opencv: cv2.resize 探究(源码)

  我们 习惯的坐标表示 是 先 x 横坐标，再 y 纵坐标。在图像处理中，这种惯性思维尤其需要担心。

  因为在计算机中，图像是以矩阵的形式保存的，先行后列。所以，一张 宽×高×颜色通道＝480×256×3 的图片会保存在一个 256×480×3 的三维张量中。图像处理时也是按照这种思想进行计算的（其中就包括 OpenCV 下的图像处理），即 高×宽×颜色通道。

  但是问题来了，cv2.resize这个api却是个小例外。因为它的参数输入却是 宽×高×颜色通道。

  查看官方文档 ：

resize Resizes an image. C++: void resize(InputArray src, OutputArray dst, Size dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR )¶ Python: cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) → dst C: void cvResize(const CvArr* src, CvArr* dst, int interpolation=CV_INTER_LINEAR ) Python: cv.Resize(src, dst, interpolation=CV_INTER_LINEAR) → None Parameters: src – input image. dst – output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src. dsize – output image size; if it equals zero, it is computed as: exttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))} Either dsize or both fx and fy must be non-zero.

  由以下语段可知， cv2.resize 的 dsize 的参数输入是 x轴×y轴，即 宽×高：

dst – output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.

  自己写了一个代码实例来验证它：

import cv2
import numpy as np
import random


seq = [random.randint(0, 255) for _ in range(256*480*3)]
mat = np.resize(seq, new_shape=[256, 480, 3])
print (mat.shape = {}.format(mat.shape))
cv2.imwrite(origin_pic.jpg, mat)
origin_pic = cv2.imread(./origin_pic.jpg)
print (origin_pic.shape = {}.format(origin_pic.shape))
resize_pic = cv2.resize(src=origin_pic,
                        dsize=(int(origin_pic.shape[1] * 2),
                               int(origin_pic.shape[0] * 1))
                        )
print (resize_pic.shape = {}.format(resize_pic.shape))
cv2.imshow(resize_pic, resize_pic)
cv2.waitKey(0)
cv2.destroyAllWindows()

  Output：

mat.shape = (256, 480, 3)
origin_pic.shape = (256, 480, 3)
resize_pic.shape = (256, 960, 3)

  成功应验了文档里的参数说明。