def __init__(self):
classify_image.maybe_download_and_extract() # what does it do?
self._session = tf.Session()
classify_image.create_graph()
self._cv_bridge = CvBridge() # what does this do?
self._sub = rospy.Subscriber('image', Image, self.callback, queue_size=1) # callback is the argument if we callback or # NOTE:
self._pub = rospy.Publisher('result', String, queue_size=1)
self.score_threshold = rospy.get_param('~score_threshold', 0.1) # ~ means private name
self.use_top_k = rospy.get_param('~use_top_k', 5)
def __init__(self):
classify_image.maybe_download_and_extract()
self._session = tf.Session()
classify_image.create_graph()
self._cv_bridge = CvBridge()
self._sub = rospy.Subscriber('image', Image, self.callback, queue_size=1)
self._pub = rospy.Publisher('result', String, queue_size=1)
self.score_threshold = rospy.get_param('~score_threshold', 0.1)
self.use_top_k = rospy.get_param('~use_top_k', 5)
def __init__(self):
classify_image.maybe_download_and_extract()
self._session = tf.Session()
classify_image.create_graph()
self._cv_bridge = CvBridge()
self._sub = rospy.Subscriber('image',
Image,
self.callback,
queue_size=1)
self._pub = rospy.Publisher('result', String, queue_size=1)
self.score_threshold = rospy.get_param('~score_threshold', 0.1)
self.use_top_k = rospy.get_param('~use_top_k', 5)
def __init__(self):
#构造函数有个API可从tensorflow.org下载训练好的inception-v3
classify_image.maybe_download_and_extract()
#创建一个session对象
self._session = tf.Session()
classify_image.create_graph()
#为opencv图像转换创建一个cv_bridge
self._cv_bridge = CvBridge()
#节点的发布者和订阅着
self._sub = rospy.Subscriber('image',
Image,
self.callback,
queue_size=1)
self._pub = rospy.Publisher('result', String, queue_size=1)
self.score_threshold = rospy.get_param('~score_threshold', 0.1)
self.use_top_k = rospy.get_param('~use_top_k', 5)
def main(argv=None):
classify_image.maybe_download_and_extract()
classify_image.create_graph()
basedir = os.path.dirname(__file__)
with tf.Session() as sess:
data = np.loadtxt('embedding.tsv')
embedding_var = tf.Variable(data, name='embedding_var')
# prepare projector config
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name
summary_writer = tf.train.SummaryWriter(os.path.join(
basedir, 'logdir'))
# link metadata
#metadata_path = os.path.join(basedir, 'logdir', 'metadata.tsv')
#with open(metadata_path, 'w') as f:
# for name in files:
# f.write('%s\n' % name)
#embedding.metadata_path = metadata_path
# write to sprite image file
image_path = os.path.join(basedir, 'logdir', 'sprite.jpg')
#size = int(math.sqrt(len(images))) + 1
#while len(images) < size * size:
# images.append(np.zeros((100, 100, 3), dtype=np.uint8))
#rows = []
#for i in range(size):
# rows.append(tf.concat(1, images[i*size:(i+1)*size]))
#jpeg = tf.image.encode_jpeg(tf.concat(0, rows))
#with open(image_path, 'wb') as f:
# f.write(sess.run(jpeg))
embedding.sprite.image_path = image_path
embedding.sprite.single_image_dim.extend([64, 64])
# save embedding_var
projector.visualize_embeddings(summary_writer, config)
sess.run(tf.variables_initializer([embedding_var]))
saver = tf.train.Saver([embedding_var])
saver.save(sess, os.path.join(basedir, 'logdir', 'model.ckpt'))
def run_top_k_predictions_on_image(image):
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
# Download the incepion-v3 model
ic.maybe_download_and_extract()
# Creates graph from saved GraphDef.
ic.create_graph()
with tf.Session() as sess:
# Convert the PNG image to a height x width x 3 (channels) Numpy array,
# for example using PIL, then feed the 'DecodeJpeg:0' tensor:
image_content = Image.open(image)
image_array = np.array(image_content)[:, :, 0:3] # Select RGB channels only.
