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gaussianFilter.py
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gaussianFilter.py
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import sys
import numpy as np
import scipy.ndimage as nd
import pycuda.tools as tools
from pycuda.gpuarray import to_gpu
from pycuda.compiler import SourceModule
import time
import pycuda.driver as driver
import mokas_gpu as mkGpu
import skimage.filters as filters
def gaussianFilter(stackImages, radius, sigma, device=0):
startTime = time.time()
stack32 = np.asarray(stackImages, dtype=np.float32)
need_mem = 2 * stack32.nbytes + np.dtype(np.float32).itemsize * (2 * radius + 1)**2
print("Total memory to be used: %.2f GB" % (need_mem/1e9))
current_dev, ctx, (free_mem_gpu, total_mem_gpu) = mkGpu.gpu_init(device)
if need_mem < free_mem_gpu:
#change with parallel filtering
stack32 = do_gaussianFilter(stack32, radius, sigma)
stackImages_filtered = stack32
else:
nsplit = int(float(need_mem)/free_mem_gpu) + 1
print("Splitting images in %d parts..." % nsplit)
stack32s = np.array_split(stack32, nsplit, 0)
print("Done")
switchTimes = np.array([])
switchSteps = np.array([])
for k, stack32 in enumerate(stack32s):
print("Calculation split %i" % k)
#change with parallel filtering
stack32 = do_gaussianFilter(stack32, radius, sigma)
if not k:
stackImages_filtered = stack32
print(stackImages_filtered.shape)
else:
stackImages_filtered = np.vstack((stackImages_filtered, stack32))
print(stackImages_filtered.shape)
print('Analysing done in %f seconds' % (time.time()-startTime))
# Close cuda device
success = mkGpu.gpu_deinit(current_dev, ctx)
if not success:
print("There is a problem with the device %i" % device)
return stackImages_filtered
def do_gaussianFilter(stackImages, radius, sigma):
"""
Return a matrix with the positions of a step in a sequence for each pixel
Parameters:
---------------
stackImages: int32 : 3D Array of images
useKernel : string
step = [-1]*5 +[1]*5
zero = [-1]*5 +[0] + [1]*5
"""
# Convert to int32
dim_z, dim_y, dim_x = stackImages.shape
dim_Z, dim_Y, dim_X = np.int32(stackImages.shape)
block_X = 256
block_Y = 1
grid_X, grid_Y = dim_x*dim_y*dim_z / block_X if dim_x*dim_y*dim_z % block_X==0 else dim_x*dim_y*dim_z / block_X +1 , 1
print("Print grid dimensions: ", grid_X, grid_Y)
auxStack = np.zeros((dim_z , dim_y, dim_x), dtype=np.float32)
radius32 = np.int32(radius)
sigma32 = np.float32(sigma)
kerGaussian = np.array([np.exp(-(float(R))**2/(2*sigma**2)) for R in range(-radius,radius+1)]).astype(np.float32)#(1./(np.sqrt(2*np.pi)*sigma))*
#print kerGaussian
print(dim_X,dim_Y,dim_Z)
print(stackImages.shape)
#print stackImages
#((float(R)-radius)**2/(2*sigma**2))
#Host to Device copy
stack_gpu = to_gpu(stackImages)
print("Stack_gpu copied")
auxStack_gpu = to_gpu(auxStack)
print("auxiliary Stack_gpu copied")
kerGaussian_gpu = to_gpu(kerGaussian)
print("gaussian kernel copied")
print("Data transfered to GPU")
print("Tokenizing filter")
mod1 = SourceModule("""
__global__ void d_gaussian_filter(float* stack_gpu, float* auxStack_gpu, float* cGaussian, int dim_x, int dim_y, int dim_z, int r)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int z = idx / (dim_x*dim_y);
int x = (idx % (dim_x*dim_y) ) % dim_x;
int y = (idx % (dim_x*dim_y) ) / dim_x;
if (x>=dim_x || y>=dim_y || z>=dim_z)
{
return;
}
float sum = 0.0;
float factor=0.0;
float t = 0.0;
for (int i0 = -r; i0 <= r; i0++)
{ int i=i0;
if(x+i0>=dim_x || x+i0<0){i=-i0;}
for (int j0 = -r; j0 <= r; j0++)
{ int j=j0;
if(y+j0>=dim_y || y+j0<0){j=-j0;}
factor = cGaussian[r+i0] * cGaussian[r+j0];
t += factor * stack_gpu[z*dim_x*dim_y+(y+j)*dim_x+x+i];
sum += factor;
}
}
auxStack_gpu[idx] = t/sum;
}
""")
print("Tokenizing copy_kernel")
mod2 = SourceModule("""
__global__ void copy_kernel(float* stack_gpu, float* auxStack_gpu)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
stack_gpu[idx] = auxStack_gpu[idx];
}
""")
print("Defining kernel filter")
func_gaussianFilter = mod1.get_function("d_gaussian_filter")
func_copyKernel = mod2.get_function("copy_kernel")
#Function calls
print("Ready to calculate the filter")
func_gaussianFilter(stack_gpu, auxStack_gpu, kerGaussian_gpu, dim_X, dim_Y, dim_Z, radius32, block=(block_X, block_Y, 1),
grid=(grid_X, grid_Y))
func_copyKernel(stack_gpu,auxStack_gpu, block=(block_X, block_Y, 1),
grid=(grid_X, grid_Y))
# func_gaussianFilter(stack_gpu, auxStack_gpu, kerGaussian_gpu, dim_X, dim_Y, dim_Z, radius32, block=(block_X, block_Y, 1),
# grid=(grid_X, grid_Y))
# func_copyKernel(stack_gpu,auxStack_gpu, block=(block_X, block_Y, 1),
# grid=(grid_X, grid_Y))
print("Done.")
print("Copy to Host filtered images")
stackImages = stack_gpu.get()
print("Done")
# As an alternative
#driver.memcpy_dtoh(switch, switch_gpu)
#driver.memcpy_dtoh(levels, levels_gpu)
#Free GPU memory
print("Clearing memory of GPU")
stack_gpu.gpudata.free()
auxStack_gpu.gpudata.free()
kerGaussian_gpu.gpudata.free()
#print stackImages_filtered
return stackImages
if __name__ == "__main__":
import matplotlib.pyplot as plt
#stackImages = sys.argv[1]
#radius = sys.argv[2]
pic = int(sys.argv[1])
regions = np.zeros((10,300,100))
for i in range(10):
regions[i,5*i:5+5*i,5*i:5+5*i]=1
#regions[:,6:,6:] = 1
sigma = 2
im_filtered = gaussianFilter(regions, radius=10, sigma=sigma)
im = [filters.gaussian(r, sigma=sigma) for r in regions]
fig1, ax = plt.subplots(1,3,sharex=True, sharey=True)
ax[0].imshow(regions[pic])
ax[1].imshow(im[pic])
ax[2].imshow(im_filtered[pic])
plt.show()