Image processing method and apparatus

Abstract

The application discloses an image processing method and device, and belongs to the technical field of electronic equipment. The method comprises the following steps: acquiring a first image and target physiological data of a user shooting the first image; determining a target emotion category of the user based on the target physiological data; determining a first image processing parameter based on the target emotion category of the user; and processing the first image based on the first image processing parameter to obtain a second image.

Description

Technical Field

[0001] This application belongs to the field of electronic equipment technology, and specifically relates to an image processing method and apparatus. Background Technology

[0002] With the widespread adoption of wearable devices, users can easily collect their personal physiological data. At the same time, image editing applications are emerging in large numbers, and users' demand for personalized image expression is constantly growing.

[0003] Currently, the main technologies related to image personalization processing include: Images are processed based on preset image processing parameters such as hue, contrast, and saturation. These applications are characterized by fixed parameters and strong versatility, but the processed image effects are monotonous and lack variation.

[0004] Alternatively, computer vision and deep learning technologies can be used to analyze image content and automatically perform scene recognition, face beautification, background blurring, and other processing. However, these technologies only focus on the content analysis of the image itself, resulting in a single image effect that is difficult to meet the differentiated visual needs of different scenarios. Summary of the Invention

[0005] The purpose of this application is to provide an image processing method and apparatus that allows the processed image to be presented differently according to changes in the user's target physiological data.

[0006] In a first aspect, embodiments of this application provide an image processing method, including: Acquire the first image and the target physiological data of the first image captured by the user; Based on the target physiological data, the user's target emotion category is determined; Based on the user's target emotion category, determine the first image processing parameters; The first image is processed based on the first image processing parameters to obtain the second image.

[0007] Secondly, embodiments of this application provide an image processing apparatus, including: The acquisition module is used to acquire the first image and the target physiological data of the first image captured by the user; The determination module is used to determine the target emotion category of the user based on the target physiological data; Based on the user's target emotion category, determine the first image processing parameters; The processing module is used to process the first image based on the first image processing parameters to obtain the second image.

[0008] Thirdly, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0009] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0010] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0011] In a sixth aspect, embodiments of this application provide a computer program product stored in a storage medium, which is executed by at least one processor to implement the method described in the first aspect.

[0012] In this embodiment, a first image and target physiological data of the first image taken by the user are first acquired. Based on the target physiological data, the user's target emotion category is determined. Then, based on the user's target emotion category, first image processing parameters are determined. The first image is processed based on the first image processing parameters to obtain a second image. Different target physiological data for taking the first image result in different first image processing parameters, thus resulting in different second images. The first image processing parameters are no longer preset to static fixed values, but are dynamically mapped to measurable target physiological data. When the target physiological data changes, the first image processing parameters change accordingly, so that the processed second image can be presented differently according to changes in the user's target physiological data, with stronger adaptability and personalized expression capabilities, and can better adapt to differentiated visual needs in different scenarios. Attached Figure Description

[0013] Figure 1 This is a schematic flowchart of an image processing method provided in some embodiments of this application; Figure 2 These are schematic diagrams of interface displays provided in some embodiments of this application; Figure 3 These are schematic diagrams of interface displays provided in some embodiments of this application; Figure 4 These are schematic diagrams illustrating user interaction with electronic devices provided in some embodiments of this application; Figure 5 These are schematic diagrams showing the sharing confirmation interface provided in some embodiments of this application; Figure 6 This is a schematic flowchart illustrating the image processing method provided in some embodiments of this application; Figure 7 These are schematic diagrams of the structure of an image processing apparatus provided in some embodiments of this application; Figure 8 These are structural block diagrams of electronic devices provided in some embodiments of this application; Figure 9 These are structural block diagrams of electronic devices provided in some embodiments of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0015] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0016] The terminology used in the implementation section of this application is only for explaining specific embodiments of this application and is not intended to limit this application. The terminology involved in the embodiments of this application is explained below.

[0017] The image processing method provided in this application will be described below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0018] It should be noted that the image processing method provided in this application can be executed by electronic devices such as mobile phones, tablets, laptops, PDAs, and in-vehicle electronic devices, which have the capability to acquire physiological data and process images. Some embodiments of this application use electronic devices as the executing entity to illustrate the image processing method provided in this application.

[0019] like Figure 1 As shown in the figure, this application provides an image processing method, which may specifically include the following steps: Step 101: Acquire the first image and the target physiological data of the first image captured by the user.

[0020] Specifically, the user triggers the electronic device to take a picture to obtain the first image, and the device is connected through the corresponding software development kit (SDK) of the wearable device, thereby synchronously acquiring the target physiological data of the first image taken by the user.

