Method for changing automobile body color in automobile image and algorithm structure thereof
An algorithm structure and image technology, applied in the field of image processing, can solve problems such as low availability rate, unsatisfactory effect, uneven color, etc.
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Embodiment 1
[0095] An embodiment of the present invention provides a method for modifying a local area of an image, the method comprising: S1) acquiring a first image and a pixel vector, and determining a local area in the first image; S2) using the pixel vector in the local Intra-region mapping generates local regions having pixel features corresponding to said pixel vectors;
[0096] Such as figure 1 , the local area can be set as the position of the vehicle body, and the first image can be set as having the background BG( figure 1 box with two dotted four-pointed stars) and an image of a car with body color a x a ( figure 1 The area with slash in is the front windshield or window glass), and the pixel feature of the pixel vector is the color feature (let it be color b).
[0097] Specifically, determining the local area in the first image in step S1) includes:
[0098] S101) Determine an object in the first image;
[0099] S102) Perform edge detection and image segmentation on th...
Embodiment 2
[0128] Such as figure 1 , an algorithm structure for changing a local area of an image, the algorithm structure includes: a generative confrontation network, which is used to map the input first image and the input pixel vector in the local area of the first image to generate a The local area of the pixel feature corresponding to the pixel vector.
[0129] Optionally, the generative model of the generative confrontation network includes: an encoder, a hidden layer space vector, and a decoder; the input layer of the encoder is used to receive the data of the first image; the hidden layer space The vector is used to transfer the data output by the output layer of the encoder to the input layer of the decoder; the hidden layer of the decoder is used to receive the data output by the input layer of the decoder and collect and fuse the Data of the hidden layer of the encoder; wherein, the hidden layer of the decoder is also used to receive the pixel vector and output a local...
Embodiment 3
[0144] Such as Figure 5 , a training method for changing an image local area algorithm, the training method includes:
[0145] S1) Obtain a set of labeled sample data, wherein the set of labeled sample data includes different images of objects with different pixel characteristics, and the local area of the image where the object is located and corresponding to the object has a labeled mask area and also has a The labeled pixel vector corresponding to the pixel feature;
[0146] S2) Build a generative confrontation network, the generative confrontation network includes a generative model and a discriminant model, and the generative model has the characteristics of updating historical output data to current input data and obtaining partial data consistent with the characteristics of historical input data The loop function of the current output and the loop function is used to construct the loop consistency loss and the reconstruction loss, the generative model also has a dec...
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