mirror of
https://github.com/PacktPublishing/Hands-On-GPU-Programming-with-CUDA-C-and-Python-3.x-Second-Edition.git
synced 2025-07-21 12:51:06 +02:00
97 lines
2.5 KiB
Python
Executable File
97 lines
2.5 KiB
Python
Executable File
from __future__ import division
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import pycuda.autoinit
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from pycuda import gpuarray
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import numpy as np
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from skcuda import fft
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from skcuda import linalg
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from matplotlib import pyplot as plt
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def cufft_conv(x , y):
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x = x.astype(np.complex64)
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y = y.astype(np.complex64)
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if (x.shape != y.shape):
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return -1
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plan = fft.Plan(x.shape, np.complex64, np.complex64)
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inverse_plan = fft.Plan(x.shape, np.complex64, np.complex64)
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x_gpu = gpuarray.to_gpu(x)
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y_gpu = gpuarray.to_gpu(y)
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x_fft = gpuarray.empty_like(x_gpu, dtype=np.complex64)
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y_fft = gpuarray.empty_like(y_gpu, dtype=np.complex64)
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out_gpu = gpuarray.empty_like(x_gpu, dtype=np.complex64)
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fft.fft(x_gpu, x_fft, plan)
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fft.fft(y_gpu, y_fft, plan)
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linalg.multiply(x_fft, y_fft, overwrite=True)
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fft.ifft(y_fft, out_gpu, inverse_plan, scale=True)
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conv_out = out_gpu.get()
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return conv_out
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def conv_2d(ker, img):
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padded_ker = np.zeros( (img.shape[0] + 2*ker.shape[0], img.shape[1] + 2*ker.shape[1] )).astype(np.float32)
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padded_ker[:ker.shape[0], :ker.shape[1]] = ker
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padded_ker = np.roll(padded_ker, shift=-ker.shape[0]//2, axis=0)
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padded_ker = np.roll(padded_ker, shift=-ker.shape[1]//2, axis=1)
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padded_img = np.zeros_like(padded_ker).astype(np.float32)
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padded_img[ker.shape[0]:-ker.shape[0], ker.shape[1]:-ker.shape[1]] = img
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out_ = cufft_conv(padded_ker, padded_img)
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output = out_[ker.shape[0]:-ker.shape[0], ker.shape[1]:-ker.shape[1]]
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return output
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gaussian_filter = lambda x, y, sigma : (1 / np.sqrt(2*np.pi*(sigma**2)) )*np.exp( -(x**2 + y**2) / (2 * (sigma**2) ))
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def gaussian_ker(sigma):
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ker_ = np.zeros((2*sigma+1, 2*sigma+1))
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for i in range(2*sigma + 1):
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for j in range(2*sigma + 1):
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ker_[i,j] = gaussian_filter(i - sigma, j - sigma, sigma)
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total_ = np.sum(ker_.ravel())
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ker_ = ker_ / total_
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return ker_
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if __name__ == '__main__':
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latte = np.float32(plt.imread('latte.jpg')) / 255
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latte_blurred = np.zeros_like(latte)
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ker = gaussian_ker(30)
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for k in range(3):
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latte_blurred[:,:,k] = conv_2d(ker, latte[:,:,k])
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fig, (ax0, ax1) = plt.subplots(1,2)
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fig.suptitle('Gaussian Filtering', fontsize=20)
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ax0.set_title('Before')
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ax0.axis('off')
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ax0.imshow(latte)
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ax1.set_title('After')
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ax1.axis('off')
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ax1.imshow(latte_blurred)
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plt.tight_layout()
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plt.subplots_adjust(top=.85)
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plt.show()
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