CN116485841A - Motion rule identification method and device based on multiple wide angles - Google Patents
Motion rule identification method and device based on multiple wide angles Download PDFInfo
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Abstract
The invention discloses a motion rule identification method and device based on multiple wide angles. Wherein the method comprises the following steps: collecting wide-angle image data through high-precision equipment; marking the wide-angle image data according to a preset marking rule to obtain marked image data; performing angle division operation on the marked image data to obtain divided image data; and extracting characteristic parameters in the divided image data, and inputting the characteristic parameters into a motion recognition model to obtain a recognition result. The invention solves the problem that the motion rule recognition method in the prior art generally adopts the techniques of pattern recognition, deep learning and the like. However, these methods have certain limitations in terms of recognition angles, dynamic rules and the like, and are difficult to meet the technical problem of high-efficiency and accurate recognition requirements in complex scenes.
Description
Technical Field
The invention relates to the field of image motion recognition, in particular to a motion rule recognition method and device based on multiple wide angles.
Background
Along with the continuous development of intelligent science and technology, intelligent equipment is increasingly used in life, work and study of people, and the quality of life of people is improved and the learning and working efficiency of people is increased by using intelligent science and technology means.
Currently, in various scenes, people need to recognize and judge the motion law or state of a moving object, such as crowd motion in video monitoring, gesture recognition in man-machine interaction, target tracking in robot control, and the like. Therefore, the development of a high-efficiency and accurate motion rule recognition method has important significance. However, the motion rule recognition method in the prior art generally adopts pattern recognition, deep learning and other technologies. However, these methods have certain limitations in terms of recognition angles, dynamic rules and the like, and are difficult to meet the requirements of efficient and accurate recognition in complex scenes. Therefore, the method aims to provide a motion rule recognition method based on multiple wide angles, and more accurate motion rule recognition is realized through multi-scale and multi-angle feature extraction and comprehensive analysis.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a motion rule recognition method and a motion rule recognition device based on multiple wide angles, which at least solve the problem that the motion rule recognition method in the prior art generally adopts the technologies of pattern recognition, deep learning and the like. However, these methods have certain limitations in terms of recognition angles, dynamic rules and the like, and are difficult to meet the technical problem of high-efficiency and accurate recognition requirements in complex scenes.
According to an aspect of the embodiment of the present invention, there is provided a motion rule recognition method based on multiple wide angles, including: collecting wide-angle image data through high-precision equipment; marking the wide-angle image data according to a preset marking rule to obtain marked image data; performing angle division operation on the marked image data to obtain divided image data; and extracting characteristic parameters in the divided image data, and inputting the characteristic parameters into a motion recognition model to obtain a recognition result.
Optionally, the marking the wide-angle image data according to a preset marking rule, and obtaining marked image data includes: generating the preset marking rule according to the wide-angle requirement of the user; each of the wide-angle image data is marked using the preset marking rule.
Optionally, the performing the angle division operation on the marker image data to obtain divided image data includes: and dividing the marked image data according to the wide angle type to obtain divided image data.
Optionally, before the extracting the characteristic parameters in the divided image data and inputting the characteristic parameters into the motion recognition model to obtain a recognition result, the method further includes: training the motion recognition model based on historical motion recognition data.
According to another aspect of the embodiment of the present invention, there is also provided a motion rule recognition apparatus based on multiple wide angles, including: the acquisition module is used for acquiring wide-angle image data through high-precision equipment; the marking module is used for marking the wide-angle image data according to a preset marking rule to obtain marked image data; the dividing module is used for performing angle dividing operation on the marked image data to obtain divided image data; and the extraction module is used for extracting the characteristic parameters in the divided image data and inputting the characteristic parameters into the motion recognition model to obtain a recognition result.
Optionally, the marking module includes: the generation unit is used for generating the preset marking rule according to the wide-angle requirement of the user; and a marking unit configured to mark each of the wide-angle image data using the preset marking rule.
Optionally, the dividing module includes: and the dividing unit is used for dividing the marked image data according to the wide-angle type to obtain divided image data.
Optionally, the apparatus further includes: and the training module is used for training the motion recognition model according to the historical motion recognition data.
According to another aspect of the embodiment of the present invention, there is further provided a nonvolatile storage medium, where the nonvolatile storage medium includes a stored program, and when the program runs, the program controls a device in which the nonvolatile storage medium is located to execute a motion rule identifying method based on multiple wide angles.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a multi-wide angle based motion rule recognition method when executed.
