from matplotlib import pyplot as plt

import numpy as np

import os

import six.moves.urllib as urllib

import sys

import tarfile

import tensorflow as tf

import zipfile

import glob

from collections import defaultdict

from io import StringIO

from PIL import Image

import DrawBox

import cv2

from object_detection.utils import label_map_util

from object_detection.utils import visualization_utils as vis_util

SaveFilename='D:/FasterR-CNNImageTest/11/'

if not os.path.exists(SaveFilename):

    os.mkdir(SaveFilename)

with tf.device('/cpu:0'):

    cap = cv2.VideoCapture(0)

    PATH_TO_CKPT = 'pb/frozen_inference_graph.pb'

    PATH_TO_LABELS = os.path.join('dataset', 'pascal_label_map.pbtxt')

    NUM_CLASSES = 2

    # Load a (frozen) 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='')

    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)

    # # Detection

    cnt = 0

    PATH_TO_TEST_IMAGES_DIR = 'E:/PythonOpenCVCode/BalanceGeneratePicture/TestSetSaveImage/Test/JPEGImages'

    with detection_graph.as_default():

        with tf.Session(graph=detection_graph) as sess:

            # while True:  # for image_path in TEST_IMAGE_PATHS:    #changed 20170825

            # Definite input and output Tensors for detection_graph

            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

            # 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 represent how level of confidence for each of the objects.

            # 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')

            num_detections = detection_graph.get_tensor_by_name('num_detections:0')

            for pidImage in glob.glob(PATH_TO_TEST_IMAGES_DIR + "/*.jpg"):

                TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, pidImage)]

                # Size, in inches, of the output images.

                IMAGE_SIZE = (12, 8)

                for image_path in TEST_IMAGE_PATHS:

                    image = Image.open(image_path)

                    # the array based representation of the image will be used later in order to prepare the

                    # result image with boxes and labels on it.

                    image_np = load_image_into_numpy_array(image)

                    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]

                    image_np_expanded = np.expand_dims(image_np, axis=0)

                    # Actual detection.

                    (boxes, scores, classes, num) = sess.run(

                        [detection_boxes, detection_scores, detection_classes, num_detections],

                        feed_dict={image_tensor: image_np_expanded})

                    # Visualization of the results of a detection.

                    # print(boxes)

                    DrawBox.visualize_boxes_and_labels_on_image_array(

                        image_np,

                        np.squeeze(boxes),

                        np.squeeze(classes).astype(np.int32),

                        np.squeeze(scores),

                        category_index,

                        max_boxes_to_draw=400,

                        use_normalized_coordinates=True,

                        groundtruth_box_visualization_color='red',

                        line_thickness=8)

                    #plt.figure(figsize=IMAGE_SIZE)

                    cv2.imwrite(SaveFilename + os.path.basename(image_path), image_np)

                    # plt.imshow(image_np)

                    cnt = cnt + 1

                    print(image_path)