import numpy as np

import os

import six.moves.urllib as urllib

import sys

import tarfile

import tensorflow as tf

import zipfile

from collections import defaultdict

from io import StringIO

from matplotlib import pyplot as plt

from PIL import Image

# 这里改成你下载的object_detection包的位置

sys.path.append(r"E:\学习资料\人工智能\models-master\research")

from object_detection.utils import ops as utils_ops

if tf.__version__ < '1.4.0':

  raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')

%matplotlib inline

from object_detection.utils import label_map_util

from object_detection.utils import visualization_utils as vis_util

matplotlib.use('Agg',warn=False, force=True)

# What model to download.

MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'

MODEL_FILE = MODEL_NAME + '.tar.gz'

DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# 这里的路径也需要修改

PATH_TO_LABELS = os.path.join(r'E:\学习资料\人工智能\models-master\research\object_detection\data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

opener = urllib.request.URLopener()

opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)

tar_file = tarfile.open(MODEL_FILE)

for file in tar_file.getmembers():

  file_name = os.path.basename(file.name)

  if 'frozen_inference_graph.pb' in file_name:

    tar_file.extract(file, os.getcwd())

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)

def run_inference_for_single_image(image, graph):

  with graph.as_default():

    with tf.Session() as sess:

      # Get handles to input and output tensors

      ops = tf.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.get_default_graph().get_tensor_by_name(

              tensor_name)

      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.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

IMAGE_SIZE = (36, 24)

#这里设置图片路径

mydir=r'E:\学习资料\人工智能\models-master\research\object_detection\test_images'

# mydir = 'G:\壁纸'

for filename in os.listdir(mydir):

    if os.path.splitext(filename)[1] == '.jpg':

        filepath=os.path.join(mydir, filename)

        print(filepath)

        image = Image.open(filepath)

  # 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.

        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)

        fig1 = plt.gcf()

        plt.figure(figsize=IMAGE_SIZE)

        plt.imshow(image_np)