如何在Tensorflow对象检测API中计数对象
如何在Tensorflow对象检测API中计数对象
提问于 2017-08-07 17:06:06
我正在执行https://github.com/tensorflow/tensorflow，这是一个检测图像中物体的例子。
我想要获得检测到的物体的计数，下面是给我在图像中绘制的检测到的物体的代码。但是我不能得到检测到的物体的数量。
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=1)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)这是给出实际目标检测的代码块，如下图所示：
如何获取对象计数？
回答 4
Stack Overflow用户
发布于 2017-08-07 18:08:39
简单解决boxes.shape打印长度问题
print(len(boxes.shape))Stack Overflow用户
发布于 2017-10-02 21:49:15
您应该手动检查分数和清点对象。代码如下：
#code to test image start
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
#code to test image finish
#add this part to count objects
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
#count is the number of objects detectedStack Overflow用户
发布于 2017-08-20 02:54:22
需要注意的是，框的数量始终是100。
如果您查看实际绘制方框的代码，即vis_util.visualize_boxes_and_labels_on_image_array函数，您将看到它们定义了一个阈值-- min_score_thresh=.5 --以将绘制的方框限制为得分大于0.5的那些检测。您可以将其视为仅绘制精确检测概率>50%的方框。您可以向上或向下调整此阈值，以增加绘制的框数。但是，如果你把它减得太低，你会得到很多不准确的框。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/45543154
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