From 58f6f6c754267cb59128b54670f409475b351454 Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Wed, 25 Sep 2024 10:27:53 +0800 Subject: [PATCH 01/27] Update PaddleDetection Python --- README.md | 7 +- configs/LZ-Picodet.yaml | 25 +++++ example/periphery/adc/README.md | 2 +- example/periphery/gpio/README.md | 2 +- example/periphery/usart/README.md | 2 +- example/vision/classification/README.md | 2 +- .../vision/classification/python/README.md | 2 +- ...t_paddleclas.py => test_classification.py} | 6 +- example/vision/detetcion/README.md | 49 ++++++++ example/vision/detetcion/python/README.md | 106 ++++++++++++++++++ .../vision/detetcion/python/test_detection.py | 44 ++++++++ 11 files changed, 237 insertions(+), 10 deletions(-) create mode 100644 configs/LZ-Picodet.yaml rename example/vision/classification/python/{test_paddleclas.py => test_classification.py} (86%) create mode 100644 example/vision/detetcion/README.md create mode 100644 example/vision/detetcion/python/README.md create mode 100644 example/vision/detetcion/python/test_detection.py diff --git a/README.md b/README.md index 4c39c5e..ee1e946 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,9 @@ * [凌智视觉模块烧录镜像指南](./docs/introductory_tutorial/burn_image.md) * [凌智视觉模块连接设备指南](./docs/introductory_tutorial/connect_device_using_ssh.md) +* [凌智视觉模块 Python 开发环境搭建指南](./docs/introductory_tutorial/python_development_environment.md) + ## 🔌 外设控制教程 @@ -43,7 +46,7 @@ Lockzhiner Vision Module 拥有丰富的 IO 接口，其接口图片如下图所 这一部分教程将引导你从零开始训练并部署各种深度学习模型，包括分类模型、检测模型、分割模型和 OCR 模型。通过这些实战教程，你将学习到如何利用 Lockzhiner Vision Module 的高性能计算能力以及借助 Paddle 生态系统的丰富资源，轻松实现视觉任务的训练和部署。 * [分类模型部署指南](./example/vision/classification/README.md) -* [检测模型部署指南(支持中)]() +* [检测模型部署指南](./example/vision/detetcion/README.md) * [分割模型部署指南(支持中)]() * [OCR 模型部署指南(支持中)]() diff --git a/configs/LZ-Picodet.yaml b/configs/LZ-Picodet.yaml new file mode 100644 index 0000000..9ef9bd1 --- /dev/null +++ b/configs/LZ-Picodet.yaml @@ -0,0 +1,25 @@ +config: + mean: + - 0.406 + - 0.485 + - 0.456 + std: + - 0.225 + - 0.224 + - 0.229 + +load_onnx: + inputs: + - x + input_size_list: + - + - 1 + - 3 + - 320 + - 320 + outputs: + - p2o.DequantizeLinear.881 + +build: + do_quantization: false + dataset: \ No newline at end of file diff --git a/example/periphery/adc/README.md b/example/periphery/adc/README.md index 59a4291..ab65dab 100644 --- a/example/periphery/adc/README.md +++ b/example/periphery/adc/README.md @@ -53,5 +53,5 @@ 请参考以下教程使用 C++ 或 Python 在凌智视觉模块上部署 ADC 例程： -* [凌智视觉模块 ADC C++ 部署指南](./cpp/README.md) + * [凌智视觉模块 ADC Python 部署指南](./python/README.md) \ No newline at end of file diff --git a/example/periphery/gpio/README.md b/example/periphery/gpio/README.md index c0d31b1..f436160 100644 --- a/example/periphery/gpio/README.md +++ b/example/periphery/gpio/README.md @@ -62,5 +62,5 @@ GPIO（General Purpose Input/Output，通用输入/输出）是一种在嵌入 请参考以下教程使用 C++ 或 Python 在凌智视觉模块上部署 GPIO 例程： -- [凌智视觉模块 GPIO C++ 部署指南](./cpp/README.md) + - [凌智视觉模块 GPIO Python 部署指南](./python/README.md) diff --git a/example/periphery/usart/README.md b/example/periphery/usart/README.md index bd0f7e7..9698468 100644 --- a/example/periphery/usart/README.md +++ b/example/periphery/usart/README.md @@ -51,5 +51,5 @@ 请参考以下教程使用 C++ 或 Python 在凌智视觉模块上部署串口例程： -- [凌智视觉模块串口 C++ 部署指南](./cpp/README.md) + - [凌智视觉模块串口 Python 部署指南](./python/README.md) \ No newline at end of file diff --git a/example/vision/classification/README.md b/example/vision/classification/README.md index 2959a30..f3f3943 100644 --- a/example/vision/classification/README.md +++ b/example/vision/classification/README.md @@ -211,7 +211,7 @@ AI Studio 是基于百度深度学习开源平台飞桨的人工智能学习与 训练完模型后，请参考以下教程使用 C++ 或 Python 在凌智视觉模块上部署分类模型例程： -* [凌智视觉模块分类模型 C++ 部署指南](./cpp/README.md) + * [凌智视觉模块分类模型 Python 部署指南](./python/README.md) ## 5 各模型性能指标 diff --git a/example/vision/classification/python/README.md b/example/vision/classification/python/README.md index 708452d..7df673b 100644 --- a/example/vision/classification/python/README.md +++ b/example/vision/classification/python/README.md @@ -169,7 +169,7 @@ if __name__ == "__main__": 请使用 Electerm Ssh 并在命令行中执行以下命令: ```bash -python test_paddleclas.py LZ-MobileNetV3.rknn +python test_classification.py LZ-MobileNetV3.rknn ``` 运行程序后，屏幕上开始打印标签索引，分类置信度，并在一段时间后输出 FPS 值 diff --git a/example/vision/classification/python/test_paddleclas.py b/example/vision/classification/python/test_classification.py similarity index 86% rename from example/vision/classification/python/test_paddleclas.py rename to example/vision/classification/python/test_classification.py index c24bf90..1540b5c 100644 --- a/example/vision/classification/python/test_paddleclas.py +++ b/example/vision/classification/python/test_classification.py @@ -6,7 +6,7 @@ import sys if __name__ == "__main__": args = sys.argv if len(args) != 2: - print("Need model path. Example: python test_paddleclas.py LZ-MobileNetV3.rknn") + print("Need model path. Example: python test_classification.py LZ-MobileNetV3.rknn") exit(1) model = PaddleClas() @@ -23,14 +23,14 @@ if __name__ == "__main__": read_index = 0 total_time_ms = 0 for i in range(30): - start_time = time.time() ret, mat = video_capture.read() if ret is False: continue + start_time = time.time() result = model.predict(mat) end_time = time.time() total_time_ms += end_time - start_time read_index += 1 - print(result.label_id, result.confidence) + print(result.label_id, result.score) print(f"FPS is {1.0 / (total_time_ms/read_index)}") diff --git a/example/vision/detetcion/README.md b/example/vision/detetcion/README.md new file mode 100644 index 0000000..1087caa --- /dev/null +++ b/example/vision/detetcion/README.md @@ -0,0 +1,49 @@ +

凌智视觉模块检测模型部署指南

+ +发布版本：V0.0.0 + +日期：2024-09-11 + +文件密级：□绝密 □秘密 □内部资料 ■公开 + +--- + +**免责声明** + +本文档按**现状**提供，福州凌睿智捷电子有限公司（以下简称**本公司**）不对本文档中的任何陈述、信息和内容的准确性、可靠性、完整性、适销性、适用性及非侵权性提供任何明示或暗示的声明或保证。本文档仅作为使用指导的参考。 + +由于产品版本升级或其他原因，本文档可能在未经任何通知的情况下不定期更新或修改。 + +**读者对象** + +本教程适用于以下工程师： + +- 技术支持工程师 +- 软件开发工程师 + +**修订记录** + +| **日期** | **版本** | **作者** | **修改说明** | +| :--------- | -------- | -------- | ------------ | +| 2024/09/11 | 0.0.0 | 郑必城 | 初始版本 | + +## 1 简介 + +## 2 图像数据标注 + +## 3 使用 AIStudio 训练检测模型 + +## 4 在凌智视觉模块上部署模型 + +训练完模型后，请参考以下教程使用 C++ 或 Python 在凌智视觉模块上部署分类模型例程： + + +* [凌智视觉模块检测模型 Python 部署指南](./python/README.md) + +## 5 各模型性能指标 + +以下测试数据为模型执行 Predict 函数运行 1000 次耗时的平均时间 + +| 检测模型 | FPS(帧/s) | 精度(%) | +|:-------:|:----:|:----:| +|LZ-Picodet||| \ No newline at end of file diff --git a/example/vision/detetcion/python/README.md b/example/vision/detetcion/python/README.md new file mode 100644 index 0000000..8e90375 --- /dev/null +++ b/example/vision/detetcion/python/README.md @@ -0,0 +1,106 @@ +

