The problem is while using the camera and passing frames by ImageStream,
I keep getting src->data == nullptr from the c++ code (OpenCV) while invoking the imdecode function. How come?
I have debugged the issue and I have found out in the getByte function,
I get:
img.height = 480
img.width = 720
In my opinion each plane should be of length 345600 (480*720)
but I got:
img.planes[0].bytes.length = 368592
img.planes[1].bytes.length = 184271
img.planes[2].bytes.length = 184271
How can it be the image's planes don't have the same length??
The following block is from the flutter app:
initCamera() {
cameraController = CameraController(cameras[0], ResolutionPreset.medium);
cameraController.initialize().then((value) {
if (!mounted) return;
setState(() {
cameraController.startImageStream((imageStream) {
cameraImage = imageStream;
print("Frame number: $frameCounter");
frameCounter++;
res = imageMetaData.applyConvertToMatrix(cameraImage);
// runModel();
});
});
});
}
Calling the c++ code by ffi:
Uint8List getBytes(CameraImage img) {
final WriteBuffer allBytes = WriteBuffer();
//Write all the values from a Uint8List into the buffer.
img.planes.forEach((plane) => allBytes.putUint8List(plane.bytes));
return allBytes.done().buffer.asUint8List();
}
Pointer<Uint8> intListToArray(Uint8List list) {
final Pointer<Uint8> ptr = calloc.allocate<Uint8>(list.length);
for (var i = 0; i < list.length; i++) {
ptr.elementAt(i).value = list[i];
}
return ptr;
}
Future<bool> applyConvertToMatrix(CameraImage img) async {
func_convert_to_matrix = nativeAddLib.lookup<NativeFunction<Pointer<Void> Function(
Pointer<Uint8>, Pointer<Int32>)>>("convert_from_img_array_to_matrix")
.asFunction();
Uint8List bytes = getBytes(img);
Pointer<void> ptr;
Pointer<Uint8> imgBytes;
imgByteLength.value = bytes.length;
imgBytes = intListToArray(bytes);
ptr = func_convert_to_matrix(imgBytes, imgByteLength); //invoking the c++ code
return true;
}
The C++ code:
void *convert_from_img_array_to_matrix(
unsigned char *img,
int32_t *imgLengthBytes) {
cv::Mat *src = new Mat();
int32_t len_bytes = *imgLengthBytes;
std::vector<unsigned char> m;
__android_log_print(ANDROID_LOG_VERBOSE, "NATIVE", "Len bytes: %d", len_bytes);
while ( len_bytes>=0 )
{
m.push_back(*(img++));
len_bytes--;
}
*src = cv::imdecode(m, cv::IMREAD_UNCHANGED);
if (src->data == nullptr)
{
__android_log_print(ANDROID_LOG_VERBOSE, "NATIVE", "You reached nullptr");
return nullptr;
}
if (DEBUG_NATIVE)
__android_log_print(ANDROID_LOG_VERBOSE, "NATIVE", "convert_from_img_array_to_matrix() --- len before:%d len after:%d width:%d height:%d",
*imgLengthBytes, src->step[0] * src->rows,
src->cols, src->rows);
*imgLengthBytes = src->step[0] * src->rows;
return src;
}
Can someone suggest what am I doing wrong?
