Python + OpenCV2 Series: 2-picture manipulation

These are equivalent to my study notes, so there is no strong structural and full introduction, please forgive me.

1 reading and writing images

Here is a brief example of loading images, printing dimensions, converting formats, and saving images as. png:

 #  -*-coding:utf-8-*- import   Cv2import NumPy as NP 
  #   read-in image  im = Cv2.imread ( " .. /data/empire.jpg   " )  #   print image size  h, w = Im.shape[:2 print   H, W  #   The image in the original JPG format is a PNG format image  Cv2.imwrite ( " . /images/ch10/ch10_p210_reading-and-writing-images.png  , IM) 

# Note: Imread default read is the RGB format, so even if the original image is a grayscale image, read out is still three channels, so after Imread can add parameters

# Note: Here is the relative path: \ and/is no difference, ' and ' is no difference ... /means return to the previous level directory,./indicates the same level directory as the source file.

# Note: the function imread () returns the image as a standard NumPy array.

1.1 Related NotesCv2.imread

Python: cv2. Imread (filename[, flags])

Parameters:

filename –name of file to is 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 The current implementation The alpha channel, if any, was stripped from the output image. Use negative value if you need the alpha channel. =0 Return a grayscale image. It would be nice to use this if it was a grayscale image. For example: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 is currently supported: For JPEG, it can be a quality ( cv_imwrite_jpeg_quality ) from 0 to (the higher is the better). Default value is 95. For PNG, it can being 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 image RGB, HSV channel separation

# convert BGR to r,g,bb,g,r = cv2.split (IM)#  Convert BGR to HSVimage_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)

Note: RGB channels are indexed in B G R which are different from Matlab.

# Note: Any channels could is split using cv2.split, pay attention to the sequence of channels

2.1 Related Notes

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 is 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 operation of the image matrix (point multiplication, copy, intercept, 1 to n-dimensional matrix)

# Mask Seed 3D Matrix
Seed_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#mask_singel_channel = Np.tile (seed_mask_ Single_channel_list[1], (70,70)) [: 200,:200= mask_singel_channel * Image_new_sample #表示点乘

# Note: The operation of the matrix is performed using the NumPy class library.

3.1 Related Notes

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, a object whose __array__ method returns an array, or any (nested) sequ ence. dtype : data-type, optional The desired data-type for the array. If not given, then the type is determined as the minimum type required to the objects in the sequence. This argument can is 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 would only be made if __array__ returns a copy, if obj was a nested sequence, or if a copy is needed to SA Tisfy any of the other requirements (Dtype, order, etc). order : {' C ', ' F ', ' A '}, optional Specify the order of the array. If order is ' C ' (default) and then the array would be in c-contiguous order (Last-index varies the fastest). If order is ' F ', then the returned array would be in fortran-contiguous order (First-index varies the fastest). If order is ' A ' and then the returned array could be in any order (either C, fortran-contiguous, or even discontiguous). Subok : bool, optional If True, then sub-classes'll be Passed-through, otherwise the returned array would be forced to be a base-class array (de Fault). ndmin : int, optional Specifies the minimum number of dimensions that the resulting array should has. Ones'll is pre-pended to the shape as needed to meet this requirement.

Returns:

out: Ndarray An array object satisfying the specified requirements.

e.g. the outermost layer is always [], so if it is a 1-dimensional one [],2 dimension is 2, n-Dimension is n

>>> 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< c10>],       [3, 4]])

Python + OpenCV2 Series: 2-picture manipulation