[英]Output predicted bounding boxes trained on TensorFlow Object Detection API
[英]Tensorflow object detection API: output boxes for probability less than 50%
我指的是 Tensorflow 对象检测 API ( https://github.com/tensorflow/models/tree/master/research/object_detection ):这是我正在使用的检测代码的 IPython 笔记本 ( https://github.com/ tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb )。 在此文件中,输出值设置为绘制概率大于 50% 的框检测代码:
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')
#myFile = open('example2.csv', 'w')
i=0
#boxeslist=[]
new_boxes = []
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=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
如何更改代码,使其以 > 10% 的概率在对象周围输出框
应该很容易。
如您所见,本教程调用函数“vis_util.visualize_boxes_and_labels_on_image_array”,其参数为:
image
boxes
classes
scores
category_index
use_normalized_coordinates
line_thickness
如果在文件中搜索:'research/object_detection/utilis/visualization_utils.py',您可以找到该函数并查看您可以设置的其他参数。
其中您可以找到: min_score_tresh
设置为.5
如果你设置:
min_score_tresh=.1
应该得到想要的结果。
小心,原因将是sh
简单的方法是在“vis_util.visualize_boxes_and_labels_on_image_array”中添加“min_score_thresh”来设置检测阈值:
# 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,
min_score_thresh=.1) # <<======== Add this line for threshold
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