Noob mistake I was only referencing the parents brain from the child's instead of copying it directly. It's a neural network that can grow in size (or shrink) through genetics. I was going to post it but it doesn't fit on this without me having to type a larger ratio of words to code oh look it works now
public class Animal1 : MonoBehaviour
{
public class Neuron1
{
public int[] children;
public float[] weights;
public float bias;
public float value;
public Neuron1()
{
children = new int[10];
for (int i = 0; i < 10; i++)
{
children[i] = -1;
}
weights = new float[10];
for (int i = 0; i < 10; i++)
{
weights[i] = 1;
}
bias = 0;
value = 0;
}
}
float sightRange = 2;
Vector2 left = new Vector2(-.4f,.6f);
Vector2 right = new Vector2(.4f, .6f);
RaycastHit2D hitLeft;
RaycastHit2D hitForward;
RaycastHit2D hitRight;
public LayerMask animalLayerMask;
[System.NonSerialized]
public int food = 0;
int foodCounter = 0;
int foodForReproduction = 3;
float noFoodCounter = 0;
int lifeWithoutFood = 20;
int maxBrainSize = 100;
[System.NonSerialized]
public Neuron1[] brain1 = new Neuron1[100]; //first three and last three are inputs and outputs and are never mutated
int childrenMaxAmount = 5;
float biasInitRange = 2;
float weightInitRange = 2;
int outputSize = 2;
int inputSize = 3;
GameObject manager;
[System.NonSerialized]
public bool firstGen = false;
[System.NonSerialized]
public GameObject parent1;
[System.NonSerialized]
public bool directCopy = false;
float maxTravelRange = 5;
Color color;
float colorMutationRate = .1f;
public GameObject animal1Prefab1;
bool debugBool = false;
void Start()
{
manager = GameObject.Find("Manager Object");
if (firstGen == true)
{
InitializeBrain(3, 2, 20);
firstGen = false;
transform.GetComponent<SpriteRenderer>().color = new Color(Random.Range(0f, 1f), Random.Range(0f, 1f), Random.Range(0f, 1f), 1);
}
else
{
if (parent1.GetComponent<Animal1>().brain1[0].bias == 1234)
{
Debug.Log("parent passing brain after deletion");
}
Mutate(parent1, 5, 1f, .5f);
transform.GetComponent<SpriteRenderer>().color = color;
}
}
void Update()
{
if (brain1[0].bias == 1234)
{
GetComponent<SpriteRenderer>().color = Color.black;
}
//teleportation
if (transform.position.x > maxTravelRange)
{
Vector2 newPosition = new Vector2(-maxTravelRange, transform.position.y);
transform.position = newPosition;
}
if (transform.position.x < -maxTravelRange)
{
Vector2 newPosition = new Vector2(maxTravelRange, transform.position.y);
transform.position = newPosition;
}
if (transform.position.y > maxTravelRange)
{
Vector2 newPosition = new Vector2(transform.position.x, -maxTravelRange);
transform.position = newPosition;
}
if (transform.position.y < -maxTravelRange)
{
Vector2 newPosition = new Vector2(transform.position.x, maxTravelRange);
transform.position = newPosition;
}
//input
hitLeft = Physics2D.Raycast(transform.position, transform.TransformDirection(left), sightRange, ~animalLayerMask); //left
hitForward = Physics2D.Raycast(transform.position, transform.TransformDirection(Vector2.up), sightRange, ~animalLayerMask); //forward
hitRight = Physics2D.Raycast(transform.position, transform.TransformDirection(right), sightRange, ~animalLayerMask); //right
if (hitLeft)
{
brain1[0].value = 1;
}
if (hitForward)
{
brain1[1].value = 1;
}
if (hitRight)
{
brain1[2].value = 1;
}
//output
Think();
// one output for speed, another for turning
int rotDir = 1;
if (brain1[maxBrainSize - 1].value < 0)
{
rotDir = -1;
}
Vector3 eulerRotation = new Vector3(0, 0, (45 * rotDir + (transform.rotation.eulerAngles.z % 360)) % 360);
transform.rotation = Quaternion.RotateTowards(transform.rotation, Quaternion.Euler(eulerRotation), Mathf.Abs(brain1[maxBrainSize - 1].value) * 25 * Time.deltaTime);
if (brain1[maxBrainSize - 2].value > 0) // so they can't go backwards
{
transform.Translate(Vector2.up * (brain1[maxBrainSize - 2].value * .1f) * Time.deltaTime);
}
//reset outputs
brain1[maxBrainSize - 2].value = 0;
brain1[maxBrainSize - 1].value = 0;
// reset inputs
brain1[0].value = 0;
brain1[1].value = 0;
brain1[2].value = 0;
noFoodCounter += Time.deltaTime;
if (food > 0)
{
foodCounter++;
food = 0;
noFoodCounter = 0;
}
if (foodCounter > foodForReproduction)
{
foodCounter = 0;
Reproduce();
}
if (noFoodCounter > lifeWithoutFood/2)
{
foodCounter = 0;
}
if (noFoodCounter > lifeWithoutFood)
{
Destroy(gameObject);
}
}
void InitializeBrain(int inputs, int outputs, int sizeExcludingInputsAndOutputs)
{
for (int i = 0; i < sizeExcludingInputsAndOutputs + inputs; i++)
{
brain1[i] = new Neuron1();
}
