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from os import listdir
import sys
from pathlib import Path
import numpy as np
sys.path.append(str(Path(__file__).resolve().parent.parent))
from typing import Union
import pandas as pd
import tensorflow as tf
from plant_leave_diseases_model import __version__ as _version
from plant_leave_diseases_model.config.core import TRAINED_MODEL_DIR, config
from plant_leave_diseases_model.processing.data_manager import convert_image_to_array, get_class_file_list, get_model_file_name_path, load_leaf_disease_dataset, load_model, load_test_dataset
from plant_leave_diseases_model.processing.data_setup import test_directory,class_file_path
def make_prediction(*, test_dir_img_file_path) -> dict:
"""Make a prediction using a saved model """
image_list=[]
#x_test, y_test = load_leaf_disease_dataset(test_dir_img_file_path)
root_dir = listdir(test_dir_img_file_path)
for plant_image in root_dir :
print("plant_image::",plant_image)
img_file_path=str(test_dir_img_file_path)+"/"+str(plant_image)
if plant_image.endswith(".jpg") == True or plant_image.endswith(".JPG") == True:
image_list.append(convert_image_to_array(img_file_path))
np_image_list = np.array(image_list, dtype=np.float16) / config.model_config.scaling_factor
print("np_image_list.shape:",np_image_list.shape)
results = {"predictions": None, "version": _version}
load_model_and_predict(np_image_list)
pred_labels = model_file_name = get_model_file_name_path()
results = {"predictions": pred_labels, "version": _version}
return results
def load_model_and_predict(x_test):
model_file_name = get_model_file_name_path()
print("loading mode file:",model_file_name)
model = load_model(file_name = model_file_name)
predictions = model.predict(x_test,verbose = 0)
###########################################
# Geting array of master class array
###########################################
master_class_arr=get_class_file_list(class_file_path)
print("master_class_arr:",master_class_arr)
print(predictions)
pred_labels = []
for pred in predictions:
np.multiply(np.array(pred), 100000)
max_index = np.multiply(np.array(pred), 100000).argmax()
leaf_pred_label=master_class_arr[max_index]
print("load_model_and_predict:max_index::",max_index,"master_class_arr:",master_class_arr[max_index])
pred_labels.append(leaf_pred_label)
return pred_labels
if __name__ == "__main__":
# Define directory where test images are loaded
test_dir_img_file_path=test_directory+"/Apple___Apple_scab"
print("test_dir_img_file_path::",test_dir_img_file_path)
make_prediction(test_dir_img_file_path = test_dir_img_file_path) |