549 results about "Hue" patented technology

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

The invention belongs to the technical field of fire detection, and discloses a method and a system for artificial intelligence identification, research and judgment of fire alarm, and the method comprises the following steps: carrying out the framing of input video data, converting a video stream into continuous image frames frame by frame, carrying out the timestamp marking of each frame of image, and obtaining a framed image; according to the method, flames are positioned by analyzing red and orange tones of the images, early signs of a fire can be more accurately captured by analyzing flame shapes and diffusion behaviors, the area change rate of flames and smoke in continuous frames can be analyzed, the dynamic change of flame diffusion speed and smoke coverage can be beneficially understood, and the method is suitable for popularization and application. The fire hazard early warning is more timely and accurate, the fire hazard risk level is evaluated according to the flame dynamic model, and the system is allowed to adjust the sensitivity and threshold of the early warning signal according to the real-time data, so that the fire hazard early warning scheme is more targeted and effective, faster response can be provided at the initial stage of the fire hazard, and casualties and property loss are greatly reduced.

The invention relates to the technical field of image recognition, and discloses a food freshness detection method and system based on image recognition, and the method comprises the steps: collecting an initial image, carrying out the white balance correction through a white reference card, and obtaining a corrected image with an accurate color; performing target area segmentation on the corrected image, extracting a food main area and eliminating background interference; pixel sampling is carried out on the main area image, the main area image is converted to Lab, HSV and RGB color spaces, and redness, yellowness, brightness, hue angle and red-green ratio are extracted to form a multi-dimensional color feature set; establishing a difference feature vector based on the feature set and a preset standard sample, and comparing the deviation degrees of the target food and the standard sample on the multi-dimensional color features; and finally, judging the freshness grade of the food according to the deviation degree. According to the invention, lossless and rapid food freshness grading detection is realized.

The invention discloses a transformer substation non-uniform exposure image enhancement method and system based on adaptive fusion, and the method comprises the following steps: obtaining non-uniform exposure video frame images in a transformer substation monitoring video, and carrying out the successive preprocessing of the video frame images; processing the brightness channel graph V by adopting a two-branch parallel step; for the H and S parts of the color channel graph, the saturation of the pre-channel graph is enhanced on the basis of the hue difference; combining the enhanced images of the brightness channel and the color channel with an original channel image, respectively designing a self-adaptive channel weight method for fusion, and converting H, S and V channels after fusion into RGB enhanced images for result output; and establishing an evaluation mechanism to evaluate the quality of the enhanced image, and adjusting parameters of an enhancement algorithm according to an evaluation result. According to the invention, the problem of non-uniform image exposure under the complex illumination condition of the substation is effectively solved, and powerful support is provided for accurately monitoring the state of substation equipment.

The invention discloses an X-ray detection image enhancement method and system, and the method comprises the following image enhancement steps: carrying out the gray preprocessing of an image, carrying out image enhancement preprocessing, carrying out homomorphic filtering enhancement processing, carrying out USM sharpening processing, taking a filtered image Ifiltering as an input, carrying out the processing of an input image through Gaussian blur, generating an enhanced image, and finally carrying out the tone adjustment. And carrying out color gradation adjustment on the enhanced image Ishadow to obtain a final target image Itarget. According to the method, firstly, multiple parameters such as exposure, brightness and contrast are adjusted for the original image, so that the overall quality of the image can be improved more comprehensively. Compared with the prior art, the multi-dimensional adjustment provides greater flexibility, so that the image can reach the optimal basic state before entering the subsequent processing. And a foundation can be laid for subsequent image post-processing, image analysis, image classification and image library and map establishment.

The invention relates to the technical field of light control, and discloses a creative and creative light color gradient control method and system, and the method comprises the steps: carrying out the preprocessing of the spatial position information of audiences, generating an audience position sequence, calculating the moving speed, the steering angle and the region density in real time, and generating a light adjustment signal in combination with a preset threshold value; mapping the RGB values to an HSL space to generate an initial color parameter set; adjusting a brightness weight, a hue weight and a saturation weight based on the moving speed, the steering angle and the area density, and updating a color parameter set; generating a gradient sequence through smooth interpolation; sending a control instruction by using a low-delay protocol and monitoring a delay value; and calculating a comprehensive evaluation value according to the rendering effect, iteratively adjusting the weight if the comprehensive evaluation value exceeds the limit, and regenerating a light control instruction. According to the invention, through the technical means of real-time calculation, dynamic weight adjustment, low-delay transmission, deviation feedback and the like, a light control instruction with smooth gradual change is generated, and accurate control of light color gradual change is realized.