for (int i = maxBrainSize - outputs; i < maxBrainSize; i++) // outputs
{
brain1[i] = new Neuron1();
}
// all neurons in between the inputs and outputs
for (int i = inputs; i < sizeExcludingInputsAndOutputs + inputs; i++)
{
brain1[i].bias = Random.Range(-biasInitRange, biasInitRange);
for (short j = 0; j < childrenMaxAmount -1; j++) // -1 so there is always at least one free space for a child
{
int rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
bool alreadyContains = true;
int errorCounter = 0;
while (alreadyContains) // making sure none are children of themselves, and that it doesn't already have this child
{
alreadyContains = false;
for (short h = 0; h < childrenMaxAmount; h++)
{
if (brain1[i].children[h] == rnd1) // if it already has this child
{
alreadyContains = true;
rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
while (rnd1 == i) // and its not itself
{
rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
}
break;
}
}
errorCounter++;
if (errorCounter > 100) // so we don't get stuck in small network sizes
{
goto outside2;
}
}
brain1[i].children[j] = rnd1;
brain1[i].weights[j] = Random.Range(-weightInitRange, weightInitRange);
if (Random.Range(1, 10) > 8)
{
break; // so not every neuron has the same amount of children
}
}
outside2:
{
}
}
// assign children of inputs
for (int i = 0; i < inputs; i++)
{
for (int j = 0; j < childrenMaxAmount; j++) // assigning all max children (sometimes) to inputs, their amounts of children will never change
{
int rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
bool alreadyContains = true;
int errorCounter = 0;
while (alreadyContains)
{
alreadyContains = false;
for (short h = 0; h < childrenMaxAmount; h++)
{
if (brain1[i].children[h] == rnd1)
{
alreadyContains = true;
rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
}
}
errorCounter++;
if (errorCounter > 1000) // so we don't get stuck in small network sizes
{
goto outside1;
}
}
brain1[i].children[j] = rnd1;
//brain1[i].weights[j] = 100; // input weight
}
outside1:
{
}
}
RemoveDeadNeurons();
// assign parents of outputs
int brainEnd = maxBrainSize - outputs;
for (int i = brainEnd; i < maxBrainSize; i++)
{
for (short j = 0; j < childrenMaxAmount; j++) // same amount of parents as inputs have children
{
int rnd1 = Random.Range(inputs, brainEnd);
while (brain1[rnd1] == null)
{
rnd1 = Random.Range(inputs, brainEnd);
}
bool alreadyContains = true;
int errorCounter = 0;
while (alreadyContains) //making sure we dont already have this parent
{
alreadyContains = false;
for (short h = 0; h < childrenMaxAmount; h++)
{
if (brain1[rnd1].children[h] == i)
{
alreadyContains = true;
rnd1 = Random.Range(inputs, brainEnd);
while (brain1[rnd1] == null)
{
rnd1 = Random.Range(inputs, brainEnd);
}
}
}
errorCounter++;
if (errorCounter > 1000) // so we don't get stuck
{
goto outside3;
}
}
for (int h = 0; h < childrenMaxAmount; h++) //adding to next available child space of the parent
{
if (brain1[rnd1].children[h] == -1)
{
brain1[rnd1].children[h] = i;
brain1[rnd1].weights[h] = Random.Range(-weightInitRange, weightInitRange);
break;
}
}
}
outside3:
{
}
}
}
void Think()
{
int[] activatedChildrenIndexes = new int[500];
int[] workingArray = new int[500];
int workingArrayCounter = 0;
short layerCounter = 0;
for (int i = 0; i < 500; i++)
{
activatedChildrenIndexes[i] = -1;
}
//initializing inputs as first activated children layer to be calculated
activatedChildrenIndexes[0] = 0;
activatedChildrenIndexes[1] = 1;
activatedChildrenIndexes[2] = 2;
int thoughtDepth = 6; //keep this as low as possible, will change to make it a percent of the amount of neurons that already exist
for (int x = 0; x < thoughtDepth; x++)
{
for (int i = 0; i < 500; i++) // cycle through activated children and add their activated children to a list
{
int currentNodeIndex = activatedChildrenIndexes[i];
if (currentNodeIndex == -1)
{
layerCounter++;
break;
}
//activation function
if (brain1[currentNodeIndex].value <= 0)
{
brain1[currentNodeIndex].value = 0;
}
else
{
brain1[currentNodeIndex].value = 1;
}
for (int j = 0; j < childrenMaxAmount; j++)
{
if (brain1[currentNodeIndex].children[j] == -1)
{
break;
}
brain1[brain1[currentNodeIndex].children[j]].value
+= (brain1[currentNodeIndex].value * brain1[currentNodeIndex].weights[j]) + brain1[currentNodeIndex].bias;
bool activatedChildIsInNextLayer = false;
for (int h = 0; h < 500; h++)
{
if (brain1[currentNodeIndex].children[j] == activatedChildrenIndexes[h])
{
activatedChildIsInNextLayer = true;
}
}
if (brain1[brain1[currentNodeIndex].children[j]].value > 0
|| layerCounter == 0
