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• Histogram dim3 griddim code#

ThreadID=(threadIdx.y*blockDim.x)+threadIdx.xĪnd to determine the unique block ID within a 2-D grid, you would use the following formula:īlockID=(blockIdx.y*gridDim.x)+blockIdx.x To determine the unique thread ID in a 2-D block, you would use the following formula: With a 1-D kernel, the unique thread ID within a block is the same as the x component of the threadIdx variable.Īnd the unique block ID within a grid is the same as the x component of the blockIdx variable: The built-in variables gridDim, blockDim, blockIdx, and threadIdx are each 3-component structs with members x, y, z. The number of blocks within a gird can be determined within a kernel by using the built-in variable gridDim and the number of threads within a block can be determined by using the built-in variable blockDim.Ī thread block is uniquely identified in a kernel function by using the built-in variable blockIdx and a thread within a block is uniquely identified in a kernel function by using the built-in variable threadIdx. Maximum x-, y-, or z-dimension of a grid of thread blocks Maximum dimensionality of a grid of thread blocks Compute Capability Technical Specifications The maximum number of threads that can be assigned to a thread block is 512 for devices with compute capability 1.x and 1024 threads for devices that support compute capability 2.0. The thread block is partitioned into individual threads and for all compute capabilities, threads can be partitioned into 1, 2, or 3 dimensions. This image only shows 2-dimensional grid, but if the graphics device supports compute capability 2.0, then the grid of thread blocks can actually be partitioned into 1, 2 or 3 dimensions, otherwise if the device supports compute capability 1.x, then thread blocks can be partitioned into 1, or 2 dimensions (in this case, then the 3rd dimension should always be set to 1). To get a raw file from a jpg one the args must be: python imageConverter.py image_name.In the image above we see that this example grid is divided into nine thread blocks (3×3), each thread block consists of 9 threads (3×3) for a total of 81 threads for the kernel grid. This way of “serialising pixels simplifies the image reading procedure on our CUDA code, since we won’t need to import any extra library to read. raw file containing the stream of RGB values (from 0 to 255) that represent the single image (sorted: from the top left to the bottom right side of the image). Print "Unable to load the image, probably your parameters are incorrect"īefore calling CUDA program, some preprocessing is needed in order to choose the starting centroids and to better represent an RGB image in C++.įirst of all, an RGB.

NameFile,extension = os.path.splitext(inputFile) Print "#Colors: " + str(numColors) + " #Iterations: " + str(iterations) Print "Error: #Colors or #Iteration are not correct" Print "Time: " + str(T1-T0) + " seconds." #print "Printing OLD CENTROIDS: " + str(CENTROIDS) #print "Printing NEW CENTROIDS: " + str(NEW_CENTROIDS) #print "Printing CLUSTER NO " + str(CLUSTER_NO) # n_cluster is the number of cluster where the pixel belongs toĬlusterTuple = x # x position of the pixelĬlusterTuple = y # y position of the pixelĬlusterTuple = findCluster(loadedImage) # cluster no. # ClusterTuple has for each pixel in the image

OutputImage.save(name+"_cl"+str(N_CLUSTERS)+"_it"+str(N_ITERATIONS)+EXTENSION,"JPEG",quality=100) OutputImage = Image.new("RGB",IMAGE_SIZE)ĭraw.point((element,element), fill=c) python kmeans.py my_Picture.jpg no_Of_Clusters no_Of_Iterations

Histogram dim3 griddim code#

The libraries needed for this example code are PIL and Numpy. In order to get familiarity with k-means, a first prototype of the algorithm is implemented in python. K, as already mentioned, is defined by the user. The K centroids are the RGB colours of the image that will be computed iteration by iteration and, at the end, it will define the final colors of the entire image. In our domain the dataset is represented by the entire image containing its pixels.

Kmeans can be used to reduce the number of color of an RGB image for many purposes and for this reason the scheme described above is expressed in a different domain. Once every element is assigned to some cluster, for each cluster the new centroid is computed.Each element of the dataset is assigned to the closest cluster.K centroids, one of each cluster, are chosen with an heuristic method.For a better insight of this algorithm I suggest to read this.Ī brief explanation of how it works is shown below. K-Means is commonly used for cluster analysis and data mining. !! The code is available on my gitHub toskyRocker account!!