Abstract: A method for performing interactions between virtual objects in a virtual environment is performed by an electronic device. The method includes: displaying an interaction interface, the interface including one or more selectable interactions; in response to a trigger operation for any interaction, displaying an action marker of the interaction associated with a first virtual object; and when at least one second virtual object appears within an interaction range of the first virtual object and carries the action marker of the interaction, playing a marker fusion special effect based on a plurality of action markers within the interaction range, the marker fusion special effect providing an interaction special effect when the plurality of action markers converge within the interaction range. By using the above method, the interaction modes based on interactions are enriched.

Abstract: This application discloses an application interface control method performed by a computer device. The method includes: displaying an application interface in a landscape state, the application interface including operable elements, and the operable elements including at least one first operable element located in a one-handed operation region and at least one second operable element located outside the one-handed operation region; receiving a shielding operation on a light sensor, the light sensor being configured to collect light intensity of an environment in which the computer device is located, and the light sensor being located in the one-handed operation region in the landscape state; and controlling, based on the shielding operation, the application interface to move, to cause the second operable element to move into the one-handed operation region. Using the foregoing method helps improve efficiency of an one-handed operation in a landscape state.

Abstract: A method for classifying an image of a displaying base plate includes: acquiring an image to be checked; from a first predetermined-type set, determining a type of the image to be checked. The first predetermined-type set includes: a first image type, a second image type and a third image type. An image of the first image type is a no-defect image, an image of the second image type is a blurred image, and an image of the third image type is a defect image. When the type of the image to be checked is the third image type, by using a first convolutional neural network, determining a defect data of the image to be checked, wherein the defect image refers to an image of a displaying base plate having a defect, and the defect data contains a defect type of the displaying base plate in the image to be checked.

Abstract: An image processing method includes: receiving a processing instruction for an image; screening out a plurality of target images from a plurality of acquired images; determining a first feature value and a second feature value of the target area of each of the target images; determining a similarity between the first feature values of the target areas in every two target images according to all the determined first feature values, and determining a similarity between the second feature values of the target areas in every two target images according to all the determined second feature values; and, if a first target similarity is within a first preset range, and a second target similarity is within a second preset range, determining that a first image and a second image belong to the same image set.

Abstract: Provided are a method and apparatus for identifying the defect grade of a bad picture, and a storage medium. The method includes: determining the defect size of a defect from a bad picture; according to a product model corresponding to the bad picture, determining the design size of a pattern corresponding to a component that is adjacent to the position of the defect; determining the defect grade of the defect according to the defect type of the defect and a magnitude relationship between the defect size and the design size, wherein the defect grade is the degree to which the defect affects product yield.

Abstract: An image processing method includes: receiving a processing instruction for an image; screening out a plurality of target images from a plurality of acquired images; determining a first feature value and a second feature value of the target area of each of the target images; determining a similarity between the first feature values of the target areas in every two target images according to all the determined first feature values, and determining a similarity between the second feature values of the target areas in every two target images according to all the determined second feature values; and, if a first target similarity is within a first preset range, and a second target similarity is within a second preset range, determining that a first image and a second image belong to the same image set.

Abstract: A method for classifying an image of a displaying base plate includes: acquiring an image to be checked; from a first predetermined-type set, determining a type of the image to be checked. The first predetermined-type set includes: a first image type, a second image type and a third image type. An image of the first image type is a no-defect image, an image of the second image type is a blurred image, and an image of the third image type is a defect image. When the type of the image to be checked is the third image type, by using a first convolutional neural network, determining a defect data of the image to be checked, wherein the defect image refers to an image of a displaying base plate having a defect, and the defect data contains a defect type of the displaying base plate in the image to be checked.