Image processing method and device suitable for cooking equipment, equipment and medium

Abstract

The embodiment of the invention provides an image processing method and device suitable for cooking equipment, equipment and a medium. The cooking equipment is provided with a glass door, an image acquisition module arranged on the glass door and a reference light source. The method comprises the following steps: acquiring a first image of the interior of the cooking equipment shot by an image acquisition module when a reference light source is turned on, and acquiring a second image of the interior of the cooking equipment shot by the image acquisition module when the reference light source is turned off; and extracting a reflected light characteristic value of the first image, and performing reflection removal processing on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image. According to the embodiment of the invention, the reference light source is arranged to shoot the first image with the reflected light, and the reflected light image of the first image is filtered through the reflected light characteristic value of the first image and the second image, so that the influence of the reflected light on image content identification is avoided, and the image definition and the information identification degree are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to an image processing method applicable to cooking equipment and an image processing device applicable to cooking equipment. Background Art

[0002] When taking photos of food inside a steam oven, interference from reflected light from external light sources is a significant problem. This reflected light not only comes from the metal inner wall of the steam oven, but may also come from various light sources in the external environment of the device, such as kitchen lights or natural light. After these reflected lights enter the camera lens, they form irregular bright spots or highlight areas on the image, resulting in reduced image contrast and blurred details, which seriously affects image resolution and accurate assessment of food status. Especially under complex real-world imaging conditions, it becomes particularly difficult to obtain a "real" image without reflected light. In addition, due to the diversity of lighting angles, intensities, and surface characteristics of food, the reflection conditions of each shot are different, which increases the difficulty of image processing. Summary of the invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide an image processing method, apparatus, cooking device and storage medium applicable to a cooking device that overcome the above problems or at least partially solve the above problems.

[0004] According to a first aspect of an embodiment of the present invention, there is provided an image processing method applicable to a cooking device, wherein the cooking device comprises a glass door, an image acquisition module disposed on the glass door, and a reference light source; the method comprises:

[0005] Acquire a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off;

[0006] Extracting the reflected light characteristic value of the first image, and performing de-reflection processing on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image.

[0007] Optionally, the image acquisition module is deployed with a preset de-reflection model;

[0008] The extracting of the reflected light characteristic value of the first image, and performing a de-reflection process on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image includes:

[0009] The reflected light characteristic value of the first image is extracted by the preset de-reflection model, and the reflected light image in the first image is determined according to the reflected light characteristic value of the first image and the second image, and the reflected light image in the first image is removed to obtain a target image.

[0010] Optionally, extracting the reflected light characteristic value of the first image by using the preset de-reflection model includes:

[0011] Extracting a Gaussian blur parameter characteristic value of the reflected light of the first image and a peak brightness change characteristic value of the reflected light of the first image by using the preset de-reflection model;

[0012] The Gaussian blur parameter characteristic value of the reflected light of the first image represents the diffusion degree of the reflected light; the peak brightness change characteristic value of the reflected light of the first image represents the influence degree of the reflected light on the image brightness.

[0013] Optionally, determining a reflected light image in the first image according to the reflected light characteristic value of the first image and the second image, and removing the reflected light image in the first image to obtain a target image includes:

[0014] The reflected image in the first image is determined according to the Gaussian blur parameter characteristic value of the reflected light of the first image, the peak brightness change characteristic value of the reflected light of the first image, and the second image, and the reflected image in the first image is removed to obtain a target image.

[0015] Optionally, the acquiring a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off includes:

[0016] Determining whether a second image of the interior of the cooking device captured by the image acquisition module is stored during the current cooking process;

[0017] If not, the image acquisition module is called to capture and store a second image of the interior of the cooking device when the reference light source is turned off.

[0018] Optionally, the image acquisition module is arranged on the outside of the glass door, and captures the image inside the cooking device through the glass door;

[0019] The reference light source is arranged outside the glass door and emits light into the cooking device through the glass door.

[0020] Optionally, the image acquisition module is arranged at the top of the glass door; and the reference light source is arranged at the bottom of the glass door.

[0021] According to a second aspect of an embodiment of the present invention, there is provided an image processing device applicable to a cooking device, wherein the cooking device comprises a glass door, an image acquisition module disposed on the glass door, and a reference light source; the device comprises:

[0022] an acquisition module, configured to acquire a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off;

[0023] The de-reflection module is used to extract the reflected light characteristic value of the first image, and perform de-reflection processing on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image.

