Image Similarity Detection in Action with Tensorflow 2.0 - Annoy and Angular - eisbilen/ImageSimilarityDetection. . For near infrared image detection 32 Image sensors for near infrared region. 4 Product name Feature Lineup Page CCD area image sensors. InGaAs linear image sensors KMIRB0109EB 0.5 1.0 1.5 2.0 2.5 3.0 0.5 1.0 1.5 Wavelength (µm) Photosensitivity (A/W) (Typ. Tchip=25 °C) G13913 series G11620 series G11476 series G11608 series G11508. Does OpenCV support the comparison of two images, returning some value (maybe a percentage) that indicates how similar these images are? 100% would be returned if the same image was passed twice, 0% would be returned if the images were totally different. I already read a lot of similar topics here on StackOverflow. I also did quite some. Alternatives to Duplicate Image Detector for Windows, Mac, Linux, Python, Software as a Service (SaaS) and more. Filter by license to discover only free or Open Source alternatives. This list contains a total of 17 apps similar to Duplicate Image Detector. List updated: 4/25/2020 2:19:00 PM.
♦ Official Documentation
ImageAI is an easy to use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find below features supported, links to official documentations as well as articles on ImageAI
Traction
ImageAI is widely used around the world by professionals, students, research groups and businesses.
3000+ GitHub Stars
Actively developed, maintained and supported since the 2018. Mac bowl me over.
75,000+ Developers
Empowering tens of thousands of developers around with state-of-the-art AI tools and frameworks.
500,000+ Media Outreach
A robust ecosystem of comprehensive tutorials, documentations and example codes. Zoom it 1 2 4 – on screen magnifier.
60+ Countries
Bringing AI technology, tools and knowledge to individuals, teams, corporate organizations and institutions around the world.
Features
Image Recognition
♦ Official Documentation
ImageAI is an easy to use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find below features supported, links to official documentations as well as articles on ImageAI
Traction
ImageAI is widely used around the world by professionals, students, research groups and businesses.
3000+ GitHub Stars
Actively developed, maintained and supported since the 2018. Mac bowl me over.
75,000+ Developers
Empowering tens of thousands of developers around with state-of-the-art AI tools and frameworks.
500,000+ Media Outreach
A robust ecosystem of comprehensive tutorials, documentations and example codes. Zoom it 1 2 4 – on screen magnifier.
60+ Countries
Bringing AI technology, tools and knowledge to individuals, teams, corporate organizations and institutions around the world.
Features
Image Recognition
ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet.
Object Detection
ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. The model implementations provided include RetinaNet, YOLOv3 and TinyYOLOv3.
Video Detection and Analysis
ImageAI provides an extended API to detect, locate and identify 80 objects in videos and retrieve full analytical data on every frame, second and minute. This feature is supported for video files, device camera and IP camera live feed.
Custom Recognition Training
ImageAI provides API to train new image recognition models on new image datasets for custom use cases. It also provides implementations to integrate and deploy the custom image recognition models.
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ImageAI provides very powerful yet easy to use classes and functions to perform Image Object Detection and Extraction.
ImageAI allows you to perform all of these with state-of-the-art deep learning algorithms like RetinaNet, YOLOv3 and TinyYOLOv3. With ImageAI you can run detection tasks and analyse images.
Similar Image Detector
Find below the classes and their respective functions available for you to use.These classes can be integrated into any traditional python program you are developing, be it a website, Windows/Linux/MacOS application or a systemthat supports or part of a Local-Area-Network.
imageai.Detection.ObjectDetection
This ObjectDetection class provides you function to perform object detection on any image or set of images, using pre-trained models that was trained onthe COCO dataset. The models supported are RetinaNet, YOLOv3 and TinyYOLOv3. This means you can detect and recognize 80 different kind ofcommon everyday objects. To get started, download any of the pre-trained model that you want to use via the links below.
Once you have downloaded the model of your choice, you should create a new instance of the ObjectDetection class as seen in the sample below:
Once you have created an instance of the class, you can use the functions below to set your instance property and start detecting objects in images.