[0021] Step 102: Based on the target physiological data, determine the user's target emotion category.

[0022] Specifically, by analyzing the target's physiological data, the user's emotional category when the first image was taken can be determined. This emotional category is the target emotional category, such as excitement, frustration, or irritability.

[0023] Step 103: Determine the first image processing parameters based on the user's target emotion category.

[0024] Specifically, different emotion categories correspond to different image processing parameters. By using the correspondence between emotion categories and image processing parameters, the image processing parameters corresponding to the user's target emotion category can be determined as the first image processing parameter.

[0025] The first image processing parameters include, but are not limited to, parameters at at least one of the following levels: Parameters used to control color attributes, such as color temperature, saturation, contrast, and brightness; Parameters of the material layer used to control surface texture, such as: film tension, water-like silk effect, degree of graininess, and metal sharpening intensity; Parameters used to control the lighting environment of the light effect layer, such as: vignette, halo, local highlight flash, light and shadow flow, etc. Parameters used to control the timing behavior layer, such as: breathing rate, development speed, animation rhythm, etc. Parameters used to control the interaction response layer for human-computer interaction feedback, such as trigger delay, trigger condition, bounce / rollback, etc.

[0026] The aforementioned tone layer, material layer, lighting effect layer, temporal behavior layer, and interaction response layer all have their corresponding state mappings. State mapping refers to the rules that dynamically map changes in measurable state variables such as wakefulness and volatility to the adjustment direction or magnitude of image processing parameters at each level.

[0027] The specific examples of each level, the parameters of each level, and the state mapping of each level are shown in Table 1: Table 1

[0028] The state mapping at each level can be configured as needed.

[0029] For example, the state mapping of the tone layer: an increase in wakefulness corresponds to an increase in color temperature / contrast; a decrease in wakefulness corresponds to a decrease in color temperature / contrast.

[0030] Examples of state mapping for material layers: increased volatility corresponds to film tension / unstable edges; stable volatility corresponds to enhanced silk-like texture on the water surface.

[0031] For example, the state mapping of the light effect layer is as follows: high wake-up corresponds to local bright flashing; low wake-up corresponds to slow breathing light and shadow.

[0032] For an example of state mapping in the temporal behavior layer: respiratory rate is associated with the rhythm of heart rate / respiratory estimation, and this mapping rule can be "enabled" or "disabled".

[0033] For an example of state mapping in the interaction response layer: high wake-up corresponds to a shorter trigger latency; low wake-up corresponds to a longer trigger latency.

[0034] Step 104: Process the first image based on the first image processing parameters to obtain the second image.

[0035] Specifically, the first image is processed based on the first image processing parameters. An example of the processing order of the above layers is: tone layer, material layer, lighting effect layer, temporal behavior layer, and interactive response layer, to obtain the processed second image.

[0036] In the above embodiments of this application, a first image and target physiological data of the first image taken by the user are first acquired. Based on the target physiological data, the user's target emotion category is determined. Then, based on the user's target emotion category, first image processing parameters are determined. The first image is processed based on the first image processing parameters to obtain a second image. Different target physiological data for taking the first image result in different first image processing parameters for processing the first image, thus resulting in different second images. The first image processing parameters are no longer preset as static fixed values, but are dynamically mapped to measurable target physiological data. When the target physiological data changes, the first image processing parameters change accordingly, so that the processed second image can be presented differently according to changes in the user's target physiological data, with stronger adaptability and personalized expression capabilities, and can better adapt to differentiated visual needs in different scenarios.

[0037] In some optional embodiments of this application, step 101, acquiring the first image and the target physiological data of the first image captured by the user, includes: The first image is captured using a camera; Raw physiological data of the user is collected by a wearable device within a first time window; wherein, the first time window includes: the time when the first image was captured, at least one time before the time of capture, and at least one time after the time of capture; The raw physiological data include at least one of the following: heart rate, heart rate variability, skin temperature, and skin electrical activity; Based on the raw physiological data, determine the physiological data fluctuation and the physiological data change trend; The target physiological data is generated based on the fluctuation of the physiological data and the trend of the physiological data changes.

[0038] Specifically, the user triggers the electronic device to take a picture, capturing the first image through the camera. The electronic device then obtains raw physiological data from the wearable device via a health data application programming interface (API), covering a first time window including the moment of capture. The first time window includes not only the moment of capture and at least one moment before the capture, but also at least one moment after the capture. For example, the first time window might be a 10-minute window before and after the capture.

