opencv中的dnn模块很强大，可以加载现在主流的深度学习模型，darknet、caffe、tensorflow等，我本人最近在使用tensorflow进行目标检测，用的是ssdlite-mobilenet-v2。tensorflow的object_detection api使用起来很方便，而且大佬研究出来的模型也都没毛病，效果很好，换换数据集，调调参数就ok。训练过程不再本篇博客的讨论范围内，假定现在已经训练完毕，并且生成了pb文件。

一般我们恢复模型都是要用到tensorflow，大致代码如下所示：

import numpy as np
import tensorflow as tf
import cv2 as cv
 
# Read the graph.
with tf.gfile.FastGFile('frozen_inference_graph.pb', 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
 
with tf.Session() as sess:
    # Restore session
    sess.graph.as_default()
    tf.import_graph_def(graph_def, name='')
 
    # Read and preprocess an image.
    img = cv.imread('example.jpg')
    rows = img.shape[0]
    cols = img.shape[1]
    inp = cv.resize(img, (300, 300))
    inp = inp[:, :, [2, 1, 0]]  # BGR2RGB
 
    # Run the model
    out = sess.run([sess.graph.get_tensor_by_name('num_detections:0'),
                    sess.graph.get_tensor_by_name('detection_scores:0'),
                    sess.graph.get_tensor_by_name('detection_boxes:0'),
                    sess.graph.get_tensor_by_name('detection_classes:0')],
                   feed_dict={'image_tensor:0': inp.reshape(1, inp.shape[0], inp.shape[1], 3)})
 
    # Visualize detected bounding boxes.
    num_detections = int(out[0][0])
    for i in range(num_detections):
        classId = int(out[3][0][i])
        score = float(out[1][0][i])
        bbox = [float(v) for v in out[2][0][i]]
        if score > 0.3:
            x = bbox[1] * cols
            y = bbox[0] * rows
            right = bbox[3] * cols
            bottom = bbox[2] * rows
            cv.rectangle(img, (int(x), int(y)), (int(right), int(bottom)), (125, 255, 51), thickness=2)
 
cv.imshow('TensorFlow MobileNet-SSD', img)
cv.waitKey()

如果电脑没有tensorflow想要恢复模型怎么办？那就可以考虑下opencv了，opencv加载tensorflow模型需要pb文件和pbtxt文件，pbtxt是可以根据pb文件生成的，在opencv的源代码中进入sample/dnn 文件夹中，由于我们的模型是ssd因此找到以下两个文件

tf_text_graph_ssd.py

tf_text_graph_common.py

在命令行执行

python tf_text_graph_ssd.py --input ssdlite.pb --config pipeline.config --output ssdlite.pbtxt

ssdlite.pb是自己训练的pb模型，pipeline是训练时的配置文件，生成pb时会有，ssdlite.pbtxt就是生成的文件用于opencv恢复模型

但是我本人在这里运行时报错了，如下图所示：

在github中有人提到训练完的模型需要转换一下

import tensorflow as tf
from tensorflow.tools.graph_transforms import TransformGraph
 
with tf.gfile.FastGFile('ssdlite.pb', 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    graph_def = TransformGraph(graph_def, ['image_tensor'], ['detection_boxes', 'detection_classes', 'detection_scores', 'num_detections'], ['sort_by_execution_order'])
    with tf.gfile.FastGFile('ssdlite_new.pb', 'wb') as f:
        f.write(graph_def.SerializeToString())#保存新的模型

转化完pb模型之后再运行上面的代码，成功生成pbtxt文件

然后用opencv恢复模型

net = cv2.dnn.readNetFromTensorflow(weightsPath,configPath)

大致代码如下：

import cv2 as cv
 
cvNet = cv.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'graph.pbtxt')
 
img = cv.imread('test.jpg')
rows = img.shape[0]
cols = img.shape[1]
cvNet.setInput(cv.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
cvOut = cvNet.forward()
 
for detection in cvOut[0,0,:,:]:
    score = float(detection[2])
    if score > 0.3:
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        cv.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)
 
cv.imshow('img', img)
cv.waitKey()

在此需要感谢dkurt大神的转换代码，顺便提一下ssdlite的速度是真的快，pc端cpu下大约60ms一帧，如果需要移植到arm平台上，可借助腾讯的ncnn，在移动端加速效果也是很棒的。

参考的一些资料：

https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API

https://github.com/opencv/opencv/issues/11560