凌智视觉模块检测模型 Python 部署指南

+ +发布版本：V0.0.0 + +日期：2024-09-11 + +文件密级：□绝密 □秘密 □内部资料 ■公开 + +--- + +**免责声明** + +本文档按**现状**提供，福州凌睿智捷电子有限公司（以下简称**本公司**）不对本文档中的任何陈述、信息和内容的准确性、可靠性、完整性、适销性、适用性及非侵权性提供任何明示或暗示的声明或保证。本文档仅作为使用指导的参考。 + +由于产品版本升级或其他原因，本文档可能在未经任何通知的情况下不定期更新或修改。 + +**读者对象** + +本教程适用于以下工程师： + +- 技术支持工程师 +- 软件开发工程师 + +**修订记录** + +| **日期** | **版本** | **作者** | **修改说明** | +| :--------- | -------- | -------- | ------------ | +| 2024/09/11 | 0.0.0 | 郑必城 | 初始版本 | + +## 1 简介 + +接下来让我们基于 Python 来部署 PaddleDetection 分类模型。在开始本章节前： + +- 请确保你已经参考 [凌智视觉模块检测模型部署指南](../README.md) 对模型进行了充分训练。 +- 请确保你已经按照 [开发环境搭建指南](../../../../docs/introductory_tutorial/python_development_environment.md) 正确配置了开发环境。 + +## 2 Python API 文档 + +```python +``` + +## 3 项目介绍 + +为了方便大家入手，我们做了一个简易的图像分类例程。该程序可以使用摄像头进行端到端推理。 + +```python +from lockzhiner_vision_module.cv2 import VideoCapture +from lockzhiner_vision_module.vision import PaddleClas +import time + +if __name__ == "__main__": + model = PaddleClas() + if model.initialize("LZ-MobileNetV2_x0_25.rknn") is False: + print("Failed to initialize PaddleClas") + exit(1) + + video_capture = VideoCapture() + if video_capture.open(0) is False: + print("Failed to open capture") + exit(1) + + while True: + read_index = 0 + total_time_ms = 0 + for i in range(30): + ret, mat = video_capture.read() + if ret is False: + continue + + start_time = time.time() + result = model.predict(mat) + end_time = time.time() + total_time_ms += (end_time - start_time) + read_index += 1 + print(result.label_id, result.confidence) + print(f"FPS is {1.0 / (total_time_ms/read_index)}") +``` + +## 4 上传并测试 Python 程序 + +参考 [连接设备指南](../../../../docs/introductory_tutorial/connect_device_using_ssh.md) 正确连接 Lockzhiner Vision Module 设备。 + +![](../../../../docs/introductory_tutorial/images/connect_device_using_ssh/ssh_success.png) + +请使用 Electerm Sftp 依次上传以下两个文件: + +- 进入存放 **test_paddleclas.py** 脚本文件的目录，将 **test_paddleclas.py** 上传到 Lockzhiner Vision Module +- 进入存放 **LZ-MobileNetV2_x0_25.rknn(也可能是其他模型)** 模型存放的目录（模型存放在训练模型后下载的 output 文件夹内），将 **LZ-MobileNetV2_x0_25.rknn** 上传到 Lockzhiner Vision Module + +![](images/stfp_0.png) + +![](images/stfp_1.png) + +请使用 Electerm Ssh 并在命令行中执行以下命令: + +```bash +python test_paddleclas.py LZ-MobileNetV3.rknn +``` + +运行程序后，屏幕上开始打印标签索引，分类置信度，并在一段时间后输出 FPS 值 + +![alt text](result_0.png) + +## 5 其他 + +如果你需要使用 C++ 来部署 PaddleClas 请参考[凌智视觉模块分类模型 C++ 部署指南](../cpp/README.md)。 diff --git a/example/vision/detetcion/python/test_detection.py b/example/vision/detetcion/python/test_detection.py new file mode 100644 index 0000000..2966372 --- /dev/null +++ b/example/vision/detetcion/python/test_detection.py @@ -0,0 +1,44 @@ +from lockzhiner_vision_module.cv2 import VideoCapture +from lockzhiner_vision_module.vision import PaddleDetection +import time +import sys + +if __name__ == "__main__": + args = sys.argv + if len(args) != 2: + print("Need model path. Example: python test_detection.py LZ-MobileNetV3.rknn") + exit(1) + + model = PaddleDetection() + if model.initialize(args[1]) is False: + print("Failed to initialize PaddleClas") + exit(1) + + video_capture = VideoCapture() + if video_capture.open(0) is False: + print("Failed to open capture") + exit(1) + + while True: + read_index = 0 + total_time_ms = 0 + for i in range(30): + ret, mat = video_capture.read() + if ret is False: + continue + + start_time = time.time() + result = model.predict(mat) + end_time = time.time() + total_time_ms += end_time - start_time + read_index += 1 + + print(f"result size is {result.size()}") + for i in range(result.size()): + box = result.boxes[i] + score = result.scores[i] + label_id = result.label_ids[i] + print( + f"(x,y,w,h,score,label_id)[{i}]: [{box.x},{box.y},{box.width},{box.height},{score},{label_id}]" + ) + print(f"FPS is {1.0 / (total_time_ms/read_index)}") -- Gitee From d21d7ad668e9d6f85cc7cf08b083feafdf34fe99 Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Wed, 25 Sep 2024 10:31:00 +0800 Subject: [PATCH 02/27] Update python example --- .../vision/classification/python/README.md | 16 +++++++++---- example/vision/detetcion/python/README.md | 24 +++++++++++++++---- 2 files changed, 30 insertions(+), 10 deletions(-) diff --git a/example/vision/classification/python/README.md b/example/vision/classification/python/README.md index 7df673b..4599d79 100644 --- a/example/vision/classification/python/README.md +++ b/example/vision/classification/python/README.md @@ -64,14 +64,14 @@ class ClassificationResult: return self.classification_result.label_id @property - def confidence(self): + def score(self): """ 获取分类结果的标签 ID。 Returns: float: 分类结果的置信度，表示识别结果的可靠程度，范围为 0 到 1。 """ - return self.classification_result.confidence + return self.classification_result.score class PaddleClas: @@ -122,10 +122,16 @@ class PaddleClas: from lockzhiner_vision_module.cv2 import VideoCapture from lockzhiner_vision_module.vision import PaddleClas import time +import sys if __name__ == "__main__": + args = sys.argv + if len(args) != 2: + print("Need model path. Example: python test_classification.py LZ-MobileNetV3.rknn") + exit(1) + model = PaddleClas() - if model.initialize("LZ-MobileNetV2_x0_25.rknn") is False: + if model.initialize(args[1]) is False: print("Failed to initialize PaddleClas") exit(1) @@ -145,9 +151,9 @@ if __name__ == "__main__": start_time = time.time() result = model.predict(mat) end_time = time.time() - total_time_ms += (end_time - start_time) + total_time_ms += end_time - start_time read_index += 1 - print(result.label_id, result.confidence) + print(result.label_id, result.score) print(f"FPS is {1.0 / (total_time_ms/read_index)}") ``` diff --git a/example/vision/detetcion/python/README.md b/example/vision/detetcion/python/README.md index 8e90375..a690b6f 100644 --- a/example/vision/detetcion/python/README.md +++ b/example/vision/detetcion/python/README.md @@ -45,12 +45,18 @@ ```python from lockzhiner_vision_module.cv2 import VideoCapture -from lockzhiner_vision_module.vision import PaddleClas +from lockzhiner_vision_module.vision import PaddleDetection import time +import sys if __name__ == "__main__": - model = PaddleClas() - if model.initialize("LZ-MobileNetV2_x0_25.rknn") is False: + args = sys.argv + if len(args) != 2: + print("Need model path. Example: python test_detection.py LZ-MobileNetV3.rknn") + exit(1) + + model = PaddleDetection() + if model.initialize(args[1]) is False: print("Failed to initialize PaddleClas") exit(1) @@ -70,9 +76,17 @@ if __name__ == "__main__": start_time = time.time() result = model.predict(mat) end_time = time.time() - total_time_ms += (end_time - start_time) + total_time_ms += end_time - start_time read_index += 1 - print(result.label_id, result.confidence) + + print(f"result size is {result.size()}") + for i in range(result.size()): + box = result.boxes[i] + score = result.scores[i] + label_id = result.label_ids[i] + print( + f"(x,y,w,h,score,label_id)[{i}]: [{box.x},{box.y},{box.width},{box.height},{score},{label_id}]" + ) print(f"FPS is {1.0 / (total_time_ms/read_index)}") ``` -- Gitee From 050f1da0dbd564f0bb527d622869d406bbc52f0a Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Wed, 25 Sep 2024 11:05:31 +0800 Subject: [PATCH 03/27] Update Capture Python Example --- example/periphery/capture/README.md | 202 +------------- .../capture/{ => cpp}/CMakeLists.txt | 0 example/periphery/capture/cpp/README.md | 261 ++++++++++++++++++ .../{ => cpp}/images/build_example.png | Bin .../capture/{ => cpp}/images/result_0.png | Bin .../capture/{ => cpp}/images/sftp.png | Bin .../capture/{ => cpp}/test_capture.cc | 0 example/periphery/capture/python/README.md | 101 +++++++ .../periphery/capture/python/test_capture.py | 32 +++ 9 files changed, 399 insertions(+), 197 deletions(-) rename example/periphery/capture/{ => cpp}/CMakeLists.txt (100%) create mode 100644 example/periphery/capture/cpp/README.md rename example/periphery/capture/{ => cpp}/images/build_example.png (100%) rename example/periphery/capture/{ => cpp}/images/result_0.png (100%) rename example/periphery/capture/{ => cpp}/images/sftp.png (100%) rename example/periphery/capture/{ => cpp}/test_capture.cc (100%) create mode 100644 example/periphery/capture/python/README.md create mode 100644 example/periphery/capture/python/test_capture.py diff --git a/example/periphery/capture/README.md b/example/periphery/capture/README.md index a1be379..1717a63 100644 --- a/example/periphery/capture/README.md +++ b/example/periphery/capture/README.md @@ -1,4 +1,4 @@ -