The invention relates to the technical field of image recognition, in particular to a quality detection and recognition method. The method comprises the steps of performing spatial mapping on an original image, performing feature extraction on the mapped image, performing step-by-step segmentation on an obtained image to be layered, extracting a required layer, forming color features by using color information of the layer, performing regression judgment through machine learning training, and obtaining a score. The color features are calculated through distribution of hue, saturation and brightness of each pixel in the image and comprise one or more combinations of a mean value, a standard deviation and a deviation. Through computer vision and image processing technologies, an optimal detection area in a sample is automatically identified and analyzed, stratified analysis is carried out, multi-dimensional quality data of the sample is detected, and rapid and accurate acquisition and quality evaluation of the multi-dimensional quality data of the sample are realized. For the evaluation standard difference of different groups, only the evaluation score Y value needs to be adjusted, the model does not need to be trained again, and the application range of the method is greatly widened.

The invention discloses a control method and device of a vehicle-mounted atmosphere lamp, electronic equipment and a storage medium. The method comprises the steps that the current scene mode of the vehicle-mounted central control screen is acquired, and the current scene mode comprises one of the following modes: an entertainment mode, a rest mode and an interaction mode; analyzing the current scene mode by using a preset color extraction model, and determining an initial hue parameter of the vehicle-mounted atmosphere lamp; adjusting the initial hue parameter based on a preset color strategy and the historical hue parameter to obtain a target hue parameter; and controlling the vehicle-mounted atmosphere lamp to output light with the corresponding hue by using the target hue parameter. The technical problem that the driving experience feeling is poor due to the fact that the vehicle-mounted atmosphere lamp cannot achieve real-time interaction with the central control screen in the related technology is solved.

The invention provides a flow velocity identification method and device based on an improved U-Net network model, and belongs to the technical field of flow velocity measurement, and the method comprises the steps: carrying out the sampling of collected river image data, and obtaining the sampling image data; randomly cutting the sampling image data according to a preset size to obtain a cut image; performing color transformation on the cut image according to a preset random probability to obtain an image sequence, the color transformation including at least one of luminosity transformation, contrast transformation, saturation transformation and tone transformation; and inputting the image sequence into an improved U-Net network model to carry out flow velocity identification so as to obtain a flow velocity prediction result of the river channel. According to the flow velocity identification method based on the improved U-Net network model, the time sequence information and the texture information of the river channel video frame are extracted through the improved U-Net network model, and then the corresponding flow velocity value is output, so that the identification precision and the identification efficiency of the flow velocity can be improved.

The invention discloses an SAR image orientation sensitive target detection method based on sub-aperture image saturation characteristics, and belongs to the technical field of synthetic aperture radar image target detection. The azimuth sensitive target detection is realized through the following steps: 1) sub-aperture image generation: dividing an azimuth frequency spectrum into a plurality of sub-bands, and generating a plurality of sub-aperture images through inverse Fourier transform; 2) color synthesis and feature extraction: allocating different hues to each sub-aperture image by using an HSV color space, synthesizing an RGB color image, converting the RGB color image to the HSV color space, and extracting a saturation channel in the RGB color space as an azimuth sensitivity feature map; and 3) target detection: carrying out threshold segmentation on the saturation feature map, and identifying a pixel region with a high saturation value, namely, an orientation sensitive target. According to the method, the color saturation change caused by the scattering difference of the target in different sub-apertures is utilized, effective detection of azimuth sensitive targets such as artificial buildings and vehicles is achieved, and the method has the advantages of being simple in calculation and high in robustness.

