from imageai import Prediction from imageai.Prediction import ImagePrediction import os execution_path = os.getcwd() # print(execution_path) prediction = ImagePrediction() prediction.setModelTypeAsMobileNetV2() # decide what model we're going to use prediction.setModelPath(os.path.join(execution_path, "mobilenet_v2.h5")) prediction.loadModel() predictions, probabilities = prediction.classifyImage(os.path.join( execution_path, "house.jpg"), result_count=5) for eachPrediction, eachProbability in zip(predictions, probabilities): print(eachPrediction, " : ", eachProbability)
#Purpose: using the imageai library to initialize an image regocnition program in python from imageai.Prediction import ImagePrediction import os execution_path = os.getcwd() #current working directory prediction = ImagePrediction() prediction.setModelTypeAsMobileNetV2( ) #we will be using he "Mobile Net V2" model prediction.setModelPath(os.path.join(execution_path, 'mobilenet_v2.h5')) prediction.loadModel() predictions, probabilities = prediction.classifyImage( os.path.join(execution_path, 'house.jpg'), result_count=10) #producing 10 predictions for house.jpg for eachPrediction, eachProbability in zip(predictions, probabilities): print(eachPrediction, " : ", f'%{eachProbability}') print() predictions, probabilities = prediction.classifyImage( os.path.join(execution_path, 'godzilla.jpg'), result_count=10) #producing 10 predictions for house.jpg for eachPrediction, eachProbability in zip(predictions, probabilities): print(eachPrediction, " : ", f'%{eachProbability}') print() predictions, probabilities = prediction.classifyImage( os.path.join(execution_path, 'giraffe.jpg'), result_count=10) #producing 10 predictions for house.jpg