From 8e29b5d4478ceb6c45f75dd43a9ff3403d5b7347 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=AC=A2=E4=B9=90=E9=A9=AC?= Date: Fri, 9 Aug 2024 12:49:34 +0800 Subject: [PATCH 1/2] =?UTF-8?q?=E9=80=82=E9=85=8D=E5=AE=89=E5=85=A8?= =?UTF-8?q?=E5=B8=BD=E8=AF=86=E5=88=AB310b?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../HelmetIdentification/Models/dy_resize.py | 75 --------- .../Models/modify_yolov5s_slice.py | 146 ------------------ contrib/HelmetIdentification/README.md | 98 +++--------- .../HelmetIdentification/Test/test_select.py | 33 ++++ ...64\346\230\216\346\226\207\346\241\243.md" | 6 +- 5 files changed, 63 insertions(+), 295 deletions(-) delete mode 100644 contrib/HelmetIdentification/Models/dy_resize.py delete mode 100644 contrib/HelmetIdentification/Models/modify_yolov5s_slice.py create mode 100644 contrib/HelmetIdentification/Test/test_select.py diff --git a/contrib/HelmetIdentification/Models/dy_resize.py b/contrib/HelmetIdentification/Models/dy_resize.py deleted file mode 100644 index b3478cfd7..000000000 --- a/contrib/HelmetIdentification/Models/dy_resize.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import sys -import onnx - -model_path = sys.argv[1] -model = onnx.load(model_path) - -def remove_node(graph, nodelist): - """ - Remove Node - """ - max_idx = len(graph.node) - rm_cnt = 0 - for i in range(len(graph.node)): - if i < max_idx: - gn = graph.node[i - rm_cnt] - if gn.name in nodelist: - print("remove {} total {}".format(gn.name, len(graph.node))) - graph.node.remove(gn) - max_idx -= 1 - rm_cnt += 1 - - -def replace_scales(ori_list, scales_name): - """ - Replace Scales name: - Leave the first two items of the input attribute of Resize unchanged - and the third item--scales name is modified - param:ori_list is the value of Resize.input - """ - n_list = [] - for j, x in enumerate(ori_list): - if j < 2: - n_list.append(x) - if j == 3: - n_list.append(scales_name) - return n_list - -# Replace Resize node -for k in range(len(model.graph.node)): - n = model.graph.node[k] - if n.op_type == "Resize": - model.graph.initializer.append( - onnx.helper.make_tensor('scales{}'.format(k), onnx.TensorProto.FLOAT, [4], [1, 1, 2, 2]) - ) - newnode = onnx.helper.make_node( - 'Resize', - name=n.name, - inputs=replace_scales(n.input, 'scales{}'.format(k)), - outputs=n.output, - coordinate_transformation_mode='asymmetric', - cubic_coeff_a=-0.75, - mode='nearest', - nearest_mode='floor' - ) - model.graph.node.remove(model.graph.node[k]) - model.graph.node.insert(k, newnode) - print("replace {} index {}".format(n.name, k)) - -node_list = ['Constant_330', 'Constant_375'] -remove_node(model.graph, node_list) -onnx.checker.check_model(model) -onnx.save(model, sys.argv[1].split('.')[0] + "_dbs.onnx") diff --git a/contrib/HelmetIdentification/Models/modify_yolov5s_slice.py b/contrib/HelmetIdentification/Models/modify_yolov5s_slice.py deleted file mode 100644 index 4984b6756..000000000 --- a/contrib/HelmetIdentification/Models/modify_yolov5s_slice.py +++ /dev/null @@ -1,146 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import sys -import onnx -# Get the largest int value -INT_MAX = sys.maxsize -# Read model save path -model_path = sys.argv[1] -# Load the model -model = onnx.load(model_path) - -def get_node_by_name(nodes, name): - """ - gain node by names - """ - for n in nodes: - if n.name == name: - return n - return -1 - - -# remove node of Slice_24 -model.graph.node.remove(get_node_by_name(model.graph.node, "Slice_24")) -model.graph.node.remove(get_node_by_name(model.graph.node, "Slice_34")) - -# Set the output size of Transpose after slice4 and slice24,slice9, slice19, slice29, slice39 -prob_info1 = onnx.helper.make_tensor_value_info('to_slice9', onnx.TensorProto.FLOAT, [1, 3, 640, 320]) -prob_info3 = onnx.helper.make_tensor_value_info('to_slice19', onnx.TensorProto.FLOAT, [1, 3, 640, 320]) -prob_info5 = onnx.helper.make_tensor_value_info('from_slice9', onnx.TensorProto.FLOAT, [1, 3, 320, 320]) -prob_info6 = onnx.helper.make_tensor_value_info('from_slice19', onnx.TensorProto.FLOAT, [1, 3, 320, 320]) -prob_info7 = onnx.helper.make_tensor_value_info('from_slice29', onnx.TensorProto.FLOAT, [1, 3, 320, 320]) -prob_info8 = onnx.helper.make_tensor_value_info('from_slice39', onnx.TensorProto.FLOAT, [1, 3, 320, 320]) -# Transpose after slice4 -node1 = onnx.helper.make_node( - 'Transpose', - inputs=['171'], - outputs=['to_slice9'], - perm=[0, 1, 3, 2] -) -# Transpose after slice24 -node3 = onnx.helper.make_node( - 'Transpose', - inputs=['181'], - outputs=['to_slice19'], - perm=[0, 1, 3, 2] -) -# add Transpose after slice9 -node5 = onnx.helper.make_node( - 'Transpose', - inputs=['from_slice9'], - outputs=['176'], - perm=[0, 1, 3, 2] -) -# add Transpose after slice19 -node6 = onnx.helper.make_node( - 'Transpose', - inputs=['from_slice19'], - outputs=['186'], - perm=[0, 1, 3, 2] -) -# add Transpose after slice29 -node7 = onnx.helper.make_node( - 'Transpose', - inputs=['from_slice29'], - outputs=['196'], - perm=[0, 1, 3, 2] -) -# add Transpose after slice39 -node8 = onnx.helper.make_node( - 'Transpose', - inputs=['from_slice39'], - outputs=['206'], - perm=[0, 1, 3, 2] -) -# add the above node -model.graph.node.append(node1) -model.graph.node.append(node3) -model.graph.node.append(node5) -model.graph.node.append(node6) -model.graph.node.append(node7) -model.graph.node.append(node8) - -# slice9 slice19 Change shaft -model.graph.initializer.append(onnx.helper.make_tensor('starts_9', onnx.TensorProto.INT64, [1], [0])) -model.graph.initializer.append(onnx.helper.make_tensor('ends_9', onnx.TensorProto.INT64, [1], [INT_MAX])) -model.graph.initializer.append(onnx.helper.make_tensor('axes_9', onnx.TensorProto.INT64, [1], [2])) -model.graph.initializer.append(onnx.helper.make_tensor('steps_9', onnx.TensorProto.INT64, [1], [2])) -# add Slice_9 -newnode1 = onnx.helper.make_node( - 'Slice', - name='Slice_9', - inputs=['to_slice9', 'starts_9', 'ends_9', 'axes_9', 'steps_9'], - outputs=['from_slice9'], -) -# remove Original node Slice_9 -model.graph.node.remove(get_node_by_name(model.graph.node, "Slice_9")) -model.graph.node.insert(9, newnode1) -# add Slice_19 -newnode2 = onnx.helper.make_node( - 'Slice', - name='Slice_19', - inputs=['to_slice19', 'starts_9', 'ends_9', 'axes_9', 'steps_9'], - outputs=['from_slice19'], -) -# remove Original node Slice_19 -model.graph.node.remove(get_node_by_name(model.graph.node, "Slice_19")) -model.graph.node.insert(19, newnode2) - -# slice29 slice39 Change shaft -model.graph.initializer.append(onnx.helper.make_tensor('starts_29', onnx.TensorProto.INT64, [1], [1])) -model.graph.initializer.append(onnx.helper.make_tensor('ends_29', onnx.TensorProto.INT64, [1], [INT_MAX])) -model.graph.initializer.append(onnx.helper.make_tensor('axes_29', onnx.TensorProto.INT64, [1], [2])) -model.graph.initializer.append(onnx.helper.make_tensor('steps_29', onnx.TensorProto.INT64, [1], [2])) -# add Slice_29 -newnode3 = onnx.helper.make_node( - 'Slice', - name='Slice_29', - inputs=['to_slice9', 'starts_29', 'ends_29', 'axes_29', 'steps_29'], - outputs=['from_slice29'], -) -# remove Original node Slice_29 -model.graph.node.remove(get_node_by_name(model.graph.node, "Slice_29")) -model.graph.node.insert(29, newnode3) -# add Slice_39 -newnode4 = onnx.helper.make_node( - 'Slice', - name='Slice_39', - inputs=['to_slice19', 'starts_29', 'ends_29', 'axes_29', 'steps_29'], - outputs=['from_slice39'], -) -# remove Original node Slice_39 -model.graph.node.remove(get_node_by_name(model.graph.node, "Slice_39")) -model.graph.node.insert(39, newnode4) -# Save the modified onnx model -onnx.save(model, sys.argv[1].split('.')[0] + "_t.onnx") diff --git a/contrib/HelmetIdentification/README.md b/contrib/HelmetIdentification/README.md index 7779b786b..d95910c4d 100644 --- a/contrib/HelmetIdentification/README.md +++ b/contrib/HelmetIdentification/README.md @@ -6,11 +6,15 @@ ### 1.1 支持的产品 -本项目以昇腾Atlas 500 A2 / Atlas 200I DK A2为主要的硬件平台。 +本项目基于mxVision SDK进行开发，以Atlas 500 A2/Atlas 200I DK A2为主要的硬件平台。 ### 1.2 支持的版本 -本样例配套的CANN版本为[7.0.RC1](https://www.hiascend.com/software/cann/commercial)，MindX SDK版本为[5.0.RC3](https://www.hiascend.com/software/Mindx-sdk)。 +本样例配套的MxVision版本、CANN版本、Driver/Firmware版本如下所示： +| MxVision版本 | CANN版本 | Driver/Firmware版本 | +|--------------- | ---------------------------------- | ----------| +| 5.0.0 | 7.0.0 | 23.0.0| +|6.0.RC2 | 8.0.RC2 | 24.1.RC2| MindX SDK安装前准备可参考《用户指南》，[安装教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/quickStart/1-1安装SDK开发套件.md) @@ -30,8 +34,6 @@ MindX SDK安装前准备可参考《用户指南》，[安装教程](https://git ├──HelmetDetection.pipline # 安全帽识别推理流程pipline ├──imgclass.names # 模型所有可识别类 ├──main.py # 推理运行程序 - ├──modify_yolov5s_slice.py #slice算子修改脚本 - ├──dy_resize.py # resize算子修改 ├──utils.py # 数据处理及可视化脚本 ├── plugins ├──MxpiSelectedFrame # 跳帧插件 @@ -57,11 +59,11 @@ MindX SDK安装前准备可参考《用户指南》，[安装教程](https://git 环境依赖软件和版本如下表： | 软件 | 版本 | 说明 | 获取方式 | -| ------------------- | ------------ | ----------------------------- | ------------------------------------------------------------ | -| mxVision | 5.0.RC3 | mxVision软件包 | [链接](https://www.hiascend.com/software/Mindx-sdk) | -| Ascend-CANN-toolkit | 7.0.RC1 | Ascend-cann-toolkit开发套件包 | [链接](https://www.hiascend.com/software/cann/commercial) | -| 操作系统 | ubuntu 22.04 | 操作系统 | Ubuntu官网获取 | -| opencv-python | 4.5.2.54 | 用于识别结果画框 | python3 -m pip install opencv-python | +| ------------------- | ------------ | ----------------------------- | ------------------------------------------------------------ | +| opencv-python | 4.10.0.54 | 用于识别结果画框 | python3 -m pip install opencv-python| +| libgl1-mesa-glx |23.0.4-0ubuntu1~22.04.1 |GL库（opencv-python可能会依赖GL）|apt install libgl1-mesa-glx| +| live555|1.10|实现视频转rstp进行推流|[live555使用教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/Live555离线视频转RTSP说明文档.md)| +|ffmpeg|2021-08-08-git-ac0408522a | 实现mp4格式视频转为264格式视频 |[ffmpeg使用教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/pc端ffmpeg安装教程.md) @@ -78,52 +80,17 @@ MindX SDK安装前准备可参考《用户指南》，[安装教程](https://git ##### 1.1模型与软件依赖 - 所用模型与软件依赖如下表所示。若使用A200I DK A2运行，推荐使用PC转换模型，具体方法可参考A200I DK A2资料。 + 所用模型与软件依赖如下表所示。若使用A200I DK A2运行，推荐使用PC转换模型，具体方法可参考A200I DK A2资料。模型相关信息可参考[原项目链接](https://github.com/PeterH0323/Smart_Construction) | 软件名称 | 版本 | 获取方式 | | ----------------------- | -------- | ------------------------------------------------------------ | -| pytorch | 1.5.1 | [pytorch官网](https://pytorch.org/get-started/previous-versions/) | -| ONNX | 1.7.0 | pip install onnx==1.7.0 | -| helmet_head_person_s.pt | v2.0 | [原项目链接](https://github.com/PeterH0323/Smart_Construction)(选择项目中yolov5s权重文件，权重文件保存在README所述网盘中) | | YOLOv5_s.onnx | YOLOv5_s | [链接](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/HelmetIdentification/model.zip) | - - -##### 1.2 pt文件转换为onnx文件 +##### 1.2 onnx文件转换为om文件 1. 可直接获取已经转换好的YOLOv5_s.onnx文件，链接如1.1所示。此模型已经完成优化，不再使用dy_resize.py、modify_yolov5s_slice.py进行优化。可直接转换为om模型。 -2. 若尝试pt文件转换为onnx文件，可获取[原项目](https://github.com/PeterH0323/Smart_Construction)代码，下载至本地。安装环境依赖**requirements.txt**在原项目中已给出（原项目使用pytorch 1.5.1框架），pt文件转换为onnx文件所需第三方库**ONNX**如1.1中方式安装。 - -3. 通过上述1.1中链接获取模型文件helmet_head_person_s.pt，下载到本地后保存至原项目weights文件中。使用原项目中的export.py将pt文件转换为onnx格式文件。运行： - -```shell -python3 ./models/export.py --weights ./weights/helmet_head_person_s.pt --img 640 --batch 1 -``` - -其中onnx算子版本为opset_version=11。转换完成后权重文件helmet_head_person_s.onnx改名为YOLOv5_s.onnx上传至服务器任意目录下。 - - - -##### 1.3 onnx文件转换为om文件 - - 转换完成后onnx脚本上传至服务器任意目录后先进行优化。 - -1. 利用附件脚本dy_resize.py修改模型resize算子。该模型含有动态Resize算子（上采样），通过计算维度变化，改为静态算子，不影响模型的精度，运行如下命令： - -```shell -python3 modify_yolov5s_slice.py YOLOv5_s.onnx -``` - -2. 然后利用modify_yolov5s_slice.py脚本修改模型slice算子，运行如下命令： - -```bash -python3 modify_yolov5s_slice.py YOLOv5_s.onnx -``` - -可以得到修改好后的YOLOv5_s.onnx模型 - -3. 最后运行atc-env脚本将onnx转为om模型，运行命令如下。 +3. 修改atc-env脚本中的路径，运行atc-env脚本将onnx转为om模型，运行命令如下。 ```shell sh atc-env.sh @@ -193,7 +160,7 @@ cmake .. make -j ``` -编译成功后将产生**libmxpi_selectedframe.so**文件，文件生成位置在build目录下。将其复制至SDK的插件库中(./MindX_SDK/mxVision/lib/plugins) +编译成功后将产生**libmxpi_selectedframe.so**文件，文件生成位置在build目录下。将其复制至SDK的插件库中(./MindX_SDK/mxVision/lib/plugins)，并修改权限为440 注：[插件编译生成教程](https://gitee.com/ascend/docs-openmind/blob/master/guide/mindx/sdk/tutorials/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99.md)在《SDK用户手册》深入开发章节 @@ -242,10 +209,10 @@ test.264可替换成任意上传至当前目录的[264格式文件](https://gite 然后切换目录至main.py所在目录下，运行命令： ```shell -python3.9.2 main.py +python3.9 main.py ``` -即可得到输出结果，输出结果将原来的两路视频分为两个文件保存，utils.py中的oringe_imgfile用于设置图像输出路径,用户需手动建立输出文件output，文件路径可自定义设置。本项目文件放置规范如下： +即可得到输出结果，输出结果将原来的两路视频分为两个文件保存，utils.py中的oringe_imgfile用于设置图像输出路径,用户需**手动建立**输出文件output，文件路径可自定义设置。本项目文件放置规范如下： ![image3](https://gitee.com/liu-kai6334/mindxsdk-referenceapps/raw/master/contrib/HelmetIdentification/image/image3.jpg) @@ -264,7 +231,7 @@ python3.9.2 main.py ##### 3.1 性能测试 -性能测试使用脚本performance_test_main.py，该脚本与main.py大体相同，不同之处是在performance_test_main.py中添加了时间戳测试，测试数据为mxpi_rtspsrc拉取的视频流。两路视频尺寸分别取多组不同尺寸的视频做对比。推理三百帧图片后取平均时间值，设置如下环境变量： +性能测试使用脚本Test/performance_test_main.py，该脚本与main.py大体相同，不同之处是在performance_test_main.py中添加了时间戳测试，测试数据为mxpi_rtspsrc拉取的视频流。两路视频尺寸分别取多组不同尺寸的视频做对比。推理三百帧图片后取平均时间值，设置如下环境变量： ```shell export PYTHONPATH=/usr/local/python3.9.2/bin:${MX_SDK_HOME}/python:{path} @@ -275,7 +242,7 @@ export PYTHONPATH=/usr/local/python3.9.2/bin:${MX_SDK_HOME}/python:{path} 运行如下命令得到结果： ```shell -python3 performance_test_main.py +python3.9 performance_test_main.py ``` 注：1.与运行main.py时相同，运行performance_test_main.py时要先使用live555进行推流。**测试视频**上传至[链接](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/HelmetIdentification/test_video.zip)，该视频为不同尺寸不同帧率的同一视频。如test64036830_158s.264为尺寸640×640，帧率30，时长158s的视频。 @@ -304,7 +271,7 @@ python3 performance_test_main.py 依据数据集中ImageSets文件夹中test.txt文件，从原始数据集中筛选出测试数据集，该程序**test_select.py**放在源码根目录Test中，在同目录下创建文件夹TestImages用来存储筛选的数据。在该目录下运行命令： ```shell -python3.9.2 test_select.py +python3.9 test_select.py ``` 程序运行后在根目录Test中会存放筛选出的测试集图片共1517张。 @@ -316,7 +283,7 @@ python3.9.2 test_select.py 运行命令： ```shell -python3.9.2 parse_voc.py +python3.9 parse_voc.py ``` ###### 3.2.4 推理运行 @@ -334,7 +301,7 @@ cls conf x0 y0 x1 y1 运行命令： ```shell -python3.9.2 testmain.py +python3.9 testmain.py ``` 注：testmain.py中直接写入了pipline，其中mxpi_modelinfer插件四个参数的配置与HelmetDetection.pipline完全相同。 @@ -348,29 +315,14 @@ python3.9.2 testmain.py 运行命令： ```shell -python3.9.2 map_calculate.py --label_path ./ground-truth --npu_txt_path ./detection-test-result/ -na -np +python3.9 map_calculate.py --label_path ./ground-truth --npu_txt_path ./detection-test-result/ -na -np ``` 即可得到输出。其中precision、recall和map记录在**output/output.txt**文件中。 +## 4 常见问题 - -## 5 软件依赖说明 - -推理中涉及到第三方软件依赖如下表所示。 - -| 依赖软件 | 版本 | 说明 | -| -------- | ------------------------- | ------------------------------ | -| live555 | 1.09 | 实现视频转rstp进行推流 | -| ffmpeg | 2021-08-08-git-ac0408522a | 实现mp4格式视频转为264格式视频 | - -注：1.[live555使用教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/Live555离线视频转RTSP说明文档.md) - -​ 2.[ffmpeg使用教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/pc端ffmpeg安装教程.md) - -## 6 常见问题 - -### 6.1 图片格式问题 +### 4.1 图片格式问题 **问题描述：** diff --git a/contrib/HelmetIdentification/Test/test_select.py b/contrib/HelmetIdentification/Test/test_select.py new file mode 100644 index 000000000..43114d83a --- /dev/null +++ b/contrib/HelmetIdentification/Test/test_select.py @@ -0,0 +1,33 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +import cv2 + +with open("ImageSets/Main/test.txt", "r") as f: + data = f.readlines() + text_data = [] + for line in data: + line_new = line.strip('\n') # Remove the newline character of each element in the list + text_data.append(line_new) + print(text_data) + +path = 'JPEGImages' +save_path = 'TestImages' + +for item in os.listdir(path): + file_name = item.split('.')[0] + if file_name in text_data: + img = cv2.imread(path + '/' + item) + cv2.imwrite(save_path + '/' + file_name + ".jpg", img) \ No newline at end of file diff --git "a/docs/\345\217\202\350\200\203\350\265\204\346\226\231/Live555\347\246\273\347\272\277\350\247\206\351\242\221\350\275\254RTSP\350\257\264\346\230\216\346\226\207\346\241\243.md" "b/docs/\345\217\202\350\200\203\350\265\204\346\226\231/Live555\347\246\273\347\272\277\350\247\206\351\242\221\350\275\254RTSP\350\257\264\346\230\216\346\226\207\346\241\243.md" index dab4f2dd6..6aaed2f16 100644 --- "a/docs/\345\217\202\350\200\203\350\265\204\346\226\231/Live555\347\246\273\347\272\277\350\247\206\351\242\221\350\275\254RTSP\350\257\264\346\230\216\346\226\207\346\241\243.md" +++ "b/docs/\345\217\202\350\200\203\350\265\204\346\226\231/Live555\347\246\273\347\272\277\350\247\206\351\242\221\350\275\254RTSP\350\257\264\346\230\216\346\226\207\346\241\243.md" @@ -23,7 +23,11 @@ cd live/ make ``` - +若编译过程中报错：struct std::atomic_flag has no member named 'test' +修改config.linux文件： +``` +CPLUSPLUS_FLAGS = (原有配置项) -std=c++2a +``` 最后就会在当前目录下生成mediaServer 文件夹，有一个live555MediaServer可执行文件 ## 4. 运行 -- Gitee From 89d7bb6f3d69a8ebbe8eaf89402e826c2e25b1da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=AC=A2=E4=B9=90=E9=A9=AC?= Date: Wed, 14 Aug 2024 09:47:19 +0800 Subject: [PATCH 2/2] =?UTF-8?q?=E5=AE=89=E5=85=A8=E5=B8=BD=E8=AF=86?= =?UTF-8?q?=E5=88=AB=E5=88=A0=E9=99=A4310B=E5=88=86=E6=94=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Models/HelmetDetection.pipline | 147 --- .../Models/Helmet_yolov5.cfg | 10 - .../Models/aipp_YOLOv5.config | 37 - .../HelmetIdentification/Models/atc-env.sh | 10 - .../Models/imgclass.names | 3 - contrib/HelmetIdentification/Models/main.py | 81 -- contrib/HelmetIdentification/Models/utils.py | 123 --- contrib/HelmetIdentification/README.md | 334 ------- .../Test/map_calculate.py | 941 ------------------ .../HelmetIdentification/Test/parse_voc.py | 94 -- .../Test/performance_test_main.py | 90 -- .../HelmetIdentification/Test/test_select.py | 33 - contrib/HelmetIdentification/Test/testmain.py | 162 --- contrib/HelmetIdentification/build.sh | 37 - contrib/HelmetIdentification/image/image1.jpg | Bin 6684 -> 0 bytes contrib/HelmetIdentification/image/image2.jpg | Bin 3501 -> 0 bytes contrib/HelmetIdentification/image/image3.jpg | Bin 1223 -> 0 bytes contrib/HelmetIdentification/image/image4.jpg | Bin 44203 -> 0 bytes .../plugins/MxpiSelectedFrame/CMakeLists.txt | 30 - .../MxpiSelectedFrame/MxpiSelectedFrame.cpp | 96 -- .../MxpiSelectedFrame/MxpiSelectedFrame.h | 80 -- .../plugins/MxpiSelectedFrame/build.sh | 36 - 22 files changed, 2344 deletions(-) delete mode 100644 contrib/HelmetIdentification/Models/HelmetDetection.pipline delete mode 100644 contrib/HelmetIdentification/Models/Helmet_yolov5.cfg delete mode 100644 contrib/HelmetIdentification/Models/aipp_YOLOv5.config delete mode 100644 contrib/HelmetIdentification/Models/atc-env.sh delete mode 100644 contrib/HelmetIdentification/Models/imgclass.names delete mode 100644 contrib/HelmetIdentification/Models/main.py delete mode 100644 contrib/HelmetIdentification/Models/utils.py delete mode 100644 contrib/HelmetIdentification/README.md delete mode 100644 contrib/HelmetIdentification/Test/map_calculate.py delete mode 100644 contrib/HelmetIdentification/Test/parse_voc.py delete mode 100644 contrib/HelmetIdentification/Test/performance_test_main.py delete mode 100644 contrib/HelmetIdentification/Test/test_select.py delete mode 100644 contrib/HelmetIdentification/Test/testmain.py delete mode 100644 contrib/HelmetIdentification/build.sh delete mode 100644 contrib/HelmetIdentification/image/image1.jpg delete mode 100644 contrib/HelmetIdentification/image/image2.jpg delete mode 100644 contrib/HelmetIdentification/image/image3.jpg delete mode 100644 contrib/HelmetIdentification/image/image4.jpg delete mode 100644 contrib/HelmetIdentification/plugins/MxpiSelectedFrame/CMakeLists.txt delete mode 100644 contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.cpp delete mode 100644 contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.h delete mode 100644 contrib/HelmetIdentification/plugins/MxpiSelectedFrame/build.sh diff --git a/contrib/HelmetIdentification/Models/HelmetDetection.pipline b/contrib/HelmetIdentification/Models/HelmetDetection.pipline deleted file mode 100644 index 285630d41..000000000 --- a/contrib/HelmetIdentification/Models/HelmetDetection.pipline +++ /dev/null @@ -1,147 +0,0 @@ -{ - "Detection":{ - "stream_config":{ - "deviceId":"1" - }, - "mxpi_rtspsrc0":{ - "factory":"mxpi_rtspsrc", - "props":{ - "rtspUrl":"rtsp://xxx.xxx.xxx.xxx:xxxx/xxx.264", - "channelId":"0" - }, - "next":"queuerstp0" - }, - "mxpi_rtspsrc1":{ - "factory":"mxpi_rtspsrc", - "props":{ - "rtspUrl":"rtsp://xxx.xxx.xxx.xxx:xxxx/xxx.264", - "channelId":"1" - }, - "next":"queuerstp1" - }, - "queuerstp0":{ - "props":{ - "max-size-buffers":"50" - }, - "factory":"queue", - "next":"mxpi_videodecoder0" - }, - "queuerstp1":{ - "props":{ - "max-size-buffers":"50" - }, - "factory":"queue", - "next":"mxpi_videodecoder1" - }, - "mxpi_videodecoder0":{ - "factory":"mxpi_videodecoder", - "props":{ - "deviceId":"1", - "inputVideoFormat":"H264", - "outputImageFormat":"YUV420SP_NV12", - "vdecChannelId":"0" - }, - "next":"queue01" - }, - "queue01":{ - "props":{ - "max-size-buffers":"500" - }, - "factory":"queue", - "next":"mxpi_selectedframe0" - }, - "mxpi_videodecoder1":{ - "factory":"mxpi_videodecoder", - "props":{ - "deviceId":"1", - "inputVideoFormat":"H264", - "outputImageFormat":"YUV420SP_NV12", - "vdecChannelId":"1" - }, - "next":"queue11" - }, - "queue11":{ - "props":{ - "max-size-buffers":"500" - }, - "factory":"queue", - "next":"mxpi_selectedframe1" - }, - "mxpi_selectedframe0":{ - "factory":"mxpi_selectedframe", - "next":"mxpi_parallel2serial0:0", - "props":{ - "frameNum":"2" - } - }, - "mxpi_selectedframe1":{ - "factory":"mxpi_selectedframe", - "next":"mxpi_parallel2serial0:1", - "props":{ - "frameNum":"2" - } - }, - "mxpi_parallel2serial0":{ - "factory":"mxpi_parallel2serial", - "props":{ - "dataSource":"mxpi_videodecoder0,mxpi_videodecoder1" - }, - "next":"mxpi_imageresize0" - }, - "mxpi_imageresize0":{ - "props":{ - "dataSource":"mxpi_parallel2serial0", - "resizeType": "Resizer_KeepAspectRatio_Fit", - "resizeHeight":"640", - "resizeWidth":"640" - }, - "factory":"mxpi_imageresize", - "next":"queue0" - }, - "queue0":{ - "props":{ - "max-size-buffers":"500" - }, - "factory":"queue", - "next":"mxpi_modelinfer0" - }, - "mxpi_modelinfer0":{ - "props":{ - "dataSource":"mxpi_imageresize0", - "modelPath":"./YOLOv5_s.om", - "postProcessConfigPath":"./Helmet_yolov5.cfg", - "labelPath":"./imgclass.names", - "postProcessLibPath":"./libMpYOLOv5PostProcessor.so" - }, - "factory":"mxpi_modelinfer", - "next":"queue1" - }, - "queue1":{ - "props":{ - "max-size-buffers":"50" - }, - "factory":"queue", - "next":"mxpi_motsimplesort0" - }, - "mxpi_motsimplesort0": { - "props": { - "dataSourceDetection": "mxpi_modelinfer0" - }, - "factory": "mxpi_motsimplesort", - "next": "mxpi_dataserialize0" - }, - "mxpi_dataserialize0": { - "props": { - "outputDataKeys": "ReservedFrameInfo,mxpi_modelinfer0" - }, - "factory": "mxpi_dataserialize", - "next": "appsink0" - }, - "appsink0": { - "props": { - "blocksize": "4096000" - }, - "factory": "appsink" - } - } -} diff --git a/contrib/HelmetIdentification/Models/Helmet_yolov5.cfg b/contrib/HelmetIdentification/Models/Helmet_yolov5.cfg deleted file mode 100644 index 18c08f4fe..000000000 --- a/contrib/HelmetIdentification/Models/Helmet_yolov5.cfg +++ /dev/null @@ -1,10 +0,0 @@ -CLASS_NUM=3 -BIASES_NUM=18 -BIASES=10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326 -SCORE_THRESH=0.4 -OBJECTNESS_THRESH=0.3 -IOU_THRESH=0.5 -YOLO_TYPE=3 -ANCHOR_DIM=3 -MODEL_TYPE=1 -RESIZE_FLAG=0 diff --git a/contrib/HelmetIdentification/Models/aipp_YOLOv5.config b/contrib/HelmetIdentification/Models/aipp_YOLOv5.config deleted file mode 100644 index 2ca2703c4..000000000 --- a/contrib/HelmetIdentification/Models/aipp_YOLOv5.config +++ /dev/null @@ -1,37 +0,0 @@ -aipp_op{ - aipp_mode:static - input_format : YUV420SP_U8 - - src_image_size_w : 640 - src_image_size_h : 640 - - crop: false - load_start_pos_h : 0 - load_start_pos_w : 0 - crop_size_w : 640 - crop_size_h: 640 - - csc_switch : true - rbuv_swap_switch : false - - # 色域转换 - matrix_r0c0: 256 - matrix_r0c1: 0 - matrix_r0c2: 359 - matrix_r1c0: 256 - matrix_r1c1: -88 - matrix_r1c2: -183 - matrix_r2c0: 256 - matrix_r2c1: 454 - matrix_r2c2: 0 - input_bias_0: 0 - input_bias_1: 128 - input_bias_2: 128 - - # 均值归一化 - min_chn_0 : 0 - min_chn_1 : 0 - min_chn_2 : 0 - var_reci_chn_0: 0.003921568627451 - var_reci_chn_1: 0.003921568627451 - var_reci_chn_2: 0.003921568627451} \ No newline at end of file diff --git a/contrib/HelmetIdentification/Models/atc-env.sh b/contrib/HelmetIdentification/Models/atc-env.sh deleted file mode 100644 index c8415211d..000000000 --- a/contrib/HelmetIdentification/Models/atc-env.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash - -# This is used to convert onnx model file to .om model file. -. /usr/local/Ascend/ascend-toolkit/set_env.sh # The path where Ascend-cann-toolkit is located -export Home="./path/" -# Home is set to the path where the model is located - -# Execute, transform YOLOv5 model. -atc --model="${Home}"/YOLOv5_s.onnx --framework=5 --output="${Home}"/YOLOv5_s --insert_op_conf=./aipp_YOLOv5.config --input_format=NCHW --log=info --soc_version=Ascend310B1 --input_shape="images:1,3,640,640" -# --model is the path where onnx is located. --output is the path where the output of the converted model is located \ No newline at end of file diff --git a/contrib/HelmetIdentification/Models/imgclass.names b/contrib/HelmetIdentification/Models/imgclass.names deleted file mode 100644 index a7446756b..000000000 --- a/contrib/HelmetIdentification/Models/imgclass.names +++ /dev/null @@ -1,3 +0,0 @@ -person -head -helmet \ No newline at end of file diff --git a/contrib/HelmetIdentification/Models/main.py b/contrib/HelmetIdentification/Models/main.py deleted file mode 100644 index 41b3e97c6..000000000 --- a/contrib/HelmetIdentification/Models/main.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import time -import signal -import cv2 -import numpy as np -import StreamManagerApi -import utils -import MxpiDataType_pb2 as MxpiDataType - - -def my_handler(signum, frame): - """ - :param signum: signum are used to identify the signal - :param frame: When the signal occurs, get the status of the process stack - func:Change flag of stop_stream - """ - global stop_stream - stop_stream = True - - -# exit flag -stop_stream = False -# When about to exit, get the exit signal -signal.signal(signal.SIGINT, my_handler) - -# The following belongs to the SDK Process -# init stream manager -streamManagerApi = StreamManagerApi.StreamManagerApi() -ret = streamManagerApi.InitManager() -if ret != 0: - print("Failed to init Stream manager, ret=%s" % str(ret)) - -# create streams by pipeline config file -#load pipline -with open("HelmetDetection.pipline", 'rb') as f: - pipelineStr = f.read() -ret = streamManagerApi.CreateMultipleStreams(pipelineStr) -# Print error message -if ret != 0: - print("Failed to create Stream, ret=%s" % str(ret)) - -# Obtain the inference result by specifying streamName and keyVec -# The data that needs to be obtained is searched by the plug-in name -# Stream name -streamName = b'Detection' -keyVec0 = StreamManagerApi.StringVector() -keyVec0.push_back(b"ReservedFrameInfo") -keyVec0.push_back(b"mxpi_modelinfer0") -keyVec0.push_back(b"mxpi_motsimplesort0") -keyVec0.push_back(b"mxpi_videodecoder0") -keyVec0.push_back(b"mxpi_videodecoder1") - -while True: - # exit flag - if stop_stream: - break - # Get data through GetProtobuf interface - inferResult0 = streamManagerApi.GetResult(streamName, b'appsink0', keyVec0) - # Determine whether the output is empty - if inferResult0.metadataVec.size() == 0: - print('Object detection result of model infer is null!!!') - continue - - DictStructure = utils.get_inference_data(inferResult0) - # the visualization of the inference result, save the output in the specified folder - utils.cv_visualization(DictStructure[0], DictStructure[1], DictStructure[2], DictStructure[3], DictStructure[4]) - -# Destroy All Streams -streamManagerApi.DestroyAllStreams() diff --git a/contrib/HelmetIdentification/Models/utils.py b/contrib/HelmetIdentification/Models/utils.py deleted file mode 100644 index 7aaa05f51..000000000 --- a/contrib/HelmetIdentification/Models/utils.py +++ /dev/null @@ -1,123 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import signal -import cv2 -import numpy as np -import MxpiDataType_pb2 as MxpiDataType - -def cv_visualization(img, infer, shape, frame_id, channel_id): - """ - :param img: Inference image - :param infer: Inference result - :param shape:Size before image padding - :param frame_id:Inference image id - :param channel_id:Channel id of the current inference image - func: the visualization of the inference result, save the output in the specified folder - """ - img_list2 = [] - print(shape) - # The title of the rectangle - title_l = (round(0.0002 * (shape[0] + shape[1])) + 0.35) - title_f = max(title_l - 1, 1) - # Add the inference results of head to img_list2 - for bbox0 in infer: - if bbox0[5] == 'head': - img_list2.append(bbox0) - for bbox1 in img_list2: - # Determine whether it is helmet - bboxes = {'x0': int(bbox1[0]), - 'x1': int(bbox1[1]), - 'y0': int(bbox1[2]), - 'y1': int(bbox1[3]), - 'confidence': round(bbox1[4], 4), - 'trackid': int(bbox1[6]), - 'age': int(bbox1[7]) - } - print(bboxes) - bboxes_list1 = [] - bboxes_list1.append(int(bboxes['x0'])) - bboxes_list1.append(int(bboxes['x1'])) - bboxes_list1.append(int(bboxes['y0'])) - bboxes_list1.append(int(bboxes['y1'])) - bboxes_list1 = np.array(bboxes_list1, dtype=np.int32) - # Draw rectangle - cv2.putText(img, str(bboxes['confidence']), (bboxes_list1[0], bboxes_list1[2]), 0, title_l, [225, 255, 255], thickness=title_f, - lineType=cv2.LINE_AA) - # rectangle color [255,255,255] - cv2.rectangle(img, (bboxes_list1[0], bboxes_list1[2]), (bboxes_list1[1], bboxes_list1[3]), (0, 0, 255), 2) - if bboxes['trackid'] is not None and bboxes['age'] == 1: - print("Warning:Not wearing a helmet,InferenceId:{},FrameId:{}".format(bboxes['trackid'], frame_id)) - - # Save pictures in two ways - if channel_id == 0: - oringe_imgfile = './output/one/image' + str(channel_id) + '-' + str( - frame_id) + '.jpg' - # Warning result save path - cv2.imwrite(oringe_imgfile, img) - else: - # when channel_id equal 1 - oringe_imgfile = './output/two/image' + str(channel_id) + '-' + str( - frame_id) + '.jpg' - cv2.imwrite(oringe_imgfile, img) - - -def get_inference_data(inference): - """ - :param inference:output of sdk stream inference - :return:img0, img_list1, img0_shape, frame_list0.frameId, frame_list0.channelId - """ - - # add inferennce data into DATA structure - # Frame information structure - frame_list0 = MxpiDataType.MxpiFrameInfo() - frame_list0.ParseFromString(inference.metadataVec[0].serializedMetadata) - # Target object structure - object_list = MxpiDataType.MxpiObjectList() - object_list.ParseFromString(inference.metadataVec[1].serializedMetadata) - # Get target box information - objectlist_data = object_list.objectVec - # track structure - tracklet_list = MxpiDataType.MxpiTrackLetList() - tracklet_list.ParseFromString(inference.metadataVec[2].serializedMetadata) - # Obtain tracking information - tracklet_data = tracklet_list.trackLetVec - # image structure - vision_list0 = MxpiDataType.MxpiVisionList() - vision_list0.ParseFromString(inference.metadataVec[3].serializedMetadata) - vision_data0 = vision_list0.visionVec[0].visionData.dataStr - # Get picture information - vision_info0 = vision_list0.visionVec[0].visionInfo - - # cv2 func YUV to BGR - yuv_bytes_nu = 3 - yuv_bytes_de = 2 - img_yuv = np.frombuffer(vision_data0, dtype=np.uint8) - # reshape - img_yuv = img_yuv.reshape(vision_info0.heightAligned * yuv_bytes_nu // yuv_bytes_de, vision_info0.widthAligned) - # Color gamut conversion - img0 = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR_NV12) - # put inference into dict, - img_list1 = [] - for k in range(len(object_list.objectVec)): - img_list = [round(objectlist_data[k].x0, 4), round(objectlist_data[k].x1, 4), round(objectlist_data[k].y0, 4), - round(objectlist_data[k].y1, 4), - round(objectlist_data[k].classVec[0].confidence, 4), objectlist_data[k].classVec[0].className, - tracklet_data[k].trackId, tracklet_data[k].age] - img_list1.append(img_list) - - # img0_shape is the original image size - img0_shape = [vision_info0.heightAligned, vision_info0.widthAligned] - # Output the results uniformly through the dictionary - dict_structure = [img0, img_list1, img0_shape, frame_list0.frameId, frame_list0.channelId] - return dict_structure diff --git a/contrib/HelmetIdentification/README.md b/contrib/HelmetIdentification/README.md deleted file mode 100644 index d95910c4d..000000000 --- a/contrib/HelmetIdentification/README.md +++ /dev/null @@ -1,334 +0,0 @@ -# 安全帽识别 - -## 1 介绍 - -安全帽作为工作中一样重要的安全防护用品，主要保护头部，防高空物体坠落，防物体打击、碰撞。通过识别每个人是否戴上安全帽，可以对没戴安全帽的人做出告警。本项目支持2路视频实时分析，其主要流程为:分两路接收外部调用接口的输入视频路径，将视频输入。通过视频解码将264格式视频解码为YUV格式图片。模型推理使用YOLOv5进行安全帽识别，识别结果经过后处理完成NMS得到识别框。对重复检测出的没戴安全帽的对象进行去重。最后将识别结果输出为两路，并对没佩戴安全帽的情况告警。 - -### 1.1 支持的产品 - -本项目基于mxVision SDK进行开发，以Atlas 500 A2/Atlas 200I DK A2为主要的硬件平台。 - -### 1.2 支持的版本 - -本样例配套的MxVision版本、CANN版本、Driver/Firmware版本如下所示： -| MxVision版本 | CANN版本 | Driver/Firmware版本 | -|--------------- | ---------------------------------- | ----------| -| 5.0.0 | 7.0.0 | 23.0.0| -|6.0.RC2 | 8.0.RC2 | 24.1.RC2| - -MindX SDK安装前准备可参考《用户指南》，[安装教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/quickStart/1-1安装SDK开发套件.md) - - - -### 1.3 代码目录结构与说明 - -本sample工程名称为HelmetIdentification，工程目录如下图所示： - -``` -├── Models - ├── aipp_YOLOv5.config # 模型转换配置文件 - ├──atc-env.sh # 模型转换脚本 - ├──YOLOv5_s.om #推理模型om文件 - ├──YOLOv5_s.onnx #推理模型onnx文件 - ├──Helmet_yolov5.cfg #后处理配置文件 - ├──HelmetDetection.pipline # 安全帽识别推理流程pipline - ├──imgclass.names # 模型所有可识别类 - ├──main.py # 推理运行程序 - ├──utils.py # 数据处理及可视化脚本 -├── plugins - ├──MxpiSelectedFrame # 跳帧插件 -├── Test - ├──performance_test_main.py # 性能测试脚本 - ├──test_select.py # 测试集筛选脚本 - ├──parse_voc.py # 测试数据集解析脚本 - ├──testmain.py # 测试主程序 - ├──map_calculate.py # 精度计算程序 -├── build.sh -``` - - - -### 1.5 技术实现流程图 - -image4 - - - -## 2 环境依赖 - -环境依赖软件和版本如下表： - -| 软件 | 版本 | 说明 | 获取方式 | -| ------------------- | ------------ | ----------------------------- | ------------------------------------------------------------ | -| opencv-python | 4.10.0.54 | 用于识别结果画框 | python3 -m pip install opencv-python| -| libgl1-mesa-glx |23.0.4-0ubuntu1~22.04.1 |GL库（opencv-python可能会依赖GL）|apt install libgl1-mesa-glx| -| live555|1.10|实现视频转rstp进行推流|[live555使用教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/Live555离线视频转RTSP说明文档.md)| -|ffmpeg|2021-08-08-git-ac0408522a | 实现mp4格式视频转为264格式视频 |[ffmpeg使用教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/pc端ffmpeg安装教程.md) - - - -在运行脚本main.py前（2.2章节），需要执行如下两个环境配置脚本设置环境变量,运行命令： - -```shell -. /usr/local/Ascend/ascend-toolkit/set_env.sh # Ascend-cann-toolkit开发套件包默认安装路径，根据实际安装路径修改 -. ${MX_SDK_HOME}/mxVision/set_env.sh # ${MX_SDK_HOME}替换为用户的SDK安装路径 -``` - -## 3.推理 - -#### 步骤1 模型转换 - -##### 1.1模型与软件依赖 - - 所用模型与软件依赖如下表所示。若使用A200I DK A2运行，推荐使用PC转换模型，具体方法可参考A200I DK A2资料。模型相关信息可参考[原项目链接](https://github.com/PeterH0323/Smart_Construction) - -| 软件名称 | 版本 | 获取方式 | -| ----------------------- | -------- | ------------------------------------------------------------ | -| YOLOv5_s.onnx | YOLOv5_s | [链接](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/HelmetIdentification/model.zip) | - -##### 1.2 onnx文件转换为om文件 - -1. 可直接获取已经转换好的YOLOv5_s.onnx文件，链接如1.1所示。此模型已经完成优化，不再使用dy_resize.py、modify_yolov5s_slice.py进行优化。可直接转换为om模型。 - -3. 修改atc-env脚本中的路径，运行atc-env脚本将onnx转为om模型，运行命令如下。 - -```shell -sh atc-env.sh -``` - -提示 **ATC run success** 说明转换成功。 - -脚本中包含atc命令: - -```shell ---model=${Home}/YOLOv5_s.onnx --framework=5 --output=${Home}/YOLOv5_s --insert_op_conf=./aipp_YOLOv5.config --input_format=NCHW --log=info --soc_version=Ascend310B1 --input_shape="images:1,3,640,640" -``` - -其参数如下表所示 - -| 参数名 | 参数描述 | -| ---------------- | :----------------------------------------------------------- | -| -- framework | 原始框架类型。当取值为5时，即为ONNX网络模型，仅支持ai.onnx算子域中opset v11版本的算 子。用户也可以将其他opset版本的算子（比如opset v9），通过PyTorch转换成 opset v11版本的onnx算子 | -| --model | 原始模型文件路径与文件名 | -| --output | 如果是开源框架的网络模型，存放转换后的离线模型的路径以及文件名。 | -| --soc_version | 模型转换时指定芯片版本。昇腾AI处理器的版本，可从ATC工具安装路径的“/usr/local/Ascend/ascend-toolkit/latest/arm64-linux/atc/data/platform_config”目录下 查看。 ".ini"文件的文件名即为对应的${soc_version} | -| --insert_op_conf | 插入算子的配置文件路径与文件名， 例如aipp预处理算子。 | -| --input_shape | 模型输入数据的 shape。 | -| --out_nodes | 指定输出节点,如果不指定输出节点（算子名称），则模型的输出默认为最后一层的算子信息，如果 指定，则以指定的为准 | - -其中--insert_op_conf参数为aipp预处理算子配置文件路径。该配置文件aipp_YOLOv5.config在输入图像进入模型前进行预处理。该配置文件保存在源码Models目录下。 - -注：1. [ATC模型转换工具指南](https://gitee.com/ascend/docs-openmind/blob/master/guide/mindx/sdk/tutorials/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99.md) - 2.atc-env.sh脚本内 Home 为onnx文件所在路径。 - - -#### 步骤2 模型推理 - -##### 2.1 pipline编写 - -pipline根据1.5节中技术实现流程图编写，该文件**HelmetDetection.pipline**放在源码根目录Models。 - - 注： - -1.pipline中mxpi_modelinfer用于加载yolov5安全帽识别模型。该插件包含四个参数，modelPath用于加载om模型文件。labelPath用于加载模型可识别类（imgclass.names）。postProcessLibPath用于加载后处理动态链接库文件，该模块实现NMS等后处理。postProcessConfigPath用于加载后处理所需要的配置文件（Helmet_yolov5.cfg）。本项目使用后处理文件为**libMpYOLOv5PostProcessor.so**。该后处理配置文件内容如下： - -```python -CLASS_NUM=3 -BIASES_NUM=18 -BIASES=10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326 -SCORE_THRESH=0.4 -OBJECTNESS_THRESH=0.3 -IOU_THRESH=0.5 -YOLO_TYPE=3 -ANCHOR_DIM=3 -MODEL_TYPE=1 -RESIZE_FLAG=0 -``` - -注：pipline中以上四个参数要修改为相应文件所在绝对路径。 - -2.pipline中mxpi_selectedframe插件完成视频跳帧。对于输入帧率为24fps输入视频进行每三帧抽一帧进行识别。实现8fps的帧率。 - -将目录切换至./plugins/MxpiSelectedFrame - -输入如下命令编译生成mxpi_selectedframe.so： - -```shell -mkdir build -cd build -cmake .. -make -j -``` - -编译成功后将产生**libmxpi_selectedframe.so**文件，文件生成位置在build目录下。将其复制至SDK的插件库中(./MindX_SDK/mxVision/lib/plugins)，并修改权限为440 - - 注：[插件编译生成教程](https://gitee.com/ascend/docs-openmind/blob/master/guide/mindx/sdk/tutorials/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99.md)在《SDK用户手册》深入开发章节 - -3.pipline中涉及到的**绝对路径**都要修改成用户安装sdk文件相应的路径 - -##### 2.2 运行推理 - -编写完pipline文件后即可运行推理流程进行识别，该程序**main.py**放在源码根目录Models。 - -mian.py通过调用sdk接口创建多个流完成数据接收、处理以及输出，接口调用流程图如下所示： - -image1 - -本项目通过mxpi_rtspsrc拉流输入数据，通过两路GetResult接口输出数据，一路输出带有帧信息的图片数据，一路输出带有帧信息的目标检测框和检测框跟踪信息。推理过程如下： - -首先通过[live555](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/Live555离线视频转RTSP说明文档.md)进行推流，进入到live555安装目录下mediaServer路径，上传要推流的视频在本目录下然后推流。 live555只支持特定几种格式文件，不支持MP4。 所以本地文件先要转成live555支持的格式。选择使用[ffmpeg](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/pc端ffmpeg安装教程.md)进行格式转换。 - -转换命令如下： - -```shell -ffmpeg -i xxx1.mp4 -vcodec h264 -bf 0 -g 25 -r 24 -s 1280*720 -an -f h264 xxx2.264 -``` - -注：参数如下： - -| 参数 | 作用 | -| ------- | ------------------------------------------------------ | -| -i | 表示输入的音视频路径需要转换视频 | -| -f | 强迫采用特定格式输出 | -| -r | 指定帧率输出 | -| -an | 关闭音频 | -| -s | 分辨率控制 | -| -g | 关键帧间隔控制 | -| -vcodec | 设定视频编解码器，未设定时则使用与输入流相同的编解码器 | - -转换完成后上传视频至live555安装目录下mediaServer。输入命令进行推流： - -```shell -./live555MediaServer test.264 -``` - -test.264可替换成任意上传至当前目录的[264格式文件](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/参考资料/pc端ffmpeg安装教程.md)，如要修改相应的也要在pipline中修改mxpi_rtspsrc的拉流路径 - -![image2](https://gitee.com/liu-kai6334/mindxsdk-referenceapps/raw/master/contrib/HelmetIdentification/image/image2.jpg) - -然后切换目录至main.py所在目录下，运行命令： - -```shell -python3.9 main.py -``` - -即可得到输出结果，输出结果将原来的两路视频分为两个文件保存，utils.py中的oringe_imgfile用于设置图像输出路径,用户需**手动建立**输出文件output，文件路径可自定义设置。本项目文件放置规范如下： - -![image3](https://gitee.com/liu-kai6334/mindxsdk-referenceapps/raw/master/contrib/HelmetIdentification/image/image3.jpg) - -所有数据放置于output中，one 、two为两路视频输出文件。 - -输出结果有如下几种情况： -| 序号 | 输入 | 输出 | -| ---- | ------------------------------ | ---------------------------------------------------------- | -| 1 | 两路只有一路输入 | 只打印有输入一路的输出 | -| 2 | 无输入或输入视频中无可识别对象 | 打印：Object detection result of model infer is null!!! | -| 3 | 输入视频有识别对象 | 打印每次推理的head的帧信息的尺寸与识别结果 | -| 4 | 识别对象未佩戴安全帽 | 打印：Warning:Not wearing a helmet, InferenceId：FrameId: | - -#### 步骤3 测试性能与精度 - - -##### 3.1 性能测试 - -性能测试使用脚本Test/performance_test_main.py，该脚本与main.py大体相同，不同之处是在performance_test_main.py中添加了时间戳测试，测试数据为mxpi_rtspsrc拉取的视频流。两路视频尺寸分别取多组不同尺寸的视频做对比。推理三百帧图片后取平均时间值，设置如下环境变量： - -```shell -export PYTHONPATH=/usr/local/python3.9.2/bin:${MX_SDK_HOME}/python:{path} -``` - -注：{path}设置为根目录中Models所在路径 - -运行如下命令得到结果： - -```shell -python3.9 performance_test_main.py -``` - -注：1.与运行main.py时相同，运行performance_test_main.py时要先使用live555进行推流。**测试视频**上传至[链接](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/HelmetIdentification/test_video.zip)，该视频为不同尺寸不同帧率的同一视频。如test64036830_158s.264为尺寸640×640，帧率30，时长158s的视频。 - -2.performance_test_main.py中加载pipline文件应写HelmetDetection.pipline的绝对路径 - - -##### 3.2 精度测试 - -###### 3.2.1 数据集说明 - -- 数据集来源: [Safety-Helmet-Wearing-Dataset](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/HelmetIdentification/data.zip) - -- 数据集结构 - - ``` - ├── VOC2028 - ├── Annotations # 图片解释文件，含标签等信息，与JPEGImages中图片一一对应 - ├── ImageSets # 存放txt文件 - ├── JPEGImages # 数据集原图片 - ``` - -注：将数据集中的三个文件放置于项目的根目录Test文件下，与**test_select.py**同目录。 - -###### 3.2.2测试数据集筛选 - -依据数据集中ImageSets文件夹中test.txt文件，从原始数据集中筛选出测试数据集，该程序**test_select.py**放在源码根目录Test中，在同目录下创建文件夹TestImages用来存储筛选的数据。在该目录下运行命令： -```shell -python3.9 test_select.py -``` - -程序运行后在根目录Test中会存放筛选出的测试集图片共1517张。 - -###### 3.2.3 测试数据集解析 - -解析测试数据集，在同级目录下生成类别文件**voc.names**、图片信息文件**VOC2028.info**和真实标签文件夹**ground_truth**， 该程序**parse_voc.py**放在源码根目录Test中。 - -运行命令： - -```shell -python3.9 parse_voc.py -``` - -###### 3.2.4 推理运行 - -依据编写的pipline业务流，对测试数据集进行推理，输出结果保存在同级目录**detection-test-result**文件夹中，该文件需要手动建立。程序**testmain.py**文件放在源码根目录Test中。 - -注：输出推理结果文件txt中数据格式为： - -```shell -cls conf x0 y0 x1 y1 -``` - -其中cls表示识别区域所属类别，conf表示识别置信度，(x0,y0)表示识别区域左上角点的坐标，(x1,y1)表示识别区域右下角点的坐标。 - -运行命令： - -```shell -python3.9 testmain.py -``` - -注：testmain.py中直接写入了pipline，其中mxpi_modelinfer插件四个参数的配置与HelmetDetection.pipline完全相同。 - -###### 3.2.5 精度计算 - -推理完成后，依据图片真实标签和推理结果，计算精度。输出结果保存在同级目录**output**文件夹中，该文件需要手动建立。程序map_calculate.py文件放在源码根目录Test中。 - -注：测试数据集中图片有两类标签"person"(负样本，未佩戴安全帽)和"hat"(正样本，佩戴安全帽)。模型输出标签有三类"person"、"head"、"helmet"，其中"head"与真实标签"person"对应，"helmet"与真实标签"hat"对应。在**map_calculate.py**文件中做了对应转换处理。 - -运行命令： - -```shell -python3.9 map_calculate.py --label_path ./ground-truth --npu_txt_path ./detection-test-result/ -na -np -``` - -即可得到输出。其中precision、recall和map记录在**output/output.txt**文件中。 - -## 4 常见问题 - -### 4.1 图片格式问题 - -**问题描述：** - -E0628 10:14:48.309166 8155 DvppImageDecoder.cpp:152] [mxpi_imagedecoder0] [2006] [DVPP:ecode jpeg or jpg fail] - -**解决方案：** - -本项目只支持jpg图片输入 如输入其他格式会报如上错误 - diff --git a/contrib/HelmetIdentification/Test/map_calculate.py b/contrib/HelmetIdentification/Test/map_calculate.py deleted file mode 100644 index a50316b3b..000000000 --- a/contrib/HelmetIdentification/Test/map_calculate.py +++ /dev/null @@ -1,941 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd - -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import glob -import json -import os -import shutil -import operator -import sys -import argparse -import math -import numpy as np - -MINOVERLAP = 0.5 # default value (defined in the PASCAL VOC2012 challenge) -top_margin = 0.15 # in percentage of the figure height -bottom_margin = 0.05 # in percentage of the figure height -parser = argparse.ArgumentParser() -parser.add_argument('-na', '--no-animation', - help="no animation is shown.", action="store_true") -parser.add_argument('-np', '--no-plot', - help="no plot is shown.", action="store_true") -parser.add_argument( - '-q', '--quiet', help="minimalistic console output.", action="store_true") -parser.add_argument('-i', '--ignore', nargs='+', type=str, - help="ignore a list of classes.") -parser.add_argument('--set-class-iou', nargs='+', type=str, - help="set IoU for a specific class.") - -parser.add_argument('--label_path', default="./ground-truth") -parser.add_argument('--npu_txt_path', default="./detection-results") - -args = parser.parse_args() - - -# if there are no classes to ignore then replace None by empty list -if args.ignore is None: - args.ignore = [] - -specific_iou_flagged = False -if args.set_class_iou is not None: - specific_iou_flagged = True - -# make sure that the cwd() is the location of -# the python script (so that every path makes sense) -os.chdir(os.path.dirname(os.path.abspath(__file__))) - -GT_PATH = args.label_path -DR_PATH = args.npu_txt_path -# if there are no images then no animation can be shown -IMG_PATH = os.path.join(os.getcwd(), 'TestImages_Pre') -if os.path.exists(IMG_PATH): - for dirpath, dirnames, files in os.walk(IMG_PATH): - if