[0024] Optionally, the image acquisition module is deployed with a preset de-reflection model; the de-reflection module includes:

[0025] The de-reflection submodule is used to extract the reflected light characteristic value of the first image through the preset de-reflection model, and determine the reflected light image in the first image according to the reflected light characteristic value of the first image and the second image, and remove the reflected light image in the first image to obtain a target image.

[0026] Optionally, the de-reflection submodule includes:

[0027] an extraction unit, configured to extract a Gaussian blur parameter characteristic value of the reflected light of the first image and a peak brightness change characteristic value of the reflected light of the first image through the preset de-reflection model;

[0028] The Gaussian blur parameter characteristic value of the reflected light of the first image represents the diffusion degree of the reflected light; the peak brightness change characteristic value of the reflected light of the first image represents the influence degree of the reflected light on the image brightness.

[0029] Optionally, the de-reflection submodule includes:

[0030] A de-reflection unit is used to determine a reflected image in the first image according to a Gaussian blur parameter characteristic value of the reflected light of the first image, a peak brightness change characteristic value of the reflected light of the first image, and the second image, and remove the reflected image in the first image to obtain a target image.

[0031] Optionally, the acquisition module includes:

[0032] a judgment submodule, used for judging whether a second image of the interior of the cooking device taken by the image acquisition module is stored during the current cooking process;

[0033] If not, the image acquisition module is called to capture and store a second image of the interior of the cooking device when the reference light source is turned off.

[0034] Optionally, the image acquisition module is arranged on the outside of the glass door, and captures the image inside the cooking device through the glass door;

[0035] The reference light source is arranged outside the glass door and emits light into the cooking device through the glass door.

[0036] Optionally, the image acquisition module is arranged at the top of the glass door; and the reference light source is arranged at the bottom of the glass door.

[0037] According to a third aspect of the present invention, a cooking device is provided, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the image processing method applicable to the cooking device as described in any one of the above items are implemented.

[0038] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the image processing method applicable to a cooking device as described in any one of the above items are implemented.

[0039] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0040] An embodiment of the present invention provides an image processing method applicable to a cooking device, wherein the cooking device has a glass door, an image acquisition module disposed on the glass door, and a reference light source; a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off are obtained; a reflected light characteristic value of the first image is extracted, and, based on the reflected light characteristic value of the first image and the second image, a de-reflection process is performed on the first image to obtain a target image. The embodiment of the present invention captures a first image with reflected light by setting a reference light source, and filters the reflected light image of the first image by using the reflected light characteristic value of the first image and the second image, thereby avoiding the influence of reflected light on the recognition of image content and significantly improving image clarity and information recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flowchart of a method for image processing applicable to a cooking device provided by an embodiment of the present invention;

[0042] Figure 2It is a structural block diagram of an image processing device applicable to cooking equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] One of the core concepts of an embodiment of the present invention is to set a reference light source to capture a first image with reflected light, and to filter the reflected image of the first image through the reflected light characteristic value of the first image and the second image, thereby avoiding the influence of reflected light on image content recognition and significantly improving image clarity and information recognition.

[0045] Reference Figure 1 , shows a flowchart of an image processing method applicable to a cooking device provided by an embodiment of the present invention, wherein the method may specifically include the following steps:

[0046] Step 101, acquiring a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off;

[0047] In actual use, the cooking device may be a steam oven, which is a multifunctional kitchen appliance that combines steam cooking and traditional baking functions, and can provide a healthier and more uniform way of cooking. A steam oven integrates the two main functions of steam cooking and traditional baking, and can complete a variety of cooking tasks in the same device, such as steaming, baking, and roasting. Steam cooking can retain more nutrients in the ingredients and reduce the use of oil, thereby providing a healthier diet option. At the same time, uniform heating ensures that the food reaches the ideal degree of doneness inside and outside, avoiding the problem of overcooking or uneven cooking. Modern steam ovens are usually equipped with intelligent control systems, allowing users to remotely operate through mobile phone applications or other smart devices, set temperature, time and mode, and even receive cooking suggestions and recipe recommendations. A steam oven is a multifunctional kitchen appliance that combines steam cooking and traditional baking functions, and can provide a healthier and more uniform way of cooking. A steam oven integrates the two main functions of steam cooking and traditional baking, and can complete a variety of cooking tasks in the same device, such as steaming, baking, and roasting.