|| brain1[currentNodeIndex].children[j] > maxBrainSize - outputSize)
{
if (!activatedChildIsInNextLayer)
{
workingArray[workingArrayCounter] = brain1[currentNodeIndex].children[j];
if (workingArrayCounter < 499)//to prevent out of bounds indexing
{
workingArrayCounter++;
}
}
}
if (brain1[brain1[currentNodeIndex].children[j]].value < 0 && brain1[currentNodeIndex].children[j] < maxBrainSize - outputSize)
{
brain1[brain1[currentNodeIndex].children[j]].value = 0; //resetting negative nodes that didn't get added to next layer
}
}
brain1[currentNodeIndex].value = 0; // resetting value after passing to children
}
for (int i = workingArrayCounter; i < 500; i++)
{
workingArray[i] = -1;
}
workingArrayCounter = 0;
for (int i = 0; i < 500; i++)
{
activatedChildrenIndexes[i] = workingArray[i];
}
}
}
void Mutate(GameObject parent, float mutationRate, float weightsMutate, float biasMutate)
{
color = parent.GetComponent<SpriteRenderer>().color;
for (int i = 0; i < maxBrainSize; i++) // initialized to its parent's brain
{
if (parent.GetComponent<Animal1>().brain1[i] != null)
{
brain1[i] = new Neuron1();
brain1[i].bias = parent.GetComponent<Animal1>().brain1[i].bias;
for (int j = 0; j < childrenMaxAmount; j++)
{
brain1[i].children[j] = parent.GetComponent<Animal1>().brain1[i].children[j];
brain1[i].weights[j] = parent.GetComponent<Animal1>().brain1[i].weights[j];
}
}
}
for (int i = inputSize; i < maxBrainSize - outputSize; i++)
{
if (brain1[i] != null)
{
if (Random.Range(0, 100) < mutationRate) // it will be mutated
{
switch (Random.Range(1, 5))
{
case 1: //change one of its weights
int weightCounter = 0;
for (int k = 0; k < childrenMaxAmount; k++) // counting its weights
{
if (brain1[i].children[k] != -1)
{
weightCounter++;
}
else
{
break;
}
}
brain1[i].weights[Random.Range(0, weightCounter)] += Random.Range(-weightsMutate, weightsMutate);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
case 2: //change its bias
brain1[i].bias += Random.Range(-biasMutate, biasMutate);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
case 3: //break a connection/ remove neuron
int childrenCounter = 0;
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[i].children[k] != -1)
{
childrenCounter++;
}
else
{
break;
}
}
int rndChild = Random.Range(0, childrenCounter);
for (int s = rndChild; s < childrenMaxAmount - 1; s++) // shift children list down starting from rndChild and to the second to last element
{
brain1[i].children[s] = brain1[i].children[s + 1];
brain1[i].weights[s] = brain1[i].weights[s + 1];
}
brain1[i].children[childrenMaxAmount - 1] = -1; // removing the last child after shifting the list down one
brain1[i].weights[childrenMaxAmount - 1] = 0;
RemoveDeadNeurons();
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
case 4: //add a new child if possible
int childrenCounter2 = 0;
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[i].children[k] != -1)
{
childrenCounter2++;
}
else
{
break;
}
}
if (childrenCounter2 < childrenMaxAmount) // if we have room for another child
{
int newChild = Random.Range(inputSize, maxBrainSize);
while (brain1[newChild] == null)
{
newChild = Random.Range(inputSize, maxBrainSize);
}
brain1[i].children[childrenCounter2] = newChild;
brain1[i].weights[childrenCounter2] = Random.Range(-weightInitRange, weightInitRange);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
}
break;
}
}
}
}
if (Random.Range(0, 100) < 10) // create a new neuron if space is available for one
{
for (int i = inputSize; i < maxBrainSize - outputSize; i++)
{
if (brain1[i] == null) // the first available neuron
{
brain1[i] = new Neuron1();
int rndParent = Random.Range(0, maxBrainSize - outputSize);
int childrenCounter3 = 0;
if (brain1[rndParent] != null)
{
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[rndParent].children[k] != -1)
{
childrenCounter3++;
}
else
{
break;
}
}
}
while (brain1[rndParent] == null || childrenCounter3 == childrenMaxAmount) // making sure it is both an existing neuron and that it has room for another child
{
childrenCounter3 = 0;
rndParent = Random.Range(0, maxBrainSize - outputSize);
if (brain1[rndParent] != null)
{
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[rndParent].children[k] != -1)
{
childrenCounter3++;
}
else
{
break;
}
}
}
}
brain1[rndParent].children[childrenCounter3] = i; //giving the neuron a parent
brain1[rndParent].weights[childrenCounter3] = Random.Range(-weightInitRange, weightInitRange);
int rndChild = Random.Range(inputSize, maxBrainSize);
while(brain1[rndChild] == null)
{
rndChild = Random.Range(inputSize, maxBrainSize);
