TensorFlow对象检测API将在图像上找到的对象打印到控制台 [英] TensorFlow Object Detection API print objects found on image to console
本文介绍了TensorFlow对象检测API将在图像上找到的对象打印到控制台的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试使用 TF对象检测API 返回在图像中找到的对象列表.
I'm trying to return list of objects that have been found at image with TF Object Detection API.
为此，我正在使用print([category_index.get(i) for i in classes[0]])打印已找到的对象列表，或者使用print(num_detections)显示已找到对象的数量，但是在两种情况下，它都为我提供了300个值或简单的值[300.]相应地.
To do that I'm using print([category_index.get(i) for i in classes[0]]) to print list of objects that have been found or print(num_detections) to display number of found objects, but in both cases it gives me list with 300 values or simply value [300.] correspondingly.
如何仅返回图像上的那些对象?或者，如果有任何错误，请帮助找出问题所在.
How it`s possible to return only that objects that are on image? Or if there is some mistake please help to figure out what is wrong.
我在训练时使用了更快的RCNN 模型配置文件和检查点.确保它确实在图像上检测到很少的物体，这里是:
I was using Faster RCNN models config file and checkpoints while training. Be sure it really detects few objects at image, here it is:
我的代码:
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
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
PATH_TO_CKPT = 'frozen_graph/frozen_inference_graph.pb'
PATH_TO_LABELS = 'object_detection/pascal_label_map.pbtxt'
NUM_CLASSES = 7
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)
PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images/'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 2) ]
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
sess.run(tf.global_variables_initializer())
img = 1
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
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')
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')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
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.imsave('RESULTS/' + str(img) + '.jpg', image_np)
img += 1
# Return found objects
print([category_index.get(i) for i in classes[0]])
print(boxes.shape)
print(num_detections)
哪个给出以下结果:
[{'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}]
(1, 300, 4)
[ 300.]
感谢您提供任何信息！
UPD:
感谢所有为这个问题提供帮助的人. 下面的代码正是我所需要的，它为我列出了找到的对象，以便我可以对它们执行其他操作.
Thousand thanks for everyone who helped with this question. Following line of code is exactly what I needed, it gives me list with objects that were found so I can do other operations on them.
print [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5]
推荐答案
据我所知，您进行了300次检测. visualize_boxes_and_labels_on_image_array显示了很少的内容，因为min_score_thresh=.5(这是默认值)对大多数人来说太高了.
As far as I can see you have 300 detections. visualize_boxes_and_labels_on_image_array shows very few of them because min_score_thresh=.5 (this is the default value) is too high for the most of them.
如果要将这样的过滤添加到输出中，可以编写:
If you want to add such filtering to the output you can write:
min_score_thresh = 0.5
print([category_index.get(i) for i in classes[0] if scores[0, i] > min_score_thresh)
您可以更改min_score_thresh以选择所需的阈值.打印带有类别名称的得分值可能会很有用.
You can change min_score_thresh to choose threshold value you need. It may be useful to print the score values with the category names.
这篇关于TensorFlow对象检测API将在图像上找到的对象打印到控制台的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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