基于飞桨的稻米加工品质监测系统

本项目拟解决目前农业中普遍存在的稻米过度加工问题，通过开发基于PaddlePaddle的密集粘连小目标的图像分割与目标检测算法，量化各环节稻米的加工指标，进而实现产线工艺参数的在线整定。

1. 安装PaddleX

! pip install paddlex==2.0.0

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2. 预测图片展示

本项目使用的数据集为团队从稻米生产样机中采集并标注的数据集。其中训练数据约1000张图片，测试数据约100张图片。数据增强方式包括图像的灰度降噪处理，高斯滤波，通过幂次变换对暗区域进行灰度拉伸处理等。输出的图像大小为4096*2160。
在这里插入图片描述

对test.bmp和test1.bmp进行密集黏连小目标实例分割检测，精确检测其中的稻米。

下面是两张需要检测的图片。

# 检测结果可视化
%matplotlib inline   
import matplotlib.pyplot as plt  
import numpy as np
import cv2

# 读取图片
pic1= cv2.imread('test.bmp')
pic2= cv2.imread('test1.bmp')

# 展示需要预测的图片
plt.figure(1)
plt.subplot(1,2,1)
plt.imshow(pic1)
plt.subplot(1,2,2)
plt.imshow(pic2)
plt.show()

在这里插入图片描述

本项目采用基于PaddlePaddle的Mask R-CNN的实例分割框架。相比于传统的二阶段算法Mask R-CNN为了实现像素级别的分类进行了优化。使用RoIAlign代替RoIPooling，将多尺寸提议区域进行特征对齐，原RoIPooling算法无法支持高精度的像素级别的图像分类。除了标签值和边界框偏移量的预测以外，单独开设一个分支通过全卷积网络实现像素级别分类。
网络一共包括13个conv层，13个relu层和4个pooling层，通过在Faster R-CNN网络层结构中引入掩码分支并通过主干网络进行特征提取。网络模型的示意图如下所示：

3. 模型预测

使用模型进行预测，同时使用pdx.det.visualize将结果可视化，可视化结果将保存到./output/mask_rcnn_r50_fpn下，其中threshold代表Box的置信度阈值，将Box置信度低于该阈值的框过滤不进行可视化。
在这里插入图片描述

import paddlex as pdx

# 检测结果可视化
%matplotlib inline   
import matplotlib.pyplot as plt  
import numpy as np
import cv2


# 模型预测
model = pdx.load_model('model/best_model') # 加载模型
image_name1 = 'test.bmp' # 需要预测的图片
result1 = model.predict(image_name1) # 模型预测
pdx.det.visualize(image_name1, result1, threshold=0.5, save_dir='./') # 预测结果可视化
image_name2 = 'test1.bmp' # 需要预测的图片
result2 = model.predict(image_name2) # 模型预测
pdx.det.visualize(image_name2, result2, threshold=0.5, save_dir='./') # 预测结果可视化


# 读取需要展示的图片
former_img= cv2.imread('test.bmp')
detect_img= cv2.imread('visualize_test.bmp')
former_img1= cv2.imread('test1.bmp')
detect_img1= cv2.imread('visualize_test1.bmp')


# 预测结果展示
plt.figure(1)
plt.subplot(2,2,1)
plt.imshow(former_img)
plt.subplot(2,2,2)
plt.imshow(detect_img)
plt.subplot(2,2,3)
plt.imshow(former_img1)
plt.subplot(2,2,4)
plt.imshow(detect_img1)
plt.show()
.subplot(2,2,4)
plt.imshow(detect_img1)
plt.show()


/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/distributed/parallel.py:120: UserWarning: Currently not a parallel execution environment, `paddle.distributed.init_parallel_env` will not do anything.
  "Currently not a parallel execution environment, `paddle.distributed.init_parallel_env` will not do anything."


[11-20 19:33:49 MainThread @utils.py:79] WRN paddlepaddle version: 2.1.2. The dynamic graph version of PARL is under development, not fully tested and supported


/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/parl/remote/communication.py:38: FutureWarning: 'pyarrow.default_serialization_context' is deprecated as of 2.0.0 and will be removed in a future version. Use pickle or the pyarrow IPC functionality instead.
  context = pyarrow.default_serialization_context()
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddleslim/common/analyze_helper.py:22: UserWarning: 
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

The backend was *originally* set to 'module://matplotlib_inline.backend_inline' by the following code:
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/traitlets/config/application.py", line 978, in launch_instance
    app.start()
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel/kernelapp.py", line 677, in start
    self.io_loop.start()
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/tornado/platform/asyncio.py", line 215, in start
    self.asyncio_loop.run_forever()
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/asyncio/base_events.py", line 534, in run_forever
    self._run_once()
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/asyncio/base_events.py", line 1771, in _run_once
    handle._run()
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/asyncio/events.py", line 88, in _run
    self._context.run(self._callback, *self._args)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 471, in dispatch_queue
    await self.process_one()
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 460, in process_one
    await dispatch(*args)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 367, in dispatch_shell
    await result
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 662, in execute_request
    reply_content = await reply_content
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel/ipkernel.py", line 360, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel/zmqshell.py", line 532, in run_cell
    return super().run_cell(*args, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2976, in run_cell
    raw_cell, store_history, silent, shell_futures, cell_id
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3030, in _run_cell
    return runner(coro)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner
    coro.send(None)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3258, in run_cell_async
    interactivity=interactivity, compiler=compiler, result=result)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3473, in run_ast_nodes
    if (await self.run_code(code, result,  async_=asy)):
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3553, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/tmp/ipykernel_143/1832274096.py", line 2, in <module>
    get_ipython().run_line_magic('matplotlib', 'inline')
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2418, in run_line_magic
    result = fn(*args, **kwargs)
  File "<decorator-gen-101>", line 2, in matplotlib
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/magic.py", line 187, in <lambda>
    call = lambda f, *a, **k: f(*a, **k)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/magics/pylab.py", line 99, in matplotlib
    gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3647, in enable_matplotlib
    pt.activate_matplotlib(backend)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/pylabtools.py", line 359, in activate_matplotlib
    plt.switch_backend(backend)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/pyplot.py", line 231, in switch_backend
    matplotlib.use(newbackend, warn=False, force=True)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py", line 1422, in use
    reload(sys.modules['matplotlib.backends'])
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/importlib/__init__.py", line 169, in reload
    _bootstrap._exec(spec, module)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/backends/__init__.py", line 16, in <module>
    line for line in traceback.format_stack()


  matplotlib.use('Agg')
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  if data.dtype == np.object:
W1120 19:33:52.083621   143 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W1120 19:33:52.089015   143 device_context.cc:422] device: 0, cuDNN Version: 7.6.


2022-11-20 19:33:54 [INFO]	Model[MaskRCNN] loaded.


/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/models/utils/visualize.py:218: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  color_mask = np.asarray(color, dtype=np.int)


2022-11-20 19:34:32 [INFO]	The visualized result is saved at ./visualize_test.bmp
2022-11-20 19:35:15 [INFO]	The visualized result is saved at ./visualize_test1.bmp

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分割效果展示

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稻米品质监测上位机制作

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此文章为搬运
原项目链接

# paddlepaddle # python # 人工智能
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