The affine transformations (translation, scaling, rotation, etc.) described above are all object transformations in two-dimensional space. If rotation occurs in three-dimensional space, then this transformation is a projection transformation. Projection transformation needs to first calculate the projection transformation matrix cv2.getPerspectiveTransform(src, dst) Among them, src and dst are both 4×2 two-dimensional matrices ( ndarray ), each […]

ROI is the abbreviation of Region Of Interest , which refers to the region of interest in the image. Many times, we only operate on part of the area in the image. Example cv2.imshow(“original image”, image) h, w = image.shape[:2] # 获取ROI,/表示浮点数除法，返回值是float，而//表示整数除法cy = h // 2 cx = w // 2 # ROI区域提取roi = image[cy-200:cy+200,

A binary image refers to an image with only two colors, black and white, where 0 represents black and 1 represents white (ie, 255). The general process of image binarization is to first convert the input image into a grayscale image, and then convert it into a binary image through the binarization method. This process

In affine transformations, such as resize , interpolation has actually been used. When a picture is enlarged from 100 × 100 to 200 × 200, new pixels (red dots in the figure below) will be generated, and what is the new pixel value, this is what interpolation does. There are many interpolation algorithms supported by

Normalization is to limit the data to a certain range after processing. The purpose of normalization is to make incomparable data comparable while maintaining the relative relationship between them. The normalize method is used in opencv to achieve normalization, and its function prototype is as follows cv2.normalize(src, dst, alpha=None, beta=None, norm_type=None, dtype=None, mask=None) in: src: