I am doing a nudity detection using nueral networks for my final year project. So far i found a github link to finish my project. To run this project i need to pass 2 arguments such as input_file
and -m/--model_weights
. i am using Pycharm IDE. i found a SO answer to pass argument. But in my case i need to pass arguments by using 2 lines of code. Not like giving parameter in Script Parameters box in pycharm. How can i do it?
input_file
means path of the image.
-m/--model_weights
means path of the weights. in my case weights path isdata/nsw_weights.npy.
and this is my project file.
import sys
import argparse
import tensorflow as tf
from model import OpenNsfwModel, InputType
from image_utils import create_tensorflow_image_loader
from image_utils import create_yahoo_image_loader
import numpy as np
IMAGE_LOADER_TENSORFLOW = "tensorflow"
IMAGE_LOADER_YAHOO = "yahoo"
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="Path to the input image.\
Only jpeg images are supported.")
parser.add_argument("-m", "--model_weights", required=True,
help="Path to trained model weights file")
parser.add_argument("-l", "--image_loader",
default=IMAGE_LOADER_YAHOO,
help="image loading mechanism",
choices=[IMAGE_LOADER_YAHOO, IMAGE_LOADER_TENSORFLOW])
parser.add_argument("-t", "--input_type",
default=InputType.TENSOR.name.lower(),
help="input type",
choices=[InputType.TENSOR.name.lower(),
InputType.BASE64_JPEG.name.lower()])
args = parser.parse_args()
model = OpenNsfwModel()
with tf.Session() as sess:
input_type = InputType[args.input_type.upper()]
model.build(weights_path=args.model_weights, input_type=input_type)
fn_load_image = None
if input_type == InputType.TENSOR:
if args.image_loader == IMAGE_LOADER_TENSORFLOW:
fn_load_image = create_tensorflow_image_loader(sess)
else:
fn_load_image = create_yahoo_image_loader()
elif input_type == InputType.BASE64_JPEG:
import base64
fn_load_image = lambda filename: np.array([base64.urlsafe_b64encode(open(filename, "rb").read())])
sess.run(tf.global_variables_initializer())
image = fn_load_image(args.input_file)
predictions = \
sess.run(model.predictions,
feed_dict={model.input: image})
print("Results for '{}'".format(args.input_file))
print("\tSFW score:\t{}\n\tNSFW score:\t{}".format(*predictions[0]))
if __name__ == "__main__":
main(sys.argv)
In pycharm you can setup multiple run configurations which will allow you to pass in different arguments.
The documentation has a section that shows you ways to set this up.
You may also want to consider writing tests if you have a set of specific inputs you want to confirm the results for.
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