '_x_feature.csv', 'a')
for j in range(1, l_featrue[i].shape[0]):
f_df_real.write(
str(n_idx) + ', ' + f_batch[j] +
', ' + str(l_featrue[i][n_idx].tolist()).replace(
"[", "").replace("]", "") + '\n')
n_idx += 1
f_df_real.close()
def manifoldD():
f_data = glob(os.path.join(conf.data_dir, conf.dataset, "*"))
shuffle(f_data)
f_batch = f_data[0:conf.n_batch]
x_test = [
get_image(f,
conf.n_img_pix,
is_crop=conf.is_crop,
resize_w=conf.n_img_out_pix,
is_grayscale=conf.is_gray) for f in f_batch
]
x_test = np.array(x_test).astype(np.float32)
if conf.is_gray:
s, h, w = x_test.shape
x_test = x_test.reshape(s, h, w, n_channel)
step = int(conf.n_batch / 2)
for i in range(step):
d_mnfd = [None] * 64
d_mnfd[0] = x_test[i]
d_mnfd[63] = x_test[i + step]
for j in range(1, 63):
d_mnfd[j] = d_mnfd[0] + ((d_mnfd[63] - d_mnfd[0]) / 63 * j)
d_intp = sess.run(d_img, feed_dict={g_net: d_mnfd})
save_image(
d_intp, os.path.join(checkpoint_dir,
str(i) + 'mnfd_anal_D.png'))
def main(argv):
pattern = "/gdata/CelebA/Img/img_align_celeba/*.jpg"
files = glob(pattern)
assert len(files) > 0
shuffle(files)
outfile = './data/CelebA_train_' + str(IMSIZE) + '.tfrecords'
writer = tf.python_io.TFRecordWriter(outfile)
for i, f in enumerate(files):
print(i)
image = get_image(f, IMSIZE, is_crop=True, resize_w=IMSIZE)
image = colorize(image)
assert image.shape == (IMSIZE, IMSIZE, 3)
image += 1.
image *= (255. / 2.)
image = image.astype('uint8')
image_raw = image.tostring()
label = 0
if i % 1 == 0:
print(i)
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(IMSIZE),
'width': _int64_feature(IMSIZE),
'depth': _int64_feature(3),
'image_raw': _bytes_feature(image_raw),
'label': _int64_feature(label)
}))
writer.write(example.SerializeToString())
writer.close()
def train(self, config):
tf.global_variables_initializer().run()
start_time = time.time()
if self.load(self.checkpoint_dir):
print("""Loading existing model""")
else:
print("""Initalizing new model""")
for epoch in range(config.epoch):
data = dataset_files(config.dataset)
np.random.shuffle(data)
batch_idx = min(len(data), config.train_size) // self.batch_size
for idx in range(batch_idx):
batch_files = data[idx * config.batch_size: (idx + 1) * config.batch_size]
batch = [get_image(batch_file, self.image_size) for batch_file in batch_files]
batch_images = np.array(batch).astype(np.float32)
masks = np.ones([config.batch_size, config.image_size, config.image_size, 3])
masks[:, 16:48, 16:48, :] = 0.0
for i in range(D_ITERATIONS):
_, d_loss, summary_str = self.sess.run(
[self.d_optim, self.d_loss, self.d_summary],
feed_dict={self.images: batch_images, self.masks: masks})
self.writer.add_summary(summary_str, self.global_step.eval())
for i in range(G_ITERATIONS):
_, g_loss, summary_str = self.sess.run(
[self.g_optim, self.g_loss, self.g_summary],
feed_dict={self.images: batch_images, self.masks: masks})
self.writer.add_summary(summary_str, self.global_step.eval())
print("Epoch: [{:2d}] [{:4d}/{:4d}] time: {:4.4f}, d_loss: {:.8f}, g_loss: {:.8f}".format(
epoch, idx, batch_idx, time.time() - start_time, d_loss, g_loss))
if idx % 10 == 0:
self.save(config.checkpoint_dir, self.global_step.eval())
def get_sample_data(self, config):
"""Set up the inputs and labels of sample images.
Samples are created periodically during training.