[0039] The raw physiological data within the first time window reveals the fluctuation and trend of the user's physiological data during the capture of the first image. The fluctuation refers to the statistical dispersion of the user's raw physiological data within the first time window, such as standard deviation and variance. The trend refers to the directional evolution of the user's raw physiological data over time within the first time window, including the direction and rate of change.

[0040] Depending on the wearable device's system, the available raw physiological data varies, and includes, but is not limited to, at least one of the following: Heart rate; Heart rate variability (HRV); Skin temperature; Electrodermal activity (EDA).

[0041] Example: The electronic device's system is the primary operating system, using the HealthKit health data management framework, and subscribes to the following raw physiological data via the quantifiable data type identifier HKQuantityType: HKQuantityType(.heartRate) refers to heart rate, measured in beats per minute. HKQuantityType(.heartRateVariabilitySDNN) refers to heart rate variability, measured in milliseconds. HKQuantityType(.skinTemperature) refers to skin temperature, measured in degrees Celsius or Fahrenheit.

[0042] Example: The electronic device's system is a secondary operating system, and it uses the Health Connect API to subscribe to the following raw physiological data: HeartRateRecord refers to heart rate records, which include heart rate values ​​and timestamps. HeartRateVariabilityRecord refers to a record of heart rate variability, which includes HRV values ​​and timestamps.

[0043] The sampling rates of raw physiological data from different sensors vary considerably, as shown in Table 2: Table 2

[0044] Because the sampling rates of raw physiological data from different sensors vary significantly, electronic devices need to perform spatiotemporal alignment processing on the collected raw physiological data to generate uniform raw physiological data. An example of an alignment method is as follows: First, the time axis is divided into fixed-length windows, for example, 5 seconds. For high-frequency raw physiological data such as skin conductance, statistical features such as mean, variance, and peak value are extracted after downsampling. For mid-frequency raw physiological data such as heart rate, linear interpolation is used to align the data to the window boundaries. For low-frequency raw physiological data such as heart rate variability and skin temperature, interpolation or trend fitting is used to predict the values ​​within the window.

[0045] The physiological data volatility and trend of physiological data change are determined by aligning the original physiological data. Then, based on the physiological data volatility and trend of physiological data change, target physiological data is generated. The target physiological data refers to the sequence or curve of physiological data volatility changing with time within the first time window.

[0046] In some optional embodiments of this application, the step of generating the target physiological data based on the fluctuation of the physiological data and the trend of the physiological data changes specifically includes: Based on the raw physiological data at each moment within the first time window and the user's preset physiological data, the physiological data deviation at each moment is calculated. The target physiological data is generated based on the physiological data deviation at each time point, the physiological data variability, and the physiological data change trend.

[0047] Specifically, it is necessary to pre-calculate the user's physiological baseline, i.e., preset physiological data, and the calculation method is as follows: The system continuously collects raw physiological data from users within a predetermined time period, calculates statistical baseline values ​​for each raw physiological data point (e.g., mean, standard deviation, quantile interval), and uses these baseline values ​​as preset physiological data. The predetermined time period can be up to 7 days.

[0048] The relative deviations between the raw physiological data and the preset physiological data at each time point within the first time window are calculated to obtain the physiological data deviation at each time point. For the physiological data deviation at any given time point, the specific calculation is performed using Formula 1:

[0049] in, This indicates the deviation of physiological data at that moment; Represents the raw physiological data at that moment; This represents the mean value among the preset physiological data; This represents the standard deviation in the preset physiological data.

[0050] In the above embodiments, physiological data deviation is calculated using the user's individual baseline data instead of using a preset uniform baseline data. This allows the physiological data deviation to more accurately reflect the user's individual physiological differences and avoids the problem of insufficient individual fit caused by a uniform baseline.

[0051] By analyzing physiological data deviations, fluctuations, and trends at various points in time, target physiological data can be generated. This data can also be accompanied by an availability flag, allowing users to determine whether the target physiological data is usable.

[0052] In some optional embodiments of this application, step 102, based on the target physiological data, determines the user's target emotion category, including: Based on the target physiological data, probability values ​​corresponding to each emotion category are generated; The emotion category corresponding to the maximum value among the probability values ​​is determined as the target emotion category.

[0053] In one embodiment, based on preset rules or function mapping, the target physiological data is mapped to a probability distribution of emotion categories, that is, the distribution of probability values ​​corresponding to each emotion category. This probability distribution of emotion categories is used to drive continuous expression parameters, rather than outputting discrete emotion labels.

[0054] In one embodiment, target physiological data is input into a lightweight model, which outputs a state representation fingerprint, i.e., a probability distribution of emotion categories. The emotion category corresponding to the highest probability value in the probability distribution is taken as the target emotion category.

