python双重for循环优化方法。

用python做图像处理。有些特殊需求需要用双重for循环遍历图像来操作例如下面代码

def getbinarizationimg(simg, targeth, targetw):
    print(simg.shape)
    h,w,c = simg.shape
    box = np.zeros((h, w),dtype=np.uint8)
    pole = np.zeros((h, w),dtype=np.uint8)
    
    for u in range(h):
        for v in range(w):
            if simg[u][v][0] == 0 and simg[u][v][1] == 220 and simg[u][v][2] == 220:
                box[u][v] = 255
    img_box = cv2.resize(box, (targetw, targeth), fx=0, fy=0,
interpolation=cv2.INTER_NEAREST)
    return img_box

图像大小为1024 * 2048 .主函数中测试结果如下

elapse_time =  3.5969557762145996
elapse_time =  3.608659029006958
elapse_time =  3.667614459991455
elapse_time =  3.546481132507324

直接疯掉的节奏啊。后来看到KCF算法中有个加速方法，拿来用用

from numba import jit


@jit()
def getbinarizationimg(simg, targeth, targetw):
    print(simg.shape)
    h,w,c = simg.shape
    box = np.zeros((h, w),dtype=np.uint8)
    pole = np.zeros((h, w),dtype=np.uint8)
    
    for u in range(h):
        for v in range(w):
            if simg[u][v][0] == 0 and simg[u][v][1] == 220 and simg[u][v][2] == 220:
                box[u][v] = 255
    img_box = cv2.resize(box, (targetw, targeth), fx=0, fy=0,
interpolation=cv2.INTER_NEAREST)
    return img_box

elapse_time =  0.29776477813720703
elapse_time =  0.0017366409301757812
elapse_time =  0.001975536346435547
elapse_time =  0.001725912094116211
elapse_time =  0.0018794536590576172

性能差不少啊。