雙線性插值python實現

#!/usr/bin/python
# -*- coding: utf-8 -*-
 
__author__ = 'Alex Wang'
 
import numpy as np
import cv2
import time
 
 
'''
python implementation of bilinear interpolation
'''
 
 
def bilinear_interpolation(img,out_dim):
    src_h, src_w, channel = img.shape
    dst_h, dst_w = out_dim[1], out_dim[0]
    if src_h == dst_h and src_w == dst_w:
        return img.copy()
    dst_img = np.zeros((dst_h,dst_w,3),dtype=np.uint8)
    scale_x, scale_y = float(src_w) / dst_w, float(src_h) / dst_h
    for i in range(3):
        for dst_y in range(dst_h):
            for dst_x in range(dst_w):
 
                # find the origin x and y coordinates of dst image x and y
                # use geometric center symmetry
                # if use direct way, src_x = dst_x * scale_x
                src_x = (dst_x + 0.5) * scale_x - 0.5
                src_y = (dst_y + 0.5) * scale_y - 0.5
 
                # find the coordinates of the points which will be used to compute the interpolation
                src_x0 = int(np.floor(src_x))
                src_x1 = min(src_x0 + 1 ,src_w - 1)
                src_y0 = int(np.floor(src_y))
                src_y1 = min(src_y0 + 1, src_h - 1)
 
                # calculate the interpolation
                temp0 = (src_x1 - src_x) * img[src_y0,src_x0,i] + (src_x - src_x0) * img[src_y0,src_x1,i]
                temp1 = (src_x1 - src_x) * img[src_y1,src_x0,i] + (src_x - src_x0) * img[src_y1,src_x1,i]
                dst_img[dst_y,dst_x,i] = int((src_y1 - src_y) * temp0 + (src_y - src_y0) * temp1)
 
    return dst_img
 
 
if __name__ == '__main__':
    img = cv2.imread('data/cat1.jpg')
    start = time.time()
    dst = bilinear_interpolation(img,(100,100))
    print 'cost %f seconds' % (time.time() - start)
    cv2.imshow('result',dst)
    cv2.waitKey()