这些相当于我的学习笔记，所以并没有很强的结构性和很全的介绍，请见谅。
1 读取、写入图像
下面是一个简短的载入图像、打印尺寸、转换格式及保存图像为.png的例子：
# -*- coding: utf-8 -*-
import cv2
import numpy as np
# 读入图像
im = cv2.imread(‘../data/empire.jpg‘)
# 打印图像尺寸
h, w = im.shape[:2]
print h, w
# 保存原jpg格式的图像为png格式图像
cv2.imwrite(‘../images/ch10/ch10_P210_Reading-and-Writing-Images.png‘,im)
# 注：imread默认读取的是RGB格式，所以即使原图像是灰度图，读出来仍然是三个通道，所以，在imread之后可以添加参数
# 注：这里是相对路径: \与/是没有区别的，‘’ 和 “” 是没有区别的。 ../表示返回到上一级目录下，./表示与该源码文件同一级目录下。
# 注：函数imread()将图像返回为一个标准的NumPy数组。
1.1 相关注释
cv2.imread
Python: cv2.imread(filename[, flags])
| Parameters: |
- filename – Name of file to be loaded.
- flags –
Flags specifying the color type of a loaded image:
- CV_LOAD_IMAGE_ANYDEPTH - If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit.
- CV_LOAD_IMAGE_COLOR - If set, always convert image to the color one
- CV_LOAD_IMAGE_GRAYSCALE - If set, always convert image to the grayscale one
-
- >0 Return a 3-channel color image.
-
Note
In the current implementation the alpha channel, if any, is stripped from the output image. Use negative value if you need the alpha channel.
- =0 Return a grayscale image. 如果是灰度图就用这个就好了。例如：cv2.imread‘../data/empire.jpg‘，0)
- <0 Return the loaded image as is (with alpha channel).
|
cv2.imwrite
Python: cv2.imwrite(filename, img[, params])
| Parameters: |
- filename – Name of the file.
- image – Image to be saved.
- params –
Format-specific save parameters encoded as pairs paramId_1, paramValue_1, paramId_2, paramValue_2, ... . The following parameters are currently supported:
- For JPEG, it can be a quality ( CV_IMWRITE_JPEG_QUALITY ) from 0 to 100 (the higher is the better). Default value is 95.
- For PNG, it can be the compression level ( CV_IMWRITE_PNG_COMPRESSION ) from 0 to 9. A higher value means a smaller size and longer compression time. Default value is 3.
- For PPM, PGM, or PBM, it can be a binary format flag ( CV_IMWRITE_PXM_BINARY ), 0 or 1. Default value is 1.
|
2 图像RGB, HSV 通道分离
# Convert BGR to r,g,b
b,g,r = cv2.split(im)
# Convert BGR to HSV
image_hue_saturation_value = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
h,s,v=cv2.split(image_hue_saturation_value)
# Convert BGR to gray
image_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# 注：RGB channels is indexed in B G R which is different from matlab。
# 注：Any channels could be split using cv2.split， pay attention to the sequence of channels
2.1 相关注释
Python: cv2.split(m[, mv]) → mv
| Parameters: |
- src – input multi-channel array.
- mv – output array or vector of arrays; in the first variant of the function the number of arrays must match src.channels(); the arrays themselves are reallocated, if needed.
|
Python: cv2.cvtColor(src, code[, dst[, dstCn]]) → dst
| Parameters: |
- src – input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point.
- dst – output image of the same size and depth as src.
- code – color space conversion code (see the description below).
- dstCn – number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code .
|
3 图像矩阵的操作（点乘，复制，截取，1到N维矩阵）
# mask seed 3D matrixseed_mask_single_channel_list = np.array([[[1,0,0],[0,0,0],[0,0,0]],[[0,1,0],[0,0,0],[0,0,0]],[[0,0,1],[0,0,0],[0,0,0]], [[0,0,0],[1,0,0],[0,0,0]],[[0,0,0],[0,1,0],[0,0,0]],[[0,0,0],[0,0,1],[0,0,0]], [[0,0,0],[0,0,0],[1,0,0]],[[0,0,0],[0,0,0],[0,1,0]],[[0,0,0],[0,0,0],[0,0,1]]])
# cut image
image_new_sample = image_source[:200,:200] #取前200个行和列的元素，python是从0开始的，所以0:200表示的是0-199这200个元素，取不到200.而初始位置0可以省略
#separate channel
mask_singel_channel = np.tile(seed_mask_single_channel_list[1],(70,70))[:200,:200] #第一个3*3的mask作为一个单元进行复制成为70行,70列，截取前200行,200列
single_channel_image = mask_singel_channel * image_new_sample #表示点乘
# 注：矩阵的操作用Numpy这个类库进行。
3.1 相关注释
numpy.array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)
| Parameters: |
object : array_like
An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. This argument can only be used to ‘upcast’ the array. For downcasting, use the .astype(t) method.
copy : bool, optional
If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.).
order : {‘C’, ‘F’, ‘A’}, optional
Specify the order of the array. If order is ‘C’ (default), then the array will be in C-contiguous order (last-index varies the fastest). If order is ‘F’, then the returned array will be in Fortran-contiguous order (first-index varies the fastest). If order is ‘A’, then the returned array may be in any order (either C-, Fortran-contiguous, or even discontiguous).
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).
ndmin : int, optional
Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement.
|
| Returns: |
out : ndarray
An array object satisfying the specified requirements.
|
e.g. 最外层始终都是[],所以如果是1维就一个[],2维就2个，N维就N个
>>> np.array([1, 2, 3])
array([1, 2, 3])
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])
>>> np.array([1, 2, 3], ndmin=2)
array([[1, 2, 3]])
numpy.tile(A, reps)
| Parameters: |
A : array_like
The input array.
reps : array_like
The number of repetitions of A along each axis.
|
| Returns: |
c : ndarray
The tiled output array
|
e.g.
>>> b = np.array([[1, 2], [3, 4]])
>>> np.tile(b, 2)
array([[1, 2, 1, 2],
[3, 4, 3, 4]])
>>> np.tile(b, (2, 1))
array([[1, 2],
[3, 4],
[1, 2],
[3, 4]])
时间： 2024-10-29 10:47:45
