kentsommer

read -p 'Ethernet device to use (can find with ifconfig): ' ethdev

sudo tcpdump -i $ethdev port 2368 -n | awk '{ print gensub(/(.*)\..*/,"\\1","g",$3), $4, gensub(/(.*)\..*/,"\\1","g",$5) }' | cut -d ' ' -f1

kentsommer

Keybase proof

I hereby claim:

• I am kentsommer on github.
• I am kentsommer (https://keybase.io/kentsommer) on keybase.
• I have a public key ASDq1kE3OCWdypuzymWKSK1Gx696Q1oK7TfJN2RlaJzoWAo

To claim this, I am signing this object:

kentsommer

import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.nn.parameter import Parameter

# Pool and Inject Module

kentsommer

#include <pcl/io/pcd_io.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/surface/convex_hull.h>
#include <pcl/segmentation/extract_polygonal_prism_data.h>
#include <pcl/visualization/cloud_viewer.h>

#include <iostream>

kentsommer

cmake_minimum_required(VERSION 2.6 FATAL_ERROR)
project(TABLETOP_EXTRACTION)
find_package(PCL REQUIRED)
include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
add_executable(extract extract.cpp)
target_link_libraries(extract ${PCL_LIBRARIES})

kentsommer

from keras.applications.inception_v3 import *
from keras.preprocessing import image
from keras.applications.imagenet_utils import decode_predictions
from keras import backend as K
import numpy as np

model = InceptionV3(weights='imagenet')

img_path = 'apple.jpg'
img = image.load_img(img_path, target_size=(299, 299))

kentsommer

	csv_path = "submission_kent.csv"
	csv_data = []
	csv_data.append(["image_name", "Type_1", "Type_2", "Type_3"])

	for index, prediction in enumerate(result):
		img_name = data_names[index]
		type_1 = '{0:.15f}'.format(prediction[0])
		type_2 = '{0:.15f}'.format(prediction[1])
		type_3 = '{0:.15f}'.format(prediction[2])
		line = [img_name, type_1, type_2, type_3]

kentsommer

import os
import cv2
import numpy as np
from glob import glob
from tqdm import tqdm
from keras.preprocessing import image
from keras.applications.inception_v3 import preprocess_input


def preprocess_input(x):

kentsommer

magic_number_1 = size_of_last_conv_layer
magic_number_2 = number_of_rows_in_dense / magic_number_1

def shuffle_rows(original_w):
	converted_w = np.zeros(original_w.shape)
	count = 0
	for index, row in enumerate(original_w):
		if (index % magic_number_1) == 0 and index != 0:
			count += 1
		new_index = ((index % magic_number_1) * magic_number_2) + count