[0048] Steam cooking can retain more nutrients in ingredients and reduce the use of oil, thus providing a healthier diet option. At the same time, uniform heating ensures that the food reaches the ideal degree of doneness inside and outside, avoiding overcooking or uneven cooking. Modern steam ovens are usually equipped with intelligent control systems, allowing users to remotely operate through mobile phone applications or other smart devices, set temperature, time and mode, and even receive cooking suggestions and recipe recommendations. High-quality images help users accurately judge the state of ingredients, such as the degree of doneness of meat and the softness and hardness of vegetables. This not only improves the success rate of cooking, but also allows users to adjust cooking parameters according to actual conditions to ensure that each dish can achieve the expected results. For steam ovens equipped with intelligent systems, image information can be used for automatic monitoring and analysis. The system can provide personalized cooking suggestions such as adjusting temperature, time or mode based on the color, texture and other characteristics of the ingredients, further improving the user experience. In some cases, users may not be able to directly observe the situation inside the steam oven. By capturing images through the camera of the image acquisition module, users can obtain internal information without opening the door, reducing heat loss and potential safety hazards caused by frequent door opening. High-definition images provide users with an intuitive visual feedback channel and enhance the human-computer interaction experience. Users can not only better understand the cooking process, but also enjoy more convenient and reliable cooking pleasure.

[0049] The imaging environment inside the steam oven is complex and changeable. External light sources (such as kitchen lights or natural light) will be reflected into the steam oven through the glass door or other transparent materials, resulting in irregular highlight areas or bright spots, which seriously affect the quality and resolution of the image. These reflected lights not only reduce the image contrast, but also may cause the details of the ingredients to be blurred, making it difficult for users to accurately assess the cooking progress or the status of the ingredients. In order to solve this problem, the present invention proposes a method of setting a reference light source to obtain a clearer image without reflection interference.

[0050] The reference light source is a carefully designed lighting device whose brightness, color temperature and illumination angle can be precisely controlled. When the reference light source is turned on, it can provide uniform and stable lighting conditions inside the steam oven, effectively covering the entire shooting area, while reducing or even eliminating interference from reflected light from the outside. In this way, the image acquisition module can capture more realistic and accurate images of ingredients without being affected by changes in the external environment. In addition, the reference light source can also be adjusted according to different cooking scenarios to meet the needs of various ingredients and cooking modes, further improving image quality.

[0051] Acquiring the first image taken when the reference light source is turned on is the basis of the entire image processing process. This image contains visual information of the food under ideal lighting conditions and is an important basis for subsequent image processing and analysis. Specifically, the first image can enhance the image contrast. Since the illumination provided by the reference light source is uniform and consistent, the contrast between different parts of the food is significantly enhanced, making subtle features more obvious. This is very helpful for identifying color changes, texture structures, etc. of food, especially in monitoring key stages in the cooking process (such as the degree of doneness of meat, changes in the hardness and softness of vegetables, etc.). The first image can reduce the impact of reflected light. By controlling the parameters of the reference light source, the impact of external reflected light can be largely suppressed to avoid unnecessary highlight areas or bright spots in the image. This not only improves the overall clarity of the image, but also helps to maintain the authenticity of color restoration, allowing users to understand the state of the food more intuitively. The first image can simplify the image processing algorithm. With the first image as a benchmark, the subsequent image processing steps become simpler and more efficient. For example, in the process of removing reflected light, by comparing the difference between the first image and the second image without reflected light, the reflected component can be accurately identified and filtered out without accidentally deleting important food information. In addition, the model established based on the first image can also be used to predict the appearance of food under different lighting conditions, thereby achieving intelligent image optimization.

[0052] When the reference light source is turned off, the interior of the steam oven mainly relies on external ambient light (such as kitchen light, natural light) or the weak lighting inside the device. These light sources are usually soft and evenly distributed. The second image taken under such conditions can reflect the true state of the food under natural light without being disturbed by reflected light.

[0053] Step 102: extracting the reflected light characteristic value of the first image, and performing a de-reflection process on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image.