In the embodiment of the invention, wide-angle image data are acquired by high-precision equipment; marking the wide-angle image data according to a preset marking rule to obtain marked image data; performing angle division operation on the marked image data to obtain divided image data; the characteristic parameters in the divided image data are extracted and input into a motion recognition model to obtain a recognition result, and the problem that the motion rule recognition method in the prior art generally adopts the technologies of pattern recognition, deep learning and the like is solved. However, these methods have certain limitations in terms of recognition angles, dynamic rules and the like, and are difficult to meet the technical problem of high-efficiency and accurate recognition requirements in complex scenes.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a multi-wide angle based motion rule recognition method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a motion rule recognition apparatus based on multiple wide angles according to an embodiment of the present invention;
fig. 3 is a block diagram of a terminal device for performing the method according to the invention according to an embodiment of the invention;
fig. 4 is a memory unit for holding or carrying program code for implementing a method according to the invention, according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, there is provided a method embodiment of a multi-wide angle based motion rule recognition method, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
Example 1
Fig. 1 is a flowchart of a motion rule recognition method based on multiple wide angles according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S102, acquiring wide-angle image data through high-precision equipment.
Specifically, in order to solve the problem in the prior art that the motion rule recognition method generally adopts the techniques of pattern recognition, deep learning and the like, the embodiment of the invention is used for solving the problem. However, these methods have certain limitations in terms of recognition angles, dynamic rules, and the like, and are difficult to meet the technical problem of high-efficiency and accurate recognition requirements in complex scenes, and firstly, high-precision image pickup equipment is required to collect wide-angle image data, sort and collect the wide-angle image data, and transmit the wide-angle image data to a memory for later recognition of the motion condition of a moving object in an image.
And step S104, marking the wide-angle image data according to a preset marking rule to obtain marked image data.
Optionally, the marking the wide-angle image data according to a preset marking rule, and obtaining marked image data includes: generating the preset marking rule according to the wide-angle requirement of the user; each of the wide-angle image data is marked using the preset marking rule.
Specifically, the embodiment of the invention can be applied to a sports field image monitoring scene, and after a plurality of image data collected by a plurality of wide-angle lenses are converged in each monitoring area of the sports field, in order to increase the efficiency and the accuracy of motion capture according to the needs of a user for the motion capture of the wide-angle lenses, each wide-angle image data needs to be marked, and the subsequent type division and the processing are carried out according to the marked image data.
And step S106, performing angle division operation on the marked image data to obtain divided image data.
Optionally, the performing the angle division operation on the marker image data to obtain divided image data includes: and dividing the marked image data according to the wide angle type to obtain divided image data.
Specifically, since the types of the collected images of the different wide-angle lenses are different, in the embodiment of the invention, after the collected and marked wide-angle image data, the angle dividing operation is required to be performed according to the marked image so as to determine which image data corresponding to the wide-angle with different angles and the wide-angle with different types are, and further obtain a final output result to identify the motion model.
Step S108, extracting characteristic parameters in the divided image data, and inputting the characteristic parameters into a motion recognition model to obtain a recognition result.
Optionally, before the extracting the characteristic parameters in the divided image data and inputting the characteristic parameters into the motion recognition model to obtain a recognition result, the method further includes: training the motion recognition model based on historical motion recognition data.
Through the embodiment, the problem that the motion rule recognition method in the prior art generally adopts the technologies of pattern recognition, deep learning and the like is solved. However, these methods have certain limitations in terms of recognition angles, dynamic rules and the like, and are difficult to meet the technical problem of high-efficiency and accurate recognition requirements in complex scenes.
Example two
Fig. 2 is a block diagram of a motion rule recognition apparatus based on multiple wide angles according to an embodiment of the present invention, as shown in fig. 2, the apparatus includes:
and an acquisition module 20 for acquiring wide-angle image data by high-precision equipment.
Specifically, in order to solve the problem in the prior art that the motion rule recognition method generally adopts the techniques of pattern recognition, deep learning and the like, the embodiment of the invention is used for solving the problem. However, these methods have certain limitations in terms of recognition angles, dynamic rules, and the like, and are difficult to meet the technical problem of high-efficiency and accurate recognition requirements in complex scenes, and firstly, high-precision image pickup equipment is required to collect wide-angle image data, sort and collect the wide-angle image data, and transmit the wide-angle image data to a memory for later recognition of the motion condition of a moving object in an image.
And the marking module 22 is used for marking the wide-angle image data according to a preset marking rule to obtain marked image data.
Optionally, the marking module includes: the generation unit is used for generating the preset marking rule according to the wide-angle requirement of the user; and a marking unit configured to mark each of the wide-angle image data using the preset marking rule.