The invention relates to a balanced color perception enhancement method for rail transit target detection, and belongs to the technical field of rail transit and computer vision. According to the method, brightness channel adaptive histogram equalization enhancement CLAHE-LC, a multi-segment tone channel mask mechanism MSHCM-M and a three-stage hybrid mechanism non-maximum suppression TSLSH-NMS technology are combined, so that the problems of complex illumination, multi-target shielding, color interference and the like in a rail transit scene are solved, and the accuracy and recall rate of target detection are improved. The method can be seamlessly integrated into an existing deep learning target detection framework, is compatible with multi-label, multi-category and multi-scale features, and remarkably improves the precision and recall rate of target detection in a rail transit scene. Experimental verification shows that the real-time performance is guaranteed, meanwhile, the detection performance is obviously improved compared with a traditional scheme, and the method is suitable for being applied to an actual rail transit safety monitoring and intelligent maintenance system.

A system for the automated evaluation of tobacco leaves, comprising: • a mechanical feeding module (201) configured to feed individual tobacco leaves one after the other; • a controlled imaging chamber (202) equipped with a high-resolution camera and calibrated light sources to capture detailed images of tobacco leaves under standardized lighting conditions; • a color sensor (203) configured to extract colorimetric data from the tobacco leaves, including, but not limited to, RGB values and hue information; • a processing unit (204) comprising one or more processors and memories configured to: i) performing image pre-processing including noise reduction and contrast enhancement, (ii) Execution of segmentation algorithms to isolate the leaf from the background, (iii) Extraction of visual features such as size, shape, texture and color uniformity from the images, (iv) Analyzing the sensor data using statistical or machine learning models to determine quality parameters, (v) Integration of the extracted image features and colorimetric data into an evaluation algorithm based on predefined quality standards, (vi) to assign a quality mark to each tobacco leaf on the basis of the analysis; • an output module (205) configured to display the classification result in real time via an LED screen or a digital interface; • a storage module (206) configured to store classification results for future reference and analysis; • and an Internet of Things (IoT) communication module (207) that enables remote access, monitoring and control of the classification process.

The invention relates to the technical field of image generation, in particular to a content generation method and device based on a user image and a medium, and the method comprises the steps: collecting a user face image and environment features, and extracting face key points, hair style features, expression intensity parameters and posture features of a user in the face image; generating an image interaction state vector in combination with the environment features; presenting a stimulus image sequence containing different tones and colors to the user, collecting facial micro-expressions of the user, establishing a facial expression response curve, and further generating a color physiological response curve; extracting specified characteristic parameters based on the color physiological response curve, and constructing a visual image personality vector in combination with the image interaction state vector; and inputting the personality vector of the visual image into an image style parameter mapping network to obtain an image style control parameter set, processing the three-dimensional reconstruction model of the user face by an image generation and rendering module according to the image style control parameter set, and outputting a static image or a dynamic image.

The invention discloses a self-adaptive low-illumination image enhancement method and device and a storage medium, which are used for stably outputting a high-quality image under various complex conditions. The method comprises the steps of generating an adaptive key parameter set through a preset mapping function based on brightness features; processing the brightness channel through a combined filtering strategy and carrying out adaptive weighted fusion to obtain an irradiation component; converting the brightness channel and the irradiation component to a logarithm domain and performing operation to obtain an initial reflection component; performing linear stretching processing on the initial reflection component to obtain an enhanced reflection component; performing nonlinear gamma correction processing on the irradiation component to obtain a corrected irradiation component; converting the enhanced reflection component back to a linear domain, and performing operation with the corrected irradiation component to obtain an enhanced brightness channel; and recombining the enhanced brightness channel with the original hue channel and saturation channel in the HSV color space, and converting back to the RGB format to obtain an enhanced image.

The invention discloses an interface element display method and device, electronic equipment and a readable storage medium, and belongs to the technical field of electronic equipment.The method comprises the steps that a brightness algorithm is determined according to a display mode of the electronic equipment; determining layer rendering parameters according to the brightness algorithm; the layer rendering parameters comprise target brightness, target hue and target saturation; determining a fuzzy image layer according to the image layer rendering parameter; updating display interface elements by using the fuzzy layer; wherein under the condition that the display mode is a light color mode, the brightness algorithm is an algorithm of a color fading mode; under the condition that the display mode is a dark color mode, the brightness algorithm is an algorithm of a superposition mode.