}
brain1[i].children[0] = rndChild; // giving the new neuron a child, a weight, and its bias
brain1[i].weights[0] = Random.Range(-weightInitRange, weightInitRange);
brain1[i].bias = Random.Range(-biasInitRange, biasInitRange);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
}
}
}
}
void RemoveDeadNeurons()
{
bool allGood = false;
while (!allGood)
{
for (int i = inputSize; i < maxBrainSize - outputSize; i++) // for every hidden layer neuron
{
if (brain1[i] != null) // if it's a neuron
{
if (brain1[i].children[0] == -1) // if it has no children
{
brain1[i] = null; // deleting neuron
for (int h = 0; h < maxBrainSize - outputSize; h++) //remove from other neurons children list, including from the input layer
{
if (brain1[h] != null)
{
for (int g = 0; g < childrenMaxAmount; g++)
{
if (brain1[h].children[g] == i) // if brain[h] has the removed neuron as a child
{
for (int s = g; s < childrenMaxAmount - 1; s++) // shift children list down
{
brain1[h].children[s] = brain1[h].children[s + 1];
brain1[h].weights[s] = brain1[h].weights[s + 1];
}
brain1[h].children[childrenMaxAmount - 1] = -1; // removing the last child in the list after shifting it down one
brain1[h].weights[childrenMaxAmount - 1] = 0;
}
}
}
}
goto outsideRDNLoop; // break out of for loop and recheck the brain
}
bool parentless = true;
for (int h = 0; h < maxBrainSize - outputSize; h++) // check if it is parentless
{
if (brain1[h] != null)
{
for (int g = 0; g < childrenMaxAmount; g++)
{
if (brain1[h].children[g] == i) // if it is a child of brain[h]
{
parentless = false;
}
}
}
}
if (parentless)
{
brain1[i] = null; //deleting neuron
goto outsideRDNLoop;
}
}
if (i == (maxBrainSize - outputSize) - 1) // if we reached the end of the for loop and this line, we know the brain was allGood
{
allGood = true;
}
}
outsideRDNLoop:
{
}
}
}
void Reproduce()
{
GameObject offspring = Instantiate(animal1Prefab1, new Vector2(transform.position.x + 1, transform.position.y + 1), Quaternion.identity);
offspring.GetComponent<Animal1>().parent1 = gameObject;
offspring.name = "animal1";
}
void OnDrawGizmosSelected()
{
Gizmos.color = Color.red;
Gizmos.DrawRay(transform.position, transform.TransformDirection(left) * sightRange);
Gizmos.DrawRay(transform.position, transform.TransformDirection(Vector2.up) * sightRange);
Gizmos.DrawRay(transform.position, transform.TransformDirection(right) * sightRange);
}
}
I've not run your code, just looked at it, but this looks suspicious if you are having problems with the contents of brain1 being overwritten elsewhere:
void Mutate(GameObject parent, float mutationRate, float weightsMutate, float biasMutate)
{
color = parent.GetComponent<SpriteRenderer>().color;
// This line here is suspicious:
brain1 = parent.GetComponent<Animal1>().brain1; // initialized to its parent's brain
What you are doing here is replacing the reference to the child's brain with the parent's - so if you are starting with just one parent initially, every single entity will be sharing the same brain.
I think perhaps you meant to deep copy the contents from parent's brain into the child's brain? What your assignment here is doing is just taking the reference to the parent's brain and using it as the child brain as well.
Example Deep copy:
Updated Neuron1 class:
public class Neuron1
{
public int[] children;
public float[] weights;
public float bias;
public float value;
public Neuron1()
{
children = new int[10];
for (int i = 0; i < 10; i++)
{
children[i] = -1; // to stop looping when reaching -1
}
weights = new float[10];
for (int i = 0; i < 10; i++)
{
weights[i] = 1;
}
bias = 0;
value = 0;
}
// Added clone function:
public Neuron1 Clone()
{
Neuron1 clone = new Neuron1();
for (int i = 0; i < clone.children.Length; i++)
{
clone.children[i] = this.children[i];
}
for (int i = 0; i < clone.weights.Length; i++)
{
clone.weights[i] = this.weights[i];
}
clone.bias = this.bias;
clone.value = this.value;
return clone;
}
}
Updated start of Mutate function.
void Mutate(GameObject parent, float mutationRate, float weightsMutate, float biasMutate)
{
color = parent.GetComponent<SpriteRenderer>().color;
Neuron1[] parentBrain = parent.GetComponent<Animal1>().brain1; // initialized to its parent's brain
for (int i = 0; i < brain1.Length; ++i)
{
brain1[i] = parentBrain[i].Clone();
}
Well, I have a demo scene here. In it I created an empty game object. It has a script attached to it (CSharp.cs), like this:
In my Update method I wrote
And as soon as I start the game, the game object including the script is deleted.
