# # Import packages
# import os
# import sys
# from os import listdir
# from os.path import isfile, join
#
# import cv2
# import numpy as np
# import tensorflow as tf
#
# # This is needed since the notebook is stored in the object_detection folder.
# sys.path.append("..")
#
# # Import utilites
# from utils_extractor import label_map_util
# from utils_extractor import visualization_utils as vis_util
#
# # Name of the directory containing the object detection module we're using
# MODEL_NAME = 'model'
#
# # Grab path to current working directory
# CWD_PATH = os.getcwd()
#
# # Path to frozen detection graph .pb file, which contains the model that is used
# # for object detection.
# PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, 'frozen_inference_graph.pb')
#
# # Path to label map file
# PATH_TO_LABELS = os.path.join(CWD_PATH, 'data', 'labelmap.pbtxt')
#
# # Number of classes the object detector can identify
# NUM_CLASSES = 1
#
# # Load the label map.
# # Label maps map indices to category names, so that when our convolution
# # network predicts `5`, we know that this corresponds to `king`.
# # Here we use internal utility functions, but anything that returns a
# # dictionary mapping integers to appropriate string labels would be fine
# label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
# categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
# category_index = label_map_util.create_category_index(categories)
#
# # Load the Tensorflow model into memory.
# detection_graph = tf.Graph()
# with detection_graph.as_default():
#     od_graph_def = tf.GraphDef()
#     with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
#         serialized_graph = fid.read()
#         od_graph_def.ParseFromString(serialized_graph)
#         tf.import_graph_def(od_graph_def, name='')
#
#     sess = tf.Session(graph=detection_graph)
#
# # Define input and output tensors (i.e. data) for the object detection classifier
#
# # Input tensor is the image
# image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
#
# # Output tensors are the detection boxes, scores, and classes
# # Each box represents a part of the image where a particular object was detected
# detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
#
# # Each score represents level of confidence for each of the objects.
# # The score is shown on the result image, together with the class label.
# detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
# detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
#
# # Number of objects detected
# num_detections = detection_graph.get_tensor_by_name('num_detections:0')
#
# # Load image using OpenCV and
# # expand image dimensions to have shape: [1, None, None, 3]
# # i.e. a single-column array, where each item in the column has the pixel RGB value
#
# # Path to image
# DIRECTORY_NAME = 'test_images'
#
# DIRECTORY_PATH = os.path.join(CWD_PATH, DIRECTORY_NAME)
#
# onlyfiles = [f for f in listdir(DIRECTORY_PATH) if isfile(join(DIRECTORY_PATH, f))]
#
# for filename in onlyfiles:
#
#     try:
#         image_path = DIRECTORY_PATH + "/" + filename
#         image = cv2.imread(image_path)
#         image_expanded = np.expand_dims(image, axis=0)
#
#         # Perform the actual detection by running the model with the image as input
#         (boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],
#                                                  feed_dict={image_tensor: image_expanded})
#
#         # Draw the results of the detection (aka 'visulaize the results')
#         image1, array_coord = vis_util.visualize_boxes_and_labels_on_image_array(image,
#                                                                                  np.squeeze(boxes),
#                                                                                  np.squeeze(classes).astype(np.int32),
#                                                                                  np.squeeze(scores),
#                                                                                  category_index,
#                                                                                  use_normalized_coordinates=True,
#                                                                                  line_thickness=3,
#                                                                                  min_score_thresh=0.60)
#
#         ymin, xmin, ymax, xmax = array_coord
#         shape = np.shape(image1)
#         im_width, im_height = shape[1], shape[0]
#         (left, right, top, bottom) = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
#
#         print(f"image is:- {filename}")
#         # cv2.imshow("original", image)
#
#         if ymin == xmax == xmin == ymax == 0:
#             output_image_path = DIRECTORY_PATH + "/ignored/" + filename
#             print(f"output image path:- {output_image_path}")
#             cv2.imwrite(output_image_path, image1)
#             continue
#         else:
#             output_image_path = DIRECTORY_PATH + "/detected/" + filename
#             print(f"output image path:- {output_image_path}")
#             cropped_image = image[int(top):int(bottom), int(left):int(right)]
#             cv2.imwrite(output_image_path, cropped_image)
#             # cv2.imshow('ID CARD DETECTOR', cropped_image)
#             cv2.waitKey(0)
#
#     except Exception as e:
#         print(e)
#         print(f"some exception occured with image:- {DIRECTORY_PATH + '/' + filename}")
#         continue
#
#     # Press any key to close the image
#     cv2.waitKey(0)
#
#     # Clean up
#     cv2.destroyAllWindows()