"""
if config.dataset == 'mnist':
sample_inputs = self.data_X[0:self.sample_num]
sample_labels = self.data_y[0:self.sample_num]
else:
sample_files = self.data[0:self.sample_num]
sample = [
get_image(sample_file,
input_height=self.input_height,
input_width=self.input_width,
resize_height=self.output_height,
resize_width=self.output_width,
crop=self.crop,
grayscale=self.grayscale)
for sample_file in sample_files
]
if self.grayscale:
sample_inputs = np.array(sample).astype(np.float32)[:, :, :,
None]
else:
sample_inputs = np.array(sample).astype(np.float32)
sample_z = np.random.uniform(-1, 1, size=(self.sample_num, self.z_dim))
sample_feed_dict = {
self.z: sample_z,
self.inputs: sample_inputs,
}
if config.dataset == 'mnist':
sample_feed_dict.update({self.y: sample_labels})
return sample_feed_dict
def main(argv):
pattern = "/scratch/nharness/Library/RAW/*TIF"
files = glob(pattern)
assert len(files) > 0
assert len(files) < 1000000, len(files)
shuffle(files)
outfile = '/home/nharness/idcgans/datasets/patents' + str(IMSIZE) + '.tfrecords'
writer = tf.python_io.TFRecordWriter(outfile)
for i, f in enumerate(files):
print i
image = get_image(f, IMSIZE, is_crop=True, resize_w=IMSIZE)
image = colorize(image)
assert image.shape == (IMSIZE, IMSIZE, 3)
image += 1.
image *= (255. / 2.)
image = image.astype('uint8')
#print image.min(), image.max()
# from pylearn2.utils.image import save
# save('foo.png', (image + 1.) / 2.)
image_raw = image.tostring()
label = 0
if i % 1 == 0:
print i, '\t',label
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(IMSIZE),
'width': _int64_feature(IMSIZE),
'depth': _int64_feature(3),
'image_raw': _bytes_feature(image_raw),
'label': _int64_feature(label)
}))
writer.write(example.SerializeToString())
writer.close()
def get_pictures(dir_name, collection):
image_ids = get_image_ids(collection)
for image_id in image_ids:
url = get_image_url(image_id)
picture_name = f"hubble{image_id}.{get_image_extension(url)}"
file_path = os.path.join(dir_name, picture_name)
save_image(get_image(url), file_path)
def transform_forward(path_in, path_out, checkpoint_dir):
image_input = get_image(path_in)
image_input = np.array([image_input])
image_shape = image_input.shape
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Graph().as_default(), tf.device('/cpu:0'), tf.Session(config=config) as sess:
initial_image = tf.placeholder(tf.float32,
shape=image_shape,
name='initial_image')
with tf.variable_scope("Transform_net"):
transformed_image_tensor = transform.net(initial_image)
saver = tf.train.Saver()
if os.path.isdir(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception("No checkpoint found...")
else:
saver.restore(sess, checkpoint_dir)
transformed_image = sess.run(transformed_image_tensor,
feed_dict={initial_image: image_input})
save_img(path_out, transformed_image[0])
def save_data_list(inpath, outpath, filenames, filename_bbox):
hr_images = []
lr_images = []
lr_size = int(LOAD_SIZE / LR_HR_RETIO)
cnt = 0
for key in filenames:
bbox = filename_bbox[key]
f_name = '%s/CUB_200_2011/images/%s.jpg' % (inpath, key)
img = get_image(f_name, LOAD_SIZE, is_crop=True, bbox=bbox)
img = img.astype('uint8')
hr_images.append(img)
lr_img = scipy.misc.imresize(img, [lr_size, lr_size], 'bicubic')
lr_images.append(lr_img)
cnt += 1
if cnt % 100 == 0:
print('Load %d......' % cnt)
#
print('images', len(hr_images), hr_images[0].shape, lr_images[0].shape)
#
outfile = outpath + str(LOAD_SIZE) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(hr_images, f_out)
print('save to: ', outfile)
#
outfile = outpath + str(lr_size) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_images, f_out)
print('save to: ', outfile)
def _read_by_function(self, filename1, filename_2):
array1 = get_image(filename1,
108,
is_crop=True,
resize_w=self.output_size,
is_grayscale=False)
array1 = np.array(array1, dtype=np.float32)
array2 = get_image(filename_2,
108,
is_crop=True,
resize_w=self.output_size,
is_grayscale=False)
array2 = np.array(array2, dtype=np.float32)
return array1, array2
def pinhole_demo(rotation=None, translation=None, save_fname="pinhole", idx=0):
# assignment: section 3
bfm = h5py.File(MODELS_PATH + BFM_FNAME, "r")
bfm_params, color, triangles = utils.read_bfm(bfm)
lms = utils.read_landmarks(MODELS_PATH + LM_FNAME) # landmark annotations
rotation = torch.tensor([0., 0., 0.]) if rotation is None else torch.tensor(rotation)
translation = torch.tensor([0., 0., -500.]) if translation is None else torch.tensor(translation)
G = morph.compute_G(bfm_params)
G_transformed = pinhole.transform(G, rotation, translation)
G_pinhole = pinhole.camera_model(G, rotation, translation)
save_obj(OBJ_3D_PATH + save_fname + str(idx) + "_3d.obj", G_transformed, color, triangles)
save_obj(OBJ_2D_PATH + save_fname + str(idx) + "_2d.obj", G_pinhole, color, triangles)
print("Rendering...")
