Abstract: A method for obtaining depth images implemented in an electronic device includes obtaining a first image and a second image; obtaining a predicted depth map of the first image, and calculating a first error value of the predicted depth map; determining a first transformation matrix between the first image and the second image; obtaining an instance segmentation image and obtaining a first mask image and a second mask image; obtaining a target transformation matrix; converting the predicted depth map into a first point cloud image, converting the first point cloud image into a second point cloud image, and converting the second point cloud image into a third image; calculating a second error value between the second image and the third image; obtaining a target deep learning network model; and inputting at least one image into the target deep learning network model, and obtaining at least one depth image.

Abstract: A method for managing driving applied in an electronic device which assesses distances to objects in a path of autonomous driving obtains RGB images of a scene in front of a vehicle, processes the RGB images based on a trained depth estimation model, and obtain depth images corresponding to the RGB images. The depth images are converted to 3D point cloud maps, 3D regions of interest from the 3D point cloud maps are determined according to a size of the vehicle, and the 3D regions of interest are converted into 2D regions of interest according to internal parameters of a camera. The 2D regions of interest are analyzed for obstacles. Driving continues when the 2D regions of interest have no obstacles, the vehicle is controlled to issue an alarm when obstacles are discovered.

Abstract: A method for training a depth estimation model is provided. The method includes obtaining a first left image and a first right image. A disparity map is obtained by inputting the first left image into a depth estimation model. A second right image is obtained by adding the first left image to the disparity map. The first left image is converted into a third right image. A mask image is obtained by performing a binarization processing on a pixel value of each of pixel points of the third right image. Once a loss value of the depth estimation model is obtained by calculating a mean square error of pixel values of all corresponding pixel points of the first right image, the second right image, and the mask image, a depth estimation model is iteratively trained according to the loss value.

Abstract: A method acquires a first image and a second image of a target object being inputted into the depth estimation network for outputting a depth image. A pixel posture conversion relationship between the first image and the second image is obtained. The pixel posture conversion relationship includes a position relationship between each first pixel in the first image and a second pixel in the second image, which correspond to a same part of the target object. A restored image is generated based on the depth image, the pixel posture conversion relationship, and pre-obtained camera parameters. A loss of the depth estimation network is determined based on a difference between the first image, the depth image, the restored image, and the second image for adjusting the parameters of the depth estimation network. A training apparatus, and an electronic device applying the method are also disclosed.

Abstract: A method for training an autoencoder implemented in an electronic device includes obtaining a stereoscopic image as the vehicle is in motion, the stereoscopic image includes a left image and a right image; generating a stereo disparity map according to the left image; generating a predicted right image according to the left image and the stereo disparity map; and calculating a first mean square error between the predicted right image and the right image.

Abstract: A method and system for generating depth in monocular images acquires multiple sets of binocular images to build a dataset containing instance segmentation labels as to content; training an work using the dataset with instance segmentation labels to obtain a trained autoencoder network; acquiring monocular image, the monocular image is input into the trained autoencoder network to obtain a first disparity map and the first disparity map is converted to obtain depth image corresponding to the monocular image. The method combines binocular images with instance segmentation images as training data for training an autoencoder network, monocular images can simply be input into the autoencoder network to output the disparity map. Depth estimation for monocular images is achieved by converting the disparity map to a depth image corresponding to the monocular image. An electronic device and a non-transitory storage are also disclosed.

Abstract: A method for processing images implemented in an electronic device includes obtaining images during moving of a vehicle; obtaining instance segmentation images by segmenting the images; obtaining a predicted disparity map by reconstructing the left images based on a pre-established autoencoder; generating a first error value of the autoencoder for the images according to the left image, the predicted disparity map, and the right image, generating a second error value of the autoencoder for the instance segmentation image according to the left image of instance segmentation, the predicted disparity map, and the right image of instance segmentation; establishing an autoencoder model by adjusting the autoencoder according to the first error value and the second error value; obtaining a test image as the vehicle is moving, and obtaining a target disparity map; and obtaining a depth image corresponding to the test image by converting the target disparity map.

Abstract: A method for neural network model encryption and decryption includes a first apparatus stores a neural network model and obtains hardware configuration information of the first apparatus, and obtains an encryption key accordingly; encrypts the neural network model by a predetermined encryption algorithm; a second apparatus obtains the encrypted neural network model from the first apparatus, transmits a decryption request to the first apparatus, obtains the hardware configuration information from the first apparatus, obtains a decryption key based on the hardware configuration information; and decrypts the encrypted neural network model by a predetermined decryption algorithm.

Abstract: A method for generating defective image of products applied in an electronic device includes generating first input data according to flawless sample images and a first noise vector, using an autoencoder as a generator of a Generative Adversarial Network (GAN), inputting the first input data to the generator, and generating images for training in defects. The method further includes calculating a first loss value between the flawless sample images and the defect training images, inputting the defect training images into a discriminator of the GAN, and calculating a second loss value. The method further includes obtaining an optimized GAN and taking the optimized GAN as a defective image adversarial network, obtaining flawless testing images, inputting the flawless testing images and a second noise into a generator of the defective image adversarial network, and generating images of defects by processing the flawless testing images and the second noise.

Abstract: A method for augmenting defect sample data thereof includes acquiring a positive sample image and defect category information of a surface of a product; inputting the positive sample image and the defect category information to a generative adversarial network (GAN); and generating defect sample data corresponding to the defect category information. An apparatus and a non-transitory computer readable medium for augmenting defect sample data are also disclosed.

