如下图，谷歌开源的object detection API提供了五种网络结构的fine-tuning训练权重，方便我们针对目标检测的需求进行模型训练，本文详细介绍下导出训练模型后，如何获得目标检测框的坐标。如果对使用object detection API训练模型的过程不了解，可以参考博文：https://www.cnblogs.com/White-xzx/p/9503203.html

　　　　　　　　　　　　　　　　　　　　　　　　　　

　　新建一个测试文件object_detection_test.py，该脚本读取我们已经训练好的模型文件和测试图片，进行测试，代码如下，

 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_2017_11_17'

 #MODEL_FILE = MODEL_NAME + '.tar.gz'

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

 MODEL_NAME = 'data'  # 训练过程中保存模型文件的文件夹路径

 # 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' # 训练完成导出的pb模型文件

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

 PATH_TO_LABELS = 'E:/TensorFlow/Box-object-detection/data/label_map.pbtxt' # label_map.pbtxt文件

 NUM_CLASSES = 2   # 类别总数

 #Load a (frozen) Tensorflow model into memory. 加载模型

 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='')

 #Loading label map 加载label_map

 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)

 #Helper code

 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'   # 测试图片的路径

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

 TEST_IMAGE = sys.argv[1]

 print("the test image is:", TEST_IMAGE)

 # 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(TEST_IMAGE)  # 打开图片

     # 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')   # 获取图片张量

     # Each box represents a part of the image where a particular object was detected.

     boxes = detection_graph.get_tensor_by_name('detection_boxes:0')   # 获取检测框张量

     # 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')   # 获取每个检测框的分数，即概率

     classes = detection_graph.get_tensor_by_name('detection_classes:0')  # 获取类别名称id，与label_map中的ID对应

     num_detections = detection_graph.get_tensor_by_name('num_detections:0')  # 获取检测总数

     # 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.结果可视化

     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)

     print(boxes) # 打印检测框坐标

     print(scores)   #打印每个检测框的概率

     print(classes)   # 打印检测框对应的类别

     print(category_index)  # 打印类别的索引，其是一个嵌套的字典

     final_score = np.squeeze(scores)

     count = 0

     for i in range(100):

         if scores is None or final_score[i] > 0.5: # 显示大于50%概率的检测框

             count = count + 1

     print("the count of objects is: ", count )   

     plt.figure(figsize=IMAGE_SIZE)

     plt.imshow(image_np)

     plt.show()

打开cmd，输入如下命令，

python object_detection_test.py ./test_images/2.png

运行结果如下，

目标检测框box的坐标，此处的坐标是坐标除以相应图片的长宽所得到的小数，排列顺序为[ymin , xmin , ymax , xmax]，即box检测框左上角和右下角的坐标，

同时显示的是目标检测框box的概率：

Box的标签索引和每个索引所代表的标签，如第一个box的索引为1,1的标签名为“box”，即检测框里的是“箱子”

检测图：

因为源码中将坐标与图片的长宽相除，所以显示的是小数，为了得到准确的坐标，只要乘上相应的长宽数值就可以得到坐标了，上图的检测图坐标由计算可得

[ymin , xmin , ymax , xmax] = [ 614.4 , 410.4 , 764.16 , 569.16 ]，即在y轴的坐标和使用pyplot显示的坐标相近（图中红线标出）。

接下来，我们只要将上面的测试代码稍加修改即可得到我们想要的坐标，比如获得每个检测物体的中心坐标，代码如下：

 import numpy as np

 import os

 import six.moves.urllib as urllib

 import sys

 import tarfile

 import tensorflow as tf

 import zipfile

 import time

 from collections import defaultdict

 from io import StringIO

 from matplotlib import pyplot as plt

 #plt.switch_backend('Agg')

 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_2017_11_17'

 #MODEL_FILE = MODEL_NAME + '.tar.gz'

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

 MODEL_NAME = 'E:/Project/object-detection-Game-2018-5-31/data-20180607'  # model.ckpt路径,包括frozen_inference_graph.pb文件

 # 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 = MODEL_NAME+'/label_map.pbtxt'

 #E:/Project/object-detection-Game-2018-5-31

 NUM_CLASSES = 6

 start = time.time()

 #Load a (frozen) Tensorflow model into memory.

 detection_graph = tf.Graph()

 with detection_graph.as_default():

   od_graph_def = tf.GraphDef()

   #loading ckpt file to graph

   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='')

 #Loading label map

 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)

 #Helper code

 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 = 'test_images'

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

 TEST_IMAGE = sys.argv[1]

 print("the test image is:", TEST_IMAGE)

 # Size, in inches, of the output images.

 IMAGE_SIZE = (12, 8)

 with detection_graph.as_default():

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

     # 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 image_path in TEST_IMAGE_PATHS:

     image = Image.open(TEST_IMAGE)

     # 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)

     #print(boxes)

     # for i in range(len(scores[0])):

     #     if scores[0][i]>0.5:

     #         print(scores[0][i])

     #print(scores)

     #print(classes)

     #print(category_index)

     final_score = np.squeeze(scores)

     count = 0

     for i in range(100):

         if scores is None or final_score[i] > 0.5:

             count = count + 1

     print()

     print("the count of objects is: ", count )

     (im_width, im_height) = image.size

     for i in range(count):

         #print(boxes[0][i])

         y_min = boxes[0][i][0]*im_height

         x_min = boxes[0][i][1]*im_width

         y_max = boxes[0][i][2]*im_height

         x_max = boxes[0][i][3]*im_width

         print("object{0}: {1}".format(i,category_index[classes[0][i]]['name']),

                          ',Center_X:',int((x_min+x_max)/2),',Center_Y:',int((y_min+y_max)/2))

         #print(x_min,y_min,x_max,y_max)

     end = time.time()

     seconds = end - start

     print("Time taken : {0} seconds".format(seconds))

     # plt.figure(figsize=IMAGE_SIZE)

     # plt.imshow(image_np)

     # plt.show()

运行结果如下，

转载请注明出处：https://www.cnblogs.com/White-xzx/p/9508535.html

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