Specifically, the embodiment of the invention can be applied to a sports field image monitoring scene, and after a plurality of image data collected by a plurality of wide-angle lenses are converged in each monitoring area of the sports field, in order to increase the efficiency and the accuracy of motion capture according to the needs of a user for the motion capture of the wide-angle lenses, each wide-angle image data needs to be marked, and the subsequent type division and the processing are carried out according to the marked image data.
And the dividing module 24 is used for performing angle division operation on the marked image data to obtain divided image data.
Optionally, the dividing module includes: and the dividing unit is used for dividing the marked image data according to the wide-angle type to obtain divided image data. .
Specifically, since the types of the collected images of the different wide-angle lenses are different, in the embodiment of the invention, after the collected and marked wide-angle image data, the angle dividing operation is required to be performed according to the marked image so as to determine which image data corresponding to the wide-angle with different angles and the wide-angle with different types are, and further obtain a final output result to identify the motion model.
The extracting module 26 is configured to extract the feature parameters in the divided image data, and input the feature parameters to the motion recognition model to obtain a recognition result.
Optionally, the apparatus further includes: and the training module is used for training the motion recognition model according to the historical motion recognition data.
According to another aspect of the embodiment of the present invention, there is further provided a nonvolatile storage medium, where the nonvolatile storage medium includes a stored program, and when the program runs, the program controls a device in which the nonvolatile storage medium is located to execute a motion rule identifying method based on multiple wide angles.
Specifically, the method comprises the following steps: collecting wide-angle image data through high-precision equipment; marking the wide-angle image data according to a preset marking rule to obtain marked image data; performing angle division operation on the marked image data to obtain divided image data; and extracting characteristic parameters in the divided image data, and inputting the characteristic parameters into a motion recognition model to obtain a recognition result. Optionally, the marking the wide-angle image data according to a preset marking rule, and obtaining marked image data includes: generating the preset marking rule according to the wide-angle requirement of the user; each of the wide-angle image data is marked using the preset marking rule. Optionally, the performing the angle division operation on the marker image data to obtain divided image data includes: and dividing the marked image data according to the wide angle type to obtain divided image data. Optionally, before the extracting the characteristic parameters in the divided image data and inputting the characteristic parameters into the motion recognition model to obtain a recognition result, the method further includes: training the motion recognition model based on historical motion recognition data.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a multi-wide angle based motion rule recognition method when executed.
Specifically, the method comprises the following steps: collecting wide-angle image data through high-precision equipment; marking the wide-angle image data according to a preset marking rule to obtain marked image data; performing angle division operation on the marked image data to obtain divided image data; and extracting characteristic parameters in the divided image data, and inputting the characteristic parameters into a motion recognition model to obtain a recognition result. Optionally, the marking the wide-angle image data according to a preset marking rule, and obtaining marked image data includes: generating the preset marking rule according to the wide-angle requirement of the user; each of the wide-angle image data is marked using the preset marking rule. Optionally, the performing the angle division operation on the marker image data to obtain divided image data includes: and dividing the marked image data according to the wide angle type to obtain divided image data. Optionally, before the extracting the characteristic parameters in the divided image data and inputting the characteristic parameters into the motion recognition model to obtain a recognition result, the method further includes: training the motion recognition model based on historical motion recognition data.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, fig. 3 is a schematic hardware structure of a terminal device according to an embodiment of the present application. As shown in fig. 3, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to enable communication connections between the elements. The memory 33 may comprise a high-speed RAM memory or may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, in which various programs may be stored for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the processor 31 may be implemented as, for example, a central processing unit (Central Processing Unit, abbreviated as CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 through wired or wireless connections.
Alternatively, the input device 30 may include a variety of input devices, for example, may include at least one of a user-oriented user interface, a device-oriented device interface, a programmable interface of software, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware insertion interface (such as a USB interface, a serial port, etc.) for data transmission between devices; alternatively, the user-oriented user interface may be, for example, a user-oriented control key, a voice input device for receiving voice input, and a touch-sensitive device (e.g., a touch screen, a touch pad, etc. having touch-sensitive functionality) for receiving user touch input by a user; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, for example, an input pin interface or an input interface of a chip, etc.; optionally, the transceiver may be a radio frequency transceiver chip, a baseband processing chip, a transceiver antenna, etc. with a communication function. An audio input device such as a microphone may receive voice data. The output device 32 may include a display, audio, or the like.
In this embodiment, the processor of the terminal device may include functions for executing each module of the data processing apparatus in each device, and specific functions and technical effects may be referred to the above embodiments and are not described herein again.
