【飞腾派4G版免费试用】 第二章：在PC端使用 TensorFlow2 训练目标检测模型

使用 TensorFlow2 训练目标检测模型

因为我的项目是计划在飞腾派上实现一个目标检测跟踪算法，通过算法输出控制信号控制电机跟随目标运行。在第一章完成了Ubuntu系统的构建和烧写，这几天就在研究如何训练目标检测模型和部署，经过一段时间的资料搜集和测试，目前已经顺利使用 TensorFlow2 完成了模型的训练的测试，首先描述下我测试的 PC 配置。

单个 step 实际测试大概2s+，为了加快测试，我设置了训练的step 为 300，实际测试15分钟左右完成了模型训练，这个在后续配置文件中可以看到。

PC端关键的软件配置

内核：Linux fedora 6.6.4-100.fc38.x86_64 #1 SMP PREEMPT_DYNAMIC Sun Dec 3 18:11:27 UTC 2023 x86_64 GNU/Linux

Python：Python 3.8.18 (default, Aug 28 2023, 00:00:00)

参考内容

• [How to train your own Object Detector with TensorFlow’s Object Detector API]
• [How to Train Your Own Object Detector Using TensorFlow Object Detection API]

环境准备

为了训练的方便，建议安装一个虚拟的python环境，首先创建一个新的文件夹demo，然后进入到 demo 目录。

1. 首先接着使用到 python 的 venv 模块创建一个虚拟环境。

python -m venv tf2_api_env

2. 接着激活创建的虚拟环境

▸source../tf2_api_env/bin/activate (tf2_api_env) ┏─╼[red]╾─╼[17:01:44]╾─╼[0] ┗─╼[~/Projects/ai_track_feiteng/demo2/workspace] ▸

3. 接下来的操作都在这个虚拟环境中完成，下面开始安装 tensorflow2：

pip install tensorflow==2.*

4. 下载，安装编译 models 下的 Protobuf

gitclonehttps://github.com/tensorflow/models.gitcdmodels/research/ protoc models/research/object_detection/protos/*.proto --python_out=../../

5. 下载，安装编译 coco API

pip install cython gitclonehttps://github.com/cocodataset/cocoapi.gitcdcocoapi/PythonAPI make cp -r pycocotools ./models/research/

6. 对象检测 API 安装

cdmodels/research cp object_detection/packages/tf2/setup.py . python3.8 -m pip install .

7. 测试是否安装正确

python3.8 object_detection/builders/model_builder_tf2_test.py 2023-12-14 18:30:03.462617: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round- off errors from different computation orders. To turn them off,setthe environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2023-12-14 18:30:03.463746: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2023-12-14 18:30:03.489237: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2023-12-14 18:30:03.489587: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructionsinperformance-critical operations. Toenablethe following instructions: AVX2 AVX_VNNI FMA,inother operations, rebuild TensorFlow with the appropriate compiler flags. 2023-12-14 18:30:03.994817: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT 2023-12-14 18:30:04.975870: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA nodereadfrom SysFS had negative value (-1), but there m ust be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L3552023-12-14 18:30:04.976136: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properlyifyou would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpuforhow to download and setup the required librariesforyour platform. Skipping registering GPU devices... Running tests under Python 3.8.18: /home/red/Projects/ai_track_feiteng/demo2/tf2_api_env/bin/python3.8 [ RUN ] ModelBuilderTF2Test.test_create_center_net_deepmac WARNING:tensorflow:`tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removedina future release. Please use `tf.keras.layers.BatchNorm alization` with parameter `synchronized`setto True. W1214 18:30:05.009487 140273879242560 batch_normalization.py:1531] `tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removedina futur e release. Please use `tf.keras.layers.BatchNormalization` with parameter `synchronized`setto True. /home/red/Projects/ai_track_feiteng/demo2/tf2_api_env/lib64/python3.8/site-packages/object_detection/builders/model_builder.py:1112: DeprecationWarning: The'warn'functioni s deprecated, use'warning'instead logging.warn(('Building experimental DeepMAC meta-arch.'...... 省略 ...... [ RUN ] ModelBuilderTF2Test.test_session [ SKIPPED ] ModelBuilderTF2Test.test_session [ RUN ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s I1214 18:30:21.144221 140273879242560 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor [ RUN ] ModelBuilderTF2Test.test_unknown_meta_architecture INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s I1214 18:30:21.144374 140273879242560 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_meta_architecture [ RUN ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s I1214 18:30:21.144848 140273879242560 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor ---------------------------------------------------------------------- Ran 24 testsin16.167s OK (skipped=1)