img_2d = utils.get_image(G_pinhole, color, triangles) # render img
img_lm = utils.get_landmarks(G_pinhole[:, :2], lms) # landmark coords
utils.show_face(img_2d)
utils.flip_y()
plt.savefig(PINHOLE_PATH + save_fname + str(idx) + ".pdf")
utils.show_landmarks(img_lm, indices=True) # overlays landmarks on image
plt.savefig(PINHOLE_PATH + save_fname + str(idx) + "_lm.pdf")
plt.close()
def _read_by_function(self, filename):
array = get_image(filename, 108, is_crop=True, resize_w=self.output_size,
is_grayscale=False)
real_images = np.array(array)
real_images = real_images[:,:,np.newaxis]
return real_images
class Powerup(pg.sprite.Sprite):
"""All the powerups: shield, weapon..."""
POWERUPS = {osp.basename(f).split(".")[0]: get_image(f)
for f in glob(osp.join(IMAGES_FOLDER, "powerups", "*png"))}
def __init__(self, center):
super(Powerup, self).__init__()
self.type = ("bomb", "shield", "weapon")[randrange(3)]
self.image = self.POWERUPS[self.type]
self.rect = self.image.get_rect()
self.rect.center = center
self.centery = center[1]
self.up = True
def update(self):
if self.rect.centery < self.centery - 50:
self.up = False
if self.rect.top > HEIGHT:
self.kill()
return
if self.up:
self.rect.y -= game.dt / 2
else:
self.rect.y += game.dt / 4
def writex(conf, n_img):
data_files = glob(os.path.join(conf.data_dir, conf.dataset, "*"))
shuffle(data_files)
l_f = data_files[0:n_img]
l_x = [
get_image(f,
conf.n_img_pix,
is_crop=conf.is_crop,
resize_w=conf.n_img_out_pix,
is_grayscale=conf.is_gray) for f in l_f
]
with open(os.path.join(base_dir, 'x.csv'), 'w') as file:
for i in range(len(l_x)):
file.write(
str(l_x[i].tolist()).replace("[", "").replace("]", "") + '\n')
file.close()
x_fix = np.array(l_x).astype(np.float32)
if (conf.is_gray == 1):
x_fix = x_fix.reshape(conf.n_batch, conf.n_img_out_pix,
conf.n_img_out_pix, 1)
x_fix = np.clip((x_fix + 1) * 127.5, 0, 255)
save_image(x_fix, '{}/x_fix.png'.format(base_dir))
def main(args):
model = get_model(training=False)
model.summary()
try:
model.load_weights(args[2])
print("....Previous weight data...")
except:
print("Weights not found, Aborting")
exit()
data_dir = args[0]
annotation_file = args[1]
outfile = open('outfile', 'w')
with open(os.path.join(data_dir, annotation_file), 'r') as f:
filenames = [line.split(' ')[0][2:] for line in f]
iters = 0
max_iters = 100
for img_name in filenames:
if iters == max_iters:
break
iters += 1
img = get_image(os.path.join(data_dir, img_name))
img = np.expand_dims(img, axis=0)
y_pred = model.predict(img)
label_true = img_name.split('_')[1]
label_pred, label_unprocessed = activation_to_label(y_pred)
outfile.write(label_true + ' ' + label_pred + ' ' + label_unprocessed + '\n')
outfile.close()
def extract_to(db, path, cursor, start, end, crop=False):
try:
os.mkdir(path)
os.mkdir(path + '/COCAINA')
os.mkdir(path + '/SEMCOCAINA')
except FileExistsError:
pass
ind = 0
# Colocar um número sequencial no início do nome do arquivo, para permitir treino
# aos pares caso sejam utilizadas redes siamesas
for ind, linha in enumerate(cursor):
_id = linha['_id']
container = linha['metadata']['numeroinformado']
carga = linha.get('metadata').get('carga')
if carga:
ncms = linha.get('metadata').get('carga').get('ncm')
conhecimento = linha.get('metadata').get('carga') \
.get('conhecimento')[0].get('conhecimento')
image = get_image(linha, crop=crop)
if image:
sub_path = os.path.join(path, 'COCAINA')
filename = str(ind) + '_' + str(_id) + '.jpg'
image.save(os.path.join(sub_path, filename))
print(ind, sub_path)
del image
if carga:
similar_image, similar_id = get_similar_image(db, conhecimento, ncms,
start, end, crop)
if similar_image:
sub_path = os.path.join(path, 'SEMCOCAINA')
filename = str(ind) + '_' + str(similar_id) + '.jpg'
similar_image.save(os.path.join(sub_path, filename))
print(ind, sub_path)
del similar_image
return ind
def extract_to(rows: list, crop=False, min_ratio=MIN_RATIO):
"""Receives a list, reads images and save to dir VAZIO or NVAZIO
Receives a list of MongoDB records. Each line must have _id of the image
and a metadata.carga.vazio boolean field
If crop=True, then the record MUST have a predictions.bbox, and the image
will be croped on predictions[0].bbox
"""
caminho = 'vazios'
if crop:
caminho += '_cropped'
if not os.path.exists(caminho):
os.mkdir(caminho)
for destino in ('VAZIO', 'NVAZIO'):
if not os.path.exists(os.path.join(caminho, destino)):
os.mkdir(os.path.join(caminho, destino))
count = 0
for count, row in enumerate(rows):
_id = row['_id']
vazio = row['metadata']['carga']['vazio']
destino = 'VAZIO' if vazio else 'NVAZIO'
caminho_atual = os.path.join(caminho, destino)
arquivo_atual = os.path.join(caminho_atual, str(_id)) + '.jpg'
if os.path.exists(arquivo_atual):
print(str(_id), ' existe, abortando...')