Abstract: A method for measuring a growth height of a plant, an electronic device, and a storage medium are provided. The method controls a camera device to obtain a color image and a depth image of a plant to be detected. The color image is detected by a detection model which is pre-trained, and a plurality of detection boxes which includes a plurality of plants to be detected is obtained. The color image and the depth image are aligned to create an alignment image. A plurality of target boxes is acquired from the alignment image, and depth values of the plurality of target boxes are determined. The quantity of the target boxes and a height of one or more plants to be detected are determined, no manual operations are required.

Abstract: A method for detecting defects in products from images thereof and an electronic device applying the method inputs a defect image repair data set into an autoencoder to train the autoencoder, and generates a reconstructed image, calculates a reference error value between the sample image and the reconstructed image by a preset error function, and set a threshold value based on the reference error value. The electronic device inputs an image possibly revealing a defect into the autoencoder and generates the reconstructed image corresponding to the image to be detected, and uses the preset error function to calculate the reconstruction error between the image and the reconstructed image, thereby determining whether the image being analyzed does reveal defects. When the reconstruction error is greater than the threshold value, a determination is made that a defect is revealed.

Abstract: In a product defect detection method, a detection image of a product is obtained. A first preset number of detection blocks are cut out from the detection image. The detection blocks are input into an auto-encoder to obtain reconstructed blocks and a mean square error between each detection block and the corresponding reconstructed block is calculated, an association between the mean square error and the detection block being established. Whether the product carries defective is determined according to the mean square error of each detection block. The method improves accuracy of detecting defects of the product.

Abstract: A method for optimizing the conversion of a deep learning model to process other data, applied in a device, includes converting a first deep learning model to obtain a second deep learning model, obtaining a weighting arrangement of the two models according to their deep learning frameworks and performing a quantization on the two models. A similarity in weighting between the two models is analyzed to produce a weighting analysis based on the first and second weighting arrangement and the first and second model quantization result weighting. The two models are tested to establish a model performance analysis. One or more suggestions for optimization are obtained based on the weighting analysis and the model performance analysis, and are applied to optimize the second deep learning model, an optimized second deep learning model being employed to process the other data.

Abstract: An image depth calculating device, which can calculate depth information of a binocular video with reduced computation, includes multiple modules. Modules receives a first frame information set that corresponds to a first frame time of the binocular video and establishes a first rhombic area that centers on a first pixel in a first viewing angle frame, to calculate a total pixel value of the first rhombic area. Modules further establishes a plurality of second rhombic areas that center on a plurality of second pixels of a pixel area in a second viewing angle frame to calculate a second total pixel value of each of the plurality of second rhombic areas. A depth calculating module compares the first total pixel value with each second total pixel value and calculates first frame depth information according to the result. An image depth calculating method is also provided.

Abstract: A gesture control system for a device for determining which one of a plurality of devices is to be controlled by a gesture acquires images of a gesture from each of the electronic devices; establishes a three dimensional coordinate system for the gesture image; calculates an angle between a first vector from a start point of the gesture to a center point of each electronic device and a second vector from an end point of the gesture to the center point of each electronic device. Thereby, the electronic device intended as the object to be controlled by the gesture can be determined, according to whether the angle between the first vector and the second vector is less than a preset value. A gesture control method is also provided.

Abstract: A device for recognizing control gestures and determining which one device out of a plurality is the target of control acquires images of a gesture from each electronic device. A three dimensional coordinate system for each image is established, and coordinate of a central point of each electronic device determined. Extent of gesture to the left and to the right at different depths is determined and a regression plane equation is calculated. A distance between the regression plane and center points of each electronic device is determined and the electronic device with the closest (the shortest distance) center point is determined as the target device of the control gesture. A gesture control method is also provided.

Abstract: An electronic device, which can recognize and be controlled by a gesture of a hand, obtains an image that comprises the hand and image depth levels of objects within the image. Static objects are filtered from the image. A first establishing module obtains hand coordinate information and establishes a first block comprising the hand. Modules further obtain a depth level of each pixel of the first block and counts the number of pixels of each depth level. A second establishing module obtains hand depth information according to the counting result and establishes a second block including the hand. A recognizing module detects a moving track of the hand in the second block and recognizes a gesture of the hand according to a library of gestures. A gesture recognition method is also provided.

Abstract: A gesture control method is provided. The method includes: obtaining gesture images with depth information; creating a coordinate system; determining coordinates of a center of each camera, a start position and an end position of the gesture; calculating directions and values of a first angle defined from an axle through the end position to a line connecting between the start position and the end position and at least two second angles each defined a vertical axle through a center of a camera to a line connecting the center of the camera and the start position, each second angle corresponding to a camera of an electronic device; and determining an electronic device to be a controlled device, wherein the electronic device corresponds to a second angle in a same direction with the first angle having a minimum absolute difference with the first angle.

Abstract: An image depth calculating device, which can calculate depth information of a binocular video with reduced computation, includes multiple modules. Modules receives a first frame information set that corresponds to a first frame time of the binocular video and establishes a first rhombic area that centers on a first pixel in a first viewing angle frame, to calculate a total pixel value of the first rhombic area. Modules further establishes a plurality of second rhombic areas that center on a plurality of second pixels of a pixel area in a second viewing angle frame to calculate a second total pixel value of each of the plurality of second rhombic areas. A depth calculating module compares the first total pixel value with each second total pixel value and calculates first frame depth information according to the result. An image depth calculating method is also provided.