8. 数据准备，这里为了重点关注模型训练过程，我们这里从仓库[raccoon_dataset]获取已经标注好的数据集。
   然后放在对应的目录workspace/data目录，如下所示：

▸ ls workspace/data/ object-detection.pbtxt raccoon_labels.csv test_labels.csv test.record train_labels.csv train.record

9. 模型选择和训练参数配置(重点！！！，这里为了演示不会详细介绍每一个参数的意义，具体参数的意义可以查看)

• 模型选择，现在有很多现成的模型可以加快我们的训练，我们需要在此基础上进行调参，TensorFlow2 对象检测已有的算法模型在这里 [tf2_detection_zoo]，这里我们需要从中下载一个模型进行训练，本章中我选择的是 [efficientdet_d0_coco17_tpu-32.tar.gz]。将这个模型的压缩包解压到 demo/workspace/pre_trained_models 目录下。

▸ tree -L 3 workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/ workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/ ├── checkpoint │ ├── checkpoint │ ├── ckpt-0.data-00000-of-00001 │ └── ckpt-0.index ├── pipeline.config └── saved_model ├── assets ├── saved_model.pb └── variables ├── variables.data-00000-of-00001 └── variables.index 5 directories, 7 files

这里关键的是 chekpoint 目录和 pipeline.config，checkpoint 包含了目标训练的切入点，pipeline.config 是我们后续需要调整的模型训练配置文件。

• 训练参数微调，这里为了加快介绍模型训练的过程，直接看下对该文件的 diff 文件可以更直观看到做了哪些修改

--- workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/pipeline.config 2020-07-11 08:12:31.000000000 +0800+++ workspace/models/efficientdet_d0/v2/pipeline.config 2023-12-14 14:10:58.998130084 +0800@@ -1,6 +1,6 @@model { ssd {- num_classes: 90+ num_classes: 1image_resizer { keep_aspect_ratio_resizer { min_dimension: 512@@ -131,7 +131,7 @@} } train_config {- batch_size: 128+ batch_size: 8data_augmentation_options { random_horizontal_flip { }@@ -149,29 +149,29 @@learning_rate { cosine_decay_learning_rate { learning_rate_base: 0.07999999821186066- total_steps: 300000+ total_steps: 300warmup_learning_rate: 0.0010000000474974513- warmup_steps: 2500+ warmup_steps: 25} } momentum_optimizer_value: 0.8999999761581421 } use_moving_average: false }- fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED"- num_steps: 300000+ fine_tune_checkpoint: "/home/red/Projects/ai_track_feiteng/demo2/workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0"+ num_steps: 300startup_delay_steps: 0.0 replicas_to_aggregate: 8 max_number_of_boxes: 100 unpad_groundtruth_tensors: false- fine_tune_checkpoint_type: "classification"- use_bfloat16: true+ fine_tune_checkpoint_type: "detection"+ use_bfloat16: falsefine_tune_checkpoint_version: V2 } train_input_reader: {- label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"+ label_map_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/object-detection.pbtxt"tf_record_input_reader {- input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"+ input_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/train.record"} }@@ -182,10 +182,10 @@} eval_input_reader: {- label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"+ label_map_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/object-detection.pbtxt"shuffle: false num_epochs: 1 tf_record_input_reader {- input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"+ input_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/test.record"} }

其中关键的修改点：

num_classes =1表示识别一类目标 batch_size =8表示这个参数会影响训练时候消耗的内存fine_tune_checkpoint_type:"detection"表示进行目标检测use_bfloat16:false不使用 TPUfine_tune_checkpoint:"/home/red/Projects/ai_track_feiteng/demo2/workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0"设置模型训练的切入点num_steps:300总的学习步数

10. 模型训练和导出
    经过前面的铺垫，目前已经具备了训练条件，执行如下脚本开始训练,我这边训练了大概15分钟：

#!/bin/shpython3.8 model_main_tf2.py --pipeline_config_path=./models/efficientdet_d0/v2/pipeline.config --model_dir=./models/efficientdet_d0/v2 --checkpoint_every_n=8 --num_workers=12 --alsologtostderr

