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)