凌智视觉模块摄像头使用指南

+

凌智视觉模块摄像头部署指南

发布版本：V0.0.0 @@ -59,203 +59,11 @@ Lockzhiner Vision Module 上自带了一个摄像头，该摄像头可以支持 在开始这个章节前，请确保: -- 你已经按照 [开发环境搭建指南](../../../docs/introductory_tutorial/development_environment.md) 正确配置了开发环境。 - 点击 [凌智视觉模块图片传输助手下载地址](https://gitee.com/LockzhinerAI/LockzhinerVisionModule/releases/download/v0.0.0/LockzhinerVisionModuleImageFetcher_v0.0.0.exe) 下载凌智视觉模块图片传输助手。 -## 4 API 文档 +## 4 在凌智视觉模块上部署摄像头例程 -### 4.1 读取摄像头文档 +请参考以下教程使用 C++ 或 Python 在凌智视觉模块上部署摄像头例程： -凌智视觉模块使用 opencv-mobile 来读取摄像头数据。关于摄像头读取，请参考传统的 OpenCV 摄像头 API 来实现，需要注意的是: - -- 受限于运行内存，请使用我们编译的 [OpenCV Mobile](https://gitee.com/LockzhinerAI/opencv-mobile) 库来代替传统的 OpenCV 库 -- LockzhinerVisionModule 只有一个摄像头，因此在打开摄像头时，不管输入什么参数，都只会打开这个摄像头 -- 配置摄像头长宽时，请保证长和宽都是 8 的倍数 - -### 4.2 传输摄像头数据文档 - -为了让大家直观的看到摄像头获取的数据，我们编写了凌智视觉模块图片传输助手。关于 Lockzhiner Vision Module 中的图片发送 API，请参考以下文档: - -```cpp -/** - * @brief Edit 类提供了对摄像头数据的控制接口。 - * - * 需要注意的是， Edit 传输的数据必须使用 Lockzhiner Vision Module 图片获取助手来接收 - */ -class Edit { - public: - Edit() = default; - ~Edit() = default; - - /** - * @brief 接收客户端连接 - * - * 此函数尝试阻塞等待客户端连接。 - * 成功时返回 true，失败时返回 false。 - * - * @return bool 指示操作是否成功。 - */ - bool StartAndAcceptConnection(); - - /** - * @brief 输出图片 - * - * 此函数尝试向客户端传输图片数据 - * 成功时返回 true，失败时返回 false。 - * - * @return bool 指示操作是否成功。 - */ - bool Print(const cv::Mat &mat); - - private: - int server_fd_; - int port_ = 6868; - int new_socket_; - - void BuildPacket(const cv::Mat &mat, uint8_t *buffer, uint64_t size); - bool Write(const void *buf, size_t len); -}; -``` - -## 5 项目介绍 - -为了方便大家入手，我们做了一个简易的图片传输例程。该例程接收 Lockzhiner Vision Module 图片获取助手的连接并向其传输图片数据。 - -```cmake -# CMake最低版本要求 -cmake_minimum_required(VERSION 3.10) - -project(test_adc) - -# 定义项目根目录路径 -set(PROJECT_ROOT_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../..") -# 定义 OpenCV SDK 路径 -set(OpenCV_ROOT_PATH "${PROJECT_ROOT_PATH}/third_party/opencv-mobile-4.10.0-lockzhiner-vision-module") -set(OpenCV_DIR "${OpenCV_ROOT_PATH}/lib/cmake/opencv4") -find_package(OpenCV REQUIRED) -set(OPENCV_LIBRARIES "${OpenCV_LIBS}") -# 定义 LockzhinerVisionModule SDK 路径 -set(LockzhinerVisionModule_ROOT_PATH "${PROJECT_ROOT_PATH}/third_party/lockzhiner_vision_module_sdk") -set(LockzhinerVisionModule_DIR "${LockzhinerVisionModule_ROOT_PATH}/lib/cmake/lockzhiner_vision_module") -find_package(LockzhinerVisionModule REQUIRED) - -# 配置摄像头数据 -add_executable(Test-Capture test_capture.cc) -target_include_directories(Test-Capture PRIVATE ${LOCKZHINER_VISION_MODULE_INCLUDE_DIRS}) -target_link_libraries(Test-Capture PRIVATE ${OPENCV_LIBRARIES} ${LOCKZHINER_VISION_MODULE_LIBRARIES}) -``` - -读取并传输摄像头数据的核心代码如下: - -```cpp -#include - -#include -#include -#include -#include - -int main(int argc, char *argv[]) { - int width = 640; - int height = 480; - if (argc == 3) { - width = atoi(argv[1]); - height = atoi(argv[2]); - } - - lockzhiner_vision_module::edit::Edit edit; - // 打开服务器并阻塞接收客户端连接 - if (!edit.StartAndAcceptConnection()) { - std::cout << "Failed to start and accept connection" << std::endl; - return 1; - } - std::cout << "Succeed to connect device" << std::endl; - - cv::VideoCapture cap; - // 设置摄像头长宽 - cap.set(cv::CAP_PROP_FRAME_WIDTH, width); - cap.set(cv::CAP_PROP_FRAME_HEIGHT, height); - // 打开摄像头 - cap.open(0); - - const int w = cap.get(cv::CAP_PROP_FRAME_WIDTH); - const int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT); - cv::Mat temp_mat; - while (true) { - cap >> temp_mat; - // 判断获取的图片是否为空 - if (temp_mat.empty()) { - continue; - } - // 向客户端输出图片 - if (!edit.Print(temp_mat)) { - std::cout << "Failed to print to edit" << std::endl; - break; - } - } - return 0; -} -``` - -## 6 编译项目 - -使用 Docker Destop 打开 LockzhinerVisionModule 容器并执行以下命令来编译项目 - -```bash -# 进入 Demo 目录 -cd /LockzhinerVisionModuleWorkSpace/LockzhinerVisionModule/example/periphery/capture - -# 创建编译目录 -rm -rf build && mkdir build && cd build - -# 配置交叉编译工具链 -export TOOLCHAIN_ROOT_PATH=${PWD}/../../../../../arm-rockchip830-linux-uclibcgnueabihf - -# 使用 cmake 配置项目 -cmake -DCMAKE_TOOLCHAIN_FILE=../../../../toolchains/arm-rockchip830-linux-uclibcgnueabihf.toolchain.cmake \ - -DCMAKE_BUILD_TYPE=Release \ - .. - -# 执行编译 -make -j8 -``` - -![](images/build_example.png) - -## 7 执行摄像头测试程序 - -参考 [连接设备指南](../../../docs/introductory_tutorial/connect_device_using_ssh.md) 正确连接 Lockzhiner Vision Module 设备。 - -![](../../../docs/introductory_tutorial/images/connect_device_using_ssh/ssh_success.png) - -参考之前的例程，将 **Test-Capture** 传输到 Lockzhiner Vision Module 上 - -![](images/sftp.png) - -传输完成后执行以下代码 - -```bash -chmod +x ./Test-Capture -./Test-Capture -``` - -代码开启后程序将阻塞并等待摄像头连接 - -![](images/result_0.png) - -打开下载的 Lockzhiner Vision Module 图片获取助手，点击连接设备 - -![](images/result_1.png) - -成功连接设备后，将出现摄像头的画面 - -> 提示: -> 如果摄像头的画面不够清晰，请尝试轻轻转动摄像头来调整合适的焦距 - -![](images/result_2.png) - -点击拍照保存可以将图片保存到程序保存路径下的 **LockzhinerVisionModuleImages** 文件夹中 - -![](images/result_3.png) - -你可以点击**打开保存文件夹**按钮来快速打开这个文件夹 + +* [凌智视觉模块摄像头 Python 部署指南](./python/README.md) \ No newline at end of file diff --git a/example/periphery/capture/CMakeLists.txt b/example/periphery/capture/cpp/CMakeLists.txt similarity index 100% rename from example/periphery/capture/CMakeLists.txt rename to example/periphery/capture/cpp/CMakeLists.txt diff --git a/example/periphery/capture/cpp/README.md b/example/periphery/capture/cpp/README.md new file mode 100644 index 0000000..a1be379 --- /dev/null +++ b/example/periphery/capture/cpp/README.md @@ -0,0 +1,261 @@ +