训练完成后，就可以将模型导出，使用如下命令：

python3.8 exporter_main_v2.py --pipeline_config_path=./models/efficientdet_d0/v2/pipeline.config --trained_checkpoint_dir=./models/efficientdet_d0/v2 --output_directory=./exported_models/efficientdet_d0 --input_type=image_tensor

上述命令会将模型导出到 ./exported_models/efficientdet_d0 目录，导出成功后会看到如下内容：

▸ tree -L 3 workspace/exported_models/efficientdet_d0/ workspace/exported_models/efficientdet_d0/ ├── checkpoint │ ├── checkpoint │ ├── ckpt-0.data-00000-of-00001 │ └── ckpt-0.index ├── pipeline.config └── saved_model ├── assets ├── fingerprint.pb ├── saved_model.pb └── variables ├── variables.data-00000-of-00001 └── variables.index 5 directories, 8 files

可以看到这是我们自己训练出来的模型和前面提到的和网上下载的模型efficientdet_d0_coco17_tpu-32.tar.gz解压之后的结构很像。
11. 最后就演示下训练模型的精度，这里提供了网上的一个示例代码，针对我的代码结构，我做了下微调(代码之前是在.ipynb格式文件中的，为此，我还改了一个 python 脚本用来提取其中的 python代码)，该代码会对测试图像进行检测，将识别出来的目标用框标注出来。首先看下测试的脚本：