not files: - # no image files found - args.no_animation = True -else: - args.no_animation = True - -# try to import OpenCV if the user didn't choose the option --no-animation -show_animation = False -if not args.no_animation: - try: - import cv2 - - show_animation = True - except ImportError: - print("\"cv2\" not found, please install to visualize.") - args.no_animation = True - -# try to import Matplotlib -# if the user didn't choose the option --no-plot -draw_plot = False -if not args.no_plot: - try: - import matplotlib.pyplot as plt - - draw_plot = True - except ImportError: - print("\"matplotlib\" not found,install it to get the plots.") - args.no_plot = True - - -def log_average_miss_rate(precision, fp_cumsum, num_images): - """ - log-average miss rate: - Calculated by averaging miss rates at 9 evenly spaced FPPI points - between 10e-2 and 10e0, in log-space. - output: - Lamr | log-average miss rate - Mr | miss rate - Fppi | false positives per image - - references: - "Pedestrian Detection: An Evaluation of the State of the Art." - """ - - # if there were no detections of that class - if precision.size == 0: - Lamr = 0 - Mr = 1 - Fppi = 0 - return Lamr, Mr, Fppi - - Fppi = fp_cumsum / float(num_images) - Mr = (1 - precision) - - fppi_tmp = np.insert(Fppi, 0, -1.0) - mr_tmp = np.insert(Mr, 0, 1.0) - - # Use 9 evenly spaced reference points in log-space - ref = np.logspace(-2.0, 0.0, num=9) - for i0, ref_i in enumerate(ref): - j = np.where(fppi_tmp <= ref_i)[-1][-1] - ref[i0] = mr_tmp[j] - - # log(0) is undefined, so we use the np.maximum(1e-10, ref) - Lamr = math.exp(np.mean(np.log(np.maximum(1e-10, ref)))) - - return Lamr, Mr, Fppi - - -def error(msg): - """ - throw error and exit - """ - print(msg) - sys.exit(0) - - -def is_float_between_0_and_1(value): - """ - check if the number is a float between 0.0 and 1.0 - """ - try: - Val = float(value) - if Val > 0.0 and Val < 1.0: - return True - else: - return False - except ValueError: - return False - - -def voc_ap(Rec, Prec): - """ - Calculate the AP given the recall and precision array - 1) We compute a version of the measured - precision/recall curve with precision monotonically decreasing - 2) We compute the AP as the area - under this curve by numerical integration. - --- Official matlab code VOC2012--- - Mrec=[0 ; rec ; 1]; - mpre=[0 ; prec ; 0]; - for i=numel(mpre)-1:-1:1 - mpre(i)=max(mpre(i),mpre(i+1)); - end - i=find(Mrec(2:end)~=Mrec(1:end-1))+1; - Ap=sum((Mrec(i)-Mrec(i-1)).*mpre(i)); - """ - Rec.insert(0, 0.0) # insert 0.0 at begining of list - Rec.append(1.0) # insert 1.0 at end of list - Mrec = Rec[:] - Prec.insert(0, 0.0) # insert 0.0 at begining of list - Prec.append(0.0) # insert 0.0 at end of list - mpre = Prec[:] - # This part makes the precision monotonically decreasing - # (goes from the end to the beginning) - # matlab: for i=numel(mpre)-1:-1:1 - # mpre(i)=max(mpre(i),mpre(i+1)); - - for cnt in range(len(mpre) - 2, -1, -1): - mpre[cnt] = max(mpre[cnt], mpre[cnt + 1]) - # This part creates a list of indexes where the recall changes - # matlab: cnt=find(Mrec(2:end)~=Mrec(1:end-1))+1; - j_list = [] - for j in range(1, len(Mrec)): - if Mrec[j] != Mrec[j - 1]: - j_list.append(j) # if it was matlab would be j + 1 - # The Average Precision (AP) is the area under the curve - # (numerical integration) - # matlab: Ap=sum((Mrec(j)-Mrec(j-1)).*mpre(j)); - Ap = 0.0 - for k in j_list: - Ap += ((Mrec[k] - Mrec[k - 1]) * mpre[k]) - return Ap, Mrec, mpre - - -def file_lines_to_list(path): - """ - Convert the lines of a file to a list - """ - # open txt file lines to a list - with open(path) as F: - content = F.readlines() - # remove whitespace characters like `\n` at the end of each line - content = [con.strip() for con in content] - return content - - -def draw_text_in_image(Img, text0, pos, Color, Line_width): - """ - Draws text in image - """ - Font = cv2.FONT_HERSHEY_PLAIN - fontScale = 1 - lineType = 1 - bottomLeftCornerOfText = pos - cv2.putText(Img, text0, - bottomLeftCornerOfText, - Font, - fontScale, - Color, - lineType) - text_width, _ = cv2.getTextSize(text0, Font, fontScale, lineType)[0] - return Img, (Line_width + text_width) - - -def adjust_axes(r, t, fig0, axes0): - """ - Plot - adjust axes - """ - # get text width for re-scaling - Bb = t.get_window_extent(renderer=r) - text_width_inches = Bb.width / fig0.dpi - # get axis width in inches - current_fig_width = fig0.get_figwidth() - new_fig_width = current_fig_width + text_width_inches - propotion = new_fig_width / current_fig_width - # get axis limit - x_lim = axes0.get_xlim() - axes0.set_xlim([x_lim[0], x_lim[1] * propotion]) - - -def draw_plot_func(dictionary0, n_classes0, window_title0, plot_title0, x_label0, output_path0, to_show0, - plot_color0, true_p_bar0): - """ - Draw plot using Matplotlib - """ - # sort the dictionary by decreasing value, into a list of tuples - sorted_dic_by_value = sorted(dictionary0.items(), key=operator.itemgetter(1)) - # unpacking the list of tuples into two lists - sorted_keys, sorted_values = zip(*sorted_dic_by_value) - # - if true_p_bar0 != "": - # Special case to draw in: - # - green -> TP: True Positives - # (object detected and matches ground-truth) - # - red -> FP: False Positives - # (object detected but does not match ground-truth) - # - pink -> FN: False Negatives - # (object not detected but present in the ground-truth) - - fp_sorted = [] - tp_sorted = [] - for key in sorted_keys: - fp_sorted.append(dictionary0[key] - true_p_bar0[key]) - tp_sorted.append(true_p_bar0[key]) - plt.barh(range(n_classes0), fp_sorted, align='center', color='crimson', label='False Positive') - plt.barh(range(n_classes0), tp_sorted, align='center', color='forestgreen', label='True Positive', - left=fp_sorted) - # add legend - plt.legend(loc='lower right') - # Write number on side of bar - Fig = plt.gcf() # gcf - get current figure - r = Fig.canvas.get_renderer() - for i0, val0 in enumerate(sorted_values): - fp_val = fp_sorted[i0] - tp_val = tp_sorted[i0] - fp_str_val = " " + str(fp_val) - tp_str_val = fp_str_val + " " + str(tp_val) - # trick to paint multicolor with offset: - # first paint everything and then repaint the first number - t = plt.text(val0, i0, tp_str_val, color='forestgreen', va='center', fontweight='bold') - plt.text(val0, i0, fp_str_val, color='crimson', va='center', fontweight='bold') - if i0 == (len(sorted_values) - 1): # largest bar - adjust_axes(r, t, Fig, plt.gca()) - else: - plt.barh(range(n_classes0), sorted_values, color=plot_color0) - # Write number on side of bar - Fig = plt.gcf() # gcf - get current figure - r = Fig.canvas.get_renderer() - for i0, val1 in enumerate(sorted_values): - str_val = " " + str(val1) # add a space before - if val1 < 1.0: - str_val = " {0:.2f}".format(val1) - t = plt.text(val1, i0, str_val, color=plot_color0, va='center', fontweight='bold') - # re-set axes to show number inside the figure - if i0 == (len(sorted_values) - 1): # largest bar - adjust_axes(r, t, Fig, plt.gca()) - # set window title - Fig.canvas.set_window_title(window_title0) - # write classes in y axis - plt.yticks(range(n_classes0), sorted_keys, fontsize=12) # Re-scale height accordingly - # comput the matrix height in points and inches - height_pt = n_classes0 * (12 * 1.4) # 1.4 (some spacing) - height_in = height_pt / Fig.dpi - figure_height = height_in / (1 - top_margin - bottom_margin) - # set new height, init_height = Fig.get_figheight - if figure_height > Fig.get_figheight(): - Fig.set_figheight(figure_height) - - # set plot title - plt.title(plot_title0, fontsize=14) - # set axis titles - plt.xlabel(x_label0, fontsize='large') - # adjust size of window - Fig.tight_layout() - # save the plot - Fig.savefig(output_path0) - # show image - if to_show0: - plt.show() - # close the plot - plt.close() - - -# Create a ".temp_files/" and "output/" directory -TEMP_FILES_PATH = ".temp_files" -if not os.path.exists(TEMP_FILES_PATH): # if it doesn't exist already - os.makedirs(TEMP_FILES_PATH) -output_files_path = "output" -if os.path.exists(output_files_path): # if it exist already - # reset the output directory - shutil.rmtree(output_files_path) - -os.makedirs(output_files_path) -if draw_plot: - os.makedirs(os.path.join(output_files_path, "classes")) -if show_animation: - os.makedirs(os.path.join(output_files_path, - "images", "detections_one_by_one")) - -# ground-truth -# Load each of the ground-truth files -# into a temporary ".json" file. -# Create a list of all the class names present -# in the ground-truth (gt_classes). -# get a list with the ground-truth files -ground_truth_files_list = glob.glob(GT_PATH + '/*.txt') -if len(ground_truth_files_list) == 0: - error("Error: No ground-truth files found!") -ground_truth_files_list.sort() -# dictionary with counter per class -gt_counter_per_class = {} -counter_images_per_class = {} - -gt_files = [] -for txt_file in ground_truth_files_list: - file_id = txt_file.split(".txt", 1)[0] - - file_id = os.path.basename(os.path.normpath(file_id)) - # check if there is a correspondent detection-results file - temp_path = os.path.join(DR_PATH, (file_id + ".txt")) - if not os.path.exists(temp_path): - continue - lines_list = file_lines_to_list(txt_file) - # create ground-truth dictionary - bounding_boxes = [] - is_difficult = False - already_seen_classes = [] - for line in lines_list: - try: - if "difficult" in line: - class_name, left, top, right, bottom, _difficult = line.split() - is_difficult = True - else: - class_name, left, top, right, bottom = line.split() - except ValueError: - error_msg = "Error: File " + txt_file + " in the wrong format.