[0055] Among them, the state expression fingerprint is used to drive the perceived atmosphere expression to generate stable and reproducible expression parameters, rather than outputting discrete emotion labels.

[0056] In some optional embodiments of this application, the method further includes: The maximum value among the probability values ​​is determined as the confidence level of the target emotion category; When the confidence level of the target emotion category is greater than the preset confidence level, the control imaging function is enabled. When the developing function is enabled, the second image is displayed in response to the second input according to the developing intensity corresponding to the second input. When the imaging function is enabled, in response to the third input, the target physiological data is displayed according to the imaging intensity corresponding to the third input.

[0057] Specifically, while obtaining the probability distribution, the confidence level (conf_expr) of the target emotion category is determined. The confidence level (conf_expr) of the target emotion category is the maximum probability value in the probability distribution. The confidence level (conf_expr) of the target emotion category is compared with a preset confidence level. If the confidence level (conf_expr) of the target emotion category is greater than the preset confidence level, the imaging function is enabled, and a visual cue is provided to inform the user that the imaging function is now active. Example: Figure 2 As shown, the availability of the display function is indicated by displaying ambient light indicator 27 at the edge of the second image. The enabled state indicates that the developing function is in a working state.

[0058] If the confidence level of the target emotion category, conf_expr, is less than or equal to the preset confidence level, then the development function is in a disabled state, which means that the development function is prohibited from use.

[0059] The confidence level mentioned above is used to quantify the reliability of the current raw physiological data. Its value is determined by factors such as data availability, motion interference, sampling window coverage, and signal-to-noise ratio. By constraining the confidence level, the risk of false triggering and privacy risks can be reduced.

[0060] like Figure 2 and Figure 3As shown, when the developing function is enabled, if a second user input is received, and this second input includes input to the developing control 26, or includes input to both the developing control 26 and the intensity control 21 for adjusting the developing intensity, then in response to the second input, a second image is displayed on the current interface according to the developing intensity indicated by the second input. For example: Figure 2 As shown, if input is received to the developing control 26, a second image of a specified duration is displayed on the current interface; if input is received to the intensity control 21, a second image is displayed on the current interface according to the developing intensity indicated by the intensity control. The developing intensity range of the intensity control 21 is 0% to 100%.

[0061] Controls refer to basic visual elements on the screen of electronic devices that users can interact with, including but not limited to buttons, icons, sliders, switches, text input boxes, etc.

[0062] In some embodiments of this application, the second input is used to trigger the developing control, and the second input can be a second operation. Exemplarily, the second input includes, but is not limited to: touch input or click input of the developing control by the user through a touch device such as a finger or stylus, or a voice command input by the user, or a specific gesture input by the user, or other feasible inputs. The specific input can be determined according to actual usage needs, and this embodiment of the invention does not limit it. For example, the second input can be: a click input of the developing control by the user. Or, In some embodiments of this application, the second input is used for movement input of the intensity control, and the second input can be a second operation. Exemplarily, the second input includes, but is not limited to: touch input or click input of the intensity control by the user through a touch device such as a finger or stylus, or a voice command input by the user, or a specific gesture input by the user, or other feasible inputs. The specific input can be determined according to actual usage needs, and this embodiment of the invention does not limit it. For example, the second input can be: sliding input of the intensity control by the user.

[0063] The specific gesture in this application embodiment can be any one of the following: single-click gesture, swipe gesture, drag gesture, pressure recognition gesture, long-press gesture, area change gesture, double-press gesture, and double-tap gesture; In this embodiment, the click input can be a single click, a double click, or any number of clicks, or it can be a long press or a short press.

[0064] like Figure 2As shown, when the developing function is enabled, if a third input from the user is received, including input to the developing control, or input to both the developing control and the intensity control for adjusting the developing intensity, then in response to the third input, the target physiological data 25 for a specified duration is displayed on the current interface according to the developing intensity indicated by the third input. This includes, but is not limited to, the fluctuation trajectory of heart rate, HRV, and EDV. A summary of the changes in the target physiological data over time can also be displayed, and the location of the shooting time is marked on the target physiological data with a vertical line 28. Here, the user interface (UI) refers to the complete visual area presented to the user on the screen at once, composed of controls, graphics, text, etc.

