choowilson

package global.skymind.training.segmentation.car;

import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;

choowilson

import cv2
import face_alignment
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from skimage import io
import collections
from timeit import default_timer as timer

# Run the 3D face alignment on a test image, without CUDA.
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cpu', flip_input=True, face_detector='sfd')

choowilson

def thresh_callback(val):
    threshold = val

    _,thres_out = cv.threshold(src_gray, 230, 255,  
                            cv.THRESH_BINARY) 
    canny_output = cv.Canny(thres_out, threshold, threshold * 2)
    
    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
    

choowilson

import org.datavec.image.loader.NativeImageLoader;
import org.nd4j.linalg.api.ndarray.INDArray;
import static org.bytedeco.opencv.global.opencv_highgui.*;

NativeImageLoader nil = new NativeImageLoader(416,416, 3);
INDArray image = nil.asMatrix("C:\\Users\\choowilson\\Desktop\\potato.jpg").div(255.0);
Mat mat = nil.asMat(image);
imshow("Output",mat);
waitKey(0);
destroyAllWindows();

choowilson

import org.bytedeco.ffmpeg.global.avcodec;
import org.bytedeco.ffmpeg.global.avutil;
import org.bytedeco.javacv.*;

import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.helper.opencv_core.RGB;

import org.bytedeco.opencv.opencv_videoio.VideoWriter;
import org.datavec.image.loader.NativeImageLoader;

choowilson

import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;  

public void initKeras() throws InvalidKerasConfigurationException, IOException, UnsupportedKerasConfigurationException {
      ComputationGraph model = KerasModelImport.importKerasModelAndWeights("model.h5");
}

choowilson

import java.awt.event.KeyEvent;
import java.awt.event.KeyListener;


public class MyKeylistener implements KeyListener {
    @Override
    public void keyPressed(KeyEvent keyEvent) {
        if (keyEvent.getKeyCode() == KeyEvent.VK_ENTER){
            System.out.println("ENTER Pressed");
        }

choowilson

import net.schmizz.sshj.SSHClient;
import net.schmizz.sshj.connection.channel.direct.Session;
import net.schmizz.sshj.transport.verification.PromiscuousVerifier;

import java.io.IOException;

public class sendCommand {
    public static void main(String[] args) throws IOException {
        final SSHClient ssh = new SSHClient();
        ssh.addHostKeyVerifier(new PromiscuousVerifier());

choowilson

import net.schmizz.sshj.SSHClient;
import net.schmizz.sshj.connection.channel.direct.Session;
import net.schmizz.sshj.transport.verification.PromiscuousVerifier;

import java.io.IOException;

public class sendCommand {
    public static void main(String[] args) throws IOException {
        final SSHClient ssh = new SSHClient();
        ssh.addHostKeyVerifier(new PromiscuousVerifier());

choowilson

import RPi.GPIO as GPIO
import time

GPIO.setmode(GPIO.BOARD) # Raspi board GPIO Pin Number

GPIO.setup(11, GPIO.OUT) # Output pin is GPIO Pin 17

# Initial state for LEDs:
print("Testing GPIO Pin11 out, Press CTRL+C to exit")