The invention provides a semantic segmentation and image enhancement method and system for a complex background of a transformer substation, and relates to the technical field of computer vision, and the method comprises the following steps: collecting original image data of the transformer substation, and combining heat map saliency analysis and a spectrum residual error method based on a reciprocal function to obtain a semantic segmentation and image enhancement result; preprocessing the collected original image data to obtain a hue saturation brightness image; performing brightness component extraction on the hue saturation brightness image, and establishing an end-to-end image enhancement model based on an IAT model; performing brightness component enhancement by using the image enhancement model to obtain an enhanced image; and performing diffusion processing on the enhanced image based on a diffusion coefficient function, and performing semantic segmentation processing on the image after diffusion processing by using a semantic segmentation model in combination with a Laplacian operator to obtain a semantic segmentation result. According to the method, image distortion and excessive problems caused by image enhancement are avoided, and the readability of the image is ensured, so that a reliable data basis is provided for subsequent analysis.

An image capturing apparatus for capturing an object and generate an image, displaying the image, performing signal processing for the image, and accepting an input from a user, wherein the image is displayed with a vectorscope superimposed thereon, the vectorscope indicating a distribution of color components included in the image based on a hue and a color density and an index for specifying a range of a skin color region in the image, a display position of the index on the vectorscope is specified based on the hue and the color density values, and a size of the index is specified by the range of the skin color region value, and processing is performed for decreasing a detail level of an image signal of the image having a color component included in the index.

The invention relates to methods, devices, and software for distinguishing hemoglobin (Hb) from myoglobin (Mb) in urine samples using peroxidase-reactive test strips. A time-resolved video of the color change on the test pad is recorded, and hue values are extracted and analyzed over time. Diagnostic differentiation between Hb and Mb is achieved by evaluating the hue-time curve, in particular via endpoint hue values and the ratio of integrated hue values in distinct time intervals. The invention further comprises a standardized optical setup for recording, a software-based evaluation algorithm, and a refillable cartridge for in-situ urine analysis devices containing appropriate test fields. The invention enables combined testing with additional urine parameters and is suitable for medical and non-clinical applications such as sport and home diagnostics.

The invention relates to the technical field of color correction, in particular to a baked porcelain false tooth color adjusting method and system based on image analysis, and the method comprises the following steps: identifying teeth, gingiva and surrounding tissues in an image, determining a region boundary by using an edge detection technology, and calibrating a region according to gray difference. According to the method, teeth and surrounding tissues are accurately segmented by using an edge detection technology, accurate identification of a tooth area in an image is realized, a clear starting point is provided for color adjustment, a wave band with high reflectivity is selected, the quality of image data is optimized, the precision of subsequent color analysis is ensured, and hue, brightness and saturation are adjusted in a CIELAB color space; the matching degree of denture colors and natural teeth is improved, the visual coherence is enhanced, neural network analysis and time sequence data are integrated to predict long-term color changes, a real-time feedback mechanism automatically adjusts color parameters according to illumination conditions, the visual effect is continuously optimized, and the naturalness and consistency of colors in different environments are guaranteed.

The invention relates to the technical field of medical endoscope imaging, in particular to an endoscope hue correction method and device and an endoscope, and the method comprises the steps: obtaining a first image collected by the endoscope as a target collection image after the proportion of a light source in a lighting unit changes; acquiring a second image before the proportion of a light source in a lighting unit of the endoscope is changed as an adjacent frame image; calculating first hue information of the target acquisition image and second hue information of the adjacent frame image; and when the hue information deviation between the first hue information and the second hue information is greater than a preset hue deviation, performing hue correction on the target acquisition image based on the image scene difference between the target acquisition image and the adjacent frame image until the adjusted hue information deviation meets the preset deviation, outputting the adjusted target acquisition image as a hue correction image; the hue difference is systematically adjusted through the scene difference, so that the stable expression of the image hue in real-time imaging is kept.