[0054] The first image captured when the reference light source is on contains the ideal lighting conditions as well as the reflected light components caused by the light source. In order to effectively remove these reflected lights, their locations must first be accurately identified. Reflections usually appear as irregular highlights or areas in the image, with brightness and color that are significantly different from the surrounding environment. By analyzing the characteristic values ​​such as grayscale value and color distribution in the first image, the system can accurately locate these reflective areas and mark the affected pixels. This precise identification is the basis for subsequent processing and ensures that the reflected light is not mistaken for the characteristics of the food itself. In addition to the location, it is also necessary to understand the intensity distribution of each reflective area. This helps to adopt appropriate processing strategies when removing reflected light and avoid over-correction and image distortion. By calculating the brightness change amplitude of different reflective areas, the intensity of each reflection point can be quantified. For example, some reflections may be very strong and cover a large area; while others may be faint small spots. Quantifying these intensities not only helps the system better understand the scope of the reflection, but also provides a basis for adaptive threshold adjustment, making the processing more flexible and accurate.

[0055] The extraction of reflected light eigenvalues ​​can also help the system distinguish reflected light from other important image information (such as the color and texture of ingredients). In actual applications, there may be some natural highlight areas on the surface of ingredients, which are not caused by reflections from external light sources. By comparing and analyzing the differences between reflected light eigenvalues ​​and normal ingredient characteristics, the system can protect the true appearance of ingredients while removing reflected light, ensuring that the image is both clear and real. This is especially important for smart cooking devices, because it needs to be based on high-quality images to evaluate the state of ingredients and provide accurate cooking suggestions. High-quality images are essential for the application of smart cooking devices. By extracting reflected light eigenvalues ​​and performing effective reflection processing, the generated target image has higher resolution and clearer ingredient details. This not only enables users to intuitively understand the state of ingredients and enjoy a more convenient and reliable cooking experience, but also supports functions such as automatic monitoring of cooking progress and accurate identification of ingredient types. The system can monitor the changes in color and shape of ingredients in real time based on clear images, and promptly remind users to adjust cooking parameters to ensure the best cooking results.

[0056] The first image, taken when the reference light source is on, contains ideal lighting conditions as well as reflected light components caused by the light source. These reflected lights usually appear as bright spots or areas in the image, which seriously affect the image quality and the recognition of food details. To eliminate this effect, the system first needs to accurately extract the reflected light eigenvalues ​​in the first image. These eigenvalues ​​provide key information about the location, intensity and distribution of the reflected light. At the same time, the second image is taken when the reference light source is off, reflecting the interior of the cooking equipment under natural ambient light or weak internal lighting. This image does not contain obvious reflected light components, so it can serve as an ideal benchmark without reflected light interference. Combining the reflected light eigenvalues ​​in the first image and the difference between the first and second images, the system can more accurately identify which parts are affected by the reflected light, quantify its intensity, and perform de-reflection processing.

[0057] In one embodiment, the image acquisition module is deployed with a preset de-reflection model;

[0058] The extracting of the reflected light characteristic value of the first image, and performing a de-reflection process on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image includes:

[0059] The reflected light characteristic value of the first image is extracted by the preset de-reflection model, and the reflected light image in the first image is determined according to the reflected light characteristic value of the first image and the second image, and the reflected light image in the first image is removed to obtain a target image.

[0060] The preset de-reflection model allows the image acquisition module to perform real-time processing while acquiring the image. Compared with post-processing, this method can immediately eliminate the impact of reflected light and ensure that users get instant feedback. This is especially important for smart cooking equipment, because it needs to adjust cooking parameters in real time based on clear images and provide accurate cooking suggestions. The preset de-reflection model is usually trained with a large amount of data and can work stably under various complex lighting conditions. Whether in natural light during the day or indoor lighting environment at night, the model can find the best processing solution to ensure that every image achieves the desired effect. This robustness improves the overall performance of the system and is suitable for different application scenarios.

