numpy/线性代数-快速16位直方图 [英] numpy / linear algebra - fast 16-bit histogram

问题描述

如果我有一个由uint16s组成的图像，并且想要为每个位计算一个直方图，即包含强度值的 0..65535 的矢量'x'和一个矢量 y 是具有该值的样本数，是否有矢量化的numpy/线性algreba方法来计算?

If I have an image made of uint16s and want to compute a histogram for each bit, i.e. a vector 'x' of 0..65535 that contains the intensity value, and a vector y that is the number of samples that have that value, is there a vectorized numpy / linear algreba way to compute this?

推荐答案

我使用Numpy进行了明显的处理，并且在Mac上使用图像尺寸需要300毫秒.然后我用OpenCV做同样的事情，它在9毫秒时快了33倍！

I did it the obvious way with Numpy, and using your image dimensions on my Mac, it takes 300ms. I then did the same thing with OpenCV and it is 33x faster at 9ms!

#!/usr/bin/env python3

import cv2
import numpy as np

# Dimensions - height, width
h, w = 2160, 2560

# Known image, channel0=1, channel1=3, channel2=5, channel3=65535
R =  np.zeros((h,w,4), dtype=np.uint16)
R[...,0] = 1
R[...,1] = 3
R[...,2] = 5
R[...,3] = 65535

def npHistogram(R):
    """Generate histogram using Numpy"""
    H, _ = np.histogram(R,65536)
    return H

def OpenCVHistogram(R):
    """Generate histogram using OpenCV"""
    H = cv2.calcHist([R.ravel()], [0], None, [65536], [0,65536]) 
    return H

A = npHistogram(R)
B = OpenCVHistogram(R)

#%timeit npHistogram(R)
#%timeit OpenCVHistogram(R)

结果

使用IPython，我得到了这些计时信息

Using IPython, I got these timings

%timeit npHistogram(R)
300 ms ± 11.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit OpenCVHistogram(R)
9.02 ms ± 226 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

关键字:Python，直方图，慢速，Numpy，np.histogram，加速，OpenCV，图像处理.

Keywords: Python, histogram, slow, Numpy, np.histogram, speedup, OpenCV, image processing.

这篇关于numpy/线性代数-快速16位直方图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！