But if I replace transform.gameObject wit just this then only the script disappears and the game object remains.
DestoryImmediate() is not recommended to be used, instead use Destory()
Destroy() will set the object to null at the end of the frame and whereas DestroyImmediate() function immediately set object reference to null.
DestoryImmediate can also delete prefabs/scenes/art in your project outside of playmode which is another reason you should not use it.
I have 2 numbers that are used with random.range what I need is that the combination of both is not repeated 2 times, that is to say the combination a1, b1 can never be repeated but now I can't get that to happen is for a card game that I have to develop where the same card cannot be repeated neither in the hand nor in the game.
class Meca
public List<carta> cardPlay2; //current card combination, each update resets the list
public List<carta> cartaJugada; // current combination of cards, these are saved so that in future
updates the combination is not repeated
public bool isChange;
if (isChange)
{
cardPlay2.Clear();
int num = 5;
for (int j = 0; j < num; j++)
{
cambioCarta();
}
isChange = false;
}
void cambioCarta()
{
GameObject temp = Instantiate(carta);
temp.transform.SetParent(parent);
GameObject tem2p = Instantiate(carta2);
tem2p.transform.SetParent(parent2);
}
script carta
public int Num_cartas, PaloCarta;
public Mecanic meca;
void Start()
{
meca = GameObject.FindObjectOfType<Mecanic>();
if(gameObject.tag == "carta1")
cartas();
if (gameObject.tag == "carta2")
carta2();
}
void cartas()
{
Num_cartas = UnityEngine.Random.Range(1, 11);
PaloCarta = UnityEngine.Random.Range(1, 5);
carta carta1 = new carta
{
numCarta = Num_cartas,
palo = PaloCarta
};
}
[Serializable]
public class carta
{
public int numCarta;
public int palo;
}
what I'm trying to do is this
private void controlCartaMano2()
{
Num_cartas = UnityEngine.Random.Range(1, 10);
PaloCarta = UnityEngine.Random.Range(1, 4);
for (int i = 0; i < meca.cardPlay2.Count; i++)
{
while (meca.cardPlay2[i].numCarta == Num_cartas && meca.cardPlay2[i].palo == PaloCarta)
{
Num_cartas = UnityEngine.Random.Range(1, 10);
PaloCarta = UnityEngine.Random.Range(1, 4);
}
}
}
private void controlCartaJugada()
{
for (int i = 0; i < meca.cartaJugada.Count; i++)
{
for(int j = 0; j < meca.cartaJugada.Count; j++)
{
if(meca.cartaJugada[i].numCarta == meca.cartaJugada[j].numCarta &&
meca.cartaJugada[i].palo == meca.cartaJugada[j].palo)
{
Num_cartas = UnityEngine.Random.Range(1, 10);
PaloCarta = UnityEngine.Random.Range(1, 4);
}
}
}
}
if you have any idea how to do it I would appreciate it
You can use a Fisher-Yates shuffle method. That will shuffle the array, in place. It's then a simple matter of passing over the array incrementally. Here's a generic method that will shuffle an array:
static void Shuffle<T> ( T [ ] array )
{
var _random = new System.Random ( );
for ( int i = 0, n = array.Length; i < ( n - 1 ); ++i )
{
var next = i + _random.Next ( n - i );
var item = array [ next ];
array [ next ] = array [ i ];
array [ i ] = item;
}
}
For more information, here's the Wiki page : https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle
I'm trying to process the camera image input in Flutter but I can't seem to get it to work.
I have a listener on the camera feed that launch a function that is supposed to process the images btu everytime I end up doing anything in the function the program interface freeze. My guess is that I have too many frames to process and thus the program freezes but I have no idea how to ignore old frames.