凌智视觉模块摄像头使用指南

+ +发布版本：V0.0.0 + +日期：2024-09-04 + +文件密级：□绝密 □秘密 □内部资料 ■公开 + +--- + +**免责声明** + +本文档按**现状**提供，福州凌睿智捷电子有限公司（以下简称**本公司**）不对本文档中的任何陈述、信息和内容的准确性、可靠性、完整性、适销性、适用性及非侵权性提供任何明示或暗示的声明或保证。本文档仅作为使用指导的参考。 + +由于产品版本升级或其他原因，本文档可能在未经任何通知的情况下不定期更新或修改。 + +**读者对象** + +本教程适用于以下工程师： + +- 技术支持工程师 +- 软件开发工程师 + +**修订记录** + +| **日期** | **版本** | **作者** | **修改说明** | +| :--------- | -------- | -------- | ------------ | +| 2024/09/04 | 0.0.0 | 郑必城 | 初始版本 | + +## 1 简介 + +Lockzhiner Vision Module 上自带了一个摄像头，该摄像头可以支持最大 **2304x1296(16:9)** 的分辨率。本章节中，我们将教会你如何使用 Lockzhiner Vision Module 读取 摄像头数据，并使用 Lockzhiner Vision Module 传输图片到本地，方便保存数据。 + +## 2 数据表 + +受限于解码能力，Lockzhiner Vision Module 在不同的分辨率下，摄像头的帧率是不一样的，具体可以参考如下表格: + +### 2.1 长宽比为 4:3 + +| 摄像头分辨率| FPS | +| :----: | :----: | +| 480x360(4:3) | 25 | +| 640x480(4:3) | 25 | +| 960x720(4:3) | 14 | +| 1280x960(4:3) | 13 | +| 1920x1440(4:3) | 13 | + +### 2.2 长宽比为 16:9 + +| 摄像头分辨率| FPS | +| :----: | :----: | +| 480x270(16:9) | 25 | +| 640x360(16:9) | 25 | +| 960x540(16:9) | 25 | +| 1280x720(16:9) | 15 | +| 1920x1080(16:9) | 12 | + +## 3 前期准备 + +在开始这个章节前，请确保: + +- 你已经按照 [开发环境搭建指南](../../../docs/introductory_tutorial/development_environment.md) 正确配置了开发环境。 +- 点击 [凌智视觉模块图片传输助手下载地址](https://gitee.com/LockzhinerAI/LockzhinerVisionModule/releases/download/v0.0.0/LockzhinerVisionModuleImageFetcher_v0.0.0.exe) 下载凌智视觉模块图片传输助手。 + +## 4 API 文档 + +### 4.1 读取摄像头文档 + +凌智视觉模块使用 opencv-mobile 来读取摄像头数据。关于摄像头读取，请参考传统的 OpenCV 摄像头 API 来实现，需要注意的是: + +- 受限于运行内存，请使用我们编译的 [OpenCV Mobile](https://gitee.com/LockzhinerAI/opencv-mobile) 库来代替传统的 OpenCV 库 +- LockzhinerVisionModule 只有一个摄像头，因此在打开摄像头时，不管输入什么参数，都只会打开这个摄像头 +- 配置摄像头长宽时，请保证长和宽都是 8 的倍数 + +### 4.2 传输摄像头数据文档 + +为了让大家直观的看到摄像头获取的数据，我们编写了凌智视觉模块图片传输助手。关于 Lockzhiner Vision Module 中的图片发送 API，请参考以下文档: + +```cpp +/** + * @brief Edit 类提供了对摄像头数据的控制接口。 + * + * 需要注意的是， Edit 传输的数据必须使用 Lockzhiner Vision Module 图片获取助手来接收 + */ +class Edit { + public: + Edit() = default; + ~Edit() = default; + + /** + * @brief 接收客户端连接 + * + * 此函数尝试阻塞等待客户端连接。 + * 成功时返回 true，失败时返回 false。 + * + * @return bool 指示操作是否成功。 + */ + bool StartAndAcceptConnection(); + + /** + * @brief 输出图片 + * + * 此函数尝试向客户端传输图片数据 + * 成功时返回 true，失败时返回 false。 + * + * @return bool 指示操作是否成功。 + */ + bool Print(const cv::Mat &mat); + + private: + int server_fd_; + int port_ = 6868; + int new_socket_; + + void BuildPacket(const cv::Mat &mat, uint8_t *buffer, uint64_t size); + bool Write(const void *buf, size_t len); +}; +``` + +## 5 项目介绍 + +为了方便大家入手，我们做了一个简易的图片传输例程。该例程接收 Lockzhiner Vision Module 图片获取助手的连接并向其传输图片数据。 + +```cmake +# CMake最低版本要求 +cmake_minimum_required(VERSION 3.10) + +project(test_adc) + +# 定义项目根目录路径 +set(PROJECT_ROOT_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../..") +# 定义 OpenCV SDK 路径 +set(OpenCV_ROOT_PATH "${PROJECT_ROOT_PATH}/third_party/opencv-mobile-4.10.0-lockzhiner-vision-module") +set(OpenCV_DIR "${OpenCV_ROOT_PATH}/lib/cmake/opencv4") +find_package(OpenCV REQUIRED) +set(OPENCV_LIBRARIES "${OpenCV_LIBS}") +# 定义 LockzhinerVisionModule SDK 路径 +set(LockzhinerVisionModule_ROOT_PATH "${PROJECT_ROOT_PATH}/third_party/lockzhiner_vision_module_sdk") +set(LockzhinerVisionModule_DIR "${LockzhinerVisionModule_ROOT_PATH}/lib/cmake/lockzhiner_vision_module") +find_package(LockzhinerVisionModule REQUIRED) + +# 配置摄像头数据 +add_executable(Test-Capture test_capture.cc) +target_include_directories(Test-Capture PRIVATE ${LOCKZHINER_VISION_MODULE_INCLUDE_DIRS}) +target_link_libraries(Test-Capture PRIVATE ${OPENCV_LIBRARIES} ${LOCKZHINER_VISION_MODULE_LIBRARIES}) +``` + +读取并传输摄像头数据的核心代码如下: + +```cpp +#include + +#include +#include +#include +#include + +int main(int argc, char *argv[]) { + int width = 640; + int height = 480; + if (argc == 3) { + width = atoi(argv[1]); + height = atoi(argv[2]); + } + + lockzhiner_vision_module::edit::Edit edit; + // 打开服务器并阻塞接收客户端连接 + if (!edit.StartAndAcceptConnection()) { + std::cout << "Failed to start and accept connection" << std::endl; + return 1; + } + std::cout << "Succeed to connect device" << std::endl; + + cv::VideoCapture cap; + // 设置摄像头长宽 + cap.set(cv::CAP_PROP_FRAME_WIDTH, width); + cap.set(cv::CAP_PROP_FRAME_HEIGHT, height); + // 打开摄像头 + cap.open(0); + + const int w = cap.get(cv::CAP_PROP_FRAME_WIDTH); + const int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT); + cv::Mat temp_mat; + while (true) { + cap >> temp_mat; + // 判断获取的图片是否为空 + if (temp_mat.empty()) { + continue; + } + // 向客户端输出图片 + if (!edit.Print(temp_mat)) { + std::cout << "Failed to print to edit" << std::endl; + break; + } + } + return 0; +} +``` + +## 6 编译项目 + +使用 Docker Destop 打开 LockzhinerVisionModule 容器并执行以下命令来编译项目 + +```bash +# 进入 Demo 目录 +cd /LockzhinerVisionModuleWorkSpace/LockzhinerVisionModule/example/periphery/capture + +# 创建编译目录 +rm -rf build && mkdir build && cd build + +# 配置交叉编译工具链 +export TOOLCHAIN_ROOT_PATH=${PWD}/../../../../../arm-rockchip830-linux-uclibcgnueabihf + +# 使用 cmake 配置项目 +cmake -DCMAKE_TOOLCHAIN_FILE=../../../../toolchains/arm-rockchip830-linux-uclibcgnueabihf.toolchain.cmake \ + -DCMAKE_BUILD_TYPE=Release \ + .. + +# 执行编译 +make -j8 +``` + +![](images/build_example.png) + +## 7 执行摄像头测试程序 + +参考 [连接设备指南](../../../docs/introductory_tutorial/connect_device_using_ssh.md) 正确连接 Lockzhiner Vision Module 设备。 + +![](../../../docs/introductory_tutorial/images/connect_device_using_ssh/ssh_success.png) + +参考之前的例程，将 **Test-Capture** 传输到 Lockzhiner Vision Module 上 + +![](images/sftp.png) + +传输完成后执行以下代码 + +```bash +chmod +x ./Test-Capture +./Test-Capture +``` + +代码开启后程序将阻塞并等待摄像头连接 + +![](images/result_0.png) + +打开下载的 Lockzhiner Vision Module 图片获取助手，点击连接设备 + +![](images/result_1.png) + +成功连接设备后，将出现摄像头的画面 + +> 提示: +> 如果摄像头的画面不够清晰，请尝试轻轻转动摄像头来调整合适的焦距 + +![](images/result_2.png) + +点击拍照保存可以将图片保存到程序保存路径下的 **LockzhinerVisionModuleImages** 文件夹中 + +![](images/result_3.png) + +你可以点击**打开保存文件夹**按钮来快速打开这个文件夹 diff --git a/example/periphery/capture/images/build_example.png b/example/periphery/capture/cpp/images/build_example.png similarity index 100% rename from example/periphery/capture/images/build_example.png rename to example/periphery/capture/cpp/images/build_example.png diff --git a/example/periphery/capture/images/result_0.png b/example/periphery/capture/cpp/images/result_0.png similarity index 100% rename from example/periphery/capture/images/result_0.png rename to example/periphery/capture/cpp/images/result_0.png diff --git a/example/periphery/capture/images/sftp.png b/example/periphery/capture/cpp/images/sftp.png similarity index 100% rename from example/periphery/capture/images/sftp.png rename to example/periphery/capture/cpp/images/sftp.png diff --git a/example/periphery/capture/test_capture.cc b/example/periphery/capture/cpp/test_capture.cc similarity index 100% rename from example/periphery/capture/test_capture.cc rename to example/periphery/capture/cpp/test_capture.cc diff --git a/example/periphery/capture/python/README.md b/example/periphery/capture/python/README.md new file mode 100644 index 0000000..29d31f8 --- /dev/null +++ b/example/periphery/capture/python/README.md @@ -0,0 +1,101 @@ +

凌智视觉模块摄像头 Python 部署指南

+ +发布版本：V0.0.0 + +日期：2024-09-25 + +文件密级：□绝密 □秘密 □内部资料 ■公开 + +--- + +**免责声明** + +本文档按**现状**提供，福州凌睿智捷电子有限公司（以下简称**本公司**）不对本文档中的任何陈述、信息和内容的准确性、可靠性、完整性、适销性、适用性及非侵权性提供任何明示或暗示的声明或保证。本文档仅作为使用指导的参考。 + +由于产品版本升级或其他原因，本文档可能在未经任何通知的情况下不定期更新或修改。 + +**读者对象** + +本教程适用于以下工程师： + +- 技术支持工程师 +- 软件开发工程师 + +**修订记录** + +| **日期** | **版本** | **作者** | **修改说明** | +| :--------- | -------- | -------- | ------------ | +| 2024/09/25 | 0.0.0 | 郑必城 | 初始版本 | + +## 1 简介 + +Lockzhiner Vision Module 上自带了一个摄像头，该摄像头可以支持最大 **2304x1296(16:9)** 的分辨率。本章节中，我们将教会你如何使用 Lockzhiner Vision Module 读取 摄像头数据，并使用 Lockzhiner Vision Module 传输图片到本地，方便保存数据。 + +## 2 前期准备 + +在开始这个章节前，请确保: + +- 你已经按照 [开发环境搭建指南](../../../../docs/introductory_tutorial/python_development_environment.md) 正确配置了开发环境。 +- 点击 [凌智视觉模块图片传输助手下载地址](https://gitee.com/LockzhinerAI/LockzhinerVisionModule/releases/download/v0.0.0/LockzhinerVisionModuleImageFetcher_v0.0.0.exe) 下载凌智视觉模块图片传输助手。 + +## 4 API 文档 + +### 4.1 读取摄像头文档 + +读取摄像头的 API 对齐了 OpenCV，具体 API 文档如下: + +```python + +``` + +### 4.2 传输摄像头数据文档 + +为了让大家直观的看到摄像头获取的数据，我们编写了凌智视觉模块图片传输助手。关于 Lockzhiner Vision Module 中的图片发送 API，请参考以下文档: + +```python + +``` + +## 5 项目介绍 + +为了方便大家入手，我们做了一个简易的图片传输例程。该例程接收 Lockzhiner Vision Module 图片获取助手的连接并向其传输图片数据。 + +```cpp +``` + +## 6 执行摄像头测试程序 + +参考 [连接设备指南](../../../../docs/introductory_tutorial/connect_device_using_ssh.md) 正确连接 Lockzhiner Vision Module 设备。 + +![](../../../../docs/introductory_tutorial/images/connect_device_using_ssh/ssh_success.png) + +参考之前的例程，将 **test_capture.py** 传输到 Lockzhiner Vision Module 上 + +![](images/sftp.png) + +传输完成后，返回代码执行界面，执行以下代码 + +```bash +python ./test_capture.py +``` + +代码开启后程序将阻塞并等待摄像头连接 + +![](images/result_0.png) + +打开下载的 Lockzhiner Vision Module 图片获取助手，点击连接设备 + +![](../images/result_1.png) + +成功连接设备后，将出现摄像头的画面 + +> 提示: +> 如果摄像头的画面不够清晰，请尝试轻轻转动摄像头来调整合适的焦距 + +![](../images/result_2.png) + +点击拍照保存可以将图片保存到程序保存路径下的 **LockzhinerVisionModuleImages** 文件夹中 + +![](../images/result_3.png) + +你可以点击**打开保存文件夹**按钮来快速打开这个文件夹 diff --git a/example/periphery/capture/python/test_capture.py b/example/periphery/capture/python/test_capture.py new file mode 100644 index 0000000..c14b8a3 --- /dev/null +++ b/example/periphery/capture/python/test_capture.py @@ -0,0 +1,32 @@ +from lockzhiner_vision_module.cv2 import VideoCapture +from lockzhiner_vision_module.edit import Edit +import time +import sys + +if __name__ == "__main__": + args = sys.argv + if len(args) != 1: + print("Need model path. Example: python test_capture.py") + exit(1) + + edit = Edit() + edit.start_and_accept_connection() + + video_capture = VideoCapture() + if video_capture.open(0) is False: + print("Failed to open capture") + exit(1) + + while True: + read_index = 0 + total_time_ms = 0 + for i in range(30): + start_time = time.time() + ret, mat = video_capture.read() + if ret is False: + continue + end_time = time.time() + edit.print(mat) + total_time_ms += end_time - start_time + read_index += 1 + print(f"FPS is {1.0 / (total_time_ms/read_index)}") -- Gitee From 7de2c587625c69a21b4c521b882555eee07fda33 Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Wed, 25 Sep 2024 11:26:33 +0800 Subject: [PATCH 04/27] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20API=20=E6=96=87?= =?UTF-8?q?=E6=A1=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- example/periphery/capture/python/README.md | 70 +++++++++- example/vision/detetcion/python/README.md | 154 +++++++++++++++++++++ 2 files changed, 222 insertions(+), 2 deletions(-) diff --git a/example/periphery/capture/python/README.md b/example/periphery/capture/python/README.md index 29d31f8..b70b1df 100644 --- a/example/periphery/capture/python/README.md +++ b/example/periphery/capture/python/README.md @@ -45,7 +45,50 @@ Lockzhiner Vision Module 上自带了一个摄像头，该摄像头可以支持 读取摄像头的 API 对齐了 OpenCV，具体 API 文档如下: ```python - +class VideoCapture: + """ + OpenCV 摄像头类 + """ + def __init__(self): + self.video_capture = cv2.VideoCapture() + + def open(self, index=0): + """ + 打开摄像头 + + Returns: + bool: 摄像头打开过程过是否出现逻辑错误 + """ + return self.video_capture.open(index) + + def isOpened(self): + """ + 判断摄像头是否打开成功 + + Returns: + bool: 摄像头是否打开成功 + """ + return self.video_capture.isOpened() + + def read(self): + """ + 读取摄像头数据 + + Returns: + bool: 摄像头是否成功读取到数据 + cv::Mat: 摄像头读取的图像数据 + """ + mat = self.video_capture.read() + return not mat.empty(), mat + + def release(self): + """ + 关闭摄像头 + + Returns: + bool: 是否释放成功 + """ + return self.video_capture.release() ``` ### 4.2 传输摄像头数据文档 @@ -53,7 +96,30 @@ Lockzhiner Vision Module 上自带了一个摄像头，该摄像头可以支持 为了让大家直观的看到摄像头获取的数据，我们编写了凌智视觉模块图片传输助手。关于 Lockzhiner Vision Module 中的图片发送 API，请参考以下文档: ```python - +class Edit: + """ + 图片数据传输类，用于传输 CV::Mat 到凌智图片传输助手 + """ + def __init__(self): + self.edit = edit.Edit() + + def start_and_accept_connection(self): + """ + 阻塞等待凌智图片传输助手的连接请求 + """ + return self.edit.start_and_accept_connection() + + def print(self, mat): + """ + 传输图片到凌智图片传输助手 + + Args: + mat (cv::Mat): OpenCV 图片 Array + + Returns: + bool: 传输是否成功 + """ + return self.edit.print(mat) ``` ## 5 项目介绍 diff --git a/example/vision/detetcion/python/README.md b/example/vision/detetcion/python/README.md index a690b6f..4005419 100644 --- a/example/vision/detetcion/python/README.md +++ b/example/vision/detetcion/python/README.md @@ -37,6 +37,160 @@ ## 2 Python API 文档 ```python +class Rect: + def __init__(self): + self.rect = cv2.Rect() + + def empty(self): + return self.rect.empty() + + @property + def x(self): + """ + 获取矩形左上角坐标点的 x 坐标 + + Returns: + int: 获取矩形左上角坐标点的 x 坐标 + """ + return self.rect.x + + @property + def y(self): + """ + 获取矩形左上角坐标点的 y 坐标 + + Returns: + int: 获取矩形左上角坐标点的 y 坐标 + """ + return self.rect.y + + @property + def width(self): + """ + 获取矩形的宽 + + Returns: + int: 获取矩形的宽 + """ + return self.rect.width + + @property + def height(self): + """ + 获取矩形的高 + + Returns: + int: 获取矩形的高 + """ + return self.rect.height + + +class DetectionResult: + """ + 分类结果类，用于封装和处理目标检测结果数据。 + + 该类主要提供了一个包装层，用于访问和管理由视觉模块产生的分类结果。 + """ + + def __init__(self): + self.detection_result = vision.DetectionResult() + + def empty(self): + """ + 判断结果类中是否存在结果（是否为空） + + Returns: + bool: 结果类中是否存在结果（是否为空） + """ + return self.detection_result.empty() + + @property + def size(self): + """ + 获取目标检测模型检测出来的目标总数 + + Returns: + size_t: 目标检测模型检测出来的目标总数 + """ + return self.detection_result.size() + + @property + def boxes(self): + """ + 获取目标检测模型检测出来的矩形框信息 + + Returns: + Rect: 矩形框信息 + """ + return self.detection_result.boxes + + @property + def scores(self): + """ + 获取目标检测模型检测出来的得分信息 + + Returns: + float: 得分信息 + """ + return self.detection_result.scores + + @property + def label_ids(self): + """ + 获取目标检测模型检测出来的分类标签信息 + + Returns: + int: 分类标签信息 + """ + return self.detection_result.label_ids + +class PaddleDetection: + """ + PaddleDetection 类 - 用于目标检测的 PaddlePaddle 模型封装。 + + 该类封装了 PaddleDetection 框架下的目标检测模型，提供了初始化和预测的方法。 + """ + + def __init__(self): + """ + 构造函数 - 初始化 PaddleDetection 对象。 + """ + self.model = vision.PaddleDetection() + + def initialize(self, model_path): + """ + 初始化模型 - 加载预训练的 PaddlePaddle 模型。 + + Args: + model_path (str): 模型文件的路径。 + + Returns: + bool: 初始化是否成功。 + """ + return self.model.initialize(model_path) + + def set_threshold(self, score_threshold=0.5, nms_threshold=0.5): + """ + 设置目标检测阈值 + + Args: + score_threshold (float): 目标检测得分阈值，默认为 0.5 + nms_threshold (float): 目标检测 NMS 阈值，默认为 0.5 + + """ + self.model.initialize(score_threshold, nms_threshold) + + def predict(self, input_mat): + """ + 进行预测 - 使用加载的模型对输入数据进行分类预测。 + + Args: + input_mat (cv2.Mat): 输入的图像数据，通常是一个 cv2.Mat 变量。 + + Returns: + DetectionResult: 预测结果对象，包含了分类的标签、置信度等信息。 + """ + return self.model.predict(input_mat) ``` ## 3 项目介绍 -- Gitee From cb712a9850e70aee2147c1096e5264497ad92b1e Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Wed, 25 Sep 2024 16:26:46 +0800 Subject: [PATCH 05/27] Update --- example/periphery/capture/python/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/example/periphery/capture/python/README.md b/example/periphery/capture/python/README.md index b70b1df..d81f1c7 100644 --- a/example/periphery/capture/python/README.md +++ b/example/periphery/capture/python/README.md @@ -38,9 +38,9 @@ Lockzhiner Vision Module 上自带了一个摄像头，该摄像头可以支持 - 你已经按照 [开发环境搭建指南](../../../../docs/introductory_tutorial/python_development_environment.md) 正确配置了开发环境。 - 点击 [凌智视觉模块图片传输助手下载地址](https://gitee.com/LockzhinerAI/LockzhinerVisionModule/releases/download/v0.0.0/LockzhinerVisionModuleImageFetcher_v0.0.0.exe) 下载凌智视觉模块图片传输助手。 -## 4 API 文档 +## 3 API 文档 -### 4.1 读取摄像头文档 +### 3.1 读取摄像头文档 读取摄像头的 API 对齐了 OpenCV，具体 API 文档如下: @@ -91,7 +91,7 @@ class VideoCapture: return self.video_capture.release() ``` -### 4.2 传输摄像头数据文档 +### 3.2 传输摄像头数据文档 为了让大家直观的看到摄像头获取的数据，我们编写了凌智视觉模块图片传输助手。关于 Lockzhiner Vision Module 中的图片发送 API，请参考以下文档: @@ -122,14 +122,14 @@ class Edit: return self.edit.print(mat) ``` -## 5 项目介绍 +## 4 项目介绍 为了方便大家入手，我们做了一个简易的图片传输例程。该例程接收 Lockzhiner Vision Module 图片获取助手的连接并向其传输图片数据。 ```cpp ``` -## 6 执行摄像头测试程序 +## 5 执行摄像头测试程序 参考 [连接设备指南](../../../../docs/introductory_tutorial/connect_device_using_ssh.md) 正确连接 Lockzhiner Vision Module 设备。 -- Gitee From 4afd533ac5d8702544879e4c374584dd12b24ecb Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Wed, 25 Sep 2024 16:27:51 +0800 Subject: [PATCH 06/27] update --- configs/LZ-Picodet.yaml | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/configs/LZ-Picodet.yaml b/configs/LZ-Picodet.yaml index 9ef9bd1..591bc4e 100644 --- a/configs/LZ-Picodet.yaml +++ b/configs/LZ-Picodet.yaml @@ -1,6 +1,6 @@ config: mean: - - 0.406 + - 0.406 - 0.485 - 0.456 std: @@ -10,7 +10,7 @@ config: load_onnx: inputs: - - x + - image input_size_list: - - 1 @@ -18,7 +18,14 @@ load_onnx: - 320 - 320 outputs: - - p2o.DequantizeLinear.881 + - p2o.DequantizeLinear.1705 + - p2o.DequantizeLinear.1707 + - p2o.DequantizeLinear.1619 + - p2o.DequantizeLinear.1621 + - p2o.DequantizeLinear.1533 + - p2o.DequantizeLinear.1535 + - p2o.DequantizeLinear.1445 + - p2o.DequantizeLinear.1447 build: do_quantization: false -- Gitee From 735254f6163952a9d34f9477e1768c02e2154d43 Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Thu, 26 Sep 2024 14:33:47 +0800 Subject: [PATCH 07/27] update preprocess mean and std --- configs/LZ-MobileNetV3.yaml | 12 ++++++------ configs/LZ-Picodet.yaml | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/configs/LZ-MobileNetV3.yaml b/configs/LZ-MobileNetV3.yaml index 12d2f24..cf097aa 100644 --- a/configs/LZ-MobileNetV3.yaml +++ b/configs/LZ-MobileNetV3.yaml @@ -1,12 +1,12 @@ config: mean: - - 0.5 - - 0.5 - - 0.5 + - 0.406 + - 0.456 + - 0.485 std: - - 0.5 - - 0.5 - - 0.5 + - 0.225 + - 0.224 + - 0.229 load_onnx: inputs: diff --git a/configs/LZ-Picodet.yaml b/configs/LZ-Picodet.yaml index 591bc4e..af327bb 100644 --- a/configs/LZ-Picodet.yaml +++ b/configs/LZ-Picodet.yaml @@ -1,8 +1,8 @@ config: mean: - 0.406 - - 0.485 - 0.456 + - 0.485 std: - 0.225 - 0.224 -- Gitee From 6fe0a98e6ef0f096f692f499285cbaa6a05e5efd Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Thu, 26 Sep 2024 17:06:53 +0800 Subject: [PATCH 08/27] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=89=8B=E5=86=99?= =?UTF-8?q?=E6=95=B0=E5=AD=97=E8=AF=86=E5=88=AB=E6=A1=88=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 31 ++++---- example/special/digit_handwritten_rec.md | 50 ++++++++++++ example/special/python/README.md | 76 +++++++++++++++++++ .../python/test_digit_handwritten_rec.py | 34 +++++++++ example/vision/classification/README.md | 10 +-- 5 files changed, 179 insertions(+), 22 deletions(-) create mode 100644 example/special/digit_handwritten_rec.md create mode 100644 example/special/python/README.md create mode 100644 example/special/python/test_digit_handwritten_rec.py diff --git a/README.md b/README.md index ee1e946..10a40e7 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,6 @@ ## 🔄 最新日志 - ## 💡 入门学习教程 这一部分教程旨在为你提供一个系统化的学习路径，帮助你快速上手 Lockzhiner Vision Module。通过一系列详细的教程，你将学会如何烧录镜像、连接设备、搭建开发环境和编写简单的程序。 @@ -24,7 +23,7 @@ 如果你需要开发 C++ 程序，请参考以下教程搭建开发环境: * [凌智视觉模块 C++ 开发环境搭建指南](./docs/introductory_tutorial/cpp_development_environment.md) -* [基于 C++ 编写 Hello World 程序](./example/hello_world/README.md) +* [基于 C++ 编写 Hello World 程序](./example/hello_world) --> ## 🔌 外设控制教程 @@ -35,26 +34,26 @@ Lockzhiner Vision Module 拥有丰富的 IO 接口，其接口图片如下图所 为了帮助你快速上手教程，我们提供了一系列的外设控制例程，你可以点击链接快速学习 -* [凌智视觉模块串口使用指南](./example/periphery/usart/README.md) -* [凌智视觉模块 ADC 使用指南](./example/periphery/adc/README.md) -* [凌智视觉模块 PWM 使用指南](./example/periphery/pwm/README.md) -* [凌智视觉模块 GPIO 使用指南](./example/periphery/gpio/README.md) -* [凌智视觉模块摄像头使用指南](./example/periphery/capture/README.md) +* [凌智视觉模块串口使用指南](./example/periphery/usart) +* [凌智视觉模块 ADC 使用指南](./example/periphery/adc) +* [凌智视觉模块 PWM 使用指南](./example/periphery/pwm) +* [凌智视觉模块 GPIO 使用指南](./example/periphery/gpio) +* [凌智视觉模块摄像头使用指南](./example/periphery/capture) ## 🧠 深度学习视觉部署教程 这一部分教程将引导你从零开始训练并部署各种深度学习模型，包括分类模型、检测模型、分割模型和 OCR 模型。通过这些实战教程，你将学习到如何利用 Lockzhiner Vision Module 的高性能计算能力以及借助 Paddle 生态系统的丰富资源，轻松实现视觉任务的训练和部署。 -* [分类模型部署指南](./example/vision/classification/README.md) -* [检测模型部署指南](./example/vision/detetcion/README.md) -* [分割模型部署指南(支持中)]() -* [OCR 模型部署指南(支持中)]() +* [凌智视觉模块分类模型部署指南](./example/vision/classification) +* [凌智视觉模块检测模型部署指南](./example/vision/detetcion) + + -## 👍 特色模型部署教程 +## 👍 特色部署教程 -* [手写数字检测部署指南(支持中)]() -* [人脸佩戴口罩检测部署指南(支持中)]() -* [二维码识别与检测部署指南(支持中)]() +* [凌智视觉模块手写数字识别部署指南]() + + ## 🐛 Bug反馈 @@ -84,4 +83,4 @@ Lockzhiner Vision Module 拥有丰富的 IO 接口，其接口图片如下图所 ## 📜 开源协议 -Lockzhiner Vision Module 全系列仓库遵循 [Apache License Version 2.0](./LICENSE)。 \ No newline at end of file +Lockzhiner Vision Module 全系列仓库遵循 [Apache License Version 2.0](./LICENSE)。 diff --git a/example/special/digit_handwritten_rec.md b/example/special/digit_handwritten_rec.md new file mode 100644 index 0000000..eb01e20 --- /dev/null +++ b/example/special/digit_handwritten_rec.md @@ -0,0 +1,50 @@ +

