def test_recognition_model_densenet(): predictor = ImageClassification() predictor.setModelTypeAsDenseNet121() predictor.setModelPath(os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5")) predictor.loadModel() predictions, probabilities = predictor.classifyImage(image_input=os.path.join(main_folder, main_folder, "data-images", "1.jpg")) assert isinstance(predictions, list) assert isinstance(probabilities, list) assert isinstance(predictions[0], str) assert isinstance(probabilities[0], float)
from imageai.Classification import ImageClassification import os execution_path = os.getcwd() prediction = ImageClassification() prediction.setModelTypeAsDenseNet121() prediction.setModelPath(os.path.join(execution_path, "DenseNet-BC-121-32.h5")) prediction.loadModel() predictions, probabilities = prediction.classifyImage(os.path.join( execution_path + "/images", "1.jpg"), result_count=5) for eachPrediction, eachProbability in zip(predictions, probabilities): print(eachPrediction, " : ", eachProbability)