Fastest 2D convolution or image filter in Python

Fastest 2D convolution or image filter in Python

When it comes to performing fast 2D convolution or image filtering in Python, one popular and efficient library is scipy. The scipy library provides a convolve2d function that can be used for 2D convolution. Additionally, if you are working with large datasets or need to perform convolution operations on a GPU, you might want to consider using libraries like cupy or numba.

Here's an example using scipy for 2D convolution:

import numpy as np from scipy.signal import convolve2d import time # Create a sample image image = np.random.rand(1000, 1000) # Create a kernel/filter kernel = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) # Measure the time taken for convolution start_time = time.time() # Perform 2D convolution result = convolve2d(image, kernel, mode='valid') end_time = time.time() elapsed_time = end_time - start_time print(f"Time taken for convolution: {elapsed_time} seconds")

Note that the mode='valid' parameter in convolve2d specifies that the output should only contain values where the kernel fully overlaps with the input.

For GPU acceleration, you can use cupy. Install it using:

pip install cupy

Here's an example using cupy:

import cupy as cp import time # Convert NumPy arrays to CuPy arrays image_gpu = cp.array(image) kernel_gpu = cp.array(kernel) # Measure the time taken for convolution on GPU start_time = time.time() # Perform 2D convolution on GPU result_gpu = cp.convolve2d(image_gpu, kernel_gpu, mode='valid') end_time = time.time() elapsed_time = end_time - start_time print(f"Time taken for GPU convolution: {elapsed_time} seconds")

Keep in mind that the actual performance may vary depending on the size of your images, the complexity of your convolution kernel, and the available hardware. Experimenting with different libraries and approaches is often necessary to find the most optimized solution for your specific use case.

Examples

  1. "Python fastest 2D convolution with NumPy"

    • Code Implementation:
      import numpy as np from scipy.signal import convolve2d # Image and kernel image = np.random.rand(1000, 1000) kernel = np.random.rand(50, 50) # Perform 2D convolution using NumPy result = convolve2d(image, kernel, mode='same')
    • Description: Use the convolve2d function from SciPy to perform 2D convolution with NumPy arrays.

  2. "Python fastest 2D convolution with OpenCV"

    • Code Implementation:
      import cv2 import numpy as np # Image and kernel image = np.random.rand(1000, 1000) kernel = np.random.rand(50, 50) # Perform 2D convolution using OpenCV result = cv2.filter2D(image, -1, kernel)
    • Description: Utilize the filter2D function from OpenCV to perform 2D convolution with NumPy arrays.

  3. "Python fastest 2D convolution with FFT"

    • Code Implementation:
      import numpy as np from scipy.signal import fftconvolve # Image and kernel image = np.random.rand(1000, 1000) kernel = np.random.rand(50, 50) # Perform 2D convolution using FFT result = fftconvolve(image, kernel, mode='same')
    • Description: Use the fftconvolve function from SciPy to perform 2D convolution using Fast Fourier Transform (FFT) for efficiency.

  4. "Python fastest 2D convolution with Numba"

    • Code Implementation:
      import numpy as np from numba import njit # Image and kernel image = np.random.rand(1000, 1000) kernel = np.random.rand(50, 50) # JIT-compiled function for 2D convolution using Numba @njit def convolve(image, kernel): result = np.zeros_like(image) for i in range(image.shape[0]): for j in range(image.shape[1]): result[i, j] = np.sum(image[i:i+kernel.shape[0], j:j+kernel.shape[1]] * kernel) return result # Perform 2D convolution using Numba-compiled function result = convolve(image, kernel)
    • Description: Utilize Numba to create a Just-In-Time (JIT) compiled function for a custom 2D convolution implementation.

  5. "Python fastest separable 2D convolution"

    • Code Implementation:
      import numpy as np from scipy.signal import convolve2d # Image and separable kernels image = np.random.rand(1000, 1000) kernel_x = np.random.rand(1, 50) kernel_y = np.random.rand(50, 1) # Perform separable 2D convolution using NumPy result = convolve2d(convolve2d(image, kernel_x, mode='same'), kernel_y, mode='same')
    • Description: Use two 1D convolutions along the x and y axes (separable convolution) for improved efficiency.

  6. "Python fastest 2D convolution with CuPy"

    • Code Implementation:
      import cupy as cp # Image and kernel on GPU using CuPy image = cp.random.rand(1000, 1000) kernel = cp.random.rand(50, 50) # Perform 2D convolution using CuPy result = cp.convolve2d(image, kernel, mode='same')
    • Description: Utilize CuPy, a GPU-accelerated library, to perform 2D convolution on GPU for faster computation.

  7. "Python fastest 2D convolution with Cython"

    • Code Implementation:
      import numpy as np from cython import cythonize from convolve_cython import convolve_cython # Image and kernel image = np.random.rand(1000, 1000) kernel = np.random.rand(50, 50) # Cython-optimized 2D convolution function result = convolve_cython(image, kernel)
      • Cython Implementation (convolve_cython.pyx):
        import numpy as np cimport numpy as np def convolve_cython(np.ndarray[np.float64_t, ndim=2] image, np.ndarray[np.float64_t, ndim=2] kernel): cdef int i, j cdef int image_shape_0 = image.shape[0] cdef int image_shape_1 = image.shape[1] cdef int kernel_shape_0 = kernel.shape[0] cdef int kernel_shape_1 = kernel.shape[1] cdef np.ndarray[np.float64_t, ndim=2] result = np.zeros((image_shape_0, image_shape_1), dtype=np.float64) for i in range(image_shape_0): for j in range(image_shape_1): result[i, j] = np.sum(image[i:i+kernel_shape_0, j:j+kernel_shape_1] * kernel) return result
    • Description: Use Cython to create a Python-C hybrid implementation for optimized 2D convolution.

  8. "Python fastest 2D convolution with PyTorch"

    • Code Implementation:
      import torch # Image and kernel on GPU using PyTorch image = torch.rand(1, 1, 1000, 1000) kernel = torch.rand(1, 1, 50, 50) # Perform 2D convolution using PyTorch result = torch.nn.functional.conv2d(image, kernel, padding=25)
    • Description: Utilize PyTorch, a popular deep learning library, to perform 2D convolution on GPU for faster computation.

  9. "Python fastest 2D convolution with TensorFlow"

    • Code Implementation:
      import tensorflow as tf import numpy as np # Image and kernel on GPU using TensorFlow image = tf.constant(np.random.rand(1, 1000, 1000, 1), dtype=tf.float32) kernel = tf.constant(np.random.rand(50, 50, 1, 1), dtype=tf.float32) # Perform 2D convolution using TensorFlow result = tf.nn.conv2d(image, kernel, strides=[1, 1, 1, 1], padding='SAME')
    • Description: Use TensorFlow, a popular deep learning library, to perform 2D convolution on GPU for faster computation.

  10. "Python fastest 2D convolution with scikit-image"

    • Code Implementation:
      import numpy as np from skimage import filters # Image image = np.random.rand(1000, 1000) # Gaussian filter using scikit-image result = filters.gaussian(image, sigma=2, mode='same')
    • Description: Leverage the gaussian function from scikit-image for a specific type of 2D convolution (Gaussian smoothing) which can be optimized for certain scenarios.

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