.setModelTypeAsRetinaNet() , This function sets the model type of the object detection instance you created to the RetinaNet model, which means you will be performing your object detection tasks using the pre-trained 'RetinaNet' model you downloaded from the links above. Find example code below:
.setModelTypeAsYOLOv3() , This function sets the model type of the object detection instance you created to the YOLOv3 model, which means you will be performing your object detection tasks using the pre-trained 'YOLOv3' model you downloaded from the links above. Find example code below:
.setModelTypeAsTinyYOLOv3() , This function sets the model type of the object detection instance you created to the TinyYOLOv3 model, which means you will be performing your object detection tasks using the pre-trained 'TinyYOLOv3' model you downloaded from the links above. Find example code below:
.setModelPath() , This function accepts a string which must be the path to the model file you downloaded and must corresponds to the model type you set for your object detection instance. Find example code,and parameters of the function below:
.loadModel() , This function loads the model from the path you specified in the function call above into your object detection instance. Find example code below:
.detectObjectsFromImage() , This is the function that performs object detection task after the model as loaded. It can be called many times to detect objects in any number of images. Find example code below:
– parameterinput_image (required) : This refers to the path to image file which you want to detect. You can set this parameter to the Numpy array of File stream of any image if you set the paramter input_type to 'array' or 'stream'
—parameteroutput_image_path (required only if input_type = 'file' ) : This refers to the file path to which the detected image will be saved. It is required only if input_type = 'file'
– parameterminimum_percentage_probability (optional ) : This parameter is used to determine the integrity of the detection results. Lowering the value shows more objects while increasing the value ensures objects with the highest accuracy are detected. The default value is 50.
—parameteroutput_type (optional ) : This parameter is used to set the format in which the detected image will be produced. The available values are 'file' and 'array'. The default value is 'file'. If this parameter is set to 'array', the function will return a Numpy array of the detected image. See sample below::
—parameterdisplay_percentage_probability (optional ) : This parameter can be used to hide the percentage probability of each object detected in the detected image if set to False. The default values is True.
– parameterdisplay_object_name (optional ) : This parameter can be used to hide the name of each object detected in the detected image if set to False. The default values is True.
—parameterextract_detected_objects (optional ) : This parameter can be used to extract and save/return each object detected in an image as a seperate image. The default values is False.
– parameterthread_safe (optional) : This ensures the loaded detection model works across all threads if set to true.
—returns : The returned values will depend on the parameters parsed into the detectObjectsFromImage() function. See the comments and code below
If all required parameters are set and ‘output_image_path' is set to a file path you want the detected image to be saved, the function will return:
- an array of dictionaries, with each dictionary corresponding to the objects
- detected in the image. Each dictionary contains the following property:
- name (string)
- percentage_probability (float)
- box_points (tuple of x1,y1,x2 and y2 coordinates)
'''detections = detector.detectObjectsFromImage(input_image='image.jpg', output_image_path='imagenew.jpg', minimum_percentage_probability=30)
If all required parameters are set and output_type = ‘array' ,the function will return
- a numpy array of the detected image
- an array of dictionaries, with each dictionary corresponding to the objects
- detected in the image. Each dictionary contains the following property:
- name (string)
- percentage_probability (float)
- box_points (list of x1,y1,x2 and y2 coordinates)
Similar Image Detector 2.0.0
'''returned_image, detections = detector.detectObjectsFromImage(input_image='image.jpg', output_type='array', minimum_percentage_probability=30)
the detected image to be saved, the function will return:1. an array of dictionaries, with each dictionary corresponding to the objects
- an array of string paths to the image of each object extracted from the image
'''detections, extracted_objects = detector.detectObjectsFromImage(input_image='image.jpg', output_image_path='imagenew.jpg', extract_detected_objects=True, minimum_percentage_probability=30)
- a numpy array of the detected image
- an array of dictionaries, with each dictionary corresponding to the objects
- detected in the image. Each dictionary contains the following property:* name (string)* percentage_probability (float)* box_points (list of x1,y1,x2 and y2 coordinates)
- an array of numpy arrays of each object detected in the image
'''returned_image, detections, extracted_objects = detector.detectObjectsFromImage(input_image='image.jpg', output_type='array', extract_detected_objects=True, minimum_percentage_probability=30)
.CustomObjects() , This function is used when you want to detect only a selected number of objects. It returns a dictionary of objects and their True or False values. To detect selected objects in an image, you will have to use the dictionary returned by the this function with the detectCustomObjectsFromImage() function. Find the details in the comment and code sample below:
.detectCustomObjectsFromImage(), This function have all the parameters and returns all the values the detectObjectsFromImage() functions does but a slight difference. This function let detect only selected objects in an image. Unlike the normal detectObjectsFromImage() function, this needs an extra parameter which is 'custom_object' which accepts the dictionary returned by the CustomObjects() function. In the sample below, we set the detection funtion to report only detections on persons and dogs:
Sample Image Object Detection code
Find below a code sample for detecting objects in an image:
