如何在Tensorflow对象检测API中查找边界框坐标
如何在Tensorflow对象检测API中查找边界框坐标
提问于 2019-05-14 03:10:49
我使用的是Tensorflow对象检测API代码。我训练了我的模型，并获得了很好的检测百分比。我一直在尝试获取边界框的坐标，但它一直打印出一个包含100个奇怪数组的列表。
在网上广泛搜索之后，我发现了数组中的数字的含义(边界框坐标是相对于底层图像的宽度和高度的0.0，1.0中的浮点数)。但是，我的数组与在线示例中显示的数组有很大的不同。另一件奇怪的事情是，我用少于100张图片测试了我的模块，所以怎么会有100个边界框坐标的数据。
我得到的数组；
[[3.13721418e-01 4.65148419e-01 7.11575747e-01 6.85783863e-01]
[9.78936195e-01 6.50490820e-03 9.97096300e-01 1.82596639e-01]
[9.51383412e-01 0.00000000e+00 1.00000000e+00 3.88432704e-02]
[9.85813320e-01 8.96016136e-02 9.97273505e-01 3.15960884e-01]
[9.88873005e-01 2.13812709e-01 1.00000000e+00 4.14675951e-01]
......
[4.42647263e-02 9.90755498e-01 2.57772505e-01 1.00000000e+00]
[2.69711018e-05 5.21758199e-02 6.37509704e-01 6.62899792e-01]
[0.00000000e+00 3.00989419e-01 9.92376506e-02 1.00000000e+00]
[1.87531322e-01 2.66501214e-04 4.50700432e-01 1.23927500e-02]
[9.36755657e-01 4.61095899e-01 9.92406607e-01 7.62619019e-01]]执行检测并获取边界框坐标的函数。output_dict‘’detection_boxes‘是保存上述数组的位置。
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[1], image.shape[2])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: image})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.int64)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict我希望输出是边界框的正则x，y坐标。
回答 1
Stack Overflow用户
发布于 2019-05-14 03:17:47
output_dict['detection_boxes']中的值确实是标准化格式的。通过检查您提供的数组中的值，这些值都在0到1之间，因此它们是合理的。
有100个框，因为模型始终输出相同数量的边界框。(等同于配置文件中的max_total_detections )。但并不是所有的都是有意义的，你需要根据output_dict['scores']中存储的置信度分数筛选出一些框。
若要获取常规边界框，请执行以下操作。您可以执行以下操作：
boxes = np.squeeze(output_dict['detection_boxes'])
scores = np.squeeze(output_dict['detection_scores'])
#set a min thresh score, say 0.8
min_score_thresh = 0.8
bboxes = boxes[scores > min_score_thresh]
#get image size
im_width, im_height = image.size
final_box = []
for box in bboxes:
ymin, xmin, ymax, xmax = box
final_box.append([xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height])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/56118540
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