Improvements coming with Python Importer for Simulink

.gitattributes

@@ -0,0 +1,28 @@
+* text=auto
+
+*.fig binary
+*.mat binary
+*.mdl binary diff merge=mlAutoMerge
+*.mdlp binary
+*.mex* binary
+*.mlapp binary
+*.mldatx binary
+*.mlproj binary
+*.mlx binary
+*.p binary
+*.sfx binary
+*.sldd binary
+*.slreqx binary merge=mlAutoMerge
+*.slmx binary merge=mlAutoMerge
+*.sltx binary
+*.slxc binary
+*.slx binary merge=mlAutoMerge
+*.slxp binary
+
+## Other common binary file types
+*.docx binary
+*.exe binary
+*.jpg binary
+*.pdf binary
+*.png binary
+*.xlsx binary

.gitignore

@@ -0,0 +1,2 @@
+__pycache__/
+slprj/

HumanDetection_import.json

@@ -0,0 +1,33 @@
+{
+ "SimulinkCodeImporterVersion": 1,
+ "LibraryFileName": "HumanDetection",
+ "CustomCode": {
+ "SourceFiles": ".\\detectHuman.py",
+ "GlobalVariableInterface": false
+ },
+ "FunctionsToImport": "detectHuman.detectHumanFromFrame",
+ "FunctionSettings": {
+ "Name": "detectHuman.detectHumanFromFrame",
+ "PortSpecArray": [
+ {
+ "ArgName": "image",
+ "PortName": "image",
+ "Scope": "Output",
+ "Index": "1",
+ "Type": "uint8",
+ "Size": "[300 400 3]",
+ "IsGlobal": false
+ },
+ {
+ "ArgName": "image",
+ "PortName": "image",
+ "Scope": "Input",
+ "Index": "1",
+ "Type": "uint8",
+ "Size": "[480 640 3]",
+ "IsGlobal": false
+ }
+ ],
+ "IsDeterministic": false
+ }
+}

README.MD

@@ -1,30 +1,49 @@
-Integrate Python Code into Simulink for Simulation
-
-Using Python Human Detection Algorithm in Simulink Example
-Copyright 2012-2021 The MathWorks, Inc.
-
-This example shows users how to integrate Python Human detection code into Simulink for simulation. This example reads a pre-recorded video, applies the human detection algorithm defined in Python, and generates the output video with human marked. This example includes the following files:
-
-runme.m: This file provides the prep steps when using this example. Since the Python algorithm is based on OpenCV, you will need to install the needed packages if they are not available on your computer. This file also helps set the "out of Process" execution mode to avoid possible MATLAB crashes. 
-
-
-detectHuman.py: this file contains the Python algorithm using OpenCV Histogram of Oriented Gradients (HOG) for human detection. 
-
-livedata.mp4: this file is a pre-recorded video showing several people walking. 
-
-python_HumanDetector.m: this file contains the MATLAB System Object integrating the Python human detection algorithm.
-
-videoReader.m: this file contains the MATLAB System Object reading the pre-recorded video of livedata.mp4. This can be replaced if you have DSP System Toolbox available (see below). 
-
-base_python_example_21a.slx: this file shows how Simulink can integrate Python code for simulation by using either MATLAB Function block or MATLAB System block. You could use the manual switch to select either method to bring in Python code. When you run this file, the output video will show blue boxes drawn around detected people. 
-
-If you have DSP System Toolbox available, you could use the "From Multimedia File" block to replace the videoReader System block for simpler video reading; If you have Computer Vision Toolbox available, you can use the "To video Display" block to replace the Video_with_human_detection MATLAB function block for simpler video play. 
-
-To use this example, open and run the runme.m first in MATLAB; Then start the base_python_example_21a.slx and run the simulation. 
-
-Products needed for using this example :
-
-MATLAB; 
-Simulink; 
-DSP System Toolbox (optional); 
-Computer Vision Toolbox (optional); 
+# Integrate Python Code into Simulink for Simulation 
+
+[![Open in MATLAB Online](https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg)](https://matlab.mathworks.com/open/github/v1?repo=yanndebray/Integrate_Python_code_with_Simulink&file=README.mlx)
+
+📹 Integrate OpenCV into Simulink models through Python wrapper: 
+Simulation of a Human Detection Algorithm
+
+<img src="simCV.gif" width= 500 />
+
+```matlab
+open_system("base_python_example_23a.slx")
+```
+
+This example shows users how to integrate Python Human detection code into Simulink for simulation. This example reads a pre-recorded video, apply the human detection algorithm defined in Python and generate the outpur video with human marked. This example includes the following files:
+
+ * [runme.m](runme.m): This file provides the prep steps when using this example. Since the Python algorithm is based on OpenCV, you will need install the needed packages if they are not available on your computer. This file also helps set the "out of Process" execution mode to avoid possible MATLAB crashes. And it will also set the Python path to the current folder. 
+
+
+* [detectHuman.py](detectHuman.py): this file contains the Python algorithm using OpenCV Histogram of Oriented Gradients (HOG) for human detection. 
+
+* [livedata.mp4](livedata.mp4): this file is a pre-recorded video showing several people walking. 
+
+* [python_HumanDetector.m](python_HumanDetector.m): this file contains the MATLAB System Object integrating the Python human detection algorithm.
+
+* [videoReader.m](videoReader.m): this file contains the MATLAB System Object reading the pre-recorded video of livedata.mp4. This can be replaced if you have DSP System Toolbox available.
+(If you have DSP System Toolbox available, you could use the "From Multimedia File" block to replace the videoReader System block for simpler video reading; If you have Computer Vision Toolbox available, you can use the "To video Display" block to replace the Video_with_human_detection MATLAB function block for simpler video play.)
+
+* [base_python_example_21a.slx](base_python_example_21a.slx): this file shows how Simulink can integrate Python code for simulation by using either MATLAB Function block or MATLAB System block. You could use the manual switch to select either method to bring in Python code. When you run this file, the output video will show blue boxes drawn around detected people. 
+
+
+![simCV-model.png](simCV-model.png)
+
+## How to
+
+To use this example, 
+1. open and run the runme.m first in MATLAB; 
+2. Then start the base_python_example_21a.slx 
+3. Run simulation. 
+
+
+## Product dependencies
+Products needed for using this example :
+
+* MATLAB; 
+* Simulink; 
+* DSP System Toolbox (optional); 
+* Computer Vision Toolbox (optional);
+
+&reg; 2021-2024 The MathWorks, Inc.

