A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

After you download the model file of your choice, you will need to download one more file which will be a JSON file that contains the model mapping for all the 1000 objects supported. Find the link below:

- imagenet_class_index.json

Great! Once you have downloaded these two files, start a new python project, and then copy these two files to your project folder where your python files (.py files) will be . Then create a python file and give it a name; an example is FirstPrediction.py. Then write the code below into the python file:

from imageai.Prediction import ImagePrediction
import os
execution_path = os.getcwd()
prediction = ImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath( execution_path + "\resnet50_weights_tf_dim_ordering_tf_kernels.h5")
prediction.setJsonPath(execution_path + "\imagenet_class_index.json")
prediction.loadModel()
predictions, percentage_probabilities = prediction.predictImage("C:\Users\MyUser\Downloads\sample.jpg", result_count=5)
for index in range(len(predictions)):
print(predictions[index] + " : " + percentage_probabilities[index])

Sample Result:

sports_car : 90.61029553413391
car_wheel : 5.9294357895851135
racer : 0.9972884319722652
convertible : 0.8457873947918415
grille : 0.581052340567112

from imageai.Prediction import ImagePrediction
import os

execution_path = os.getcwd()

prediction = ImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(execution_path + "\resnet50_weights_tf_dim_ordering_tf_kernels.h5")
prediction.setJsonPath(execution_path + "\imagenet_class_index.json")

predictions, percentage_probabilities = prediction.predictImage("C:\Users\MyUser\Downloads\sample.jpg", result_count=5)

for index in range(len(predictions)):
print(predictions[index] + " : " + percentage_probabilities[index])

from imageai.Prediction import ImagePrediction
import os
import threading
execution_path = os.getcwd()
prediction = ImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath( execution_path + "\resnet50_weights_tf_dim_ordering_tf_kernels.h5")
prediction.setJsonPath(execution_path + "\imagenet_class_index.json")
picturesfolder = os.environ["USERPROFILE"] + "\Pictures\"
allfiles = os.listdir(picturesfolder)
class PredictionThread(threading.Thread):
def __init__(self):
    threading.Thread.__init__(self)

def run(self):
    prediction.loadModel()
    for eachPicture in allfiles:
        if eachPicture.endswith(".png") or eachPicture.endswith(".jpg"):
            predictions, percentage_probabilities = prediction.predictImage(picturesfolder + eachPicture, result_count=1)
            for index in range(len(predictions)):
                print(predictions[index] + " : " + percentage_probabilities[index])

predictionThread = PredictionThread ()
predictionThread.start()



Sample Application

      As a demonstration of the what you can do with ImageAI, we have 
 built a complete AI powered Photo gallery for Windows called IntelliP ,  using ImageAI and UI framework Kivy. Follow this 
  link  to download page of the application and its source code.


Documentation

        The ImageAI library currently supports only image prediction via the ImagePrediction
  class. 
 
 
  

 The ImagePrediction  class  
 

 

           The ImagePrediction class can be used to perform image prediction
      in any python application by initiating it and calling the available functions below: 

            - setModelTypeAsSqueezeNet()    This function should be called should you 
      chose to use the SqueezeNet model file for the image prediction. You only need to call it once. 

            - setModelTypeAsResNet()    This function should be called should you 
      chose to use the ResNet model file for the image prediction. You only need to call it once. 

            - setModelTypeAsInceptionV3()    This function should be called should you 
      chose to use the InceptionV3Net model file for the image prediction. You only need to call it once. 

            - setModelTypeAsDenseNet    This function should be called should you 
      chose to use the DenseNet model file for the image prediction. You only need to call it once. 

            - setModelPath()    You need to call this function only once and parse the path to
       the model file path into it. The model file type must correspond to the model type you set.  

            - setJsonPath()    You need to call this function only once and parse the path to
       the imagenet_class_index.json into it. 

             - loadModel()    You need to call this function
        once only before you attempt to call the predictImage() function .

              - predictImage()    To perform image 
         prediction on an image, you will call this function and parse in the path to the image file
          you want to predict, and also state the number of predictions result you will want to 
           have returned by the function (1 to 1000 posible results). This functions returns two arrays.
           The first one is an array of predictions while the second is an array of corresponding percentage probabilities 
           for each prediction in the prediction array. You can call this function as many times as you need 
           for as many images you want to predict and at any point in your python program as far as you
            have called the required functions to set model type, set model file path, set json file path
             and load the model.  

 
 
Contact Developers

 
 Moses Olafenwa 

      Website:      https://moses.specpal.science 

      Twitter:      @OlafenwaMoses 

      Medium :      @guymodscientist 

      Facebook :      moses.olafenwa 




      John Olafenwa 

      Website:      https://john.specpal.science 

      Twitter:      @johnolafenwa 

      Medium :      @johnolafenwa 

      Facebook :      olafenwajohn