Fig. 4 is a schematic hardware structure of a terminal device according to another embodiment of the present application. Fig. 4 is a specific embodiment of the implementation of fig. 3. As shown in fig. 4, the terminal device of the present embodiment includes a processor 41 and a memory 42.
The processor 41 executes the computer program code stored in the memory 42 to implement the methods of the above-described embodiments.
The memory 42 is configured to store various types of data to support operation at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, video, etc. The memory 42 may include a random access memory (random access memory, simply referred to as RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, a processor 41 is provided in the processing assembly 40. The terminal device may further include: a communication component 43, a power supply component 44, a multimedia component 45, an audio component 46, an input/output interface 47 and/or a sensor component 48. The components and the like specifically included in the terminal device are set according to actual requirements, which are not limited in this embodiment.
The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 40 may include one or more modules that facilitate interactions between the processing component 40 and other components. For example, processing component 40 may include a multimedia module to facilitate interaction between multimedia component 45 and processing component 40.
The power supply assembly 44 provides power to the various components of the terminal device. Power supply components 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for terminal devices.
The multimedia component 45 comprises a display screen between the terminal device and the user providing an output interface. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation.
The audio component 46 is configured to output and/or input audio signals. For example, the audio component 46 includes a Microphone (MIC) configured to receive external audio signals when the terminal device is in an operational mode, such as a speech recognition mode. The received audio signals may be further stored in the memory 42 or transmitted via the communication component 43. In some embodiments, audio assembly 46 further includes a speaker for outputting audio signals.
The input/output interface 47 provides an interface between the processing assembly 40 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: volume button, start button and lock button.
The sensor assembly 48 includes one or more sensors for providing status assessment of various aspects for the terminal device. For example, the sensor assembly 48 may detect the open/closed state of the terminal device, the relative positioning of the assembly, the presence or absence of user contact with the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera or the like.
The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot, where the SIM card slot is used to insert a SIM card, so that the terminal device may log into a GPRS network, and establish communication with a server through the internet.
From the above, it will be appreciated that the communication component 43, the audio component 46, and the input/output interface 47, the sensor component 48 referred to in the embodiment of fig. 4 may be implemented as an input device in the embodiment of fig. 3.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A multi-wide angle based motion rule recognition method, comprising:
collecting wide-angle image data through high-precision equipment;
marking the wide-angle image data according to a preset marking rule to obtain marked image data;
performing angle division operation on the marked image data to obtain divided image data;
and extracting characteristic parameters in the divided image data, and inputting the characteristic parameters into a motion recognition model to obtain a recognition result.
2. The method of claim 1, wherein marking the wide-angle image data according to a preset marking rule to obtain marked image data comprises:
generating the preset marking rule according to the wide-angle requirement of the user;
each of the wide-angle image data is marked using the preset marking rule.
3. The method of claim 1, wherein performing an angle division operation on the marker image data to obtain divided image data comprises:
and dividing the marked image data according to the wide angle type to obtain divided image data.
4. The method according to claim 1, wherein before the extracting the feature parameters in the divided image data and inputting the feature parameters to a motion recognition model, the method further comprises:
training the motion recognition model based on historical motion recognition data.
5. A multi-wide angle based motion rule recognition apparatus, comprising:
the acquisition module is used for acquiring wide-angle image data through high-precision equipment;
the marking module is used for marking the wide-angle image data according to a preset marking rule to obtain marked image data;
the dividing module is used for performing angle dividing operation on the marked image data to obtain divided image data;
and the extraction module is used for extracting the characteristic parameters in the divided image data and inputting the characteristic parameters into the motion recognition model to obtain a recognition result.
6. The apparatus of claim 5, wherein the marking module comprises:
the generation unit is used for generating the preset marking rule according to the wide-angle requirement of the user;
and a marking unit configured to mark each of the wide-angle image data using the preset marking rule.
7. The apparatus of claim 5, wherein the partitioning module comprises:
and the dividing unit is used for dividing the marked image data according to the wide-angle type to obtain divided image data.
8. The apparatus of claim 5, wherein the apparatus further comprises:
and the training module is used for training the motion recognition model according to the historical motion recognition data.
9. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for executing the processor, wherein the computer readable instructions when executed perform the method of any of claims 1 to 4.
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| CN202310422719.3A CN116485841A (en) | 2023-04-19 | 2023-04-19 | Motion rule identification method and device based on multiple wide angles |
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| CN202310422719.3A CN116485841A (en) | 2023-04-19 | 2023-04-19 | Motion rule identification method and device based on multiple wide angles |
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2023
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