テンソルフローAPIで検出されたオブジェクトをカウントする

Question

私はテンソルフローオブジェクト検出チュートリアルを使用しています。私は検出されたオブジェクトの適切な数を取得します。私は、検出されたオブジェクトを数え、その数をプリントアウトする方法を見つけるためにいくつかの問題を抱えています。

私はコミュニティを見て、誰かがboxes.shape [0]でそれを行ったことを知りましたが、それを動作させることはできません。

チュートリアルをpythonに変換し、matplotlibの呼び出しを削除しました。私は、検出されたオブジェクトの数を印刷するために必要なボックスで画像を出力する必要はありません。

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. 
#get_ipython().magic('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 = 'test_inference_graph' 

# 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('training', 'object- detection.pbtxt') 

NUM_CLASSES = 1 





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, 'IMG_{}.PNG'.format(i)) for i in range(7464, 7483) ] 
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'test-latest.jpg') ] 

# Size, in inches, of the output images. 
#IMAGE_SIZE = (20, 16) 



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(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. 
     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=1) 
     plt.figure(figsize=IMAGE_SIZE) 
     plt.imshow(image_np) 
''' 
###Below always print 1 
#print(boxes.shape[0]) 
taco = [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5] 
print(len(taco)) 

Answer 1

taco = [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5] 
print(len(taco)) 

Answer 2

num変数は、検出されたボックスの数を含むべきです。