Matplotlab displays images read by OpenCV

1. Confirm the array type of the image

Using OpenCV()When a function reads an image, the second parameter (flag) determines the reading method of the image. Specifically,0、1and-1Correspond to different reading modes:

Processing images containing transparency channels is not discussed here

useJudge image type

In OpenCV, when you use()When a function reads an image, it loads the image into memory and stores it as a NumPy array.is an attribute of the NumPy array, which returns a tuple containing the dimensions of the array. By checking the length of this tuple (i.e. the number of dimensions), we can determine the type or structure of the array.

• Grayscale image: Usually a two-dimensional array, represented as(height, width)， The returned tuple length is 2.
• Color images: Usually a three-dimensional array, represented as(height, width, channels)， channelsDenote the number of color channels (for example, an RGB image has three channels, and the shape is usually(height, width, 3)）， The returned tuple length is 3.

# img is a NumPy arrayif len() == 2:
    print("This is a grayscale image")
elif len() == 3:
    print("This is a color image")

2. Use Matplotlab to display

Grayscale image

(img, cmap='gray')

understandcmap='gray'

directimshowThe default color map will be used (usuallyviridis), which is not usually the desired grayscale effect. The color map (cmap) needs to be explicitly specified as 'gray', which ensures that each pixel value in the image is correctly mapped to the grayscale level.

Color images

The color channels read by OpenCV areBGR, the color channel displayed by Matplotlib reads isRGB, so the channel needs to be converted when reading

(img[:, :, ::-1])

understandimg[:, :, ::-1]

The first dimension and the second dimension represent the height and width of the image respectively. The third dimension has three channels: red (R), green (G), and blue (B)

• :Indicates that all elements are selected.
• ::-1It is the slice syntax in Python, which means selecting elements from the end to the beginning (inverse order). The intention here is usually to flip the data of the last dimension, such as converting the RGB format to the BGR format, or vice versa.

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