text: import sys
  import os
  import argparse
  from pathlib import Path
  import time
  import json
  import tensorflow as tf
  from processing import utils
  from processing import postprocessing
  from processing.preprocessing import Preprocessor
  from processing.preprocessing import get_preprocessing_function
  from processing.postprocessing import label_images
  from processing.utils import printProgressBar
  from skimage.util import img_as_ubyte
  from sklearn.metrics import confusion_matrix
  from matplotlib import pyplot as plt
  import numpy as np
  import pandas as pd
  import logging
  
  logging.basicConfig(level=logging.INFO)
  logger = logging.getLogger(__name__)
  
  
  def get_true_classes(filenames):
      # retrieve ground truth
      #reurn 1 if there is defective, 0 if the image is great
      y_true = [1 if "good" not in filename.split("\\") else 0 for filename in filenames]
      #y_true = [0 if "hi"  in filename.split("\\") else 1 for filename in filenames]
      return y_true
  
  
  def is_defective(areas, min_area):
      """Decides if image is defective given the areas of its connected components"""
      areas = np.array(areas)
      if areas[areas >= min_area].shape[0] > 0:
          return 1
      return 0
  
  
  def predict_classes(resmaps, min_area, threshold):
      # threshold residual maps with the given threshold
      resmaps_th = resmaps > threshold
      # compute connected components
      _, areas_all = label_images(resmaps_th)
      # Decides if images are defective given the areas of their connected components
      y_pred = [is_defective(areas, min_area) for areas in areas_all]
      return y_pred
  
  
  def save_segmented_images(resmaps, threshold, filenames, save_dir):
      # threshold residual maps with the given threshold
      resmaps_th = resmaps > threshold
      # create directory to save segmented resmaps
      seg_dir = os.path.join(save_dir, "segmentation")
      if not os.path.isdir(seg_dir):
          os.makedirs(seg_dir)
      # save segmented resmaps
      for i, resmap_th in enumerate(resmaps_th):
          fname = utils.generate_new_name(filenames[i], suffix="seg")
          fpath = os.path.join(seg_dir, fname)
          plt.imsave(fpath, resmap_th, cmap="gray")
      return
  
  
  def main(args):
      # parse arguments
      model_path = args.path
      save = args.save
  
      # ============= LOAD MODEL AND PREPROCESSING CONFIGURATION ================
  
      # load model and info
      model, info, _ = utils.load_model_HDF5(model_path)
      # set parameters
      input_directory = info["data"]["input_directory"]
      architecture = info["model"]["architecture"]
      loss = info["model"]["loss"]
      rescale = info["preprocessing"]["rescale"]
      shape = info["preprocessing"]["shape"]
      color_mode = info["preprocessing"]["color_mode"]
      vmin = info["preprocessing"]["vmin"]
      vmax = info["preprocessing"]["vmax"]
      nb_validation_images = info["data"]["nb_validation_images"]
  
      # =================== LOAD VALIDATION PARAMETERS =========================
  
      model_dir_name = os.path.basename(str(Path(model_path).parent))
      finetune_dir = os.path.join(
          os.getcwd(),
          "results",
          input_directory,
          architecture,
          loss,
          model_dir_name,
          "finetuning",
      )
      subdirs = os.listdir(finetune_dir)
      for subdir in subdirs:
          logger.info(
              "testing with finetuning parameters from \n{}...".format(
                  os.path.join(finetune_dir, subdir)
              )
          )
          try:
              with open(
                  os.path.join(finetune_dir, subdir, "finetuning_result.json"), "r"
              ) as read_file:
                  validation_result = json.load(read_file)
          except FileNotFoundError:
              logger.warning("run finetune.py before testing.\nexiting script.")
              sys.exit()
  
          min_area = validation_result["best_min_area"]
          threshold = validation_result["best_threshold"]
          method = validation_result["method"]
          dtype = validation_result["dtype"]
  
