干货 | tensorflow模型导出与OpenCV DNN中使用

星标或者置顶【OpenCV学堂】 干货文章与技术教程第一时间送达

OpenCV DNN模块

Deep Neural Network - DNN 是OpenCV中的深度神经网络模块，支持基于深度学习模块前馈网络运行、实现图像与视频场景中的

• 图像分类
• 对象检测
• 图像分割

其模型导入与加载的相关API支持以下深度学习框架

• tensorflow - readNetFromTensorflow
• caffe - readNetFromCaffe
• pytorch - readNetFromTorch
• darknet - readNetFromDarknet

OpenCV3.4.1以上版本支持tensorflow1.11版本以上的对象检测框架(object detetion)模型导出使用，当前支持的模型包括以下：

也就是说通过tensorflow object detection API框架进行迁移学习训练模型，导出预测图之后，可以通过OpenCV3.4.1以上版本提供几个python脚本导出graph配置文件，然后就可以在OpenCV DNN模块中使用tensorflow相关的模型了。感觉十分方便，下面就按照操作走一波！

使用tensorflow模型

根据tensorflow中迁移学习或者下载预训练模型不同，OpenCV DNN 模块提供如下可以使用脚本生成对应的模型配置文件

• tf_text_graph_ssd.py
• tf_text_graph_faster_rcnn.py
• tf_text_graph_mask_rcnn.py

这是因为，OpenCV DNN需要根据text版本的模型描述文件来解析tensorflow的pb文件，实现网络模型加载。 对SSD对象检测模型，生成模型描述文件运行以下命令行即可( 在一行执行 )：

python tf_text_graph_ssd.py --input /path/to/model.pb --config /path/to/example.config --output /path/to/graph.pbtxt

以MobileNet-SSD v2版本为例，首先下载该模型，解压缩以后会发现里面有一个frozen_inference_graph.pb文件，使用tensorflow加载预测图进行预测的代码如下：

import tensorflow as tf
import cv2 as cv
# Read the graph.
model_dir = 'D:/tensorflow/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb'
with tf.gfile.FastGFile(model_dir, '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('D:/images/objects.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()

运行结果如下：

基于frozen_inference_graph.pb生成graph.pbtxt模型配置文件，命令行运行截图如下：

使用OpenCV DNN模块加载tensorflow模型(frozen_inference_graph.pb与graph.pbtxt)，实现预测图使用的代码如下(注意此时不需要依赖tensorflow)：

import cv2 as cv
model_path = 'D:/tensorflow/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb'
config_path = 'D:/tensorflow/ssd_mobilenet_v2_coco_2018_03_29/graph.pbtxt'
net = cv.dnn.readNetFromTensorflow(model_path, config_path)
frame = cv.imread('D:/images/objects.jpg')
rows = frame.shape[0]
cols = frame.shape[1]
net.setInput(cv.dnn.blobFromImage(frame, size=(300, 300), swapRB=True, crop=False))
cvOut = net.forward()
print(cvOut)
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(frame, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)
cv.imshow('opencv-dnn-ssd-detect', frame)
cv.waitKey()

运行结果如下(跟tensorflow中的运行结果完全一致，OpenCV DNN果然靠谱)：

OpenCV DNN 行人检测 本人尝试了基于tensorflow object detection API使用MobileNet-SSD v2迁移学习实现自定义数据集训练，导出预测图之后，使用OpenCV DNN模块的python脚本生成对象的图配置文件graph.pbtxt，通过OpenCV加载模型使用，实时预测，最后上一张运行结果图：

OpenCV DNN调用代码如下

import cv2 as cv
inference_pb = "D:/pedestrian_data/export_pb/frozen_inference_graph.pb";
graph_text = "D:/pedestrian_data/export_pb/graph.pbtxt";
# load tensorflow model
net = cv.dnn.readNetFromTensorflow(inference_pb, graph_text)
image = cv.imread("D:/python/Pedestrian-Detection/test_images/3600.jpg")
h = image.shape[0]
w = image.shape[1]
# 获得所有层名称与索引
layerNames = net.getLayerNames()
lastLayerId = net.getLayerId(layerNames[-1])
lastLayer = net.getLayer(lastLayerId)
print(lastLayer.type)
net.setInput(cv.dnn.blobFromImage(image, size=(300, 300), swapRB=True, crop=False))
cvOut = net.forward()
for detection in cvOut[0,0,:,:]:
    score = float(detection[2])
    if score > 0.5:
        left = detection[3]*w
        top = detection[4]*h
        right = detection[5]*w
        bottom = detection[6]*h
        cv.rectangle(image, (int(left), int(top)), (int(right), int(bottom)), (0, 255, 0), thickness=2)
        cv.putText(image, "Pedestrian", (int(left), int(top-10)), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 255), 1)
cv.imshow('pedestrain_demo', image)
cv.imwrite("D:/Pedestrian.png", image)
cv.waitKey(0)
cv.destroyAllWindows()