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kakittwo / led.ino
Last active November 23, 2018 18:06
led.ino
int n = 4;
int sum =0 ;
void setup() {
for (int i = 0; i < n; i++) {
pinMode(10+ i, OUTPUT);
}
@kakittwo
kakittwo / classify.py
Created November 3, 2018 17:02
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 120, 120
train_data_dir = '/home/kakitone/Desktop/kinect/data/train4/'
validation_data_dir = '/home/kakitone/Desktop/kinect/data/test4/'
nb_train_samples = 2000
@kakittwo
kakittwo / model.py
Created October 22, 2018 15:37
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from keras.models import load_model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
@kakittwo
kakittwo / model.py
Created October 22, 2018 01:06
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
@kakittwo
kakittwo / classification.py
Created October 21, 2018 23:44
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
@kakittwo
kakittwo / classification.py
Created October 21, 2018 23:43
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
@kakittwo
kakittwo / index.html
Created October 6, 2018 16:47
<html>
<head>
<title>
Speech recognition system
</title>
<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.7.1/jquery.min.js"></script>
<script>
@kakittwo
kakittwo / alignment.py
Created October 6, 2018 16:44
def decodeSpeechData(aircraftListMod, wordList):
'''
Example input
aircraftListMod = [['November', 'Eight', 'Tree', 'Two', 'One', 'Mike'], ['Juliet', 'Bravo', 'Uniform', 'Six', 'One', 'Six']]
wordList = ['November', 'Eight', 'Tree', 'Two', 'One', 'Mike', 'Monitoring']
'''
aircraft_name = []
speech = []
bestMatchScore = -1
@kakittwo
kakittwo / speech.py
Last active October 6, 2018 16:38
def formWordList():
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
import argparse
import io
speech_file = "/home/kakitone/Desktop/googlemap/final.wav"
client = speech.SpeechClient()
@kakittwo
kakittwo / gist:768e5477a5834ee84624205659e4d415
Last active August 26, 2018 14:49
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
### Subroutine to calculate likelihood for hummingbird in an image
def calculateLikelihood(x):
x = np.expand_dims(x, axis=0)