凌智视觉模块手写数字识别部署指南

+ +发布版本：V0.0.0 + +日期：2024-09-26 + +文件密级：□绝密 □秘密 □内部资料 ■公开 + +--- + +**免责声明** + +本文档按**现状**提供，福州凌睿智捷电子有限公司（以下简称**本公司**）不对本文档中的任何陈述、信息和内容的准确性、可靠性、完整性、适销性、适用性及非侵权性提供任何明示或暗示的声明或保证。本文档仅作为使用指导的参考。 + +由于产品版本升级或其他原因，本文档可能在未经任何通知的情况下不定期更新或修改。 + +**读者对象** + +本教程适用于以下工程师： + +- 技术支持工程师 +- 软件开发工程师 + +**修订记录** + +| **日期** | **版本** | **作者** | **修改说明** | +| :--------- | -------- | -------- | ------------ | +| 2024/09/26 | 0.0.0 | 郑必城 | 初始版本 | + +## 1 简介 + +手写数字识别是经常使用到的功能之一。竞品 K210 和 OpenMV 提供了 Mnist 数字识别的案例，但是这些案例基于非真实环境的数据集，因此没办法直接在真实环境中使用，为了解决这个问题，我们基于 [凌智视觉模块分类模型部署指南](../vision/classification) 训练了凌智视觉模块专用的模型，可以实现实时的数字识别功能。 + +## 2 运行前的准备 + +- 请确保你已经下载了 [凌智视觉模块手写数字识别模型](https://gitee.com/LockzhinerAI/LockzhinerVisionModule/releases/download/v0.0.0/LZ-DigitHandRecog.rknn) + +## 3 在凌智视觉模块上部署模型 + +下载模型后，请参考以下教程使用 Python 在凌智视觉模块上部署分类模型例程： + +- [凌智视觉模块分类模型 Python 部署指南](./python) + +## 4 模型性能指标 + +以下测试数据为模型执行 Predict 函数运行 1000 次耗时的平均时间 + +| 分类模型 | FPS(帧/s) | 精度(%) | +|:-------:|:----:|:----:| +|LZ-DigitHandRecog|35|96.74| diff --git a/example/special/python/README.md b/example/special/python/README.md new file mode 100644 index 0000000..1e1f535 --- /dev/null +++ b/example/special/python/README.md @@ -0,0 +1,76 @@ +

