cv2.dnn.readNetFromTensorflow提取您的Protobuf文件.pb和模型.pbtxt的配置文件以加载保存的模型。

net.forward()-运行前向传递以计算净输出。

您的检测结果，即net.forward()将给出Numpy ndarray作为输出，您可以使用它在给定的输入图像上绘制框。

您可以考虑以下示例。

import cv2

# Load a model imported from Tensorflow
tensorflowNet = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb','graph.pbtxt')

# Input image
img = cv2.imread('img.jpg')
rows,cols,channels = img.shape

# Use the given image as input,which needs to be blob(s).
tensorflowNet.setInput(cv2.dnn.blobFromImage(img,size=(300,300),swapRB=True,crop=False))

# Runs a forward pass to compute the net output
networkOutput = tensorflowNet.forward()

# Loop on the outputs
for detection in networkOutput[0,0]:

    score = float(detection[2])
    if score > 0.2:

        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows

        #draw a red rectangle around detected objects
        cv2.rectangle(img,(int(left),int(top)),(int(right),int(bottom)),(0,255),thickness=2)

# Show the image with a rectagle surrounding the detected objects 
cv2.imshow('Image',img)
cv2.waitKey()
cv2.destroyAllWindows()  

我考虑过使用Inception-SSD v2作为重量文件，可以从here下载。 以及来自此link的配置文件。