Here is my code so far
import 'package:flutter/material.dart';
import 'package:camera/camera.dart';
import 'package:image/image.dart' as imglib;
import 'package:flutter/scheduler.dart';
import 'user_data_container.dart';
class Camera extends StatefulWidget {
Camera();
#override
_CameraState createState() => new _CameraState();
}
class _CameraState extends State<Camera> {
CameraController controller;
bool isDetecting = false;
double redavg;
imglib.Image last_image;
int count = 0;
Image _image_display;
#override
void initState() {
super.initState();
SchedulerBinding.instance.addPostFrameCallback((_) {
_initializeApp();
});
}
void _initializeApp() async {
var cameras = UserDataContainer.of(context).data.cameras;
if (cameras == null || cameras.length < 1) {
print('No camera is found');
} else {
controller = new CameraController(
cameras[0],
ResolutionPreset.high,
);
controller.initialize().then((_) {
if (!mounted) {
return;
}
setState(() {});
controller.startImageStream((CameraImage img) async {
print(count.toString() + "Stream. detecting: " + isDetecting.toString());
if (!isDetecting) {
isDetecting = true;
int startTime = new DateTime.now().millisecondsSinceEpoch;
_processCameraImage(img);
// int endTime = new DateTime.now().millisecondsSinceEpoch;
// print("Detection done in " + (endTime - startTime).toString());
// isDetecting = false;
}
else{
print("It's detecting");
}
});
});
}
}
#override
void dispose() {
controller?.dispose();
super.dispose();
}
#override
Widget build(BuildContext context) {
if (controller == null || !controller.value.isInitialized) {
return Container();
}
return
Scaffold(
body: Column(
children: <Widget>[
Text("red: "+ redavg.toString() + " "+ count.toString()),
Expanded(child: last_image == null ? Container() : _image_display),
],
),
);
// );
}
void _processCameraImage(CameraImage image) async {
count = count +1;
print("p: " + image.planes.length.toString());
// if (isDetecting) {
// print("Already detecting;");
// return;
// }
// else{
// isDetecting = true;
try {
await processFrame(image);
} catch (e) {
// await handleExepction(e)
} finally {
print("Done detecting :)");
// isDetecting = false;
}
// }
}
void processFrame(CameraImage image) async {
print ("convert");
imglib.Image convertedImage = await _convertCameraImage(image);
last_image = convertedImage;
imglib.PngEncoder pngEncoder = new imglib.PngEncoder(level: 0, filter: 0);
// Convert to png
List<int> png = pngEncoder.encodeImage(last_image);
_image_display = Image.memory(png);
// var colors = colorAverage(convertedImage);
setState(() {
// redavg = colors[2];
// print(colors.toString());
});
}
imglib.Image _convertCameraImage(CameraImage image) {
int width = image.width;
int height = image.height;
var img = imglib.Image(image.planes[0].bytesPerRow, height); // Create Image buffer
const int hexFF = 0xFF000000;
final int uvyButtonStride = image.planes[1].bytesPerRow;
final int uvPixelStride = image.planes[1].bytesPerPixel;
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
final int uvIndex =
uvPixelStride * (x / 2).floor() + uvyButtonStride * (y / 2).floor();
final int index = y * width + x;
final yp = image.planes[0].bytes[index];
final up = image.planes[1].bytes[uvIndex];
final vp = image.planes[2].bytes[uvIndex];
// Calculate pixel color
int r = (yp + vp * 1436 / 1024 - 179).round().clamp(0, 255);
int g = (yp - up * 46549 / 131072 + 44 - vp * 93604 / 131072 + 91)
.round()
.clamp(0, 255);
int b = (yp + up * 1814 / 1024 - 227).round().clamp(0, 255);
// color: 0x FF FF FF FF
// A B G R
img.data[index] = hexFF | (b << 16) | (g << 8) | r;
}
}
// Rotate 90 degrees to upright
// var img1 = imglib.copyRotate(img, 90);
imglib.PngEncoder pngEncoder = new imglib.PngEncoder(level: 0, filter: 0);
// Convert to png
List<int> png = pngEncoder.encodeImage(img);
_image_display = Image.memory(png);
return img;
}
}
I am new to SWT. I am trying to create a small application. Basically it has two screens. In the first screen I have to take user credentials. It has to be validated. If its successful I have to query a table and build a tree structure. Both validation and building the tree freezes my UI. I searched stackoverflow and google. I got the below options.
Display.getDefault().asyncExec() and starting long running process as separate thread from UI thread.
But still my UI freezes.
When the user clicks on Logon button. I created a thread. As a first step I tried to show a indefinite progress bar using asyncExec. Since I have to access the Uname and Password I have to trigger another asyncexec and perform the login. If its successful populated the tree.
I triggered another asyncExec to close the progress bar.
