tensorflow object detection demo

 

官方给了一个demo是object_detection_tutorial.ipynb  https://github.com/tensorflow/models/blob/master/object_detection/object_detection_tutorial.ipynb

 

是用Jupyter notebook来运行的，由于通过xshell使用服务器，不能浏览网页，所以需要一个python版本。 这里把链接中的代码拼了起来 ，可以在python中运行

 

 

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

# This is needed to display the images.

%matplotlib inline

 

# This is needed since the notebook is stored in the object_detection folder.

sys.path.append("..")

from utils import label_map_util

 

from utils import visualization_utils as vis_util

 

# What model to download.

MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'

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'

 

# List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

 

NUM_CLASSES = 90

 

# What model to download.

MODEL_NAME = 'ssd_inception_v2_coco_11_06_2017'

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'

 

# List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('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)

 

# For the sake of simplicity we will use only 2 images:

# image1.jpg

# image2.jpg

# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.

PATH_TO_TEST_IMAGES_DIR = 'test_images'

TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]

 

# Size, in inches, of the output images.

IMAGE_SIZE = (12, 8)

 

with detection_graph.as_default():

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

    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)

      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.

      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.

      scores = detection_graph.get_tensor_by_name('detection_scores:0')

      classes = detection_graph.get_tensor_by_name('detection_classes:0')

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

      # Actual detection.

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

          [boxes, scores, classes, num_detections],

          feed_dict={image_tensor: image_np_expanded})

      # Visualization of the results of a detection.

      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.figure(figsize=IMAGE_SIZE)

      plt.imshow(image_np) 

numpy.save("/data/xueqian/output.npy",image_np)#由于无法查看图片 就先保存下来

 

 

一些训练好的模型可以在下面下载：https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md