import argparse
import os

import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from PIL import Image

from utils import visualization_utils as vis_util
from utils import label_map_util

NUM_CLASSES = 5


def parse_args(check=True):
    parser = argparse.ArgumentParser()
    parser.add_argument('--output_dir', type=str, required=True)
    parser.add_argument('--dataset_dir', type=str, required=True)
    FLAGS, unparsed = parser.parse_known_args()
    return FLAGS, unparsed


if __name__ == '__main__':
    FLAGS, unparsed = parse_args()

    PATH_TO_CKPT = os.path.join(FLAGS.output_dir, 'frozen_inference_graph.pb')
    PATH_TO_LABELS = os.path.join(FLAGS.dataset_dir, 'labels_items.txt')

    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='')

    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)

    def load_image_into_numpy_array(image):
        (im_width, im_height) = image.size
        return np.array(image.getdata()).reshape(
            (im_height, im_width, 3)).astype(np.uint8)

    test_img_path = os.path.join(FLAGS.dataset_dir, 'test.jpg')

    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')
            image = Image.open(test_img_path)
            image_np = load_image_into_numpy_array(image)
            image_np_expanded = np.expand_dims(image_np, axis=0)
            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections],
                feed_dict={image_tensor: image_np_expanded})
            vis_util.visualize_boxes_and_labels_on_image_array(
                image_np,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                category_index,
                use_normalized_coordinates=True,
                line_thickness=8)
            plt.imsave(os.path.join(FLAGS.output_dir, 'output.png'), image_np)