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predict.py
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predict.py
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import argparse
import sys
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
from model.resnet import ResNet50
from data.data import load_set
import tensorflow as tf
import numpy as np
import logging
from matplotlib import pyplot as plt
from PIL import Image
JSON_CONFIG = 'config.json'
def show_results(filename, classname, accuracy):
image = Image.open(filename)
plt.imshow(image)
plt.title("This is {} with {}%".format(classname, accuracy))
plt.show()
def interpret(filenames, predictions, classes_dict):
assert len(filenames) == predictions.shape[0]
for i, file in enumerate(filenames):
prediction = predictions[i]
class_index = np.argmax(prediction)
accuracy = prediction[class_index]
class_name = classes_dict[class_index]
return f'This is {class_name} with {accuracy * 100}% accuracy'
# show_results(file, class_name, accuracy * 100)
def predict(model_folder, image_folder, classes_dict, debug=False):
weights = os.path.join(model_folder, 'model.ckpt')
n_classes = len(classes_dict)
model = ResNet50(JSON_CONFIG, n_classes)
filenames = model.load_pred(image_folder)
predictions = model.predict(weights, debug=debug)
return interpret(filenames, predictions, classes_dict)
def main(image_path):
# parser = argparse.ArgumentParser()
# parser.add_argument("-img", "--img-folder", required=True,
# help="specify path to images to make prediction")
# parser.add_argument("-f", "--data-folder", required=True,
# help="path to Training Dataset to get class dict")
# parser.add_argument("-mod","--model-folder", required=True,
# help="specify path to folder with saved model")
# parser.add_argument("-d", "--debug", action="store_true",
# help="Use TensorFlow Debugger")
# args = parser.parse_args()
model_folder = os.path.join('trained_model/epoch203')
dataset_folder = os.path.join('image/train')
# debug = args.debug
classes_dict = load_set(dataset_folder, only_dict=True)
logging.info(classes_dict)
return predict(model_folder, image_path, classes_dict, False)
if __name__ == '__main__':
main()