Method of automatically regulating interface style based on background image
A background image, automatic adjustment technology, applied in the direction of program loading/starting, program control devices, etc., can solve the problems of excessive interface style changes, large system overhead, deprivation of the user's right to create an interface, etc., to improve the appearance of the interface. The effect of improving usability
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Embodiment 1
[0039] Please refer to figure 1 , the present invention automatically adjusts the interface style method according to the background image, and its manipulation steps are as follows:
[0040] (1) First, provide the user with a solution to automatically adjust the interface style according to the background image, but allow the user to disable this function.
[0041] (2) If this function is enabled, immediately after the user changes the background image, process A uniformly intercepts n samples S on the background image 1 to S n , which is stored in the background image sample sequence, each sample is a small part of the image background, which can be a rectangle or other shapes.
[0042] (3) Process B performs feature analysis on the samples in the background image sample sequence one by one. The features mentioned here mainly include color values (RGB data, CYMK data. The CYMK color coding scheme uses four basic tones of cyan (Cyan), magenta (Magenta), yellow (Yellow) a...
Embodiment 2
[0065] Embodiment 2: Project: "Lenovo Portable Home 2.0"
[0066] Implementation process:
[0067] 1. Make sure to select the following characteristic parameters:
[0068] R - red channel value
[0069] G——Green channel value
[0070] B - blue channel value
[0071] E - Hue
[0072] S - Saturation
[0073] L - Brightness
[0074] Therefore, the eigenvector K=(R, G, B, E, S, L). Algorithms for obtaining the above characteristic parameters from a certain pixel point are known algorithms, which will not be described below.
[0075] 2. Evenly intercept 64 pixels from the 240×320 screen to form the sample space S 1 -S 64 .
[0076] 3. Calculate the value of the feature vector K for each pixel, and obtain 64 feature vectors, which are denoted as
[0077] K 1 , K 2 , K 3 ,...,K 64
[0078] where each vector is an (R, G, B, E, S, L) tuple with six components.
[0079] 4. For each component, find the average value of the component in the 64 vectors to form K j0 ...
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