          # ====================== PREPROCESS TEST IMAGES ==========================
  
          # get the correct preprocessing function
          preprocessing_function = get_preprocessing_function(architecture)
  
          # initialize preprocessor
          preprocessor = Preprocessor(
              input_directory=input_directory,
              rescale=rescale,
              shape=shape,
              color_mode=color_mode,
              preprocessing_function=preprocessing_function,
          )
  
          # get test generator
          nb_test_images = preprocessor.get_total_number_test_images()
          test_generator = preprocessor.get_test_generator(
              batch_size=nb_test_images, shuffle=False
          )
  
          # retrieve test images from generator
          imgs_test_input = test_generator.next()[0]
  
          # retrieve test image names
          filenames = test_generator.filenames
  
          # predict on test images
          imgs_test_pred = model.predict(imgs_test_input)
  
          # instantiate TensorImages object
          tensor_test = postprocessing.TensorImages(
              imgs_input=imgs_test_input,
              imgs_pred=imgs_test_pred,
              vmin=vmin,
              vmax=vmax,
              method=method,
              dtype=dtype,
              filenames=filenames,
          )
  
          # ====================== CLASSIFICATION ==========================
  
          # retrieve ground truth
          y_true = get_true_classes(filenames)
  
          # predict classes on test images
          y_pred = predict_classes(
              resmaps=tensor_test.resmaps, min_area=min_area, threshold=threshold
          )
  
          # confusion matrix
          tnr, fp, fn, tpr = confusion_matrix(y_true, y_pred, normalize="true").ravel()
  
          # initialize dictionary to store test results
          test_result = {
              "min_area": min_area,
              "threshold": threshold,
              "TPR": tpr,
              "TNR": tnr,
              "score": (tpr + tnr) / 2,
              "method": method,
              "dtype": dtype,
          }
  
          # ====================== SAVE TEST RESULTS =========================
  
          # create directory to save test results
          save_dir = os.path.join(
              os.getcwd(),
              "results",
              input_directory,
              architecture,
              loss,
              model_dir_name,
              "test",
              subdir,
          )
  
          if not os.path.isdir(save_dir):
              os.makedirs(save_dir)
  
          # save test result
          with open(os.path.join(save_dir, "test_result.json"), "w") as json_file:
              json.dump(test_result, json_file, indent=4, sort_keys=False)
  
          # save classification of image files in a .txt file
          classification = {
              "filenames": filenames,
              "predictions": y_pred,
              "truth": y_true,
              "accurate_predictions": np.array(y_true) == np.array(y_pred),
          }
          df_clf = pd.DataFrame.from_dict(classification)
          with open(os.path.join(save_dir, "classification.txt"), "w") as f:
              f.write(
                  "min_area = {}, threshold = {}, method = {}, dtype = {}\n\n".format(
                      min_area, threshold, method, dtype
                  )
              )
              f.write(df_clf.to_string(header=True, index=True))
  
          # print classification results to console
          with pd.option_context("display.max_rows", None, "display.max_columns", None):
              print(df_clf)
  
          # save segmented resmaps
          if save:
              save_segmented_images(tensor_test.resmaps, threshold, filenames, save_dir)
  
          # print test_results to console
          print("test results: {}".format(test_result))
  
          # print precentage
          array_of_correct = np.array(y_true) == np.array(y_pred)
          true_sum=sum(array_of_correct)
          res= true_sum / len(y_pred)
          print("test results in %: ",int(res*100))
  
  if __name__ == "__main__":
      # create parser
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "-p", "--path", type=str, required=True, metavar="", help="path to saved model"
      )
      parser.add_argument(
          "-s", "--save", action="store_true", help="save segmented images",
      )
  
      args = parser.parse_args()
  
      main(args)
  
  # Examples of command to initiate testing
  # python3 test.py -p saved_models/mvtec/capsule/mvtecCAE/ssim/13-06-2020_15-35-10/mvtecCAE_b8_e39.hdf5
  