[0065] like Figure 3 As shown, the current interface displays a first control 22 for "Confirm and Save (including expression layer)", a second control 23 for "Save without expression layer", and a third control 24 for "Save after closing expression layer". When the user triggers the first control 22, the electronic device associates and saves the target physiological data and the processed second image in the current expression layer. When the user triggers the second control 23, the electronic device ignores the target physiological data and the processed second image in the current expression layer and saves only the first image. When the user triggers the third control 24, the electronic device closes the target physiological data and the processed second image displayed in the current expression layer and saves only the first image.

[0066] It should be noted that if the user chooses to turn off the preview function before taking the first image, the image will be processed directly according to the default development intensity and saving method when the development function is enabled.

[0067] In some optional embodiments of this application, the step of displaying the second image according to the development intensity corresponding to the second input specifically includes: According to the development intensity corresponding to the second input, the first image and the second image are displayed in columns or superimposed on the screen.

[0068] Specifically, based on the development intensity indicated by the user's second input, the first and second images can be displayed side-by-side or top-by-bottom on the current interface, such as... Figure 3 As shown. Alternatively, based on the development intensity indicated by the user's second input, the first and second images can be overlaid on the current interface to facilitate previewing and comparing them. Overlay display refers to presenting the first and second images together in the same display area in a mixed manner, making the content of both images visible simultaneously.

[0069] For example: Figure 4 As shown, step 401: The user opens the photo album on the electronic device and selects the first image.

[0070] Step 402: If the electronic device displays an ambient indicator light at the edge of the first image, it indicates that the developing function of the first image is turned on.

[0071] Step 403: The user presses and holds the developing control for 1 second.

[0072] Step 404: The current interface of the electronic device displays a second image, which has vignetting and color variations.

[0073] Step 405: Users can adjust the intensity control by pinching with two fingers or other methods.

[0074] Step 406: Update the development intensity of the second image and refresh the second image.

[0075] Step 407: The user selects a column display.

[0076] Step 408: Display the first image and the second image in columns on the current interface of the electronic device.

[0077] In some optional embodiments of this application, step 103, based on the user's target emotion category, determines the first image processing parameters, including: Based on the user's target emotion category, a second image processing parameter corresponding to the target emotion category is determined from multiple preset image processing parameters; In response to the first input to the second image processing parameters, the second image processing parameters are updated to obtain the updated first image processing parameters.

[0078] Specifically, different emotion categories correspond to different preset image processing parameters. The preset image processing parameters corresponding to the target emotion category can be determined through the aforementioned correspondence, and these preset image processing parameters are used as the second image processing parameter. If the user does not provide any other input for the second image processing parameter, then the second image processing parameter is used as the first image processing parameter.

[0079] If a user's first input on the second image processing parameters is received, the second image processing parameters are updated in response to the first input, and the updated second image processing parameters are used as the first image processing parameters.

[0080] In some embodiments of this application, the first input is used for editing the second image processing parameters, and the first input can be a first operation. Exemplarily, the first input includes, but is not limited to: touch input or click input of the second image processing parameters by the user through a touch device such as a finger or stylus, or a voice command input by the user, or a specific gesture input by the user, or other feasible inputs. The specific input can be determined according to actual usage needs, and this embodiment of the invention does not limit it. For example, the first input can be: a swipe input of the second image processing parameters by the user.

[0081] The specific gesture in this application embodiment can be any one of the following: single-click gesture, swipe gesture, drag gesture, pressure recognition gesture, long-press gesture, area change gesture, double-press gesture, and double-tap gesture; In this embodiment, the click input can be a single click, a double click, or any number of clicks, or it can be a long press or a short press.

[0082] In the above embodiments, based on the second image processing parameters determined according to the target emotion category, the second image processing parameters are further dynamically updated through the first input, thereby generating a personalized image that is adapted to the user's physiological state, visual perception, and interactive expression.

[0083] In some optional embodiments of this application, the method further includes: Perform at least one of the following privacy-preserving processes on the target physiological data to obtain the target data: Layered desensitization process; The target physiological data is associated with and stored with corresponding identification information; wherein, the identification information includes at least one of the following: the salted hash value of the target physiological data, and the index value of the target physiological data.

[0084] Specifically, the raw physiological data is processed and stored locally on the electronic device. To ensure data privacy, the target physiological data is stored in the EXIF ​​or extended attributes of the image file using one or more of the following methods: salted hashing, hierarchical desensitization, or storage of salted hash values ​​associated with index values. The target data may also optionally store the version number of the current expression layer, confidence level, and triggering methods for the first, second, and third inputs.

[0085] Electronic devices can also be configured to trigger a fourth control to "remove status expression". If the user triggers the fourth control, the salted hash value, index value, expression layer version number and triggering method are deleted from the EXIF ​​or extended attributes of the image file. If the corresponding expression layer cache data exists locally, it needs to be cleared. At this time, the ambient light indicator of the second image will no longer be displayed on the current interface, and subsequent browsing will only present the first image, further improving privacy protection.