#!/bin/python3.8importos# importing OS in order to make GPU visibleos.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"# do not change anything in here# specify which device you want to work on.# Use "-1" to work on a CPU. Default value "0" stands for the 1st GPU that will be usedos.environ["CUDA_VISIBLE_DEVICES"]="0"#TODO:specify your computational deviceimporttensorflowastf# import tensorflow# checking that GPU is foundiftf.test.gpu_device_name():print('GPU found')else:print("No GPU found")# other importimportnumpyasnpfromPILimportImageimportmatplotlibfrommatplotlibimportpyplotaspltfromtqdmimporttqdmimportsys# importyng sys in order to access scripts located in a different folderprint(matplotlib.get_backend()) path2scripts = ['../models/research/','../models/']#TODO:provide pass to the research foldersys.path.insert(0, path2scripts[0])# making scripts in models/research available for importsys.path.insert(0, path2scripts[1])# making scripts in models/research available for importprint(sys.path)# importing all scripts that will be needed to export your model and use it for inferencefromobject_detection.utilsimportlabel_map_utilfromobject_detection.utilsimportconfig_utilfromobject_detection.utilsimportvisualization_utilsasviz_utilsfromobject_detection.buildersimportmodel_builder#NOTE:your current working directory should be Tensorflow.#TODO:specify two pathes: to the pipeline.config file and to the folder with trained model.path2config ='exported_models/efficientdet_d0/pipeline.config'path2model ='exported_models/efficientdet_d0/'# do not change anything in this cellconfigs = config_util.get_configs_from_pipeline_file(path2config)# importing configmodel_config = configs['model']# recreating model configdetection_model = model_builder.build(model_config=model_config, is_training=False)# importing modelckpt = tf.compat.v2.train.Checkpoint(model=detection_model) ckpt.restore(os.path.join(path2model,'checkpoint/ckpt-0')).expect_partial() path2label_map ='data/object-detection.pbtxt'#TODO:provide a path to the label map filecategory_index = label_map_util.create_category_index_from_labelmap(path2label_map,use_display_name=True)defdetect_fn(image):""" Detect objects in image. Args: image: (tf.tensor): 4D input image Returs: detections (dict): predictions that model made """image, shapes = detection_model.preprocess(image) prediction_dict = detection_model.predict(image, shapes) detections = detection_model.postprocess(prediction_dict, shapes)returndetectionsdefload_image_into_numpy_array(path):"""Load an image from file into a numpy array. Puts image into numpy array to feed into tensorflow graph. Note that by convention we put it into a numpy array with shape (height, width, channels), where channels=3 for RGB. Args: path: the file path to the image Returns: numpy array with shape (img_height, img_width, 3) """returnnp.array(Image.open(path))definference_with_plot(path2images, box_th=0.25):""" Function that performs inference and plots resulting b-boxes Args: path2images: an array with pathes to images box_th: (float) value that defines threshold for model prediction. Returns: None """forimage_pathinpath2images:print('Running inference for {}... '.format(image_path), end='') image_np = load_image_into_numpy_array(image_path) input_tensor = tf.convert_to_tensor(np.expand_dims(image_np,0), dtype=tf.float32) detections = detect_fn(input_tensor)# All outputs are batches tensors.# Convert to numpy arrays, and take index [0] to remove the batch dimension.# We're only interested in the first num_detections.num_detections =int(detections.pop('num_detections')) detections = {key: value[0, :num_detections].numpy()forkey, valueindetections.items()} detections['num_detections'] = num_detections# detection_classes should be ints.detections['detection_classes'] = detections['detection_classes'].astype(np.int64) label_id_offset =1image_np_with_detections = image_np.copy() viz_utils.visualize_boxes_and_labels_on_image_array( image_np_with_detections, detections['detection_boxes'], detections['detection_classes']+label_id_offset, detections['detection_scores'], category_index, use_normalized_coordinates=True, max_boxes_to_draw=200, min_score_thresh=box_th, agnostic_mode=False, line_thickness=5) plt.figure(figsize=(15,10)) plt.imshow(image_np_with_detections)print('Done') marked_file_name="marked_"+image_path plt.savefig(marked_file_name)print('Saved {} Done'.format(marked_file_name)) matplotlib.use('TkAgg') plt.show()defnms(rects, thd=0.5):""" Filter rectangles rects is array of oblects ([x1,y1,x2,y2], confidence, class) thd - intersection threshold (intersection divides min square of rectange) """out = [] remove = [False] *len(rects)foriinrange(0,len(rects) -1):ifremove[i]:continueinter = [0.0] *len(rects)forjinrange(i,len(rects)):ifremove[j]:continueinter[j] = intersection(rects[i][0], rects[j][0]) /min(square(rects[i][0]), square(rects[j][0])) max_prob =0.0max_idx =0forkinrange(i,len(rects)):ifinter[k] >= thd:ifrects[k][1] > max_prob: max_prob = rects[k][1] max_idx = kforkinrange(i,len(rects)):if(inter[k] >= thd) & (k != max_idx): remove[k] =Trueforkinrange(0,len(rects)):ifnotremove[k]: out.append(rects[k]) boxes = [box[0]forboxinout] scores = [score[1]forscoreinout] classes = [cls[2]forclsinout]returnboxes, scores, classesdefintersection(rect1, rect2):""" Calculates square of intersection of two rectangles rect: list with coords of top-right and left-boom corners [x1,y1,x2,y2] return: square of intersection """x_overlap =max(0,min(rect1[2], rect2[2]) -max(rect1[0], rect2[0])); y_overlap =max(0,min(rect1[3], rect2[3]) -max(rect1[1], rect2[1])); overlapArea = x_overlap * y_overlap;returnoverlapAreadefsquare(rect):""" Calculates square of rectangle """returnabs(rect[2] - rect[0]) *abs(rect[3] - rect[1])definference_as_raw_output(path2images, box_th =0.25, nms_th =0.5, to_file =False, data =None, path2dir =False):""" Function that performs inference and return filtered predictions Args: path2images: an array with pathes to images box_th: (float) value that defines threshold for model prediction. Consider 0.25 as a value. nms_th: (float) value that defines threshold for non-maximum suppression. Consider 0.5 as a value. to_file: (boolean). When passed as True = > results are saved into a file. Writing format is path2image + (x1abs, y1abs, x2abs, y2abs, score, conf) for box in boxes data: (str) name of the dataset you passed in (e.g. test/validation) path2dir: (str). Should be passed if path2images has only basenames. If full pathes provided = > set False. Returs: detections (dict): filtered predictions that model made """print(f'Current data set is{data}')print(f'Ready to start inference on{len(path2images)}images!')forimage_pathintqdm(path2images):ifpath2dir:# if a path to a directory where images are stored was passed inimage_path = os.path.join(path2dir, image_path.strip()) image_np = load_image_into_numpy_array(image_path) input_tensor = tf.convert_to_tensor(np.expand_dims(image_np,0), dtype=tf.float32) detections = detect_fn(input_tensor)# checking how many detections we gotnum_detections =int(detections.pop('num_detections'))# filtering out detection in order to get only the one that are indeed detectionsdetections = {key: value[0, :num_detections].numpy()forkey, valueindetections.items()}# detection_classes should be ints.detections['detection_classes'] = detections['detection_classes'].astype(np.int64)# defining what we need from the resulting detection dict that we got from model outputkey_of_interest = ['detection_classes','detection_boxes','detection_scores']# filtering out detection dict in order to get only boxes, classes and scoresdetections = {key: valueforkey, valueindetections.items()ifkeyinkey_of_interest}ifbox_th:# filtering detection if a confidence threshold for boxes was given as a parameterforkeyinkey_of_interest: scores = detections['detection_scores'] current_array = detections[key] filtered_current_array = current_array[scores > box_th] detections[key] = filtered_current_arrayifnms_th:# filtering rectangles if nms threshold was passed in as a parameter# creating a zip object that will contain model output info asoutput_info =list(zip(detections['detection_boxes'], detections['detection_scores'], detections['detection_classes'] ) ) boxes, scores, classes = nms(output_info) detections['detection_boxes'] = boxes# format: [y1, x1, y2, x2]detections['detection_scores'] = scores detections['detection_classes'] = classesifto_fileanddata:# if saving to txt file was requestedimage_h, image_w, _ = image_np.shape file_name =f'pred_result_{data}.txt'line2write =list() line2write.append(os.path.basename(image_path))withopen(file_name,'a+')astext_file:# iterating over boxesforb, s, cinzip(boxes, scores, classes): y1abs, x1abs = b[0] * image_h, b[1] * image_w y2abs, x2abs = b[2] * image_h, b[3] * image_w list2append = [x1abs, y1abs, x2abs, y2abs, s, c] line2append =','.join([str(item)foriteminlist2append]) line2write.append(line2append) line2write =' '.join(line2write) text_file.write(line2write + os.linesep)returndetections inference_with_plot(["1.jpg","2.jpg"],0.6)