My UI freezes from Logon click till Tree population completion. Where am I going wrong.
new Thread(new Runnable()
{
private int progress = 0;
private static final int INCREMENT = 10;
#Override
public void run()
{
while (!progressBar.isDisposed())
{
Display.getDefault().asyncExec(new Runnable()
{
#Override
public void run()
{
if (!progressBar.isDisposed())
progressBar.setVisible(true);
}
});
Display.getDefault().asyncExec(new Runnable()
{
#Override
public void run()
{
String sAuth = null;
switch(tAuth.getText()){
case "Enterprise": sAuth = "secEnterprise"; break;
case "LDAP": sAuth = "secLDAP"; break;
case "Windows AD": sAuth = "secWinAD"; break;
case "SAP": sAuth = "secSAPR3"; break;
}
try {
log.info("Attempting to create enterprise session");
CoreLogic.logonEnterprise(tUSR.getText().trim(),tPWD.getText().trim(),sAuth,tCMS.getText());
if(CommonVariables.entsession){
log.info("Enterprise session created.");
CoreLogic.getUniverse();
log.info("Populating universe tree");
String[] temp;
for (Entry<Integer, UnvObj> entry : CommonVariables.unvlst.entrySet()) {
UnvObj t = entry.getValue();
temp = t.getPath().split("/");
if (temp[0].equals("Universes")){
int max = temp.length;
int i =0 ;
boolean flag = false;
TreeItem trItem = null;
do{
if(i == 0) {
if(tree.getItemCount() == 0){
flag = false;
}
else
{
for(int k = 0; k < tree.getItemCount(); k++){
if(temp[i].equals(tree.getItem(k).getText())){
i++;
trItem = tree.getItem(k);
flag =true;
break;
}
else
{
flag = false;
}
}
}
}
else{
if(trItem.getItemCount() == 0){
flag = false;
}
else
{
for(int k = 0; k < trItem.getItemCount(); k++){
if(temp[i].equals(trItem.getItem(k).getText())){
i++;
trItem = trItem.getItem(k);
flag =true;
break;
}
else
{
flag = false;
}
}
}
}
}while (flag == true && i < max);
TreeItem Item = null;
if (i == 0){
for (int k = 0; k < max; k++){
if(k == 0) {
Item = new TreeItem(tree,SWT.NONE);
Item.setText(temp[k]);
Item.setData("Type","Folder");
Image image = new Image(display,ResourceLoader.load("/images/Fld.png"));
Item.setImage(image);
}else
{
Item = new TreeItem(Item,SWT.NONE);
Item.setText(temp[k]);
Item.setData("Type","Folder");
Image image = new Image(display,ResourceLoader.load("/images/Fld.png"));
Item.setImage(image);
}
}
} else if( i < max){
for (int k = i; k < max; k++){
trItem = new TreeItem(trItem,SWT.NONE);
trItem.setText(temp[k]);
trItem.setData("Type","Folder");
Image image = new Image(display,ResourceLoader.load("/images/Fld.png"));
trItem.setImage(image);
}
}
if (i == 0){
Item = new TreeItem(Item,SWT.NONE);
Item.setText(t.getName());
Item.setData("Type",t.getKind());
Item.setData("Mapkey",entry.getKey());
if(t.getKind().equals("Universe")){
Image image = new Image(display,ResourceLoader.load("/images/Unv.ico"));
Item.setImage(image);
}
else{
Image image = new Image(display,ResourceLoader.load("/images/Unx.ico"));
Item.setImage(image);
}
} else
{
trItem = new TreeItem(trItem,SWT.NONE);
trItem.setText(t.getName());
trItem.setData("Type",t.getKind());
trItem.setData("Mapkey",entry.getKey());
if(t.getKind().equals("Universe")){
Image image = new Image(display,ResourceLoader.load("/images/Unv.ico"));
trItem.setImage(image);
}
else{
Image image = new Image(display,ResourceLoader.load("/images/Unx.ico"));
trItem.setImage(image);
}
}
}
}
log.info("Universe Tree Populated.");
sl.topControl =Universe;
Main.layout();
}else
{
log.info("Unable to create enterprise session");
MessageBox messageBox = new MessageBox(shell, SWT.ICON_WARNING | SWT.OK);
messageBox.setText("Report Extractor");
messageBox.setMessage("Unable to create the enterprise session with the provided credentials. Please verify it.");
messageBox.open();
}
}
catch(Exception exp)
{
log.error("Fail to create enterprise session",exp);
}
}
});
Display.getDefault().asyncExec(new Runnable()
{
#Override
public void run()
{
if (!progressBar.isDisposed())
progressBar.setVisible(false);
}
});
}
}
}).start();