Classification

ImageAI : Image Classification

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Introducing Jarvis and TheiaEngine.

We the creators of ImageAI are glad to announce 2 new AI projects to provide state-of-the-art Generative AI, LLM and Image Understanding on your personal computer and servers.

Install Jarvis on PC/Mac to setup limitless access to LLM powered AI Chats for your every day work, research and generative AI needs with 100% privacy and full offline capability.

Visit https://jarvis.genxr.co to get started.

TheiaEngine, the next-generation computer Vision AI API capable of all Generative and Understanding computer vision tasks in a single API call and available via REST API to all programming languages. Features include

• Detect 300+ objects ( 220 more objects than ImageAI)
• Provide answers to any content or context questions asked on an image
  • very useful to get information on any object, action or information without needing to train a new custom model for every tasks
• Generate scene description and summary
• Convert 2D image to 3D pointcloud and triangular mesh
• Semantic Scene mapping of objects, walls, floors, etc
• Stateless Face recognition and emotion detection
• Image generation and augmentation from prompt
• etc.

Visit https://www.genxr.co/theia-engine to try the demo and join in the beta testing today.

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TABLE OF CONTENTS

• 🔳 First Prediction
• 🔳 Documentation

ImageAI provides 4 different algorithms and model types to perform image prediction. To perform image prediction on any picture, take the following simple steps. The 4 algorithms provided for image prediction include MobileNetV2, ResNet50, InceptionV3 and DenseNet121. Each of these algorithms have individual model files which you must use depending on the choice of your algorithm. To download the model file for your choice of algorithm, click on any of the links below:

• MobileNetV2 (Size = 4.82 mb, fastest prediction time and moderate accuracy)
• ResNet50 by Microsoft Research (Size = 98 mb, fast prediction time and high accuracy)
• InceptionV3 by Google Brain team (Size = 91.6 mb, slow prediction time and higher accuracy)
• DenseNet121 by Facebook AI Research (Size = 31.6 mb, slower prediction time and highest accuracy)

Great! Once you have downloaded this model file, start a new python project, and then copy the model file to your project folder where your python files (.py files) will be . Download the image below, or take any image on your computer and copy it to your python project's folder. Then create a python file and give it a name; an example is FirstPrediction.py. Then write the code below into the python file:

FirstPrediction.py

from imageai.Classification import ImageClassification
import os

execution_path = os.getcwd()

prediction = ImageClassification()
prediction.setModelTypeAsResNet50()
prediction.setModelPath(os.path.join(execution_path, "resnet50-19c8e357.pth"))
prediction.loadModel()

predictions, probabilities = prediction.classifyImage(os.path.join(execution_path, "1.jpg"), result_count=5 )
for eachPrediction, eachProbability in zip(predictions, probabilities):
    print(eachPrediction , " : " , eachProbability)

Sample Result:

convertible : 52.459555864334106
sports_car : 37.61284649372101
pickup : 3.1751200556755066
car_wheel : 1.817505806684494
minivan : 1.7487050965428352

The code above works as follows:

from imageai.Classification import ImageClassification
import os

The code above imports the ImageAI library and the python os class.

execution_path = os.getcwd()

The above line obtains the path to the folder that contains your python file (in this example, your FirstPrediction.py).

prediction = ImageClassification()
prediction.setModelTypeAsResNet50()
prediction.setModelPath(os.path.join(execution_path, "resnet50-19c8e357.pth"))

In the lines above, we created and instance of the ImagePrediction() class in the first line, then we set the model type of the prediction object to ResNet by caling the .setModelTypeAsResNet50() in the second line and then we set the model path of the prediction object to the path of the model file (resnet50-19c8e357.pth) we copied to the python file folder in the third line.

predictions, probabilities = prediction.classifyImage(os.path.join(execution_path, "1.jpg"), result_count=5 )

In the above line, we defined 2 variables to be equal to the function called to predict an image, which is the .classifyImage() function, into which we parsed the path to our image and also state the number of prediction results we want to have (values from 1 to 1000) parsing result_count=5. The .classifyImage() function will return 2 array objects with the first (predictions) being an array of predictions and the second (percentage_probabilities) being an array of the corresponding percentage probability for each prediction.

for eachPrediction, eachProbability in zip(predictions, probabilities):
    print(eachPrediction, " : " , eachProbability)

The above line obtains each object in the predictions array, and also obtains the corresponding percentage probability from the percentage_probabilities, and finally prints the result of both to console.

Documentation

We have provided full documentation for all ImageAI classes and functions. Find links below:**

• Documentation - English Version https://imageai.readthedocs.io