[0061] The preset de-reflection model is pre-trained and optimized to accurately identify and quantify the impact of reflected light caused by the reference light source under different lighting conditions. The first image taken when the reference light source is turned on contains ideal lighting conditions and reflected light components. In order to effectively remove these reflected lights, the system first needs to accurately extract the reflected light feature values ​​in the first image. These feature values ​​can provide key information about the location, intensity and distribution of the reflected light, laying the foundation for subsequent processing steps. Specifically, the pre-trained reflection removal model combines the powerful pattern recognition capabilities of deep learning with the efficiency of traditional image processing algorithms. After training with a large amount of annotated data, the deep learning model can automatically learn the complex features of reflected light and distinguish it from other image information. At the same time, traditional image processing technology is used for refinement processing to ensure that the final output image is both clear and realistic. Compare the first image with the second image to find the difference between the two. Since the second image does not contain reflected light components, it is possible to determine which parts are affected by reflected light by comparing features such as grayscale value and color. According to the feature values ​​of different reflection areas, the processing threshold is dynamically adjusted to achieve effective filtering of reflected light. This method can flexibly deal with various complex reflection situations, improve processing accuracy, and ensure that the characteristics of the food itself are not deleted by mistake. Combining information at different resolution levels, the processing is gradually refined from global to local to ensure the consistency and smoothness of the image. In this way, the true appearance of the food can be retained to the maximum extent while removing the reflected light. For those areas covered by reflected light, the system can perform color and texture restoration according to the corresponding positions in the second image, restore the original color and surface features of the food, and make the final output target image both clear and realistic.

[0062] The pre-trained reflection removal model can identify and separate the reflected image portion that matches the reference light source characteristics based on the extracted reflected light feature values ​​and the reference provided by the second image through deep learning or image processing algorithms. This method ensures that unnecessary reflection interference is effectively removed without losing the internal details of the steam oven, thereby generating a high-quality target image. This method not only enhances the effect of image processing, but also improves the user's cooking experience. It is suitable for various complex imaging environments and provides solid technical support for the functional realization of smart cooking equipment. By accurately removing reflected light, the generated target image not only eliminates the interference of external light sources, but also retains the true characteristics of the ingredients, supporting more intelligent application scenarios.

[0063] In one embodiment, extracting the reflected light characteristic value of the first image by using the preset de-reflection model includes:

[0064] Extracting a Gaussian blur parameter characteristic value of the reflected light of the first image and a peak brightness change characteristic value of the reflected light of the first image by using the preset de-reflection model;

[0065] The Gaussian blur parameter characteristic value of the reflected light of the first image represents the diffusion degree of the reflected light; the peak brightness change characteristic value of the reflected light of the first image represents the influence degree of the reflected light on the image brightness.

[0066] The Gaussian blur parameter can help the system identify which areas are affected by reflected light. Reflected light usually appears as irregular highlights or areas, and its diffusion can be quantified by Gaussian distribution. By analyzing the Gaussian blur parameters of different areas, the system can accurately locate and mark the affected pixels. The Gaussian blur parameter not only indicates the location of the reflected light, but also provides key information about its diffusion range. A larger σ value means that the reflected light is more diffuse and affects a larger area of ​​the image; a smaller σ value means that the reflected light is concentrated in a smaller area. This characteristic value helps to more accurately adjust the threshold and select the appropriate filter in the subsequent processing steps. By controlling the Gaussian blur parameter, you can remove the reflected light while trying to preserve other important details in the image. For example, there may be some natural highlights on the surface of the food, which are not caused by reflections from external light sources. By comparing and analyzing the differences between the Gaussian blur parameters and the normal food characteristics, the system can protect these details during processing.

[0067] The peak brightness change eigenvalue can help the system identify which pixels have significantly higher brightness than the surrounding environment, so as to determine whether they are affected by reflected light. By calculating the difference between each pixel and the average brightness in its neighborhood, points with sudden brightness changes can be found. In addition to the position, it is also necessary to understand the brightness change amplitude of each reflection area. This helps to adopt appropriate processing strategies when removing reflected light to avoid image distortion caused by over-correction. By calculating the peak brightness change of different reflection areas, the brightness impact of each reflection point can be quantified, providing a basis for adaptive threshold adjustment. Some ingredients may have natural highlight areas themselves, and the brightness changes in these areas are not caused by reflections from external light sources. By analyzing the peak brightness change eigenvalue, the system can protect the real highlight features on the surface of the ingredients while removing reflected light, ensuring that the image is both clear and realistic.

