tomrunia

# MIT License
#
# Copyright (c) 2017 Tom Runia
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to conditions.

tomrunia

    # Initialize the camera
    camera = bpy.data.cameras.new('Camera')
    camera_obj = bpy.data.objects.new('Camera', camera)
    scene.objects.link(camera_obj)

    scene.objects.active = camera_obj
    scene.camera = camera_obj
    camera_obj.select = True

    camera_obj.location = (0, 0, 0)

tomrunia

def decode(serialized_example, sess):
    '''
    Given a serialized example in which the frames are stored as
    compressed JPG images 'frames/0001', 'frames/0002' etc., this
    function samples SEQ_NUM_FRAMES from the frame list, decodes them from
    JPG into a tensor and packs them to obtain a tensor of shape (N,H,W,3).
    Returns the the tuple (frames, class_label (tf.int64)

    :param serialized_example: serialized example from tf.data.TFRecordDataset
    :return: tuple: (frames (tf.uint8), class_label (tf.int64)

tomrunia

Block	Elements	Kernel Size	Filter depth	Output depth	Stride	Misc. Info
	Convolution	[5, 5]	3	64	[1, 1]
conv1	ReLU	-	-	-	-
	Max-pool	[3, 3]	-	-	[2, 2]
	Convolution	[5, 5]	64	64	[1, 1]
conv2	ReLU	-	-	-	-
	Max-pool	[3, 3]	-	-	[2, 2]

tomrunia

# Initialize placeholders for feeding in to the queue
self.queue_inputs       = tf.placeholder(tf.float32, shape=[None, self.config.seq_length, self.config.image_size, self.config.image_size], name="queue_inputs")
self.queue_targets      = tf.placeholder(tf.uint8, shape=[None, self.config.seq_length], name="queue_targets")

min_after_dequeue = 10000
capacity = min_after_dequeue + 3 * self.config.batch_size

q = tf.FIFOQueue(

tomrunia

def load_and_enqueue(input_dir, sess, coord, enqueue_op, queue_inputs, queue_targets, num_examples, examples_per_file=100, rewrite_targets=True):

    # Check if we have a sufficient number of HDF5 files to load all the samples
    filenames_queue = glob.glob(os.path.join(input_dir, "train/*.h5"))
    filenames_queue.sort()
    assert len(filenames_queue) > 0

    examples_available = len(filenames_queue)*examples_per_file
    num_examples = min(examples_available, num_examples)

tomrunia

require 'image'
require 'lfs'
require 'cunn'
require 'nngraph'

function segment(model, flow_mag_ang_file, minmax_file, output_file)

    local file = io.open(minmax_file)
    local minmaxes = {}
    local ind = 1;

tomrunia

filename_queue = tf.FIFOQueue(100000, [tf.string], shapes=[[]])

# ...

reader = tf.WholeFileReader()
image_filename, image_raw = reader.read(self._filename_queue)
image = tf.image.decode_jpeg(image_raw, channels=3)

# Image preprocessing
image_preproc = ...

tomrunia

import time

import numpy as np
import matplotlib.pyplot as plt
from Blockchain_test import return_data

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM

tomrunia

import time

import numpy as np
import matplotlib.pyplot as plt
from Blockchain_test import return_data

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM