Question

是否可以通过Microsoft自定义视觉model.pb文件获取检测到的特定对象的边界框？我知道我们可以通过对天蓝色的自定义视觉服务的API调用来实现。举例来说，由于存在张量，我们可以从ssd冻结推断graph.pb文件中获取边界框。我们可以对自定义视觉的model.pb文件做同样的事情吗？

这是我正在使用的代码，用于打印张量流模型和输出的操作。

detection_graph = tf.Graph()

with detection_graph.as_default():
    graph_def = tf.GraphDef()
    with tf.gfile.GFile('model.pb,'rb') as fid:
        serialized_graph = fid.read()
        graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(graph_def, name='')

with tf.Session(graph=detection_graph) as sess:
    ops = tf.get_default_graph().get_operations()
    for op in ops:
        for output in op.outputs:
            print(output.name)


Placeholder:0
layer1_conv/weights:0
layer1_conv/weights/read:0
layer1_conv/Conv2D:0
layer1_conv/biases:0
layer1_conv/biases/read:0
layer1_conv/BiasAdd:0
layer1_leaky/alpha:0
layer1_leaky/mul:0
layer1_leaky:0
pool1:0
layer2_conv/weights:0
layer2_conv/weights/read:0
layer2_conv/Conv2D:0
layer2_conv/biases:0
layer2_conv/biases/read:0
layer2_conv/BiasAdd:0
layer2_leaky/alpha:0
layer2_leaky/mul:0
layer2_leaky:0
pool2:0
layer3_conv/weights:0
layer3_conv/weights/read:0
layer3_conv/Conv2D:0
layer3_conv/biases:0
layer3_conv/biases/read:0
layer3_conv/BiasAdd:0
layer3_leaky/alpha:0
layer3_leaky/mul:0
layer3_leaky:0
pool3:0
layer4_conv/weights:0
layer4_conv/weights/read:0
layer4_conv/Conv2D:0
layer4_conv/biases:0
layer4_conv/biases/read:0
layer4_conv/BiasAdd:0
layer4_leaky/alpha:0
layer4_leaky/mul:0
layer4_leaky:0
pool4:0
layer5_conv/weights:0
layer5_conv/weights/read:0
layer5_conv/Conv2D:0
layer5_conv/biases:0
layer5_conv/biases/read:0
layer5_conv/BiasAdd:0
layer5_leaky/alpha:0
layer5_leaky/mul:0
layer5_leaky:0
pool5:0
layer6_conv/weights:0
layer6_conv/weights/read:0
layer6_conv/Conv2D:0
layer6_conv/biases:0
layer6_conv/biases/read:0
layer6_conv/BiasAdd:0
layer6_leaky/alpha:0
layer6_leaky/mul:0
layer6_leaky:0
pool6:0
layer7_conv/weights:0
layer7_conv/weights/read:0
layer7_conv/Conv2D:0
layer7_conv/biases:0
layer7_conv/biases/read:0
layer7_conv/BiasAdd:0
layer7_leaky/alpha:0
layer7_leaky/mul:0
layer7_leaky:0
layer8_conv/weights:0
layer8_conv/weights/read:0
layer8_conv/Conv2D:0
layer8_conv/biases:0
layer8_conv/biases/read:0
layer8_conv/BiasAdd:0
layer8_leaky/alpha:0
layer8_leaky/mul:0
layer8_leaky:0
m_outputs0/weights:0
m_outputs0/weights/read:0
m_outputs0/Conv2D:0
m_outputs0/biases:0
m_outputs0/biases/read:0
m_outputs0/BiasAdd:0
model_outputs:0

Placeholder:0和model_outputs:0是输入和输出。 Placeholder:0采用形状为(?,416,416,3)的张量，model_outputs:0输出形状为(1, 13, 13, 30)的张量。如果我仅检测到单个对象，如何从model_outputs:0张量获取边界框。

我要去哪里错了？欢迎任何建议。

Answer 1

您似乎正在使用python，因此可以从customvision UI（选择tensorflow选项）导出对象检测模型：

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/export-model-python

这将为您提供一个包含以下内容的zipfile：

labels.txt
model.pb
python/object_detection.py
python/predict.py

将所有内容放在一个目录中，然后简单地执行代码：

python predict.py image.jpg

嘿！这将打印出字典列表，例如

{'boundingBox': {'width': 0.92610852, 'top': -0.06989955, 'height': 0.85869097, 'left': 0.03279033}, 'tagId': 3, 'tagName': 'myTagName', 'probability': 0.24879535}

将坐标（相对于左上角）标准化为图像的宽度和高度。

这是主要的（不是我的代码！）：

def main(image_filename):
    # Load a TensorFlow model
    graph_def = tf.GraphDef()
    with tf.gfile.FastGFile(MODEL_FILENAME, 'rb') as f:
        graph_def.ParseFromString(f.read())

    # Load labels
    with open(LABELS_FILENAME, 'r') as f:
        labels = [l.strip() for l in f.readlines()]

    od_model = TFObjectDetection(graph_def, labels)

    image = Image.open(image_filename)
    predictions = od_model.predict_image(image)
    print(predictions)

，您可以根据需要进行修改。祝你好运！

有没有办法从Microsoft的自定义视觉对象检测model.pb文件中获取边界框？

1 个答案: