用SSE2指令集加速warpAffine

根据提供的引用内容，我们可以了解到SSE指令集是一种针对英特尔CPU的指令集扩展，可以加速向量运算。而warpAffine是OpenCV中的一个函数，用于对图像进行仿射变换。为了加速warpAffine函数，我们可以使用SSE2指令集。

下面是使用SSE2指令集加速warpAffine的步骤和代码实现：

1. 首先，我们需要将图像数据转换为SSE2数据类型。可以使用_mm_loadu_si128函数将8个像素点的数据加载到一个__m128i类型的变量中。

2. 接下来，我们需要对图像进行仿射变换。可以使用_mm_setr_ps函数将仿射变换矩阵中的元素加载到一个__m128类型的变量中，然后使用_mm_mul_ps函数将该变量与图像数据中的像素点进行矩阵乘法运算。

3. 最后，我们需要将SSE2数据类型转换回普通的图像数据类型。可以使用_mm_storeu_si128函数将__m128i类型的变量中的数据存储到图像数据中。

下面是使用SSE2指令集加速warpAffine的代码实现：

import cv2
import numpy as np
import time
import os
import sys
import math
import copy
from numba import jit
import numexpr as ne
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import scipy.signal as signal
import scipy.interpolate as interpolate
import scipy.optimize as optimize
import scipy.spatial.distance as distance
import scipy.ndimage as ndimage
import scipy.io as io
import skimage.io as skio
import skimage.transform as sktransform
import skimage.color as skcolor
import skimage.filters as skfilters
import skimage.segmentation as skseg

def warpAffine_sse2(img, M):
    rows, cols = img.shape[:2]
    dst = np.zeros((rows, cols), dtype=np.uint8)
    M = np.array(M).astype(np.float32)
    M = np.vstack([M, [0, 0, 1]])
    M_inv = cv2.invertAffineTransform(M)
    M_inv = np.array(M_inv).astype(np.float32)
    for i in range(rows):
        for j in range(cols):
            x, y = np.dot(M_inv, np.array([j, i, 1]))
            if x >= 0 and x < cols and y >= 0 and y < rows:
                x1, y1 = int(x), int(y)
                x2, y2 = x1 + 1, y1 + 1
                if x2 < cols and y2 < rows:
                    p1 = _mm_loadu_si128(img[y1, x1:x1+8])
                    p2 = _mm_loadu_si128(img[y2, x1:x1+8])
                    p3 = _mm_loadu_si128(img[y1, x2:x2+8])
                    p4 = _mm_loadu_si128(img[y2, x2:x2+8])
                    fx1 = x - x1
                    fx2 = 1 - fx1
                    fy1 = y - y1
                    fy2 = 1 - fy1
                    w1 = _mm_setr_ps(fx2 * fy2, fx1 * fy2, fx2 * fy1, fx1 * fy1)
                    w2 = _mm_setr_ps(1, 1, 1, 1)
                    p = _mm_add_ps(_mm_add_ps(_mm_mul_ps(p1, w1), _mm_mul_ps(p2, _mm_mul_ps(w2, _mm_set1_ps(fy1)))), _mm_add_ps(_mm_mul_ps(p3, _mm_mul_ps(w2, _mm_set1_ps(fx1))), _mm_mul_ps(p4, _mm_mul_ps(w2, _mm_mul_ps(_mm_set1_ps(fx1), _mm_set1_ps(fy1))))))
                    _mm_storeu_si128(dst[i, j:j+8], p)
    return dst

# 定义SSE2指令集函数
def _mm_loadu_si128(p):
    return np.frombuffer(p, dtype=np.uint8)

def _mm_setr_ps(a, b, c, d):
    return np.array([a, b, c, d], dtype=np.float32)

def _mm_add_ps(a, b):
    return a + b

def _mm_mul_ps(a, b):
    return a * b

def _mm_set1_ps(a):
    return np.array([a, a, a, a], dtype=np.float32)

# 测试代码
img = cv2.imread('test.jpg', 0)
rows, cols = img.shape[:2]
M = cv2.getRotationMatrix2D((cols/2, rows/2), 45, 1)
start = time.time()
dst = warpAffine_sse2(img, M)
end = time.time()
print('Time:', end - start)
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()