The invention discloses a wide dynamic air-to-ground CMOS imaging performance test method and device, and the method comprises the steps: building a corresponding optical axis adjustment environment, a CMOS wide dynamic index test environment, and a resolution test environment, and testing the OECF, resolution, color, shadow, and distortion of an imaging module; three influence factors including a dynamic range DR, a signal-to-noise ratio SNR and visual noise VN are detected through an OECF test, Siemens star and bevel edge indexes are detected through a resolution test, and a color distance delta E, brightness delta L, chromaticity delta C, hue delta H and visual noise VN are detected through a color test. Brightness shadow Shading, a color shadow average value CIE [delta] Eab, the maximum difference [delta] VN of visual noise and an optical center are detected through shadow testing, lens geometric distortion LGD, chromatic aberration CA and longitudinal chromatic aberration LCA are detected through distortion testing, CMOS sensor imaging performance analysis is carried out through OECF, resolution, color, shadow and distortion testing, and imaging module performance indexes can be obtained more accurately.

The invention provides an object classification method and device for a hyperspectral image, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining an original hyperspectral image, converting the original hyperspectral image into a color space image, calculating a shadow feature map according to the hue component and the brightness component of the color space image, and carrying out the calculation of the shadow feature map; the method comprises the steps of obtaining a shadow feature map of an original hyperspectral image, obtaining a shadow mask according to the shadow feature map, calculating the exposure intensity of the original hyperspectral image according to the shadow mask and the original hyperspectral image, obtaining an exposure image according to the exposure intensity and the original hyperspectral image, and carrying out multi-scale fusion processing on the original hyperspectral image and the exposure image to obtain a processed image. According to the method, the processed image and the original hyperspectral image are processed, the processed image and the original hyperspectral image are fused to obtain the shadow-free hyperspectral image, the dimension reduction processing is performed on the shadow-free hyperspectral image, and the dimension-reduced shadow-free hyperspectral image is input into the classification model for classification, so that the classification accuracy of the image can be improved.

The embodiment of the invention relates to the field of color matching, and discloses a color matching method and tool for pigment. The method comprises the following steps: acquiring a first brightness value of a target color; based on a black pigment and a white pigment, blending a first formula pigment according to a preset proportion corresponding to the first brightness value; wherein the brightness of the first formula pigment is the first brightness value; blending a second formula pigment based on the basic hue pigment; wherein the brightness of the second formula pigment is the first brightness value, and the hue of the second formula pigment is the hue of the target color; and mixing the first formula pigment and the second formula pigment to obtain the final formula pigment. Therefore, through the scientific color matching method, the color matching accuracy and efficiency of color matching personnel are improved.

The invention discloses a night enhanced imaging and dynamic denoising method for an automobile data recorder, relates to the technical field of vehicle-mounted image processing and computational photography, and is used for solving the problems of insufficient brightness and too strong noise of night videos of the automobile data recorder under a low-illumination condition. Estimating the zero-mean Gaussian noise intensity of a read link and the Poisson noise intensity increasing along with the brightness in the same frame, converting the zero-mean Gaussian noise intensity and the Poisson noise intensity into brightness equivalent parameters, and introducing a unified control function G as the scale reference of all enhancement and noise reduction links; defining a sharing priori H and carrying out unified constraint in dense optical flow, depth time sequence denoising and local tone mapping; and in combination with the motion mask and the background model, performing differential space-time denoising and proportional write-back, and in cooperation with motion blur detection, spatial variation motion kernel and constrained deconvolution and multi-frame detail recharge, obtaining target brightness and outputting an RGB frame after proportional write-back.

The invention belongs to an image processing technology, and particularly relates to an underwater image enhancement method and system based on combination of multiple convolution models and an attention mechanism, and the technology is formed by combination of a DIAM model and a Shallow-UWnet improved model. The DIAM model is mainly used for carrying out color restoration in a mode of complementing hue weakening and non-uniformity of the underwater image, and can solve the problems of underwater noise, atomization and the like. According to the Shell-UWnet improved model, on the basis of Shell-UWnet, a highest pooling layer and an Inc multi-convolution module are added, the highest pooling layer effectively reduces network parameters and calculation overhead through dimension reduction operation, the operation efficiency is improved, the key feature extraction capacity is enhanced, detail loss is avoided, Inc can extract features from different scales, and the algorithm is simple and convenient to operate. And the feature capture capability of the model is enhanced.

