Kh4lil

/**
 * @brief      Luminosity Kernel
 *             Converts colored image to grayscale using Luminosity algorithm
 */
__global__ void grayscale_luminosity_kernel(unsigned char* input, unsigned char* output, int width, int height, int colored_WidthStep, int grayscaled_WidthStep) {

	const int xIndex = blockIdx.x * blockDim.x + threadIdx.x;               // x index of current thread.
	const int yIndex = blockIdx.y * blockDim.y + threadIdx.y;               // y index of current thread.

	if ((xIndex < width) && (yIndex < height))

Kh4lil

/**
 * @brief      Average Kernel
 *             Converts colored image to grayscale using Average algorithm
 */
__global__ void grayscale_average_kernel(unsigned char* input, unsigned char* output, int width, int height, int colored_WidthStep, int grayscaled_WidthStep) {

	const int xIndex = blockIdx.x * blockDim.x + threadIdx.x;               // x index of current thread.
	const int yIndex = blockIdx.y * blockDim.y + threadIdx.y;               // y index of current thread.

	if ((xIndex < width) && (yIndex < height))

Kh4lil

/**
 * @brief      Lightness Kernel
 *             Converts colored image to grayscale using Lightness algorithm
 */
__global__ void grayscale_lightness_kernel(unsigned char* input, unsigned char* output, int width, int height, int colored_WidthStep, int grayscaled_WidthStep) {

	const int xIndex = blockIdx.x * blockDim.x + threadIdx.x;               // x index of current thread.
	const int yIndex = blockIdx.y * blockDim.y + threadIdx.y;               // y index of current thread.

	if ((xIndex < width) && (yIndex < height))

Kh4lil

/*
* Wrapper function
* @param      input           The input image.
* @param      input		      The output image to be modified. 
*/
void convert_color_to_grayscale(const cv::Mat& inputImage, cv::Mat& outputImage) {

	const int inputImage_ColoredBytes = inputImage.step * inputImage.rows;	                       // Calculate total number of bytes of input image.
	const int outputImage_GrayBytes = outputImage.step * outputImage.rows;                         // Calculate total number of bytes of output image.

Kh4lil

int main() {
	std::string inputImage = "model.ppm";                                             // Import image
	cv::Mat originalImage = cv::imread(inputImage); 
	cv::Mat imageResizeDoubled;  
	cv::Mat imageResizeQuadrupeled;

	std::cout << "Original image size: " << originalImage.size() << std::endl;        // Display resolution of original image. 

	// Image resizing: 
	float scale = 2;                                                                  // scale factor. To be changed from 2 to 4 if needed. 

Kh4lil

# Importing the dataset
from sklearn.datasets import load_iris
irisDataSet = load_iris()
X = irisDataSet.data
y = irisDataSet.target

# Split the data set into training and testing
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(irisDataSet['data'], irisDataSet['target'], random_state=0)

Kh4lil

# New iris with below information
X_newIris = np.array([[5.0, 2.9, 1.0, 0.2]])
predictNew = KNN_Classifier.predict(X_new)
print(predictNew)

# >>>Output: [0]

Kh4lil

# Split the data set into training and testing
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris['data'], iris['target'], random_state=0)

from sklearn.neighbors import KNeighborsClassifier
KNN_Classifier = KNeighborsClassifier(n_neighbors=1)
KNN_Classifier.fit(X_train, y_train)

Kh4lil

# Importing the dataset
from sklearn.datasets import load_iris
irisDataSet = load_iris()
X = irisDataSet.data
y = irisDataSet.target

# Plotting
import matplotlib.pyplot as plt
# 
features = irisDataSet.data.T

Kh4lil

public bool compareTrees(TreeNode p, TreeNode q) {
        //Check for baseCase: 
        if (p == null && q == null){
            return true;
        }if (p == null || q == null){
            return false;
        //Compare the values of the two nodes:
        }if (p.val != q.val){
            return false;
        }