谷歌开源的目标检测模型，选了个内存占用小的ssd_mobilenet_v1_coco_2017_11_17模型，网络下载链接：

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

程序：

import os

import numpy as np

import tensorflow as tf

from PIL import Image

from matplotlib import pyplot as plt

import ops as utils_ops

import label_map_util

import visualization_utils as vis_util

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

PATH_TO_CKPT = 'frozen_inference_graph.pb'

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

PATH_TO_LABELS = 'pascal_label_map.pbtxt'

NUM_CLASSES = 20

'''

if tf.__version__ < '1.4.0':

    raise ImportError(

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

'''

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)

# 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 = 'image\\'

TEST_IMAGE_PATHS = [os.path.join(

    PATH_TO_TEST_IMAGES_DIR, image) for image in os.listdir(PATH_TO_TEST_IMAGES_DIR)]

# Size, in inches, of the output images.

IMAGE_SIZE = (12, 8)

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

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.

    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)

    plt.figure(figsize=IMAGE_SIZE)

    plt.imshow(image_np)

plt.show()

测试运行结果：
https://pan.baidu.com/s/1VTaMnAYrDY8rNrie_dsLQg

d1420180908_172237.gif