[0086] In some optional embodiments of this application, the method further includes: In response to the fourth input, perform one of the following sharing actions: Share the first image; Share the first image, which is associated with the target physiological data; Share the second image; Share the second image, which is associated with the target physiological data; Share the second image and the target data; Share the second image and the target data, wherein the second image is associated with the target physiological data; In cases where the shared first or second image is associated with the target physiological data, an interactive control is displayed in the display interface of the first or second image, and the target physiological data is displayed when the interactive control is triggered.

[0087] Specifically, when the user clicks the share control, the current screen redirects to the share confirmation screen, such as... Figure 5 As shown, the sharing confirmation interface displays the fifth control 51, the sixth control 52, the seventh control 53, the eighth control 54, the ninth control 55, and the tenth control 56.

[0088] Specifically, if the fifth control is triggered, only the first image is shared, and this first image is not associated with the target physiological data. If the sixth control is triggered, the first image is shared, and this first image is associated with the target physiological data; the target physiological data will only be displayed when the user triggers the interactive control of the first image. If the seventh control is triggered, only the second image is shared, and this second image is not associated with the target physiological data. If the eighth control is triggered, the second image is shared, and this second image is associated with the target physiological data; the target physiological data will only be displayed when the user triggers the interactive control of the second image. If the ninth control is triggered, both the second image and the target data are shared. If the tenth control is triggered, both the second image and the target data are shared, and this second image is associated with the target physiological data; the target physiological data will only be displayed when the user triggers the interactive control of the second image.

[0089] After the user selects a sharing option, the electronic device generates and shares the content according to the selected option. If the user cancels sharing, the user is returned to the photo browsing interface.

[0090] The above embodiments ensure users' right to choose and be informed about the content they share, which is beneficial to privacy protection and user experience.

[0091] It should be noted that electronic devices provide expression layer settings options on the settings page or the camera interface, allowing users to decide whether to enable status expression, the raw physiological data used in the calculation, the style and default development intensity of the ambient indicator light, and whether to provide an expression layer preview after shooting. Users can complete the above expression layer settings as needed.

[0092] like Figure 6 As shown, the above image processing flow is illustrated through a specific embodiment: Step 601: User sets the expression layer.

[0093] Step 602: Acquire the first image through the camera and collect the user's raw physiological data within the first time window through the wearable device.

[0094] Step 603: Based on the raw physiological data, determine the fluctuation of physiological data and the trend of physiological data change.

[0095] Step 604: Generate target physiological data based on the fluctuation of physiological data and the trend of physiological data changes.

[0096] Step 605: Based on the target physiological data, generate probability values ​​for each emotion category.

[0097] Step 606: Determine the emotion category corresponding to the maximum value among the probability values ​​as the target emotion category, and determine the maximum value among the probability values ​​as the confidence level of the target emotion category.

[0098] Step 607: When the confidence level of the target emotion category is greater than the preset confidence level, the control imaging function is enabled.

[0099] Step 608: When the imaging function is enabled, in response to the second input, display the second image according to the imaging intensity corresponding to the second input; in response to the third input, display the target physiological data according to the imaging intensity corresponding to the third input.

[0100] Step 609: Determine the first image processing parameters based on the user's target emotion category; Step 610: Process the first image based on the first image processing parameters to obtain the second image.

[0101] The target physiological data in the above embodiments can be used in other scenarios, such as music playlist atmosphere sorting, e-book reading background adjustment, and dynamic themes for game interfaces. Furthermore, the application process of the aforementioned multidimensional target physiological data can be applied in scenarios that integrate heterogeneous time-series data, such as medical diagnosis and sports training. Moreover, the visualization display methods for the aforementioned second image, target data, and target physiological data can be extended to augmented reality (AR) or virtual reality (VR) scenarios, adjusting the visual effects of the virtual environment according to the user's physiological state.

[0102] In summary, in this embodiment, different target physiological data lead to different first image processing parameters, resulting in different second images. The first image processing parameters are no longer preset to static fixed values, but rather dynamically mapped to measurable target physiological data. When the target physiological data changes, the first image processing parameters change accordingly, allowing the processed second image to be presented differently based on changes in the user's target physiological data. This provides stronger adaptability and personalized expression, better meeting the differentiated visual needs of different scenarios. Furthermore, the confidence level constraint reduces the risk of accidental triggering and privacy risks.