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
# 1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
# float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor, {'DecodeJpeg:0': image_array})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = ic.NodeLookup()
top_k = predictions.argsort()[-ic.FLAGS.num_top_predictions:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
def __init__(self,
model=None,
type_='segmentation',
img_shape=None,
input_tensor=None):
classify_image.maybe_download_and_extract()
self._session = tf.Session()
self.tensor_2_run = model
init = tf.initialize_all_variables()
self._session.run(init)
#classify_image.create_graph()
self._cv_bridge = CvBridge()
self.input_tensor = input_tensor
self.img_shape = img_shape
self.type_ = type_
self._sub = rospy.Subscriber('image',
Image,
self.callback,
queue_size=1)
print('found camera')
self._pub = rospy.Publisher('result', String, queue_size=1)
print('setup publishing')
self.count = 0
print("len : " + str(len(recv_data)) + " total_len : " + str(length))
if len(recv_data) == length:
break
print("end while")
arr = np.array(recv_data)
bytearr = arr.tobytes()
print("recv_data length : " + str(len(recv_data)))
image = Image.open(io.BytesIO(bytearr))
image.save('python.jpg')
#server code
classify_image.maybe_download_and_extract()
session = tf.Session()
classify_image.create_graph()
host = "192.168.0.2"
port = 12345
buffer_size = 1024
score_threshold = 0.1
use_top_k = 5
s = socket.socket()
try:
s.bind((host, port))
s.listen(5)
def main(argv=None):
classify_image.maybe_download_and_extract()
classify_image.create_graph()
basedir = os.path.dirname(__file__)
with tf.Session() as sess:
pool3 = sess.graph.get_tensor_by_name('pool_3:0')
jpeg_data = tf.placeholder(tf.string)
thumbnail = tf.cast(
tf.image.resize_images(tf.image.decode_jpeg(jpeg_data),
[100, 100]), tf.uint8)
outputs = []
files = []
images = []
for filename in os.listdir('images'):
if not filename.endswith('.JPG'):
continue
print('process %s...' % filename)
files.append(filename)
with open(os.path.join(basedir, 'images', filename), 'rb') as f:
data = f.read()
results = sess.run([pool3, thumbnail], {
'DecodeJpeg/contents:0': data,
jpeg_data: data
})
outputs.append(results[0])
images.append(results[1])
embedding_var = tf.Variable(tf.pack([tf.squeeze(x) for x in outputs],
axis=0),
trainable=False,
name='pool3')
# prepare projector config
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name
summary_writer = tf.train.SummaryWriter(os.path.join(
basedir, 'logdir'))
# link metadata
metadata_path = os.path.join(basedir, 'logdir', 'metadata.tsv')
with open(metadata_path, 'w') as f:
for name in files:
f.write('%s\n' % name)
embedding.metadata_path = metadata_path
# write to sprite image file
image_path = os.path.join(basedir, 'logdir', 'sprite.jpg')
size = int(math.sqrt(len(images))) + 1
while len(images) < size * size:
images.append(np.zeros((100, 100, 3), dtype=np.uint8))
rows = []
for i in range(size):
rows.append(tf.concat(1, images[i * size:(i + 1) * size]))
jpeg = tf.image.encode_jpeg(tf.concat(0, rows))
with open(image_path, 'wb') as f:
f.write(sess.run(jpeg))
embedding.sprite.image_path = image_path
embedding.sprite.single_image_dim.extend([100, 100])
# save embedding_var
projector.visualize_embeddings(summary_writer, config)
sess.run(tf.variables_initializer([embedding_var]))
saver = tf.train.Saver([embedding_var])
saver.save(sess, os.path.join(basedir, 'logdir', 'model.ckpt'))
def _load_graph(self):
classify_image.maybe_download_and_extract()
classify_image.create_graph()