continue
image = get_image(row, crop, min_ratio)
if image:
image.save(arquivo_atual)
print('%s arquivos exportados...' % count)
return count
def post(self):
try:
parser = reqparse.RequestParser()
parser.add_argument("image", required=True, location=['form', 'args', 'json', 'files'])
args = parser.parse_args(strict=True)
if "FileStorage" in args.image:
parser.replace_argument('image', type=FileStorage, required=True, location='files')
args = parser.parse_args(strict=True)
img, byte_image = get_image(args.image)
img_compressed = compress_image(img, byte_image)
img_compressed.seek(0)
return send_file(
img_compressed,
mimetype='image/jpeg',
as_attachment=True,
attachment_filename="compressed_image.jpg"
)
except ImageException as e:
return {
"status_code": 400,
"message": str(e)
}
except Exception as e:
return {
"status_code": 500,
"message": "Internal Server Error."
}
def spookjoke():
name = random.choice(utils.file_to_list('Sp00k.txt'))
path_name = slugify(name,
word_boundary=True, separator="_")
path = "{0}/{1}".format("spook", path_name)
tweet_image = utils.get_image(path)
name = re.sub(r' \([^)]*\)', '', name)
m = "Oh oh! Looks like your command was stolen by {0}!! #Sp00ky".format(name)
return m, tweet_image
def skip_enemy(self):
print("Jumping over enemy ...")
image = get_image("enemy-mushroom.png", self.emulator)
searching = True;
while(searching):
coordinates = pyautogui.locateCenterOnScreen(image)
if coordinates is not None:
x, y = coordinates[0], coordinates[1]
print(x, y)
searching = False;
def get_image(note_id):
note = None
try:
note = Note.objects.get(pk=note_id)
except ObjectDoesNotExist:
return {RSP_CODE : RC_NO_SUCH_NOTE}
if note.image_path is None:
return {RSP_CODE : RC_NO_IMAGE}
result = {RSP_CODE : RC_SUCESS}
result['image'] = utils.get_image(note.image_path)
return result
def waifu(gender, args="", otp=False, DISCORD=False, user_id=False):
if gender == 0:
list_name = "Waifu"
end_tag = "1girl+solo"
else:
list_name = "Husbando"
end_tag = "-1girl+-female"
result = ""
lines = utils.file_to_list(
os.path.join(settings['list_loc'],
list_name + " List.txt"))
args = ' '.join(args.split()).lower()
matched = []
ignore = ["high-school-dxd", "love-live",
"aoki-hagane-no-arpeggio", "kantai-collection",
"aikatsu", "akb0048", "idolmaster",
"idolmaster-cinderella-girls"]
if len(args) > 4:
for entry in lines:
if slugify(entry[1], word_boundary=True) in ignore:
continue
if slugify(args, word_boundary=True) ==\
slugify(entry[1], word_boundary=True):
matched.append(entry)
