Outer contour point labeling method and device, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides an outer contour point labeling method and device, electronic equipment and a readable storage medium, and relates to the technical field of image processing. A to-be-processed image is obtained, the to-be-processed image comprises a target area, the target area represents the area where a to-be-labeled object is located, and three-dimensional model parameters of the to-be-labeled object in the target area are obtained; and a three-dimensional model is constructed based on the three-dimensional model parameters and comprises a three-dimensional outer contour point candidate area and a three-dimensional outer contour vertex. A three-dimensional outer contour point candidate region is projected to the to-be-labeled object to obtain a two-dimensional outer contour point candidate region of the to-be-labeled object. Based on a convex hull algorithm, screening is performed from the two-dimensional outer contour point candidate region to obtain a target outer contour point. Finally, the target outer contour point on the to-be-marked object is marked to obtain an outer contour point marking result. Thus, labeling of the two-dimensional human body outer contour can be rapidly and accurately achieved so that a large amount of training data can be obtained, and therefore the accuracy of the training result is improved.

Description

Technical field[0001]The present invention relates to the field of image processing, and in particular, there is a method, apparatus, an electronic device, and a readable storage medium of outer contour point.Background technique[0002]In the field of image processing, the human outer contour consists of a plurality of two-dimensional outer contour points having clear semantics. The human body outer contour point is detected by the training model, which can be applied to the body of the body body.[0003]At present, there is no disclosure or similar data set including human outer contours in the industry. The existing source of data sets mainly relies on manual marking, less data volume, slow labeling, and labor cost. However, model training requires a lot of training data, and the number of training data has seriously affects the training results.[0004]How to quickly accurately implement the marks of the two-dimensional human body outline, to get a lot of training data is the problem ...

AI Technical Summary

Problems solved by technology

Existing sources of data sets mainly rely on manual labeling, the amount of data is small, the labeling speed is slow and the labor cost is

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