tensorflow模型导出与OpenCVDNN中使用

　　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)  # 检测 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          # 绘制         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()
　　本文转自 OpenCV学堂