"""Script for predicting the params for the diffraction image.

# Args
    -p: Path to the image.
    -ds: Path to the scalar for the features.
    -ps: Path to the scalar for the output variables.
    -mo: Path to the model outputs.

# Returns
    None. Saves the model prediction.
"""
from keras.models import load_model
import cv2
import argparse
import numpy as np
from diffraction_image_rcg.finding_center import prepare_model_image
from sklearn.externals import joblib

ap = argparse.ArgumentParser()
ap.add_argument("-p", "--path", required=True, help="path to load image")
ap.add_argument(
    "-ds",
    "--data_scaler",
    required=False,
    default="../resources/model_output/data.scaler",
    help="path to scalar for features",
)
ap.add_argument(
    "-ps",
    "--params_scaler",
    required=False,
    default="../resources/model_output/params.scaler",
    help="path to load scalar for output variables",
)
ap.add_argument(
    "-mo",
    "--model_output",
    required=False,
    default="../resources/model_output/",
    help="path for model outputs",
)
args = vars(ap.parse_args())

path_params_scalar = args["params_scaler"]
path_data_scalar = args["data_scaler"]

print("[INFO] loading image...")
image = cv2.imread(args["path"], cv2.IMREAD_GRAYSCALE)
image = prepare_model_image(image)
input_x = image[100, 100:201]
input_x = np.reshape(input_x, (1, 101))

sc_data = joblib.load(path_data_scalar)
input_x = sc_data.transform(input_x)

print("[INFO] loading model...")
model = load_model("../resources/model_output/model.model")

print("[INFO] predicting...")
preds = model.predict(input_x)

sc_params = joblib.load(path_params_scalar)
preds = sc_params.inverse_transform(preds)
print("[INFO] showing image...")
text = "R = {0:.2f} H = {1:.2f}".format(preds[0, 0], preds[0, 1])
print(text)
output = image.copy()
cv2.putText(output, text, (15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0), 1)
cv2.imwrite("{}prediction_output.png".format(args["model_output"]), output)

"""Script for predicting the params for the diffraction image.

# Args
    -p: Path to the image.
    -ds: Path to the scalar for the features.
    -ps: Path to the scalar for the output variables.
    -mo: Path to the model outputs.

# Returns
    None. Saves the model prediction.
"""
from keras.models import load_model
import cv2
import argparse
import numpy as np
from diffraction_image_rcg.finding_center import prepare_model_image
from sklearn.externals import joblib

ap = argparse.ArgumentParser()
ap.add_argument("-p", "--path", required=True, help="path to load image")
ap.add_argument(
    "-ds",
    "--data_scaler",
    required=False,
    default="../resources/model_output/data.scaler",
    help="path to scalar for features",
)
ap.add_argument(
    "-ps",
    "--params_scaler",
    required=False,
    default="../resources/model_output/params.scaler",
    help="path to load scalar for output variables",
)
ap.add_argument(
    "-mo",
    "--model_output",
    required=False,
    default="../resources/model_output/",
    help="path for model outputs",
)
args = vars(ap.parse_args())

path_params_scalar = args["params_scaler"]
path_data_scalar = args["data_scaler"]

print("[INFO] loading image...")
image = cv2.imread(args["path"], cv2.IMREAD_GRAYSCALE)
image = prepare_model_image(image)
input_x = image[100, 100:201]
input_x = np.reshape(input_x, (1, 101))

sc_data = joblib.load(path_data_scalar)
input_x = sc_data.transform(input_x)

print("[INFO] loading model...")
model = load_model("../resources/model_output/model.model")

print("[INFO] predicting...")
preds = model.predict(input_x)

sc_params = joblib.load(path_params_scalar)
preds = sc_params.inverse_transform(preds)
print("[INFO] showing image...")
text = "R = {0:.2f} H = {1:.2f}".format(preds[0, 0], preds[0, 1])
print(text)
output = image.copy()
cv2.putText(output, text, (15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0), 1)
cv2.imwrite("{}prediction_output.png".format(args["model_output"]), output)

Script for predicting the params for the diffraction image.  # Args     -p: Path to the image.     -ds: Path to the scalar for the features.     -ps: Path to the scalar for the output variables.     -mo: Path to the model outputs.  # Returns     None. Saves the model prediction.