tensorflow模型导出与OpenCVDNN中使用

1、OpenCV DNN1 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 - readNetFromDarknetOpenCV3.4.1以上版本支持tensorflow1.11版本以上的对象检测框架(object detet

2、ion) 模型导出使用，当前支持的模型包括以下：ModelVersionMobileNet-SSD v12017_11_17weightsconfigMobileNet-SSD v1 PPN2018_07_03weightsconfigMobileNet-SSD v22018_03_29weightsconfigInception-SSD v22017_11_17weightsconfigPaster-RCNN Inception v22018_01_28weightsconfigFaster-RCNN Res Net-502018_01_23weightsconfig也就是说通过 tenso

3、rflow 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真块中使用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t ensorf l ow模型OpenCV DNN 模块根据tensoflow中迁移学习或者下载预训练模型不同,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.pytf_text_graph_faster_rcnn.pytf_text_graph_mask_rcnn.py

4、这是因为，OpenCV DNNB要由g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力口载预

5、测图进行预测的代码如下：import tensorflow as tfimport cv2 as cv# Read the graph.model_dir = 'D:/tensorflow/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_g raph.pb'with tf.gfile.FastGFile(model_dir, 'rb') as f:graph_def = tf.GraphDef()graph_def.ParseFromString(f.read()with tf.Session() as ses

6、s:# Restore sessionsess.graph.as_default()tf.import_graph_def(graph_def, name='')# Read and preprocess an image.img = cv.imread('D:/images/objects.jpg')rows = img.shape0cols = img.shape1inp = cv.resize(img, (300, 300)inp = inp:, :, 2, 1,0 # BGR2RGB# Run the modelout = sess.run(sess.g

7、raph.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.shape0, in

8、p.shape1, 3)# Visualize detected bounding boxes.num_detections = int(out00)for i in range(num_detections):classId = int(out30i)score = float(out10i)bbox = float(v) for v in out20iif score > 0.3:x = bbox1 * colsy = bbox0 * rowsright = bbox3 * colsbottom = bbox2 * rowscv.rectangle(img, (int(x), int

9、(y), (int(right), int(bottom), (125, 255, 51), thick ness=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模型酉己置文件，命令行运行截图如下：使用 OpenCV DNN®块力口载 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tensorflo

10、w)import cv2 as cvmodel_path = 'D:/tensorflow/ssd_mobilenet_v2_coco_2018_03_29/frozenJnference_ 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&#

11、39;)rows = frame.shape0cols = frame.shape1net.setInput(cv.dnn.blobFromImage(frame, size=(300, 300), swapRB=True, crop=Fal se)cvOut = net.forward()print(cvOut)for detection in cvOut0,0,:,:score = float(detection2)if score > 0.3:left = detection3 * colstop = detection4 * rowsright = detection5 * co

12、lsbottom = detection6 * rowscv.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 DNNM然靠谱)：OpenCV DNN行人检测本人尝试了基于 tensoflow object detection API使用 MobileNet-SSDv2

13、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 OpenCVto 载模型使用，实时预测，最后上一张运行结果图：OpenCV DNNM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 tensor

14、flow modelnet = cv.dnn.readNetFromTensoflow(inference_pb, graph_text) image = cv.imread("D:/python/Pedestrian-Detection/test_images/3600.jpg")h = image.shape0w = image.shape1# 获得所有层名称与索引layerNames = net.getLayerNames()lastLayerld = net.getLayerId(layerNames-1)lastLayer = net.getLayer(lastL

15、ayerId)print(lastLayer.type)# 检测net.setInput(cv.dnn.blobFromImage(image, size=(300, 300), swapRB=True, crop=Fal se)cvOut = net.forward()for detection in cvOut0,0,:,:score = float(detection2)if score > 0.5:left = detection3*wtop = detection4*hright = detection5*wbottom = detection6*h#绘制cv.rectangle(image, (int(left), int(top), (int(right), int(bottom), (0, 255, 0), thic