# It's not really that random if there isn't that many people matched.
if len(matched) > 4:
result = random.choice(matched)
if not result:
result = random.choice(lines)
name, show, otp_img = result
if otp:
return name, show, otp_img
path_name = slugify(name,
word_boundary=True, separator="_")
path = os.path.join(list_name, path_name)
tweet_image = utils.get_image(path)
if not tweet_image and not DISCORD:
tags = [name.replace(" ", "_"), "solo", "-genderswap", end_tag]
tweet_image = utils.get_image_online(tags, 0, 1,
"", path)
name = re.sub(r' \([^)]*\)', '', name)
m = "Your {0} is {1} ({2})".format(list_name.title(),
name, show)
if user_id:
st = "{} ({})".format(name, show)
count_command(list_name, st,
os.path.join(settings['user_count_loc'], user_id))
count_command(list_name, "{} ({})".format(name, show),
settings['count_file'])
return m, tweet_image
def prepare_data(self):
""" Treat the pictures """
tmp = 'test' if self.test else 'train'
print " ---- Loading "+tmp+" set ----"
for i, img_id in tqdm(enumerate(self.images_id)):
features = get_image(self.path, img_id, width=self.width)
if self.X is None:
self.n_features = features.shape[0]
self.X = np.zeros((self.n_images, self.n_features),
dtype=np.uint8)
if not features.shape[0] == self.n_features:
print "Error on image {}".format(img_id)
self.X[i, :] = features
print "Feature_matrix shape: {}".format(self.X.shape)
def mywaifu(user_id, gender):
if gender == 0:
gender = "Waifu"
filename = "users_waifus.json"
else:
gender = "Husbando"
filename = "users_husbandos.json"
user_waifus_file = open(
os.path.join(settings['list_loc'], filename), 'r',
encoding='utf-8')
user_waifus = json.load(user_waifus_file)
user_waifus_file.close()
for user in user_waifus['users']:
if int(user['twitter_id']) == user_id:
break
if int(user['twitter_id']) != user_id:
count_trigger("mywaifu")
m = ("I don't know who your {0} is!\n"
"Use {1}Register!\n"
"Help: {2}").format(gender, gender,
config_get('Help URLs', 'include_name'))
return m, False
tags = user['name'] + user['tags']
if user.get('max_page'):
max_page = user['max_page']
else:
max_page = 20
path_name = slugify(user['name'],
word_boundary=True, separator="_")
path = os.path.join(settings['image_loc'],
gender.lower(), path_name)
ignore_list = "user_ignore/{0}".format(user['twitter_id'])
tweet_image = utils.get_image_online(tags, user['web_index'],
max_page, ignore_list)
if not tweet_image:
tweet_image = utils.get_image(path, ignore_list)
if not tweet_image:
m = ("Failed to get an image (website could be offline).\n"
"Help: {0}").format(config_get('Help URLs', 'website_offline'))
remove_one_limit(user_id, "my" + gender.lower())
return m, False
if datetime.datetime.now().isoweekday() == 3:
m = "#{0}Wednesday".format(gender)
else:
m = "#{0}AnyDay".format(gender)
return m, tweet_image
def main(argv):
pattern = "/home/ian/imagenet/ILSVRC2012_img_train_t1_t2/n*/*JPEG"
files = glob(pattern)
assert len(files) > 0
assert len(files) > 1000000, len(files)
shuffle(files)
dirs = glob("/home/ian/imagenet/ILSVRC2012_img_train_t1_t2/n*")
assert len(dirs) == 1000, len(dirs)
dirs = [d.split('/')[-1] for d in dirs]
dirs = sorted(dirs)
str_to_int = dict(zip(dirs, range(len(dirs))))
outfile = '/media/NAS_SHARED/imagenet/imagenet_train_labeled_' + str(IMSIZE) + '.tfrecords'
writer = tf.python_io.TFRecordWriter(outfile)
for i, f in enumerate(files):
print i
image = get_image(f, IMSIZE, is_crop=True, resize_w=IMSIZE)
image = colorize(image)
assert image.shape == (IMSIZE, IMSIZE, 3)
image += 1.
image *= (255. / 2.)
image = image.astype('uint8')
#print image.min(), image.max()
# from pylearn2.utils.image import save
# save('foo.png', (image + 1.) / 2.)
image_raw = image.tostring()
class_str = f.split('/')[-2]
label = str_to_int[class_str]
if i % 1 == 0:
print i, '\t',label
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(IMSIZE),
'width': _int64_feature(IMSIZE),
'depth': _int64_feature(3),
'image_raw': _bytes_feature(image_raw),
'label': _int64_feature(label)
}))
writer.write(example.SerializeToString())
writer.close()
def random_list(list_name, args="", DISCORD=False):
gender = "waifu"
hashtag = ""
search_for = ""
m = False
lines = False
show_series = False
scrape_images = True
if list_name == "Shipgirl":
if "aoki" in args:
lines = utils.file_to_list('Shipgirl Aoki.txt')
elif "all" in args:
if "otp" in args:
list_name += " OTP"
lines = utils.file_to_list('Shipgirl All OTP.txt')
else:
lines = utils.file_to_list('Shipgirl Aoki.txt')
lines += utils.file_to_list('Shipgirl.txt')
else:
hashtag = "#Kancolle"
if "otp" in args:
list_name += " OTP"
lines = utils.file_to_list('Shipgirl OTP.txt')
else:
lines = utils.file_to_list('Shipgirl.txt')
elif list_name == "Touhou":
hashtag = "#Touhou"
if "otp" in args:
list_name += " OTP"
lines = utils.file_to_list('Touhou OTP.txt')
else:
lines = utils.file_to_list('Touhou.txt')
elif list_name == "Vocaloid":
hashtag = "#Vocaloids"
if "otp" in args:
list_name += " OTP"
lines = utils.file_to_list('Vocaloid OTP.txt')
else:
lines = utils.file_to_list('Vocaloid.txt')
elif list_name == "Imouto":
list_name = "Imouto"
show_series = True
lines = utils.file_to_list('Imouto.txt')
elif list_name == "Idol":
show_series = True
if "love live" in args or "lovelive" in args:
search_for = "Love Live!"
