An image processing method and device

Abstract

The invention discloses an image processing method and device. The image processing method comprises the following steps: reading a to-be-synthesized image; reading a to-be-cutout image, performing RGB channel splitting on the to-be-cutout image and respectively calculating edge pixel average values of the three channel images; performing binarization processing on the images of the three channelsby using the average values, and combining binarization images of the three channels to obtain a superposed binarized image so as to record target profile information of the binarized image; readingthe target profile information to generate a position of a synthetic image in the to-be-synthesized image; putting the to-be-cutout image on the position of the synthetic image so as to generate the synthetic image, and saving target categories and the generated positions as tag files, wherein numbers of the target object categories and names of the target object categories in the to-be-cutout image are configured in advance, and the numbers of the categories correspond to the names of the categories. According to the image processing method and device disclosed by the invention, by utilizingimage cutout and synthesis, image data required to be learned deeply are acquired and automatic generation of the corresponding labels is realized.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image processing method and device. Background technique [0002] The current image matting and synthesis methods are aimed at complex background images, so most of them are relatively complicated; the training data (images) of the current deep learning model (for image-related tasks such as classification and detection) usually need to be taken manually, and the same target Objects in different scenes usually have the problem of repeated shooting (for example, in different scenes of a bottle of Coke in the refrigerator and on the table, its placement may be the same, but its image data needs to be shot separately); for deep learning For the image target detection task in the model, in addition to the image itself, the training data also needs image tags (including the name of the image, the category name of the target object contained in the image, and the location of the targ...

AI Technical Summary

Problems solved by technology

[0002] The current image matting and synthesis methods are aimed at complex background images, so most of them are relatively complicated; the training data (images) of the current deep learning model (for image-related tasks such as classification and detection) usually need to be taken manually, and the same target Objects in different scenes usually have the problem of repeated shooting (for example, in different scenes of a bottle of Coke in the refrigerator and on the table, its placement may be the same, but its image data needs to be shot separately); for deep learning For the image target detection task in the model, in addition to the image itself, the training data also needs image tags (including the name of the image, the category name of the target object contained in the image, and the location of the target object). a lot of energy and time

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