\n" - error_msg += " Expected: \n" - error_msg += " Received: " + line - error(error_msg) - # check if class is in the ignore list, if yes skip - if class_name == "hat": - class_name = "helmet" - elif class_name == "person": - class_name = "head" - if class_name in args.ignore: - continue - bbox = left + " " + top + " " + right + " " + bottom - if is_difficult: - bounding_boxes.append( - {"class_name": class_name, "bbox": bbox, - "used": False, "difficult": True}) - is_difficult = False - else: - bounding_boxes.append( - {"class_name": class_name, "bbox": bbox, "used": False}) - # count that object - if class_name in gt_counter_per_class: - gt_counter_per_class[class_name] += 1 - else: - # if class didn't exist yet - gt_counter_per_class[class_name] = 1 - - if class_name not in already_seen_classes: - if class_name in counter_images_per_class: - counter_images_per_class[class_name] += 1 - else: - # if class didn't exist yet - counter_images_per_class[class_name] = 1 - already_seen_classes.append(class_name) - - # dump bounding_boxes into a ".json" file - new_temp_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json" - gt_files.append(new_temp_file) - with open(new_temp_file, 'w') as outfile: - json.dump(bounding_boxes, outfile) - - -gt_classes = list(gt_counter_per_class.keys()) -print(gt_counter_per_class) -# let's sort the classes alphabetically -gt_classes = sorted(gt_classes) -n_classes = len(gt_classes) - -# """ -# Check format of the flag --set-class-iou (if used) -# e.g. check if class exists -# """ -if specific_iou_flagged: - n_args = len(args.set_class_iou) - error_msg = \ - '\n --set-class-iou [class_1] [IoU_1] [class_2] [IoU_2] [...]' - if n_args % 2 != 0: - error('Error, missing arguments. Flag usage:' + error_msg) - # Its format is [class_1] [IoU_1] [class_2] [IoU_2] - specific_iou_classes = args.set_class_iou[::2] # even - iou_list = args.set_class_iou[1::2] # odd - if len(specific_iou_classes) != len(iou_list): - error('Error, missing arguments. Flag usage:' + error_msg) - for tmp_class in specific_iou_classes: - if tmp_class not in gt_classes: - error('Error, unknown class \"' + tmp_class + - '\". Flag usage:' + error_msg) - for num in iou_list: - if not is_float_between_0_and_1(num): - error('IoU must be [0.0,1.0].usage:' + error_msg) - -# """ -# detection-results -# Load each of the detection-results files -# into a temporary ".json" file. -# """ -# get a list with the detection-results files -dr_files_list = glob.glob(DR_PATH + '/*.txt') - -dr_files_list.sort() - -for class_index, class_name in enumerate(gt_classes): - bounding_boxes = [] - for txt_file in dr_files_list: - # the first time it checks - # if all the corresponding ground-truth files exist - file_id = txt_file.split(".txt", 1)[0] - - file_id = os.path.basename(os.path.normpath(file_id)) - temp_path = os.path.join(GT_PATH, (file_id + ".txt")) - if class_index == 0: - if not os.path.exists(temp_path): - error_msg = "Error. File not found: {}\n".format(temp_path) - error(error_msg) - lines = file_lines_to_list(txt_file) - for line in lines: - try: - sl = line.split() - tmp_class_name, confidence, left, top, right, bottom = sl - except ValueError: - error_msg = "Error: File " + txt_file + " wrong format.\n" - error_msg += " Expected: \n" - error_msg += " Received: " + line - error(error_msg) - if tmp_class_name == class_name: - bbox = left + " " + top + " " + right + " " + bottom - bounding_boxes.append( - {"confidence": confidence, - "file_id": file_id, "bbox": bbox}) - - # sort detection-results by decreasing confidence - bounding_boxes.sort(key=lambda x: float(x['confidence']), reverse=True) - with open(TEMP_FILES_PATH + "/" + class_name + "_dr.json", 'w') as outfile: - json.dump(bounding_boxes, outfile) - -# """ -# Calculate the AP for each class -# """ -sum_AP = 0.0 -ap_dictionary = {} -lamr_dictionary = {} -# open file to store the output -with open(output_files_path + "/output.txt", 'w') as output_file: - output_file.write("# AP and precision/recall per class\n") - count_true_positives = {} - for class_index, class_name in enumerate(gt_classes): - count_true_positives[class_name] = 0 - # """ - # Load detection-results of that class - # """ - dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json" - dr_data = json.load(open(dr_file)) - - # """ - # Assign detection-results to ground-truth objects - # """ - nd = len(dr_data) - tp = [0] * nd # creates an array of zeros of size nd - fp = [0] * nd - count = 0 - for idx, detection in enumerate(dr_data): - file_id = detection["file_id"] - if show_animation: - # find ground truth image - ground_truth_img = glob.glob1(IMG_PATH, file_id + ".*") - if len(ground_truth_img) == 0: - error("Error. Image not found with id: " + file_id) - elif len(ground_truth_img) > 1: - error("Error. Multiple image with id: " + file_id) - else: # found image - # Load image - img = cv2.imread(IMG_PATH + "/" + ground_truth_img[0]) - # load image with draws of multiple detections - img_cumulative_path = output_files_path - img_cumulative_path += "/images/" + ground_truth_img[0] - if os.path.isfile(img_cumulative_path): - img_cumulative = cv2.imread(img_cumulative_path) - else: - img_cumulative = img.copy() - # Add bottom border to image - bottom_border = 60 - BLACK = [0, 0, 0] - img = cv2.copyMakeBorder( - img, 0, bottom_border, - 0, 0, cv2.BORDER_CONSTANT, value=BLACK) - # assign detection-results to ground truth object if any - # open ground-truth with that file_id - gt_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json" - ground_truth_data = json.load(open(gt_file)) - ovmax = -1 - gt_match = -1 - # load detected object bounding-box - bb = [float(x) for x in detection["bbox"].split()] - for obj in ground_truth_data: - # look for a class_name match - if obj["class_name"] == class_name: - bbgt = [float(x) for x in obj["bbox"].split()] - bi = [max(bb[0], bbgt[0]), max(bb[1], bbgt[1]), - min(bb[2], bbgt[2]), min(bb[3], bbgt[3])] - iw = bi[2] - bi[0] + 1 - ih = bi[3] - bi[1] + 1 - if iw > 0 and ih > 0: - # compute overlap (IoU) - ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + \ - (bbgt[2] - bbgt[0] + 1) * \ - (bbgt[3] - bbgt[1] + 1) - iw * ih - ov = iw * ih / ua - if ov > ovmax: - ovmax = ov - gt_match = obj - - # assign detection as true positive/don't care/false positive - if show_animation: - status = "NO MATCH FOUND!" - # set minimum overlap - min_overlap = MINOVERLAP - if specific_iou_flagged: - if class_name in specific_iou_classes: - index = specific_iou_classes.index(class_name) - min_overlap = float(iou_list[index]) - if ovmax >= min_overlap: - if "difficult" not in gt_match: - if not bool(gt_match["used"]): - # true positive - tp[idx] = 1 - gt_match["used"] = True - count_true_positives[class_name] += 1 - # update the ".json" file - with open(gt_file, 'w') as f: - f.write(json.dumps(ground_truth_data)) - if show_animation: - status = "MATCH!" - else: - # false positive (multiple detection) - fp[idx] = 1 - if show_animation: - status = "REPEATED MATCH!" - else: - # false positive - fp[idx] = 1 - count += 1 - if ovmax > 0: - status = "INSUFFICIENT OVERLAP" - - # """ - # Draw image to show animation - # """ - if show_animation: - height, widht = img.shape[:2] - # colors (OpenCV works with BGR) - white = (255, 255, 255) - light_blue = (255, 200, 100) - green = (0, 255, 0) - light_red = (30, 30, 255) - # 1st line - margin = 10 - v_pos = int(height - margin - (bottom_border / 2.0)) - text = "Image: " + ground_truth_img[0] + " " - img, line_width = draw_text_in_image( - img, text, (margin, v_pos), white, 0) - text = "Class [" + str(class_index) + "/" + \ - str(n_classes) + "]: " + class_name + " " - img, line_width = draw_text_in_image( - img, text, - (margin + line_width, v_pos), - light_blue, line_width) - if ovmax != -1: - color = light_red - if status == "INSUFFICIENT OVERLAP": - text = "IoU: {0:.2f}% ".format( - ovmax * 100) - text += "< {0:.2f}% ".format(min_overlap * 100) - else: - text = "IoU: {0:.2f}% ".format( - ovmax * 100) - text += ">= {0:.2f}% ".format(min_overlap * 100) - color = green - img, _ = draw_text_in_image( - img, text, (margin + line_width, v_pos), - color, line_width) - # 2nd line - v_pos += int(bottom_border / 2.0) - rank_pos = str(idx + 1) # rank position (idx starts at 0) - text = "Detection #rank: " + rank_pos - temp_conf = float(detection["confidence"]) - text += " conf: {0:.2f}% ".format(temp_conf * 100) - img, line_width = draw_text_in_image( - img, text, (margin, v_pos), white, 0) - color = light_red - if status == "MATCH!": - color = green - text = "Result: " + status + " " - img, line_width = draw_text_in_image( - img, text, (margin + line_width, v_pos), color, line_width) - - font = cv2.FONT_HERSHEY_SIMPLEX - # if there is intersections between the bounding-boxes - if ovmax > 0: - bbgt = [int(round(float(x))) - for x in gt_match["bbox"].split()] - cv2.rectangle(img, (bbgt[0], bbgt[1]), - (bbgt[2], bbgt[3]), light_blue, 2) - cv2.rectangle(img_cumulative, (bbgt[0], bbgt[1]), - (bbgt[2], bbgt[3]), light_blue, 2) - cv2.putText(img_cumulative, class_name, - (bbgt[0], bbgt[1] - 5), font, 0.6, - light_blue, 1, cv2.LINE_AA) - bb = [int(i) for i in bb] - cv2.rectangle(img, (bb[0], bb[1]), - (bb[2], bb[3]), color, 2) - cv2.rectangle(img_cumulative, - (bb[0], bb[1]), (bb[2], bb[3]), color, 2) - cv2.putText(img_cumulative, class_name, - (bb[0], bb[1] - 5), font, - 0.6, color, 1, cv2.LINE_AA) - # show image - cv2.imwrite("result.jpg", img) - # save image to output - output_img_path = output_files_path - output_img_path += "/images/detections_one_by_one/" - output_img_path += class_name + "_detection" - output_img_path += str(idx) + ".jpg" - cv2.imwrite(output_img_path, img) - # save the image with all the objects drawn to it - cv2.imwrite(img_cumulative_path, img_cumulative) - - print("f_count:", count) - # compute precision/recall - cumsum = 0 - for idx, val in enumerate(fp): - fp[idx] += cumsum - cumsum += val - cumsum = 0 - for idx, val in enumerate(tp): - tp[idx] += cumsum - cumsum += val - rec = tp[:] - for idx, val in enumerate(tp): - rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name] - prec = tp[:] - for idx, val in enumerate(tp): - prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx]) - - ap, mrec, mprec = voc_ap(rec[:], prec[:]) - sum_AP += ap - text = "{0:.2f}%".format(ap * 100) + " = " + class_name + " AP " - # """ - # Write to output.txt - # """ - rounded_prec = ['%.2f' % elem for elem in prec] - rounded_rec = ['%.2f' % elem for elem in rec] - output_file.write(text + "\n Precision: " + str(rounded_prec) + - "\n Recall :" + str(rounded_rec) + "\n\n") - if not args.quiet: - print(text) - ap_dictionary[class_name] = ap - - n_images = counter_images_per_class[class_name] - lamr, mr, fppi = log_average_miss_rate( - np.array(rec), np.array(fp), n_images) - lamr_dictionary[class_name] = lamr - - # """ - # Draw plot - # """ - if draw_plot: - plt.plot(rec, prec, '-o') - # add a new penultimate point to the list (mrec[-2], 0.0) - # since the last line segment - # (and respective area) do not affect the AP value - area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]] - area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]] - plt.fill_between(area_under_curve_x, 0, - area_under_curve_y, alpha=0.2, edgecolor='r') - # set window title - fig = plt.gcf() # gcf - get current figure - fig.canvas.set_window_title('AP ' + class_name) - # set plot title - plt.title('class: ' + text) - # set axis titles - plt.xlabel('Recall') - plt.ylabel('Precision') - # optional - set axes - axes = plt.gca() # gca - get current axes - axes.set_xlim([0.0, 1.0]) - axes.set_ylim([0.0, 1.05]) # .05 to give some extra space - # Alternative option -> wait for button to be pressed - # Alternative option -> normal display - # save the plot - fig.savefig(output_files_path + "/classes/" + class_name + ".png") - plt.cla() # clear axes for next plot - - if show_animation: - cv2.destroyAllWindows() - - output_file.write("\n# mAP of all classes\n") - mAP = sum_AP / n_classes - text = "mAP = {0:.2f}%".format(mAP * 100) - output_file.write(text + "\n") - print(text) - -# """ -# Draw false negatives -# """ -if show_animation: - pink = (203, 192, 255) - for tmp_file in gt_files: - ground_truth_data = json.load(open(tmp_file)) - # get name of corresponding image - start = TEMP_FILES_PATH + '/' - img_id = tmp_file[tmp_file.find( - start) + len(start):tmp_file.rfind('_ground_truth.json')] - img_cumulative_path = output_files_path + "/images/" + img_id + ".jpg" - img = cv2.imread(img_cumulative_path) - if img is None: - img_path = IMG_PATH + '/' + img_id + ".jpg" - img = cv2.imread(img_path) - # draw false negatives - for obj in ground_truth_data: - if not obj['used']: - bbgt = [int(round(float(x))) for x in obj["bbox"].split()] - cv2.rectangle(img, (bbgt[0], bbgt[1]), - (bbgt[2], bbgt[3]), pink, 2) - cv2.imwrite(img_cumulative_path, img) - -# remove the temp_files directory -shutil.rmtree(TEMP_FILES_PATH) - -# """ -# Count total of detection-results -# """ -# iterate through all the files -det_counter_per_class = {} -for txt_file in dr_files_list: - file_id = txt_file.split(".txt", 1)[0] - - # get lines to list - lines_list = file_lines_to_list(txt_file) - for line in lines_list: - class_name = line.split()[0] - # check if class is in the ignore list, if yes skip - if class_name in args.ignore: - continue - # count that object - if class_name in det_counter_per_class: - det_counter_per_class[class_name] += 1 - else: - # if class didn't exist yet - det_counter_per_class[class_name] = 1 - -dr_classes = list(det_counter_per_class.keys()) -print("dr_class:", dr_classes) - -# """ -# Plot the total number of occurences of each class in the ground-truth -# """ -if draw_plot: - window_title = "ground-truth-info" - plot_title = "ground-truth\n" - plot_title += "(" + str(len(ground_truth_files_list)) + \ - " files and " + str(n_classes) + " classes)" - x_label = "Number of objects per class" - output_path = output_files_path + "/ground-truth-info.png" - to_show = False - plot_color = 'forestgreen' - draw_plot_func( - gt_counter_per_class, - n_classes, - window_title, - plot_title, - x_label, - output_path, - to_show, - plot_color, - '', - ) - -# """ -# Write number of ground-truth objects per class to results.txt -# """ -with open(output_files_path + "/output.txt", 'a') as output_file: - output_file.write("\n# Number of ground-truth objects per class\n") - for class_name in sorted(gt_counter_per_class): - output_file.write(class_name + ": " + - str(gt_counter_per_class[class_name]) + "\n") - -# """ -# Finish counting true positives -# if class exists in detection-result -# but not in ground-truth -# then there are no true positives in that class -# """ -for class_name in dr_classes: - if class_name not in gt_classes: - count_true_positives[class_name] = 0 -print("count_true_p:", count_true_positives) -# """ -# Plot the total number of occurences of -# each class in the "detection-results" folder -# """ -if draw_plot: - window_title = "detection-results-info" - # Plot title - plot_title = "detection-results\n" - plot_title += "(" + str(len(dr_files_list)) + " files and " - count_non_zero_values_in_dictionary = sum( - int(x) > 0 for x in list(det_counter_per_class.values())) - plot_title += str(count_non_zero_values_in_dictionary) - plot_title += " detected classes)" - # end Plot title - x_label = "Number of objects per class" - output_path = output_files_path + "/detection-results-info.png" - to_show = False - plot_color = 'forestgreen' - true_p_bar = count_true_positives - draw_plot_func( - det_counter_per_class, - len(det_counter_per_class), - window_title, - plot_title, - x_label, - output_path, - to_show, - plot_color, - true_p_bar - ) - -# """ -# Write number of detected objects per class to output.txt -# """ -with open(output_files_path + "/output.txt", 'a') as output_file: - output_file.write("\n# Number of detected objects per class\n") - for class_name in sorted(dr_classes): - n_det = det_counter_per_class[class_name] - text = class_name + ": " + str(n_det) - text += " (tp:" + str(count_true_positives[class_name]) + "" - text += ", fp:" + str(n_det - count_true_positives[class_name]) + ")\n" - output_file.write(text) - -# """ -# Draw log-average miss rate plot (Show lamr of all classes in decreasing order) -# """ -if draw_plot: - window_title = "lamr" - plot_title = "log-average miss rate" - x_label = "log-average miss rate" - output_path = output_files_path + "/lamr.png" - to_show = False - plot_color = 'royalblue' - draw_plot_func( - lamr_dictionary, - n_classes, - window_title, - plot_title, - x_label, - output_path, - to_show, - plot_color, - "" - ) - -# """ -# Draw mAP plot (Show AP's of all classes in decreasing order) -# """ -if draw_plot: - window_title = "mAP" - plot_title = "mAP = {0:.2f}%".format(mAP * 100) - x_label = "Average Precision" - output_path = output_files_path + "/mAP.png" - to_show = True - plot_color = 'royalblue' - draw_plot_func( - ap_dictionary, - n_classes, - window_title, - plot_title, - x_label, - output_path, - to_show, - plot_color, - "" - ) diff --git a/contrib/HelmetIdentification/Test/parse_voc.py b/contrib/HelmetIdentification/Test/parse_voc.py deleted file mode 100644 index 587027762..000000000 --- a/contrib/HelmetIdentification/Test/parse_voc.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd - -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import argparse -import xml.etree.ElementTree as ET - -CLASSES = ('person', 'hat') - -def main(arg): - """ - gain ground-truth - """ - info = open('./VOC2028.info', 'w') - cls = open('./voc.names', 'w') - for i in CLASSES: - cls.write(i) - cls.write('\n') - - for idx, jpg_name in enumerate(os.listdir(arg.img_path)): - key_name = jpg_name.split('.')[0] - xml_name = os.path.join(arg.ann_path, key_name + '.xml') - #parse xml - tree = ET.parse(xml_name) - root = tree.getroot() - size = root.find('size') - width = size.find('width').text - height = size.find('height').text - info.write('{} {} {} {}'.format(idx, os.path.join(arg.img_path, jpg_name), width, height)) - info.write('\n') - - with open('{}/{}'.format(arg.gtp, key_name + '.txt'), 'w') as f: - for obj in root.iter('object'): - difficult = int(obj.find('difficult').text) - cls_name = obj.find('name').text.strip().lower() - if cls_name not in CLASSES: - continue - xml_box = obj.find('bndbox') - xmin = (float(xml_box.find('xmin').text)) - ymin = (float(xml_box.find('ymin').text)) - xmax = (float(xml_box.find('xmax').text)) - ymax = (float(xml_box.find('ymax').text)) - - if difficult: - comment = '{} {} {} {} {} {}'.format(cls_name, xmin, ymin, xmax, ymax, 'difficult') - else: - comment = '{} {} {} {} {}'.format(cls_name, xmin, ymin, xmax, ymax) - f.write(comment) - f.write('\n') - - -def err_msg(msg): - """ - print error message - """ - print('-' * 55) - print("The specified '{}' file does not exist".format(msg)) - print('You can get the correct parameter information from -h') - print('-' * 55) - exit() - - -def check_args(param): - """ - check input args - """ - if not os.path.exists(param.img_path): - err_msg(param.img_path) - if not os.path.exists(param.ann_path): - err_msg(param.ann_path) - if not os.path.exists(param.gtp): - os.makedirs(param.gtp) - return param - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Parse the VOC2028 dataset label') - parser.add_argument("--img_path", default="TestImages", help='The image path') - parser.add_argument("--ann_path", default="Annotations", help='Origin xml path') - parser.add_argument("--gtp", default="ground-truth/", help='The ground true file path') - args = parser.parse_args() - args = check_args(args) - main(args) - \ No newline at end of file diff --git a/contrib/HelmetIdentification/Test/performance_test_main.py b/contrib/HelmetIdentification/Test/performance_test_main.py deleted file mode 100644 index 7350d8ddf..000000000 --- a/contrib/HelmetIdentification/Test/performance_test_main.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import time -import signal -import cv2 -import numpy as np -import StreamManagerApi -import MxpiDataType_pb2 as MxpiDataType -import utils - - -def my_handler(signum, frame): - """ - :param signum: signum are used to identify the signal - :param frame: When the signal occurs, get the status of the process stack - func:Change flag of stop_stream - """ - global stop_stream - stop_stream = True - - -# exit flag -stop_stream = False -# When about to exit, get the exit signal -signal.signal(signal.SIGINT, my_handler) - -# The following belongs to the SDK Process -# init stream manager -streamManagerApi = StreamManagerApi.StreamManagerApi() -ret = streamManagerApi.InitManager() -if ret != 0: - print("Failed to init Stream manager, ret=%s" % str(ret)) - -# create streams by pipeline config file -#load pipline -with open("HelmetDetection.pipline", 'rb') as f: - pipelineStr = f.read() -ret = streamManagerApi.CreateMultipleStreams(pipelineStr) -# Print error message -if ret != 0: - print("Failed to create Stream, ret=%s" % str(ret)) - -# Obtain the inference result by specifying streamName and keyVec -# The data that needs to be obtained is searched by the plug-in name -# Stream name -streamName = b'Detection' -keyVec0 = StreamManagerApi.StringVector() -keyVec0.push_back(b"ReservedFrameInfo") -keyVec0.push_back(b"mxpi_modelinfer0") -keyVec0.push_back(b"mxpi_motsimplesort0") -keyVec0.push_back(b"mxpi_videodecoder0") -keyVec0.push_back(b"mxpi_videodecoder1") - -i = 0 -t_all = 0 -while True: - # exit flag - if stop_stream: - break - i += 1 - if i > 300: - break - t1 = time.time() - inferResult0 = streamManagerApi.GetResult(streamName, b'appsink0', keyVec0) - if inferResult0.metadataVec.size() == 0: - print('Object detection result of model infer is null!!!') - continue - - DictStructure = utils.get_inference_data(inferResult0) - # the visualization of the inference result, save the output in the specified folder - utils.cv_visualization(DictStructure[0], DictStructure[1], DictStructure[2], DictStructure[3], DictStructure[4]) - t2 = time.time() - t_diff = t2-t1 - t_all += t_diff -t_ave = t_all/i -print("e2e Time:{}".format(t_ave)) - -# Destroy All Streams -streamManagerApi.DestroyAllStreams() diff --git a/contrib/HelmetIdentification/Test/test_select.py b/contrib/HelmetIdentification/Test/test_select.py deleted file mode 100644 index 43114d83a..000000000 --- a/contrib/HelmetIdentification/Test/test_select.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import shutil -import cv2 - -with open("ImageSets/Main/test.txt", "r") as f: - data = f.readlines() - text_data = [] - for line in data: - line_new = line.strip('\n') # Remove the newline character of each element in the list - text_data.append(line_new) - print(text_data) - -path = 'JPEGImages' -save_path = 'TestImages' - -for item in os.listdir(path): - file_name = item.split('.')