hashtag = "#LoveLive"
if "otp" in args:
list_name = "Love Live! OTP"
show_series = False
lines = utils.file_to_list('Idol Love Live OTP.txt')
elif "cinderella" in args or "cinderella" in args:
search_for = "Idolmaster Cinderella Girls"
hashtag = "#Idolmaster"
elif "idolmaster" in args or "idolm@ster" in args:
search_for = "Idolmaster"
hashtag = "#Idolmaster"
elif "akb0048" in args:
search_for = "AKB0048"
hashtag = "#akb0048"
elif "wake" in args:
search_for = "Wake Up Girls!"
hashtag = "#WUG_JP"
elif "aikatsu" in args:
search_for = "Aikatsu!"
hashtag = "#Aikatsu"
if "male" in args:
list_name = "Male Idol"
lines = utils.file_to_list('Idol Males.txt')
else:
if not lines:
lines = utils.file_to_list('Idol.txt')
if search_for:
temp_lines = []
for line in lines:
if line[1] == search_for:
temp_lines.append(line)
lines = temp_lines
del temp_lines
elif list_name == "Shota":
show_series = True
gender = "husbando"
lines = utils.file_to_list('Shota.txt')
elif list_name == "Onii":
list_name = "Onii-chan"
show_series = True
gender = "husbando"
lines = utils.file_to_list('Onii-chan.txt')
elif list_name == "Onee":
list_name = "Onee-chan"
show_series = True
lines = utils.file_to_list('Onee-chan.txt')
elif list_name == "Sensei":
show_series = True
if "female" in args:
lines = utils.file_to_list('Sensei Female.txt')
elif "male" in args:
gender = "husbando"
lines = utils.file_to_list('Sensei Male.txt')
else:
lines = utils.file_to_list('Sensei Male.txt')
lines += utils.file_to_list('Sensei Female.txt')
elif list_name == "Senpai":
show_series = True
if "female" in args:
lines = utils.file_to_list('Senpai Female.txt')
elif "male" in args:
gender = "husbando"
lines = utils.file_to_list('Senpai Male.txt')
else:
lines = utils.file_to_list('Senpai Male.txt')
lines += utils.file_to_list('Senpai Female.txt')
elif list_name == "Kouhai":
show_series = True
if "female" in args:
lines = utils.file_to_list('Kouhai Female.txt')
elif "male" in args:
gender = "husbando"
lines = utils.file_to_list('Kouhai Male.txt')
else:
lines = utils.file_to_list('Kouhai Male.txt')
lines += utils.file_to_list('Kouhai Female.txt')
elif list_name == "Monstergirl":
show_series = True
scrape_images = True
lines = utils.file_to_list('Monstergirl.txt')
elif list_name == "Witchgirl":
hashtag = "#s_witch"
show_series = False
scrape_images = True
lines = utils.file_to_list('Witchgirl.txt')
elif list_name == "Tankgirl":
hashtag = "#garupan"
show_series = False
scrape_images = True
lines = utils.file_to_list('Tankgirl.txt')
# Under heavy stuff random.choice can be very weak
# so just a quick way to make sure it's 'random random'
random.shuffle(lines)
entry = random.choice(lines)
if list_name.endswith("OTP"):
names = entry.split("(x)")
if list_name == "Touhou":
tags = "{0}+{1}+2girls+yuri+touhou+-asai_genji+-comic".format(
names[0].replace(" ", "_"),
names[1].replace(" ", "_"))
if "love live" in list_name.lower():
tags = "{0}+{1}+2girls+yuri+-comic".format(
names[0].replace(" ", "_"),
names[1].replace(" ", "_"))
else:
tags = "{0}+{1}+yuri+2girls+-comic".format(
names[0].replace(" ", "_"),
names[1].replace(" ", "_"))
name = "{0}(x){1}".format(names[0], names[1])
else:
if isinstance(entry, list):
name = entry[0]
show = entry[1]
else:
name = entry
if scrape_images:
tags = "{0}+solo".format(name.replace(" ", "_"))
path_name = slugify(name,
word_boundary=True, separator="_")
path = "{0}/{1}".format(gender.lower(), path_name)
tweet_image = utils.get_image(path)
if scrape_images and not DISCORD or not tweet_image and not DISCORD:
tweet_image_temp = utils.get_image_online(tags, 0, 1, "", path)
if tweet_image_temp is not False:
tweet_image = tweet_image_temp
name = re.sub(r' \([^)]*\)', '', name)
hashtag = "" # TODO: Temp (testing with twitter)
if show_series:
m = "Your {0} is {1} ({2}) {3}".format(list_name, name,
show, hashtag)
elif list_name.endswith("OTP"):
