Image beautifying method and mobile terminal
A mobile terminal and image technology, applied in image data processing, graphics and image conversion, instruments, etc., can solve problems such as low efficiency, poor beautification effect, and complicated beautification operation, so as to reduce manual operation, strong robustness, and improve image quality. The effect of beautifying efficiency and beautifying effect
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Embodiment 1
[0027] refer to figure 1 , which shows a flow chart of the steps of an image beautification method according to Embodiment 1 of the present invention, which may specifically include the following steps:
[0028] Step 101, acquiring face feature points in the original image.
[0029] Wherein, the original image is a selfie imported by the user or a picture containing a face image taken by other people, and the image is to be beautified; the feature points of the face in the original image include: facial features (eyes, eyebrows, nose, mouth, ears) position and contour point position.
[0030] The technology for detecting facial feature points is very mature, and the more classic algorithms include ASM (Active ShapeModel, Active Shape Model) and AAM (Active Appearance Model, Active Appearance Model). ASM is a point distribution model. The coordinates of key points are sequentially concatenated to obtain shape vectors, such as the shape vectors of eyes, lips, ears, noses, and ...
Embodiment 2
[0043] refer to figure 2 , shows a flow chart of the steps of an image beautification method according to Embodiment 2 of the present invention, which may specifically include the following steps:
[0044] Step 201, acquiring face feature points in the original image.
[0045] For this step, reference may be made to the detailed description of step 101, which will not be repeated here.
[0046] Step 202, process the original image and the facial feature points into the regression model through a preset regression model to obtain an optimal parameter set; wherein, the preset regression model takes multiple image samples as input , the regression model with the smallest loss value of the target parameter group corresponding to each image sample is obtained through training.
[0047] It can be seen from the detailed description of step 102 that the target parameter set of each image is the optimal parameter set of each image.
[0048] In the embodiment of the present inventio...
Embodiment 3
[0084] refer to Figure three , shows a structural block diagram of a mobile terminal according to Embodiment 3 of the present invention.
[0085] The mobile terminal 300 includes: a face feature point acquisition module 301 , a parameter group acquisition module 302 , and an image adjustment module 303 .
[0086] The functions of each module and the interaction between each module are introduced in detail below.
[0087] The face feature point acquisition module 301 is used to acquire the face feature points in the original image.
[0088] The parameter set acquisition module 302 is used to process the original image and the facial feature points acquired by the facial feature point acquisition module through a preset regression model to obtain an optimal parameter set; wherein the The preset regression model is a regression model trained according to multiple image samples and target parameter groups corresponding to each image sample.
[0089] The image adjustment module...
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