这个脚本会检测当前目录下的 1.jpg 和 2.jpg 文件，然后将识别出来概率>0.5的目标用框框起来，并分别命名为marked_1.jpg和marked_2.jpg。原始图像分别是：

执行脚本进行检测处理：

▸source../t./b.py 2023-12-15 06:28:50.519691: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off,setthe environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2023-12-15 06:28:50.520813: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2023-12-15 06:28:50.545707: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2023-12-15 06:28:50.546025: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructionsinperformance-critical operations. Toenablethe following instructions: AVX2 AVX_VNNI FMA,inother operations, rebuild TensorFlow with the appropriate compiler flags. 2023-12-15 06:28:50.990588: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT 2023-12-15 06:28:51.480008: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error 2023-12-15 06:28:51.480053: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic informationforhost: fedora 2023-12-15 06:28:51.480057: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: fedora 2023-12-15 06:28:51.480104: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.146.2 2023-12-15 06:28:51.480114: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.146.2 2023-12-15 06:28:51.480117: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.146.2 No GPU found TkAgg ['../models/','../models/research/','/home/red/Projects/ai_track_feiteng/demo2/workspace','/usr/lib64/python38.zip','/usr/lib64/python3.8','/usr/lib64/python3.8/lib-dynload','/home/red/.local/lib/python3.8/site-packages','/usr/lib64/python3.8/site-packages','/usr/lib/python3.8/site-packages'] Running inferencefor1.jpg... Done Saved marked_1.jpg Done Running inferencefor2.jpg... Done Saved marked_2.jpg Done

检测后并处理的图像是：

因为训练的次数较少，导致识别的准确度并不是特别高，但是整个训练和演示的流程的还是完整的。希望能对大家了解 TensorFlow2 进行目标检测有所帮助。

这里再附下，提取.ipynb格式文件中python代码的示例代码：

#!/bin/python3.8importjsonimportsysimportosfrompathlibimportPath out_file_name=Path(sys.argv[1]).stem+'.py'withopen(sys.argv[1],'r')asf: text=json.load(f)iflen(sys.argv) >2: out_file_name = sys.argv[2]print('args:{}nout_file:{}'.format(sys.argv[1:], out_file_name))withopen(out_file_name,'w')asfp: fp.writelines("#!/bin/python3.8nn")forxintext['cells']:ifx['cell_type'] =="code": fp.writelines([i.rstrip()+'n'foriinx['source']])

下一章，我会介绍如何获取图像数据，标柱图像，然后进行模型训练，敬请期待。

审核编辑 黄宇