name_one = re.sub(r' \([^)]*\)', '', names[0])
name_two = re.sub(r' \([^)]*\)', '', names[1])
name = "{0} x {1}".format(name_one, name_two)
if not m:
m = "Your {0} is {1} {2}".format(list_name, name, hashtag)
if not list_name.endswith("OTP"):
count_trigger(name, gender)
return m, tweet_image
def mywaifu(user_id, gender, DISCORD=False, SKIP_DUP_CHECK=False):
tweet_image = False
if gender == 0:
gender = "Waifu"
filename = "users_waifus.json"
else:
gender = "Husbando"
filename = "users_husbandos.json"
user_waifus_file = open(
os.path.join(settings['list_loc'], filename), 'r',
encoding='utf-8')
user_waifus = json.load(user_waifus_file)
user_waifus_file.close()
for user in user_waifus['users']:
if int(user['twitter_id']) == int(user_id):
break
if int(user['twitter_id']) != int(user_id):
count_trigger("mywaifu")
m = ("I don't know who your {0} is!\n"
"Use {1}Register!\n"
"Help: {2}").format(gender, gender,
config_get('Help URLs', 'include_name'))
return m, False
tags = user['name'] + user['tags']
if user.get('max_page'):
max_page = user['max_page']
else:
max_page = 20
path_name = slugify(user['name'],
word_boundary=True, separator="_")
path = os.path.join(settings['image_loc'],
gender.lower(), path_name)
if DISCORD:
ignore_list = "user_ignore/discord_{0}".format(user['twitter_id'])
tweet_image = utils.get_image(path, ignore_list)
else:
ignore_list = "user_ignore/{0}".format(user['twitter_id'])
if not DISCORD:
tweet_image = utils.get_image_online(tags, user['web_index'],
max_page, ignore_list,
path=path)
if not tweet_image:
tweet_image = utils.get_image(path, ignore_list)
if not tweet_image and SKIP_DUP_CHECK:
tweet_image = utils.get_image(path)
if not tweet_image:
m = ("Failed to grab a new image!\nThe main image website could be offline.\n"
"Help: {0}").format(config_get('Help URLs', 'website_offline'))
remove_one_limit(user_id, "my" + gender.lower())
return m, False
if datetime.datetime.now().isoweekday() == 3:
# TODO: Remember hashtags here
m = "{0}Wednesday".format(gender)
else:
m = "{0}AnyDay".format(gender)
if DISCORD:
# @user's x is x
m = " {gender} is {name}!".format(
gender=gender, name=user['name'].replace("_", " ").title())
return m, tweet_image
def set_image(self, filename):
image = utils.get_image(filename, (64, 64))
self.button.set_image(image)
""" The theme starts here """
#ui.Entry.font_desc = pango.FontDescription('Danube 8')
#ui.Label.font_desc = pango.FontDescription('Danube 8')
@override(ui.Label)
def do_render(self):
"""the label is looking for an background_image attribute and if it is found
it paints it"""
if getattr(self, "background_image", None):
self.background_image.render(self.graphics, self.width, self.height)
ui.Button.images = {
"normal": get_image("themes/assets/button_normal.png", 3, 3, 3, 3),
"highlight": get_image("themes/assets/button_highlight.png", 3, 3, 3, 3),
"pressed": get_image("themes/assets/button_pressed.png", 3, 3, 3, 3),
"disabled": get_image("themes/assets/button_disabled.png", 3, 3, 3, 3),
}
ui.Button.font_desc = pango.FontDescription('Serif 10')
@override(ui.Button)
def do_render(self, state=None):
""" Properties that affect rendering:
state: normal / highlight / pressed
focused: True / False
enabled: True / False
"""
state = state or self.state
def __init__ (self, debug_level=0, args=[], optlist=[]) :
ChallengeBase.__init__(self, debug_level)
self.message ="""
usage: %s [-h] [-v] [-d] [ -k key-key ] [ -c cert ] [ -i image-id ]\
[ -f flavor-id ] -n FQDN-name
image-id -f flavor-id
-h - usage help
-v - verbose / debug output
-d - delete objects if they existed in cloud before creating new one
-k - certificate pritate key (see -c below)
-c - public certificate ( see -k above)
-n - FQDN name like www.myexample.com
-i - specify image-id or use the default for Ubuntu 10.04
-f - specify flavor-id or use the default for the smallest cloud server
""" % (PROGRAM_NAME)
