import numpy as np
import os
import tensorflow as tf
import cv2
import sys
sys.path.append("C:\\Users\\notebook\\Documents\\GitHub\\models\\research\\")
sys.path.append("C:\\Users\\notebook\\Documents\\GitHub\\models\\research\\object_detection\\utils")

from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util


PATH_TO_FROZEN_GRAPH = 'training/frozen_inference_graph.pb'
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with tf.io.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

PATH_TO_LABELS = 'training/label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

category_index = label_map_util.create_category_index_from_labelmap(
    PATH_TO_LABELS, use_display_name=True)


def run_inference_for_single_image(image, graph):
    if 'detection_masks' in tensor_dict:
        # The following processing is only for single image
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(
            tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [
                                   real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [
                                   real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[0], image.shape[1])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
    image_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name('image_tensor:0')

    # Run inference
    output_dict = sess.run(tensor_dict,
                           feed_dict={image_tensor: np.expand_dims(image, 0)})

    # all outputs are float32 numpy arrays, so convert types as appropriate
    output_dict['num_detections'] = int(output_dict['num_detections'][0])
    output_dict['detection_classes'] = output_dict[
        'detection_classes'][0].astype(np.uint8)
    output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
    output_dict['detection_scores'] = output_dict['detection_scores'][0]
    if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
    return output_dict


with detection_graph.as_default():
    with tf.compat.v1.Session() as sess:
        # Get handles to input and output tensors
        ops = tf.compat.v1.get_default_graph().get_operations()
        all_tensor_names = {output.name for op in ops for output in op.outputs}
        tensor_dict = {}
        for key in [
            'num_detections', 'detection_boxes', 'detection_scores',
            'detection_classes', 'detection_masks'
        ]:
            tensor_name = key + ':0'
            if tensor_name in all_tensor_names:
                tensor_dict[key] = tf.compat.v1.get_default_graph().get_tensor_by_name(
                    tensor_name)

        TEST_IMAGE_DIR_PATH = input("Please enter path to directory with your test images: ")

        for dir_path, dirs, files in os.walk(TEST_IMAGE_DIR_PATH):
            os.chdir(dir_path)
            for f in files:
                image_np = cv2.imread(f)
                # Actual detection.
                output_dict = run_inference_for_single_image(
                    image_np, detection_graph)
                # Visualization of the results of a detection.
                vis_util.visualize_boxes_and_labels_on_image_array(
                    image_np,
                    output_dict['detection_boxes'],
                    output_dict['detection_classes'],
                    output_dict['detection_scores'],
                    category_index,
                    instance_masks=output_dict.get('detection_masks'),
                    use_normalized_coordinates=True,
                    line_thickness=8) 
                cv2.imshow('object_detection', image_np)
                key_pressed = cv2.waitKey(0) & 0xFF
                if key_pressed == 27:
                    cv2.destroyAllWindows()
                    sys.exit(0)
                elif key_pressed == ord('q'):
                    cv2.destroyAllWindows()