[0068] In one embodiment, determining a reflected light image in the first image according to the reflected light characteristic value of the first image and the second image, and removing the reflected light image in the first image to obtain a target image includes:

[0069] The reflected image in the first image is determined according to the Gaussian blur parameter characteristic value of the reflected light of the first image, the peak brightness change characteristic value of the reflected light of the first image, and the second image, and the reflected image in the first image is removed to obtain a target image.

[0070] According to the Gaussian blur parameters and peak brightness change characteristic values ​​of different reflection areas, the processing threshold is dynamically adjusted to achieve effective filtering of reflected light. This method can flexibly cope with various complex reflection situations, improve processing accuracy, and ensure that the characteristics of the food itself are not deleted by mistake. Morphological operations such as dilation and corrosion are used to remove isolated small-area reflection spots while protecting the outline of a large range of food. This helps to maintain the overall structure and detail clarity of the image and avoid image distortion due to over-correction. Combining information at different resolution levels, the image is gradually refined from global to local to ensure consistency and smoothness. In this way, the true appearance of the food can be retained to the maximum extent while removing the reflected light. For those areas covered by the reflected light, the system can perform color and texture repair according to the corresponding positions in the second image to restore the original color and surface characteristics of the food, so that the final output target image is both clear and real.

[0071] In one embodiment, the acquiring of a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off includes:

[0072] Determining whether a second image of the interior of the cooking device captured by the image acquisition module is stored during the current cooking process;

[0073] If not, the image acquisition module is called to capture and store a second image of the interior of the cooking device when the reference light source is turned off.

[0074] The second image is taken with the reference light source turned off, reflecting the interior of the cooking device under natural ambient light or weak internal lighting. This image does not contain reflected light components, so it can serve as an ideal benchmark without reflected light interference. By comparing with the first image (taken with the reference light source turned on), the system can more accurately identify and quantify the impact of reflected light. If the second image has been stored during the cooking process, the system can use it directly for reflection processing; if not, it needs to be re-shot and stored in time. This is because the lighting conditions and ingredient placement may be different each time you cook, ensuring that the latest second image can provide the most accurate comparison benchmark, thereby improving the accuracy of reflected light removal. By first determining whether the second image exists and then deciding whether to reshoot, you can avoid duplication of work and save time and computing resources. Especially in smart cooking equipment, efficient use of resources is very important to improve user experience.

[0075] In one embodiment, the image acquisition module is disposed outside the glass door and captures the image inside the cooking device through the glass door;

[0076] The reference light source is arranged outside the glass door and emits light into the cooking device through the glass door.

[0077] Placing the image acquisition module and reference light source outside the glass door facilitates installation, debugging and daily maintenance. Technicians do not need to enter the high temperature and high humidity cooking environment, reducing maintenance costs and complexity. This external installation method makes the design of the entire intelligent cooking device more concise, avoids the layout of complex circuits and hardware inside, and improves the reliability and stability of the system. Shooting through the glass door can effectively isolate adverse factors such as high temperature, steam and oil smoke inside the cooking equipment, protect the image acquisition module and reference light source from damage, thereby ensuring image quality and equipment life. The reference light source is set on the outside, and can provide uniform and controllable lighting conditions to the inside of the cooking equipment by precisely adjusting the angle and intensity. This helps to improve image contrast, reduce shadows, and make food details more clearly visible. Although the glass door may introduce some reflected light, the system can effectively identify and remove these reflected interferences through the preset de-reflection model and the benchmark provided by the second image to generate a high-quality target image.

[0078] Placing key components on the outside avoids the possibility of users directly contacting high-temperature areas, improving safety in use. In particular, when turning on or off cooking equipment, users will not feel inconvenienced or have safety hazards due to internal equipment. Shooting through the glass door allows users to intuitively observe the cooking process, enhancing the human-computer interaction experience. High-definition images not only provide users with visual feedback, but also support smart cooking equipment for automatic monitoring and personalized suggestions. At the same time, based on clear target images, the system can more accurately monitor changes in the color and shape of ingredients, adjust cooking parameters in time, and ensure the best cooking results. High-quality images enable the system to better identify different types of ingredients, provide personalized cooking suggestions, and enhance user experience. Users can intuitively understand the status of ingredients through high-definition images and enjoy a more convenient and reliable cooking process.