self.cert_use_default = False
self.cert_key_name = None
self.cert_key = None
self.cert_name = None
self.cert = None
self.opt_delete = False
self.fqdn_name = None
self.domain_name = None
self.domain = None
self.recs = None
self.lb=None
self.lb_name = None
self.vip=None
self.vip_address = None
self.nodes=[]
self.image = None
self.image_id = None
self.flavor = None
self.flavor_id = None
self.server_prefix=None
self.cs_count=3
self.server_passord = "SecretP@ss1"
self.servers = []
self.storage_name = None
self.volumes=[]
self.network_name = None
self.network_range = "192.168.100.0/24"
self.network = None
self.network_id = None
for o, val in optlist:
if o == "-n":
self.fqdn_name = val
self.domain_name = ".".join( self.fqdn_name.split('.')[1:] )
self.domain=None
self.recs = None
elif o == "-d":
self.opt_delete = True
elif o == "-i":
self.image_id = val
elif o == "-f":
self.flavor_id = val
elif o == "-k":
self.cert_key_name = val
elif o == "-c":
self.cert_name = val
elif o == "-i":
self.image_id = val
elif o == "-f":
self.flavor_id = val
if not self.fqdn_name :
self.usage()
sys.exit(-1)
#default values for some variables
self.image = None
if not self.image_id :
self.image_id = utils.get_image(self.cs)
self.flavor= None
if not self.flavor_id :
self.flavor_id = utils.get_flavor(self.cs)
self.lb_name = self.fqdn_name
self.storage_name = self.fqdn_name
self.server_prefix = self.fqdn_name
self.network_name = self.fqdn_name
if not self.cert_key_name and not self.cert_name :
self.cert_use_default = True
self.cert_key_name = self.fqdn_name + ".key"
self.cert_key="""-----BEGIN RSA PRIVATE KEY-----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-----END RSA PRIVATE KEY-----
"""
self.cert_name = self.fqdn_name + ".crt"
self.cert = """-----BEGIN CERTIFICATE-----
def __init__ (self, debug_level=0, args=[], optlist=[]) :
ChallengeBase.__init__(self, debug_level)
self.message ="""
usage: %s [-h] [-v] [-d] [ -s ssh-key ] [ -e error-page ] [ -c container-name ] [ -i image-id ] [ -f flavor-id ] -n FQDN-name
image-id -f flavor-id
-h - usage help
-v - verbose / debug output
-d - delete objects if they existed in cloud before creating new one
-n - FQDN name like www.myexample.com
-s - path to your ssh public key (not priv!)
-e - path to a html file that will be served from the LB when all pool members are down
-c - name of cloud files container-name to store the backup data
-i - specify image-id or use the default for Ubuntu 10.04
-f - specify flavor-id or use the default for the smallest cloud server
""" % (PROGRAM_NAME)
self.optlist = optlist
self.args = args
self.opt_delete = False
self.fqdn_name = None
self.domain_name = None
self.domain = None
self.recs = None
self.ssh_public_key_name = None
self.ssh_public_key = None
self.error_page_name = None
self.error_page_path = None
self.error_page = None
self.container_name = None
self.container = None
self.image = None
self.image_id = None
self.flavor = None
self.flavor_id = None
self.server_prefix=None
self.cs_count=2
self.servers = []
self.lb=None
self.lb_name =None
self.vip=None
self.vip_address = None
self.nodes=[]
for o, val in optlist:
if o == "-n":
self.fqdn_name = val
self.domain_name = ".".join( self.fqdn_name.split('.')[1:] )
self.domain=None
self.recs = None
elif o == "-s":
self.ssh_public_key_name = val
elif o == "-e":
self.error_page_path = val
elif o == "-d":
self.opt_delete = True
elif o == "-c":
self.container_name = val
elif o == "-i":
self.image_id = val
elif o == "-f":
self.flavor_id = val
if not self.fqdn_name :
self.usage()
sys.exit(-1)
#default values for some variables
if not self.container_name :
self.container_name = self.fqdn_name
self.image = None
if not self.image_id :
self.image_id = utils.get_image(self.cs)
self.flavor= None
if not self.flavor_id :
self.flavor_id = utils.get_flavor(self.cs)
self.lb_name = self.fqdn_name
self.server_prefix = self.fqdn_name
