The present invention discloses a system for generating machine learning sample images based on augmented reality. The system comprises: a face data construction module configured to recognize faces and construct face data information, wherein the face data information comprises a face key point, posture data, face frame data and an image camera field angle; a model construction module configuredto generate material data and a three-dimensional model; and a tag generation module configured to generate tag information corresponding to face data and storage the tag information. The steps mentioned above can completely perform sample collection and tagging processing with complete automatic implementation, can effectively improve the efficiency whatever the entertainment or information safety aspects, and can improve the completion quality and the rendering speed of the three-dimensional model.

System and method for generating machine learning sample images based on augmented reality

The invention discloses a method for automatically establishing artificial intelligent image recognizing training materials and annotation files. The method is characterized by comprising the following steps of S1, designing a 3D model and scene, and conducting three-dimensional modeling through CAD type software numbers; S2, synthesizing training images, reading the 3D model, materials and background information in the step S1 through tool software, conducting simulating camera shooting on the 3D model, materials and background to obtain shot images with different distances, angles, time durations and scenes, combining the images obtained through simulation and the object materials, synthesizing and outputting the training images, and recording the model space positions of all focused objects under current states during outputting; S3, establishing the object annotation files, and conducting annotation on the model space position information, recorded in the step S3, of all the focused objects to generate the annotation files; S4, storing the annotation files. By means of the method, the high-quality training materials and annotation files can be rapidly generated.

Method for automatically establishing artificial intelligent image recognizing training materials and annotation files

The invention provides an image tagging method and apparatus, a storage medium and an electronic device The image tagging method comprises the steps of determining target types corresponding to targets needed to be tagged according to target information in an obtained scene image; then according to a preset target space sequence, sorting the target types, and generating a target sorting list; andfinally according to the target sorting list, tagging the targets in the scene image in sequence By sorting the targets in the scene image according to the reasonable space sequence in advance, image tagging of a complex scene is adapted, the image tagging efficiency is improved, and while the tagging accuracy is ensured, a large amount of manpower costs can be saved

Image tagging method and apparatus, storage medium and electronic device

The invention provides a rendered-image acquiring method, device and system and a storage medium. The method includes: acquiring real images for specific vision task training, and using the real images to train a rendering model on the basis of adversarial learning until training ends, wherein the rendering model includes a renderer and a classifier, and the classifier is used for distinguishing whether an input image is real or is obtained by rendering; acquiring rendering parameters, which are used by the renderer when training of the rendering model ends, to use the same as best rendering parameters, and using the renderer to generate best rendered images, which are used for specific vision task training, on the basis of the best rendering parameters. According to the rendered-image acquiring method, device and system and the storage medium of the embodiment of the invention, adversarial learning is utilized to train the renderer and the classifier, the rendering parameters of besteffect are enabled to be fully automatically selected for the renderer of specific logic and a specific computer vision task, thus the best rendered images are generated, and effect of the computer vision task can be validly and highly efficiently improved.

Rendered-image acquiring method, device and system and storage medium

本願の実施例はセマンティックセグメンテーションモデルの訓練方法および装置、電子機器、ならびに記憶媒体を開示し、そのうち、方法は、セマンティックセグメンテーションモデルによって、少なくとも一つのラベル付けされていない画像に対して、セマンティックセグメンテーションを行い、初歩のセマンティックセグメンテーション結果を、前記ラベル付けされていない画像のカテゴリとして得ることと、畳み込みニューラルネットワークによって、少なくとも一つの前記ラベル付けされていない画像のカテゴリ、および少なくとも一つのラベル付けされた画像のカテゴリに基づき、少なくとも二つの画像がそれぞれ対応するサブ画像およびサブ画像に対応する特徴を得ることと、少なくとも二つのサブ画像のカテゴリ、および少なくとも二つのサブ画像間の特徴距離に基づき、セマンティックセグメンテーションモデルを訓練することと、を含み、前記少なくとも二つの画像は、少なくとも一つの前記ラベル付けされていない画像および少なくとも一つの前記ラベル付けされた画像を含み、前記少なくとも二つのサブ画像には対応する画像のカテゴリが含まれている。本願の上記実施例により訓練されて得られたセマンティックセグメンテーションモデルは、セマンティックセグメンテーションにおいて高い正解率を得ることができる。【選択図】図１

セマンティックセグメンテーションモデルの訓練方法および装置、電子機器、ならびに記憶媒体

The embodiments of the present invention disclose a training data generating method, a generating apparatus, and an image semantic segmentation method thereof. The training data generating method includes: setting a corresponding category label for an object model in a three-dimensional stereoscopic scene; acquiring a corresponding plurality of scene graphs by changing scene parameters of the three-dimensional stereoscopic scene; and rendering the object model into the monochrome material corresponding to the category label of a model; acquiring a semantic segmentation map corresponding to each scene graph based on the rendered object model; and storing each scene graph and the semantic segmentation map corresponding to the corresponding scene graph as a group of training data. The methodcan quickly generate a large number of scene images and semantic segmentation pictures that meet different usage requirements and meet the requirements of use, greatly saving time and manpower.

Training data generating method, generating device and image semantic segmentation method

A training data generation method and generation apparatus, and an image semantics segmentation method therefor, the training data generation method comprising: setting a corresponding category label for an object model in a three-dimensional stereoscopic scene (201); acquiring several corresponding scene images by changing scene parameters of the three-dimensional stereoscopic scene (202); rendering the object model as a monochrome material corresponding to the category label of the object model (203); acquiring a semantics segmentation image corresponding to each scene image based on the rendered object model (204); and storing each scene image and the semantics segmentation image corresponding to each scene image as a set of training data (205). The method can quickly generate a large number of scene images and semantic segmentation images that meet the usage requirements and reflect different times, thereby greatly saving time and human labour.

Training data generation method and generation apparatus, and image semantics segmentation method therefor

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Training data generating method, generating device and image semantic segmentation method

Training data generation method and generation apparatus, and image semantics segmentation method therefor