from imageai.Prediction import ImagePrediction
import os
execution_path = os.getcwd()
prediction = ImagePrediction()
prediction.setModelTypeAsMobileNetV2()
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)
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)
#Date: July 18, 2021
#Author: Terry Su
#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(