[0079] In one embodiment, the image acquisition module is disposed at the top of the glass door; and the reference light source is disposed at the bottom of the glass door.

[0080] Placing the image acquisition module at the top of the glass door provides a wider viewing angle, ensuring that the entire interior of the cooking equipment can be captured. This position arrangement allows the camera to cover the entire cooking area, avoiding blind spots caused by limited viewing angles, thereby improving the overall coverage of the image. Top mounting helps achieve a near-vertical shooting angle and reduces perspective distortion caused by tilt angles. This not only makes the shape and size of the ingredients more accurately reflected in the image, but also improves the accuracy of subsequent image processing algorithms. Placing the reference light source at the bottom of the glass door, away from the image acquisition module, can effectively reduce direct optical interference between the two. The large spatial distance between the top camera and the bottom light source reduces the possibility of the light source directly shining on the lens and reduces unnecessary reflected light entering the image. The design of the bottom light source imitates the effect of natural light illuminating an object from below, making the reflected light more uniform and controllable. This approach helps reduce highlights and irregular reflections, improving the authenticity and visual effects of the image.

[0081] The uniform lighting conditions provided by the bottom light source, combined with the wide-angle coverage of the top camera, can form good contrast in the image. This helps to highlight the color and texture characteristics of the ingredients, providing a more accurate assessment of the food status for smart cooking equipment. Mounting the image acquisition module on the top can prevent it from being directly exposed to high temperature, steam and oil smoke, extending the service life of the equipment. At the same time, the bottom light source is also easier to dissipate heat, reducing the risk of failure due to overheating. The position of the top camera allows users to intuitively view the cooking process through an external display or mobile device, enhancing the human-computer interaction experience. The high-definition image not only provides visual feedback, but also supports real-time monitoring and personalized cooking suggestions. This layout design simplifies the structure of the system and is easy to install and maintain. Technicians do not need to enter the high temperature and high humidity cooking environment, reducing maintenance costs and complexity.

[0082] An embodiment of the present invention provides an image processing method applicable to a cooking device, wherein the cooking device has a glass door, an image acquisition module disposed on the glass door, and a reference light source; a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off are obtained; a reflected light characteristic value of the first image is extracted, and, based on the reflected light characteristic value of the first image and the second image, a de-reflection process is performed on the first image to obtain a target image. The embodiment of the present invention captures a first image with reflected light by setting a reference light source, and filters the reflected light image of the first image by using the reflected light characteristic value of the first image and the second image, thereby avoiding the influence of reflected light on the recognition of image content and significantly improving image clarity and information recognition.

[0083] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0084] Reference Figure 2 , shows a structural block diagram of an image processing device applicable to a cooking device provided by an embodiment of the present invention, which may specifically include the following modules:

[0085] An acquisition module 201, configured to acquire a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off;

[0086] The de-reflection module 202 is used to extract the reflected light characteristic value of the first image, and perform de-reflection processing on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image.

[0087] In one embodiment, the image acquisition module is deployed with a preset de-reflection model; the de-reflection module includes:

[0088] The de-reflection submodule is used to extract the reflected light characteristic value of the first image through the preset de-reflection model, and determine the reflected light image in the first image according to the reflected light characteristic value of the first image and the second image, and remove the reflected light image in the first image to obtain a target image.

[0089] In one embodiment, the anti-reflective submodule comprises:

[0090] an extraction unit, configured to extract a Gaussian blur parameter characteristic value of the reflected light of the first image and a peak brightness change characteristic value of the reflected light of the first image through the preset de-reflection model;

[0091] The Gaussian blur parameter characteristic value of the reflected light of the first image represents the diffusion degree of the reflected light; the peak brightness change characteristic value of the reflected light of the first image represents the influence degree of the reflected light on the image brightness.

[0092] In one embodiment, the anti-reflective submodule comprises:

[0093] A de-reflection unit is used to determine a reflected image in the first image according to a Gaussian blur parameter characteristic value of the reflected light of the first image, a peak brightness change characteristic value of the reflected light of the first image, and the second image, and remove the reflected image in the first image to obtain a target image.

[0094] In one embodiment, the acquisition module includes:

[0095] a judgment submodule, used for judging whether a second image of the interior of the cooking device taken by the image acquisition module is stored during the current cooking process;

[0096] If not, the image acquisition module is called to capture and store a second image of the interior of the cooking device when the reference light source is turned off.