[0103] The image processing method provided in this application can be executed by an image processing device. This application uses an image processing device executing the image processing method as an example to illustrate the image processing device provided in this application.

[0104] like Figure 7 As shown, this application embodiment also provides an image processing apparatus 700, specifically including: The acquisition module 701 is used to acquire the first image and the target physiological data of the first image captured by the user; The determination module 702 is used to determine the target emotion category of the user based on the target physiological data; Based on the user's target emotion category, determine the first image processing parameters; The processing module 703 is used to process the first image based on the first image processing parameters to obtain a second image.

[0105] Optionally, when acquiring the first image and the target physiological data captured by the user in the first image, the acquisition module 701 is specifically used for: The first image is captured using a camera; Raw physiological data of the user within a first time window is collected by a wearable device; wherein, the first time window includes: the time when the first image was captured, at least one time before the capture time, and at least one time after the capture time; the raw physiological data includes at least one of the following: heart rate, heart rate variability, skin temperature, and skin electrical activity; Based on the raw physiological data, determine the physiological data fluctuation and the physiological data change trend; The target physiological data is generated based on the fluctuation of the physiological data and the trend of the physiological data changes.

[0106] Optionally, when the acquisition module 701 generates the target physiological data based on the physiological data fluctuation and the physiological data change trend, it is specifically used for: Based on the raw physiological data at each moment within the first time window and the user's preset physiological data, the physiological data deviation at each moment is calculated. The target physiological data is generated based on the physiological data deviation at each time point, the physiological data variability, and the physiological data change trend.

[0107] Optionally, when determining the user's target emotion category based on the target physiological data, the determining module 702 is specifically used for: Based on the target physiological data, probability values ​​corresponding to each emotion category are generated; The emotion category corresponding to the maximum value among the probability values ​​is determined as the target emotion category.

[0108] Optionally, when determining the first image processing parameters based on the user's target emotion category, the determining module 702 is specifically used for: Based on the user's target emotion category, a second image processing parameter corresponding to the target emotion category is determined from multiple preset image processing parameters; In response to the first input to the second image processing parameters, the second image processing parameters are updated to obtain the updated first image processing parameters.

[0109] The image processing device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television set (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0110] The image processing device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0111] The image processing apparatus provided in this application embodiment can achieve... Figures 1 to 6 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0112] Optionally, such as Figure 8 As shown, this application embodiment also provides an electronic device 800, including a processor 801 and a memory 802. The memory 802 stores a program or instructions that can run on the processor 801. When the program or instructions are executed by the processor 801, they implement the various steps of the above-described image processing method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0113] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0114] Figure 9 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0115] The electronic device 1000 includes, but is not limited to, components such as: radio frequency unit 1001, network module 1002, audio output unit 1003, input unit 1004, sensor 1005, display unit 1006, user input unit 1007, interface unit 1008, memory 1009, and processor 1010.

[0116] Those skilled in the art will understand that the electronic device 1000 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1010 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 9 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0117] The processor 1010 is used to acquire the first image and the target physiological data of the first image captured by the user; Based on the target physiological data, the user's target emotion category is determined; Based on the user's target emotion category, determine the first image processing parameters; The first image is processed based on the first image processing parameters to obtain the second image.

[0118] Optionally, when acquiring the first image and the target physiological data captured by the user in the first image, the processor 1010 is specifically used for: The first image is captured using a camera; Raw physiological data of the user within a first time window is collected by a wearable device; wherein, the first time window includes: the time when the first image was captured, at least one time before the capture time, and at least one time after the capture time; the raw physiological data includes at least one of the following: heart rate, heart rate variability, skin temperature, and skin electrical activity; Based on the raw physiological data, determine the physiological data fluctuation and the physiological data change trend; The target physiological data is generated based on the fluctuation of the physiological data and the trend of the physiological data changes.

[0119] Optionally, when the processor 1010 generates the target physiological data based on the physiological data fluctuation and the physiological data change trend, it is specifically used for: Based on the raw physiological data at each moment within the first time window and the user's preset physiological data, the physiological data deviation at each moment is calculated. The target physiological data is generated based on the physiological data deviation at each time point, the physiological data variability, and the physiological data change trend.

[0120] Optionally, when determining the user's target emotion category based on the target physiological data, the processor 1010 is specifically configured to: Based on the target physiological data, probability values ​​corresponding to each emotion category are generated; The emotion category corresponding to the maximum value among the probability values ​​is determined as the target emotion category.

[0121] Optionally, when determining the first image processing parameters based on the user's target emotion category, the processor 1010 is specifically configured to: Based on the user's target emotion category, a second image processing parameter corresponding to the target emotion category is determined from multiple preset image processing parameters; In response to the first input to the second image processing parameters, the second image processing parameters are updated to obtain the updated first image processing parameters.

