This disclosure is related to an image generation method and apparatus. The method includes: obtaining a first body image including a target body and a first clothes image including target clothes; transforming the first clothes image based on a posture of the target body in the first body image to obtain a second clothes image, the second clothes image including the target clothes, and a posture of the target clothes matching the posture of the target body; performing feature extraction on the second clothes image, an image of a bare area in the first body image, and the first body image to obtain a clothes feature, a skin feature, and a body feature respectively; and generating a second body image based on the clothes feature, the skin feature, and the body feature, the target body in the second body image wearing the target clothes.

Disclosed in the present invention is An annotation method of arbitrary-oriented rectangular bounding box, wherein: the elements for annotation being: the coordinates of the center point C, a vector {right arrow over (CD)} formed by the center point C and a chosen vertex D, and the ratio of the vector {right arrow over (CP)} to vector {right arrow over (CD)}, where {right arrow over (CP)} is the projection of the vector {right arrow over (CE)} to {right arrow over (CD)}, and {right arrow over (CE)} is a vector formed by the center of the bounding box to one of the vertex E that close neighbor to vertex D; and it is also required that the vector {right arrow over (CP)} is in the same direction as the vector {right arrow over (CD)}, the vertex E in either of the clockwise or counterclockwise direction of the vertex D. The symbol notation of this method is (x.sub.c, y.sub.c, u, v, ρ), x.sub.c and y.sub.c are the two coordinate values of the center point C, u and v are the two components of vector {right arrow over (CD)}, ρ is the ratio of the vector {right arrow over (CP)} to vector {right arrow over (CD)}. Also let a binary value s to indicate whether the two components of the vector {right arrow over (CD)} have same sign or not to represent {right arrow over (CD)} and −{right arrow over (CD)} at once by (|u|, |v|, s), then getting a method for annotating arbitrary-oriented rectangular bounding box that one bounding box has only two representation vectors. Its symbol notation is (x.sub.c, y.sub.c, |u|, |v|, s, ρ), wherein |u| and |v| are magnitude of two components of the vector {right arrow over (CD)}. This method avoids loss inconsistency between representations of the same bounding box and is beneficial to model regression training.

A system for analyzing screenshots can include a computing device including a processor coupled to a memory and a display screen configured to display content. The system can include an application stored on the memory and executable by the processor. The application can include a screenshot receiver configured to access, from storage to which a screenshot of the content displayed on the display screen captured using a screenshot function of the computing device is stored, the screenshot including an image and a predetermined marker. The application can include a marker detector configured to detect the predetermined marker included in the screenshot. The application can include a link identifier configured to identify, using the predetermined marker, a link to a resource mapped to the image included in the screenshot, the resource accessible by the computing device via the link.

The present disclosure discloses a microwave identification method, which is implemented on at least one device, including at least one processor and at least one storage device, the method including: the at least one processor obtains microwave data; the at least one processor generates an image of one or more objects based on the microwave data; the at least one processor obtains a model of each of the one or more objects; and based on the model of each of the one or more objects, the at least one processor identifies the one or more objects in the image of the one or more objects.

This application provides a method and apparatus for updating an object recognition model in the field of artificial intelligence. In the technical solution provided in this application, a target image and first voice information of a user are obtained. The first voice information indicates a first category of a target object in the target image. A feature library of a first object recognition model is updated based on the target image and the first voice information. The updated first object recognition model includes a feature of the target object and a first label indicating the first category, and the feature of the target object corresponds to the first label. A recognition rate of an object recognition model can be improved more easily according to the technical solution provided in this application.

An object detection method is provided. In the method, raw point cloud data including a to-be-detected object is obtained, where the raw point cloud data includes annotation information for the to-be-detected object; instance point cloud data corresponding to the to-be-detected object is extracted from the raw point cloud data by using the annotation information; an object position point is determined from the raw point cloud data, and the raw point cloud data and the instance point cloud data to obtain a fused to-be-detected sample is fused based on the object position point and the to-be-detected object is detected by using the raw point cloud data and the to-be-detected sample.

An object detection method is provided. In the method, raw point cloud data including a to-be-detected object is obtained, where the raw point cloud data includes annotation information for the to-be-detected object; instance point cloud data corresponding to the to-be-detected object is extracted from the raw point cloud data by using the annotation information; an object position point is determined from the raw point cloud data, and the raw point cloud data and the instance point cloud data to obtain a fused to-be-detected sample is fused based on the object position point and the to-be-detected object is detected by using the raw point cloud data and the to-be-detected sample.

The method for generating a road edge line includes: acquiring a road image; recognizing lane line information from the road image; recognizing key point information related to the road edge from the road image; and generating the road edge line according to the lane line information and the key point information.

The present disclosure provides a landform map building method and apparatus, an electronic device and a readable storage medium, and relates to the field of image processing technologies. The method for building landform map includes: acquiring a to-be-processed image to obtain a grayscale image of the to-be-processed image; classifying pixels in the grayscale image according to grayscale values to obtain binary images corresponding to different landform categories; extracting image spot contours of image spots in the binary images, and taking the extracted image spot contours as vector graphs to obtain a vector graph set; merging, according to position information, the vector graphs corresponding to a same landform category in the vector graph set, and obtaining a first landform map according to merging results corresponding to different landform categories; and mapping, by using a preset landform type, the vector graphs corresponding to different landform categories in the first landform map, and taking a mapping result as a second landform map.

The present disclosure provides a landform map building method and apparatus, an electronic device and a readable storage medium, and relates to the field of image processing technologies. The method for building landform map includes: acquiring a to-be-processed image to obtain a grayscale image of the to-be-processed image; classifying pixels in the grayscale image according to grayscale values to obtain binary images corresponding to different landform categories; extracting image spot contours of image spots in the binary images, and taking the extracted image spot contours as vector graphs to obtain a vector graph set; merging, according to position information, the vector graphs corresponding to a same landform category in the vector graph set, and obtaining a first landform map according to merging results corresponding to different landform categories; and mapping, by using a preset landform type, the vector graphs corresponding to different landform categories in the first landform map, and taking a mapping result as a second landform map.