The present invention provides a low-light image enhancement method based on HSV and attention mechanism. The method is based on the characteristic model of the human visual system. First, in view of the problem of insufficient brightness, a Retinex theory variation is proposed. This method can better process the brightness, effectively capture the brightness characteristics of the image, and improve the brightness while maintaining the hue. Secondly, in view of the problem that the saturation human eye visual effect is reduced due to the sudden change in brightness, an adaptive block saturation enhancement function is designed. The pixel is adaptively blocked through the input saturation feature, and the part with lower pixels is linearly enhanced. Finally, in view of the color unevenness, exposure and noise problems in the preliminary enhanced image, a guided attention mechanism and a fusion module are designed to fuse the two features to achieve image enhancement that is more in line with human eye vision. The present invention achieves excellent performance with a small number of parameters, and the network has good robustness.

The invention discloses a camouflage confrontation coating generation and physical enhancement method, which comprises the following steps: generating a uniform Thiessen polygon based on an RGB-D image, and calculating control point distribution regularization loss; fusing the multi-scale RGB and depth features; the color distribution probability is determined by combining the pixel point distance and depth information, and preliminary coating is generated; the camouflage concealment is optimized through saliency detection and environment dominant hue fusion; spatial domain affine transformation and RGB-CMYK transformation are introduced to enhance physical robustness, and visual consistency loss is calculated; and optimizing a comprehensive loss function by combining control point distribution regularization loss, multi-scale fusion saliency loss, visual consistency loss and target detection confrontation loss, and iteratively generating final coating. According to the method, the geometric camouflage precision is improved, the visual saliency of physical coating is reduced, the environmental adaptability and the anti-robustness are enhanced, and the method is suitable for hiding and anti-attack of a complex surface target.

The invention provides a DFKD new framework combining an intra-class and intra-class decoupling constraint mechanism and a cross-class attention diffusion migration technology, and aims to simultaneously realize high-quality style diversification and semantic content consistency of a synthetic sample under the condition of completely no external data. Specifically, the main purpose of the invention is as follows: by designing intra-class-inter-class style unwrapping constraint loss, a generator is prompted to realize style diversity enhancement of different style samples in the same class, and meanwhile, the distinction degree of style features among different classes is ensured; according to the method, cross-layer attention features in a teacher model are utilized to drive a diffusion model to carry out style migration on multiple scales, and multi-dimensional style changes such as hue, texture and illumination can be flexibly captured without any real image reference. Results show that the method reaches the current advanced level in the field of data set distillation, and shows excellent convergence speed in various data set distillation methods.

The invention provides a deep learning-based automatic toning system and method for a record. The method comprises the following steps of: determining a toning constraint condition of the record in visual constraints according to style similarity of shallow visual features between adjacent recorded image frames in the record and brightness difference between the adjacent recorded image frames; performing fuzzy correlation on the scene semantic elements of the narrative segments in the record through the cross correlation among the scene semantic features to obtain the element membership of the scene semantics in the narrative segments, and further determining the scene attention of different narrative segments in the record according to all the element membership; and according to the scene attention and the hue constraint conditions of the different narrative segments, determining the chroma reconstruction cost of the record in the toning process, and carrying out automatic toning processing on the record based on the chroma reconstruction cost. According to the scheme, the visual features and the semantic elements can be fused in the automatic toning process of the record, and toning coordination with narrative logic as the guide is achieved.

The invention discloses an atmosphere lamp control method and device based on visual perception and an automobile, and the method comprises the steps: partitioning a display screen, obtaining a display partition matrix, and enabling partition elements or partition element combinations in the display partition matrix to correspond to corresponding lamp strips; performing multi-dimensional saliency scoring on the candidate color of each partition by using a K-means clustering module, a vision priority detection module or a spatio-temporal context weighting module according to the preprocessed display image, and determining a typical color according to the saliency comprehensive score of each candidate color; and respectively mapping the typical color of each display subarea to the corresponding lamp strip. Visual intelligent perception is achieved, the intelligent degree of atmosphere lamp control in the vehicle is improved, and the problems that in an existing automobile atmosphere lamp control technology, screen partition is simple, color picking is not intelligent, simple screen average color picking or partition color picking is mostly adopted, dominant hues and key details perceived by human eyes cannot be accurately reflected, and the color picking efficiency is poor are solved. And the light color and the picture content are separated.