[0] - if file_name in text_data: - img = cv2.imread(path + '/' + item) - cv2.imwrite(save_path + '/' + file_name + ".jpg", img) \ No newline at end of file diff --git a/contrib/HelmetIdentification/Test/testmain.py b/contrib/HelmetIdentification/Test/testmain.py deleted file mode 100644 index df6c1bb34..000000000 --- a/contrib/HelmetIdentification/Test/testmain.py +++ /dev/null @@ -1,162 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd - -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -import os -import random -import cv2 -import numpy as np -import StreamManagerApi -import MxpiDataType_pb2 as MxpiDataType - -if __name__ == '__main__': - # init stream manager - streamManagerApi = StreamManagerApi.StreamManagerApi() - ret = streamManagerApi.InitManager() - if ret != 0: - print("Failed to init Stream manager, ret=%s" % str(ret)) - exit() - - # create streams by pipeline config file - pipeline = { - "detection": { - "stream_config": { - "deviceId": "1" - }, - "appsrc0": { - "props": { - "blocksize": "409600" - }, - "factory": "appsrc", - "next": "mxpi_imagedecoder0" - }, - "mxpi_imagedecoder0": { - "factory": "mxpi_imagedecoder", - "next": "mxpi_imageresize0" - }, - "mxpi_imageresize0": { - "factory": "mxpi_imageresize", - "props": { - "dataSource": "mxpi_imagedecoder0", - "resizeType": "Resizer_KeepAspectRatio_Fit", - "resizeHeight": "640", - "resizeWidth": "640" - }, - "next": "queue0" - }, - "queue0": { - "props": { - "max-size-buffers": "500" - }, - "factory": "queue", - "next": "mxpi_modelinfer0" - }, - "mxpi_modelinfer0": { - "props":{ - "dataSource":"mxpi_imageresize0", - "modelPath":"./YOLOv5_s.om", - "postProcessConfigPath":"./Helmet_yolov5.cfg", - "labelPath":"./imgclass.names", - "postProcessLibPath":"./libMpYOLOv5PostProcessor.so" - }, - "factory": "mxpi_modelinfer", - "next": "mxpi_dataserialize0" - }, - "mxpi_dataserialize0": { - "props": { - "outputDataKeys": "mxpi_modelinfer0" - }, - "factory": "mxpi_dataserialize", - "next": "appsink0" - }, - "appsink0": { - "props": { - "blocksize": "4096000" - }, - "factory": "appsink" - } - } - } - - pipelineStr = json.dumps(pipeline).encode() - ret = streamManagerApi.CreateMultipleStreams(pipelineStr) - if ret != 0: - print("Failed to create Stream, ret=%s" % str(ret)) - exit() - - path = "./TestImages/" - for item in os.listdir(path): - img_path = os.path.join(path,item) - print("file_path:",img_path) - img_name = item.split(".")[0] - img_txt = "./detection-test-result/" + img_name + ".txt" - if os.path.exists(img_txt): - os.remove(img_txt) - dataInput = StreamManagerApi.MxDataInput() - if os.path.exists(img_path) != 1: - print("The test image does not exist.") - - with open(img_path, 'rb') as f: - dataInput.data = f.read() - - - # Inputs data to a specified stream based on streamName. - streamName = b'detection' - ret = streamManagerApi.SendData(streamName, 0, dataInput) - - if ret < 0: - print("Failed to send data to stream.") - - - # Obtain the inference result by specifying streamName and uniqueId. - infer_result = streamManagerApi.GetResult(streamName, 0) - if infer_result.errorCode != 0: - print("GetResult error. errorCode=%d, errorMsg=%s" % ( - infer_result.errorCode, infer_result.data.decode())) - - # print the infer result - - results = json.loads(infer_result.data.decode()) - img = cv2.imread(img_path) - img_shape = img.shape - print(img_shape) - bboxes = [] - key = "MxpiObject" - if key not in results.keys(): - continue - for bbox in results['MxpiObject']: - bboxes = {'x0': int(bbox['x0']), - 'x1': int(bbox['x1']), - 'y0': int(bbox['y0']), - 'y1': int(bbox['y1']), - 'confidence': round(bbox['classVec'][0]['confidence'], 4), - 'text': bbox['classVec'][0]['className']} - text = "{}{}".format(str(bboxes['confidence']), " ") - print(bboxes) - L1 = [] - L1.append(int(bboxes['x0'])) - L1.append(int(bboxes['x1'])) - L1.append(int(bboxes['y0'])) - L1.append(int(bboxes['y1'])) - L1.append(bboxes['confidence']) - L1.append(bboxes['text']) - print(L1) - - with open(img_txt,"a+") as f: - content = '{} {} {} {} {} {}'.format(L1[5], L1[4], L1[0], L1[2], L1[1], L1[3]) - f.write(content) - f.write('\n') - - # destroy streams - streamManagerApi.DestroyAllStreams() diff --git a/contrib/HelmetIdentification/build.sh b/contrib/HelmetIdentification/build.sh deleted file mode 100644 index 4d14c15a3..000000000 --- a/contrib/HelmetIdentification/build.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -set -e -current_folder="$( cd "$(dirname "$0")" ;pwd -P )" - - -SAMPLE_FOLDER=( -/plugins/MxpiSelectedFrame/ -) - - -err_flag=0 -for sample in "${SAMPLE_FOLDER[@]}";do - cd "${current_folder}/${sample}" - bash build.sh || { - echo -e "Failed to build ${sample}" - err_flag=1 - } -done - -if [ ${err_flag} -eq 1 ]; then - exit 1 -fi -exit 0 \ No newline at end of file diff --git a/contrib/HelmetIdentification/image/image1.jpg b/contrib/HelmetIdentification/image/image1.jpg deleted file mode 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zybb~WAhOQuaVK&}0B1$fZ%EK>fh#0ye=6BA3LcI1aecBZM4gP-YY?g#hMi3wt>LY1 zA`4;8Cuj{sBtIiL$X~v5m_y8%1u<4~>ok%&@&%=N5t;o5=)=GXB4$05#OdWW!Ev0N zozezZi?n~0<}y${bqhK1Dow)<3tCjjg-4OztP4@i*8k}X`38Pok1ao!S<_tM@g533 Ndwm$R+FcPDe*+WoWfTAa diff --git a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/CMakeLists.txt b/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/CMakeLists.txt deleted file mode 100644 index d0e93415e..000000000 --- a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/CMakeLists.txt +++ /dev/null @@ -1,30 +0,0 @@ -cmake_minimum_required(VERSION 3.5.2) - -add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0) -add_definitions(-Dgoogle=mindxsdk_private) -set(PLUGIN_NAME "mxpi_selectedframe") -set(TARGET_LIBRARY ${PLUGIN_NAME}) -set(MX_SDK_HOME "$ENV{MX_SDK_HOME}") - -include_directories(${MX_SDK_HOME}/include) -include_directories(${MX_SDK_HOME}/opensource/include) -include_directories(${MX_SDK_HOME}/opensource/include/gstreamer-1.0) -include_directories(${MX_SDK_HOME}/opensource/include/glib-2.0) -include_directories(${MX_SDK_HOME}/opensource/lib/glib-2.0/include) -include_directories(${MX_SDK_HOME}/include/MxTools/PluginToolkit) -include_directories(${MX_SDK_HOME}/lib) - -link_directories(${MX_SDK_HOME}/lib) -link_directories(${MX_SDK_HOME}/opensource/lib) - -add_compile_options(-std=c++11 -fPIC -fstack-protector-all -pie -Wno-deprecated-declarations) -add_compile_options("-DPLUGIN_NAME=${PLUGIN_NAME}") -add_library(${TARGET_LIBRARY} SHARED MxpiSelectedFrame.cpp) - -target_link_libraries(${TARGET_LIBRARY} glib-2.0 gstreamer-1.0 gobject-2.0 gstbase-1.0 gmodule-2.0) -target_link_libraries(${TARGET_LIBRARY} plugintoolkit mxbase mxpidatatype) -target_link_libraries(${TARGET_LIBRARY} -Wl,-z,relro,-z,now,-z,noexecstack -s) - -install(TARGETS ${TARGET_LIBRARY} LIBRARY DESTINATION ${PROJECT_SOURCE_DIR}/dist/lib) - -# ${MX_SDK_HOME}设置为远程SDK安装路径 \ No newline at end of file diff --git a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.cpp b/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.cpp deleted file mode 100644 index bcf707e82..000000000 --- a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.cpp +++ /dev/null @@ -1,96 +0,0 @@ -/* - * Copyright(C) 2021. Huawei Technologies Co.,Ltd. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "MxpiSelectedFrame.h" -#include "MxBase/Log/Log.h" -using namespace MxBase; -using namespace MxTools; -using namespace MxPlugins; - -APP_ERROR MxpiSelectedFrame::Init(std::map> &configParamMap) -{ - LogInfo << "Begin to initialize MxpiSelectedFrame(" << pluginName_ << ")."; - // get parameters from website. - SelectedFrameNum_ = *std::static_pointer_cast(configParamMap["frameNum"]); - LogInfo << "skip frame nunmber(" << SelectedFrameNum_ << ")."; - LogInfo << "End to initialize MxpiSelectedFrame(" << pluginName_ << ")."; - return APP_ERR_OK; -} - -APP_ERROR MxpiSelectedFrame::DeInit() -{ - LogInfo << "Begin to deinitialize MxpiSelectedFrame(" << pluginName_ << ")."; - LogInfo << "End to deinitialize MxpiSelectedFrame(" << pluginName_ << ")."; - return APP_ERR_OK; -} - -APP_ERROR MxpiSelectedFrame::Process(std::vector &mxpiBuffer) -{ - MxpiBuffer *inputMxpiBuffer = mxpiBuffer[0]; - MxpiMetadataManager mxpiMetadataManager(*inputMxpiBuffer); - auto errorInfoPtr = mxpiMetadataManager.GetErrorInfo(); - if (errorInfoPtr != nullptr) { - LogWarn << "Input data is invalid, element(" << pluginName_ << ") plugin will not be executed rightly."; - SendData(0, *inputMxpiBuffer); - return APP_ERR_COMM_FAILURE; - } - - if (SelectedFrameNum_ == 0) { - SendData(0, *inputMxpiBuffer); - } else { - count++; - if ((count % (SelectedFrameNum_ + 1)) == 0) { - count = 0; - SendData(0, *inputMxpiBuffer); - } else { - MxpiBufferManager::DestroyBuffer(inputMxpiBuffer); - } - } - return APP_ERR_OK; -} - -std::vector> MxpiSelectedFrame::DefineProperties() -{ - std::vector> properties; - auto prop1 = std::make_shared>(ElementProperty { - UINT, - "frameNum", - "frameNum", - "the number of selected frame", - 0, 0, 100 - }); - properties.push_back(prop1); - return properties; -} - -MxpiPortInfo MxpiSelectedFrame::DefineInputPorts() -{ - MxpiPortInfo inputPortInfo; - std::vector> value = {{"ANY"}}; - GenerateStaticInputPortsInfo(value, inputPortInfo); - return inputPortInfo; -} - -MxpiPortInfo MxpiSelectedFrame::DefineOutputPorts() -{ - MxpiPortInfo outputPortInfo; - std::vector> value = {{"ANY"}}; - GenerateStaticOutputPortsInfo(value, outputPortInfo); - return outputPortInfo; -} -namespace { - MX_PLUGIN_GENERATE(MxpiSelectedFrame) -} \ No newline at end of file diff --git a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.h b/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.h deleted file mode 100644 index de76e850c..000000000 --- a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/MxpiSelectedFrame.h +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright(C) 2021. Huawei Technologies Co.,Ltd. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MXPLUGINS_MXPISKIPFRAME_H -#define MXPLUGINS_MXPISKIPFRAME_H - -#include "MxBase/ErrorCode/ErrorCode.h" -#include "MxTools/PluginToolkit/base/MxPluginGenerator.h" -#include "MxTools/PluginToolkit/buffer/MxpiBufferManager.h" -#include "MxTools/PluginToolkit/metadata/MxpiMetadataManager.h" -#include "MxTools/Proto/MxpiDataType.pb.h" - -/** - * This plugin is used for skip frame. - */ -namespace MxPlugins { - class MxpiSelectedFrame : public MxTools::MxPluginBase { - public: - /** - * @description: Init configs. - * @param configParamMap: config. - * @return: Error code. - */ - APP_ERROR Init(std::map> &configParamMap) override; - - /** - * @description: DeInit device. - * @return: Error code. - */ - APP_ERROR DeInit() override; - - /** - * @description: MxpiSelectedFrame plugin process. - * @param mxpiBuffer: data receive from the previous. - * @return: Error code. - */ - APP_ERROR Process(std::vector &mxpiBuffer) override; - - /** - * @description: MxpiSelectedFrame plugin define properties. - * @return: properties. - */ - static std::vector> DefineProperties(); - - /** - * @api - * @brief Define the number and data type of input ports. - * @return MxTools::MxpiPortInfo. - */ - static MxTools::MxpiPortInfo DefineInputPorts(); - - /** - * @api - * @brief Define the number and data type of output ports. - * @return MxTools::MxpiPortInfo. - */ - static MxTools::MxpiPortInfo DefineOutputPorts(); - - private: - - uint32_t SelectedFrameNum_ = 0; - - uint32_t count = 0; - }; -} - -#endif \ No newline at end of file diff --git a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/build.sh b/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/build.sh deleted file mode 100644 index 58cbd3f76..000000000 --- a/contrib/HelmetIdentification/plugins/MxpiSelectedFrame/build.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License.mitations under the License. - -set -e - -current_folder="$( cd "$(dirname "$0")" ;pwd -P )" - -function build_plugin() { - build_path=$current_folder/build - if [ -d "$build_path" ]; then - rm -rf "$build_path" - else - echo "file $build_path is not exist." - fi - mkdir -p "$build_path" - cd "$build_path" - cmake .. - make -j - cd .. - exit 0 -} - -build_plugin -exit 0 \ No newline at end of file -- Gitee