[0122] It should be understood that, in this embodiment, the input unit 1004 may include a graphics processing unit (GPU) 10041 and a microphone 10042. The GPU 10041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1006 may include a display panel 10061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 1007 includes at least one of a touch panel 10071 and other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 may include a touch detection device and a touch controller. Other input devices 10072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, joysticks, etc., which will not be described in detail here.

[0123] The memory 1009 can be used to store software programs and various data. The memory 1009 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1009 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1009 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0124] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor 1010.

[0125] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described image processing method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0126] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0127] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described image processing method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0128] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0129] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described image processing method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0130] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0131] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0132] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. An image processing method, characterized in that, include: Acquire the first image and the target physiological data of the first image captured by the user; Based on the target physiological data, the user's target emotion category is determined; Based on the user's target emotion category, determine the first image processing parameters; The first image is processed based on the first image processing parameters to obtain the second image.

2. The method according to claim 1, characterized in that, The acquisition of the first image and the target physiological data of the first image captured by the user includes: The first image is captured using a camera; Raw physiological data of the user is collected by a wearable device within a first time window; wherein, the first time window includes: the time when the first image was captured, at least one time before the time of capture, and at least one time after the time of capture; The raw physiological data include at least one of the following: heart rate, heart rate variability, skin temperature, and skin electrical activity; Based on the raw physiological data, determine the physiological data fluctuation and the physiological data change trend; The target physiological data is generated based on the fluctuation of the physiological data and the trend of the physiological data changes.

3. The method according to claim 2, characterized in that, The process of generating the target physiological data based on the fluctuation of the physiological data and the trend of the physiological data includes: Based on the raw physiological data at each moment within the first time window and the user's preset physiological data, the physiological data deviation at each moment is calculated. The target physiological data is generated based on the physiological data deviation at each time point, the physiological data variability, and the physiological data change trend.

4. The method according to claim 1, characterized in that, Determining the user's target emotion category based on the target physiological data includes: Based on the target physiological data, probability values ​​corresponding to each emotion category are generated; The emotion category corresponding to the maximum value among the probability values ​​is determined as the target emotion category.

5. The method according to claim 1, characterized in that, The step of determining the first image processing parameters based on the user's target emotion category includes: Based on the user's target emotion category, a second image processing parameter corresponding to the target emotion category is determined from multiple preset image processing parameters; In response to the first input to the second image processing parameters, the second image processing parameters are updated to obtain the updated first image processing parameters.

6. An image processing apparatus, characterized in that, include: The acquisition module is used to acquire the first image and the target physiological data of the first image captured by the user; The determination module is used to determine the target emotion category of the user based on the target physiological data; Based on the user's target emotion category, determine the first image processing parameters; The processing module is used to process the first image based on the first image processing parameters to obtain the second image.

7. The apparatus according to claim 6, characterized in that, When acquiring the first image and the target physiological data of the first image captured by the user, the acquisition module is specifically used for: The first image is captured using a camera; Raw physiological data of the user within a first time window is collected by a wearable device; wherein, the first time window includes: the time when the first image was captured, at least one time before the capture time, and at least one time after the capture time; the raw physiological data includes at least one of the following: heart rate, heart rate variability, skin temperature, and skin electrical activity; Based on the raw physiological data, determine the physiological data fluctuation and the physiological data change trend; The target physiological data is generated based on the fluctuation of the physiological data and the trend of the physiological data changes.

8. The apparatus according to claim 7, characterized in that, When the acquisition module generates the target physiological data based on the physiological data fluctuation and the physiological data change trend, it is specifically used for: Based on the raw physiological data at each moment within the first time window and the user's preset physiological data, the physiological data deviation at each moment is calculated. The target physiological data is generated based on the physiological data deviation at each time point, the physiological data variability, and the physiological data change trend.

9. The apparatus according to claim 6, characterized in that, When determining the user's target emotion category based on the target physiological data, the determining module is specifically used for: Based on the target physiological data, probability values ​​corresponding to each emotion category are generated; The emotion category corresponding to the maximum value among the probability values ​​is determined as the target emotion category.

10. The apparatus according to claim 6, characterized in that, When determining the first image processing parameters based on the user's target emotion category, the determining module is specifically used for: Based on the user's target emotion category, a second image processing parameter corresponding to the target emotion category is determined from multiple preset image processing parameters; In response to the first input to the second image processing parameters, the second image processing parameters are updated to obtain the updated first image processing parameters.

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