凌智视觉模块手写数字识别 Python 部署指南

+ +发布版本：V0.0.0 + +日期：2024-09-26 + +文件密级：□绝密 □秘密 □内部资料 ■公开 + +--- + +**免责声明** + +本文档按**现状**提供，福州凌睿智捷电子有限公司（以下简称**本公司**）不对本文档中的任何陈述、信息和内容的准确性、可靠性、完整性、适销性、适用性及非侵权性提供任何明示或暗示的声明或保证。本文档仅作为使用指导的参考。 + +由于产品版本升级或其他原因，本文档可能在未经任何通知的情况下不定期更新或修改。 + +**读者对象** + +本教程适用于以下工程师： + +- 技术支持工程师 +- 软件开发工程师 + +**修订记录** + +| **日期** | **版本** | **作者** | **修改说明** | +| :--------- | -------- | -------- | ------------ | +| 2024/09/26 | 0.0.0 | 郑必城 | 初始版本 | + +## 1 简介 + +接下来让我们基于 Python 来部署手写数字模型。在开始本章节前： + +- 请确保你已经参考 [凌智视觉模块手写数字识别部署指南](../README.md) 正确下载了模型。 +- 请确保你已经参考 [凌智视觉模块摄像头部署指南](../../periphery/capture/README.md) 正确下载了凌智视觉模块图片传输助手。 +- 请确保你已经按照 [开发环境搭建指南](../../../../docs/introductory_tutorial/python_development_environment.md) 正确配置了开发环境。 + +## 2 Python API 文档 + +同[分类模型 Python 部署 API 文档](../../vision/classification/python/README.md) + +## 3 项目介绍 + +为了方便大家入手，我们做了一个简易的手写数字识别例程。该程序可以使用摄像头进行端到端推理，并可视化推理结果到凌智视觉模块图片传输助手。 + +```python +``` + +## 4 上传并测试 Python 程序 + +参考 [连接设备指南](../../../../docs/introductory_tutorial/connect_device_using_ssh.md) 正确连接 Lockzhiner Vision Module 设备。 + +![](../../../../docs/introductory_tutorial/images/connect_device_using_ssh/ssh_success.png) + +请使用 Electerm Sftp 依次上传以下两个文件: + +- 进入存放 **test_paddleclas.py** 脚本文件的目录，将 **test_paddleclas.py** 上传到 Lockzhiner Vision Module +- 进入存放 **LZ-MobileNetV2_x0_25.rknn(也可能是其他模型)** 模型存放的目录（模型存放在训练模型后下载的 output 文件夹内），将 **LZ-MobileNetV2_x0_25.rknn** 上传到 Lockzhiner Vision Module + +![](images/stfp_0.png) + +![](images/stfp_1.png) + +请使用 Electerm Ssh 并在命令行中执行以下命令: + +```bash +python test_classification.py LZ-MobileNetV3.rknn +``` + +运行程序后，屏幕上开始打印标签索引，分类置信度，并在一段时间后输出 FPS 值 + +![alt text](result_0.png) + +## 5 其他 + +如果你需要使用 C++ 来部署 PaddleClas 请参考[凌智视觉模块分类模型 C++ 部署指南](../cpp/README.md)。 diff --git a/example/special/python/test_digit_handwritten_rec.py b/example/special/python/test_digit_handwritten_rec.py new file mode 100644 index 0000000..ee14a09 --- /dev/null +++ b/example/special/python/test_digit_handwritten_rec.py @@ -0,0 +1,34 @@ +from lockzhiner_vision_module.cv2 import VideoCapture +from lockzhiner_vision_module.vision import PaddleClas, visualize +from lockzhiner_vision_module.edit import Edit +import sys + +if __name__ == "__main__": + args = sys.argv + if len(args) != 2: + print("Need model path. Example: python test_digit_handwritten_rec.py LZ-DigitHandRecog.rknn") + exit(1) + + edit = Edit() + edit.start_and_accept_connection() + + model = PaddleClas() + if model.initialize(args[1]) is False: + print("Failed to initialize PaddleClas") + exit(1) + + video_capture = VideoCapture() + if video_capture.open(0) is False: + print("Failed to open capture") + exit(1) + + while True: + ret, mat = video_capture.read() + if ret is False: + continue + + result = model.predict(mat) + print(f"The label_id is {result.label_id} and the score is {result.score}") + + vis_mat = visualize(mat, result) + edit.print(vis_mat) \ No newline at end of file diff --git a/example/vision/classification/README.md b/example/vision/classification/README.md index f3f3943..e7a5e16 100644 --- a/example/vision/classification/README.md +++ b/example/vision/classification/README.md @@ -47,7 +47,7 @@ Labelme 是一个 python 语言编写，带有图形界面的图像标注软件 ### 2.2 标注前的准备 -首先，请参考 [摄像头使用指南](./example/periphery/capture/README.md) 利用 **Lockzhiner Vision Module 图片获取助手** 拍摄你需要进行标注的图片，如下图所示： +首先，请参考 [摄像头使用指南](./example/periphery/capture) 利用 **Lockzhiner Vision Module 图片获取助手** 拍摄你需要进行标注的图片，如下图所示： ![](images/capture_images.png) @@ -211,8 +211,8 @@ AI Studio 是基于百度深度学习开源平台飞桨的人工智能学习与 训练完模型后，请参考以下教程使用 C++ 或 Python 在凌智视觉模块上部署分类模型例程： - -* [凌智视觉模块分类模型 Python 部署指南](./python/README.md) + +- [凌智视觉模块分类模型 Python 部署指南](./python) ## 5 各模型性能指标 @@ -220,6 +220,4 @@ AI Studio 是基于百度深度学习开源平台飞桨的人工智能学习与 | 分类模型 | FPS(帧/s) | 精度(%) | |:-------:|:----:|:----:| -|LZ-MobileNetV2_x0_25|70.57|53.21| -|LZ-MobileNetV2_x0_5|63.48|65.03| -|LZ-MobileNetV2_x1_0|49.44|72.15| \ No newline at end of file +|LZ-MobileNetV3|40|75.32| -- Gitee From 9b4d7ee5287f99e6cc72586009d60c3ae81f89a2 Mon Sep 17 00:00:00 2001 From: Zheng-Bicheng Date: Thu, 26 Sep 2024 17:20:02 +0800 Subject: [PATCH 09/27] Update docs --- example/special/python/README.md | 54 ++++++++++++++---- example/special/python/images/result_0.png | Bin 0 -> 424432 bytes example/special/python/images/stfp.png | Bin 0 -> 162348 bytes .../vision/classification/python/README.md | 10 ++-- 4 files changed, 48 insertions(+), 16 deletions(-) create mode 100644 example/special/python/images/result_0.png create mode 100644 example/special/python/images/stfp.png diff --git a/example/special/python/README.md b/example/special/python/README.md index 1e1f535..ce98ee7 100644 --- a/example/special/python/README.md +++ b/example/special/python/README.md @@ -44,33 +44,65 @@ 为了方便大家入手，我们做了一个简易的手写数字识别例程。该程序可以使用摄像头进行端到端推理，并可视化推理结果到凌智视觉模块图片传输助手。 ```python +from lockzhiner_vision_module.cv2 import VideoCapture +from lockzhiner_vision_module.vision import PaddleClas, visualize +from lockzhiner_vision_module.edit import Edit +import sys + +if __name__ == "__main__": + args = sys.argv + if len(args) != 2: + print("Need model path. Example: python test_digit_handwritten_rec.py LZ-DigitHandRecog.rknn") + exit(1) + + edit = Edit() + edit.start_and_accept_connection() + + model = PaddleClas() + if model.initialize(args[1]) is False: + print("Failed to initialize PaddleClas") + exit(1) + + video_capture = VideoCapture() + if video_capture.open(0) is False: + print("Failed to open capture") + exit(1) + + while True: + ret, mat = video_capture.read() + if ret is False: + continue + + result = model.predict(mat) + print(f"The label_id is {result.label_id} and the score is {result.score}") + + vis_mat = visualize(mat, result) + edit.print(vis_mat) ``` ## 4 上传并测试 Python 程序 参考 [连接设备指南](../../../../docs/introductory_tutorial/connect_device_using_ssh.md) 正确连接 Lockzhiner Vision Module 设备。 -![](../../../../docs/introductory_tutorial/images/connect_device_using_ssh/ssh_success.png) +![](../../../docs/introductory_tutorial/images/connect_device_using_ssh/ssh_success.png) 请使用 Electerm Sftp 依次上传以下两个文件: -- 进入存放 **test_paddleclas.py** 脚本文件的目录，将 **test_paddleclas.py** 上传到 Lockzhiner Vision Module -- 进入存放 **LZ-MobileNetV2_x0_25.rknn(也可能是其他模型)** 模型存放的目录（模型存放在训练模型后下载的 output 文件夹内），将 **LZ-MobileNetV2_x0_25.rknn** 上传到 Lockzhiner Vision Module +- 进入存放 **test_digit_handwritten_rec.py** 脚本文件的目录，将 **test_digit_handwritten_rec.py** 上传到 Lockzhiner Vision Module +- 进入存放 **LZ-DigitHandRecog.rknn** 模型存放的目录（模型存放在训练模型后下载的 output 文件夹内），将 **LZ-DigitHandRecog.rknn** 上传到 Lockzhiner Vision Module -![](images/stfp_0.png) - -![](images/stfp_1.png) +![](images/stfp.png) 请使用 Electerm Ssh 并在命令行中执行以下命令: ```bash -python test_classification.py LZ-MobileNetV3.rknn +python test_digit_handwritten_rec.py LZ-DigitHandRecog.rknn ``` -运行程序后，屏幕上开始打印标签索引，分类置信度，并在一段时间后输出 FPS 值 +运行程序后，使用凌智视觉模块图片传输助手连接设备，屏幕上开始打印标签索引和置信度，凌智视觉模块图片传输助手出现可视化的结果 -![alt text](result_0.png) +![alt text](images/result_0.png) -## 5 其他 + -如果你需要使用 C++ 来部署 PaddleClas 请参考[凌智视觉模块分类模型 C++ 部署指南](../cpp/README.md)。 + diff --git 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