[0097] In one embodiment, the image acquisition module is disposed outside the glass door and captures the image inside the cooking device through the glass door;

[0098] The reference light source is arranged outside the glass door and emits light into the cooking device through the glass door.

[0099] In one embodiment, the image acquisition module is disposed at the top of the glass door; and the reference light source is disposed at the bottom of the glass door.

[0100] An embodiment of the present invention provides an image processing method applicable to a cooking device, wherein the cooking device has a glass door, an image acquisition module disposed on the glass door, and a reference light source; a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off are obtained; a reflected light characteristic value of the first image is extracted, and, based on the reflected light characteristic value of the first image and the second image, a de-reflection process is performed on the first image to obtain a target image. The embodiment of the present invention captures a first image with reflected light by setting a reference light source, and filters the reflected light image of the first image by using the reflected light characteristic value of the first image and the second image, thereby avoiding the influence of reflected light on the recognition of image content and significantly improving image clarity and information recognition.

[0101] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0102] The embodiment of the present invention further provides a cooking device, comprising:

[0103] It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned image processing method embodiment applicable to cooking equipment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0104] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned image processing method embodiment applicable to cooking equipment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0105] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0106] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0107] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0110] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0111] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0112] The above is a detailed introduction to an image processing method suitable for cooking equipment and an image processing device suitable for cooking equipment provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An image processing method suitable for cooking equipment, characterized in that: The cooking device comprises a glass door, an image acquisition module arranged on the glass door, and a reference light source; the method comprises: Acquire a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off; Extracting the reflected light characteristic value of the first image, and performing de-reflection processing on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image.

2. The method according to claim 1, characterized in that The image acquisition module is deployed with a preset de-reflection model; The extracting of the reflected light characteristic value of the first image, and performing a de-reflection process on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image includes: The reflected light characteristic value of the first image is extracted by the preset de-reflection model, and the reflected light image in the first image is determined according to the reflected light characteristic value of the first image and the second image, and the reflected light image in the first image is removed to obtain a target image.

3. The method according to claim 2, characterized in that The extracting the reflected light characteristic value of the first image by using the preset de-reflection model includes: Extracting a Gaussian blur parameter characteristic value of the reflected light of the first image and a peak brightness change characteristic value of the reflected light of the first image by using the preset de-reflection model; The Gaussian blur parameter characteristic value of the reflected light of the first image represents the diffusion degree of the reflected light; the peak brightness change characteristic value of the reflected light of the first image represents the influence degree of the reflected light on the image brightness.

4. The method according to claim 3, characterized in that The step of determining a reflected light image in the first image according to the reflected light characteristic value of the first image and the second image, and removing the reflected light image in the first image to obtain a target image includes: The reflected image in the first image is determined according to the Gaussian blur parameter characteristic value of the reflected light of the first image, the peak brightness change characteristic value of the reflected light of the first image, and the second image, and the reflected image in the first image is removed to obtain a target image.

5. The method according to claim 1, characterized in that The acquiring of a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off includes: Determining whether a second image of the interior of the cooking device captured by the image acquisition module is stored during the current cooking process; If not, the image acquisition module is called to capture and store a second image of the interior of the cooking device when the reference light source is turned off.

6. The method according to claim 1, characterized in that The image acquisition module is arranged on the outside of the glass door and captures the image inside the cooking device through the glass door; The reference light source is arranged outside the glass door and emits light into the cooking device through the glass door.

7. The method according to claim 6, characterized in that The image acquisition module is arranged at the top of the glass door; and the reference light source is arranged at the bottom of the glass door.

8. An image processing device suitable for cooking equipment, characterized in that: The cooking device comprises a glass door, an image acquisition module arranged on the glass door, and a reference light source; the device comprises: an acquisition module, configured to acquire a first image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned on, and a second image of the interior of the cooking device taken by the image acquisition module when the reference light source is turned off; The de-reflection module is used to extract the reflected light characteristic value of the first image, and perform de-reflection processing on the first image according to the reflected light characteristic value of the first image and the second image to obtain a target image.

9. A cooking device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the image processing method applicable to a cooking device as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the image processing method applicable to a cooking device as claimed in any one of claims 1 to 7 are implemented.

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