detectHuman.py

@@ -1,22 +1,18 @@
 import time
-import imutils
+# import imutils
 import numpy as np
-from cv2 import cv2
+import cv2
+import hogClass
 
+def detectHumanFromFrame(image):
 
-class hogObject:
- detector = cv2.HOGDescriptor()
- def __init__(self):
- self.detector.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
-
-
-def getHogObject():
- return hogObject()
-
-
-def detectHumanFromFrame(image, hog):
+ hog = hogClass.hogObject()
+
  image = np.asarray(image)
- image = imutils.resize(image, width=min(400, image.shape[1]))
+ # image = imutils.resize(image, width=min(400, image.shape[1]))
+ ratio = image.shape[0] / image.shape[1]
+ new_height = int(400 * ratio)
+ image = cv2.resize(image, (400,new_height))
 
  # Detecting all the regions in the Image that has a pedestrians inside it
  (regions, _) = hog.detector.detectMultiScale(
@@ -30,8 +26,6 @@ def detectHumanFromFrame(image, hog):
 
 
 if __name__ == "__main__":
-
- hog = hogObject()
 
  cap = cv2.VideoCapture('livedata.mp4')
  ret, image = cap.read()
@@ -41,7 +35,7 @@ def detectHumanFromFrame(image, hog):
  # Reading the video stream
  ret, image = cap.read()
  if ret:
- image = detectHumanFromFrame(image, hog)
+ image = detectHumanFromFrame(image)
  cv2.imshow("Image", image)
  cv2.waitKey(5)
  if cv2.waitKey(5) & 0xFF == ord('q'):

detectHuman_detectHumanFromFrame.m

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+classdef detectHuman_detectHumanFromFrame < matlab.System
+% Python def detectHumanFromFrame imported from python module detectHuman.py
+methods(Access = protected)
+function validateInputsImpl(obj, varargin)
+if ~isempty(varargin{1})
+ validateattributes(varargin{1}, {'uint8'}, {'size',[480 640 3]});
+end
+end
+function setupImpl(obj)
+ py.importlib.import_module('detectHuman');
+end
+function [image] = stepImpl(obj,image)
+ image = uint8(py.detectHuman.detectHumanFromFrame(image));
+end
+function varargout = getOutputDataTypeImpl(obj)
+ varargout{1} = 'uint8';
+end
+function varargout = getOutputSizeImpl(obj)
+ varargout{1} = [300 400 3];
+end
+function varargout = isOutputComplexImpl(obj)
+ varargout{1} = false;
+end
+function varargout = isOutputFixedSizeImpl(obj)
+ varargout{1} = true;
+end
+end
+methods(Static, Access = protected)
+function simMode = getSimulateUsingImpl
+ simMode = 'Interpreted execution';
+end
+end
+end

hogClass.py

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+import cv2
+
+class hogObject:
+ detector = cv2.HOGDescriptor()
+ def __init__(self):
+ self.detector.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+
+def getHogObject():
+ return hogObject()

license.txt

@@ -1,11 +1,11 @@
-Copyright (c) 2020, The MathWorks, Inc.
-All rights reserved.
-Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
-1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
-2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
-3. In all cases, the software is, and all modifications and derivatives of the software shall be, licensed to you solely for use in conjunction with MathWorks products and service offerings. 
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-
-
-
+Copyright (c) 2020, The MathWorks, Inc.
+All rights reserved.
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+3. In all cases, the software is, and all modifications and derivatives of the software shall be, licensed to you solely for use in conjunction with MathWorks products and service offerings. 
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+
+

pySimCV.prj

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<MATLABProject xmlns="http://www.mathworks.com/MATLABProjectFile"/>

requirements.txt

@@ -0,0 +1,2 @@
+opencv-python
+numpy

resources/project/EEtUlUb-dLAdf0KpMVivaUlztwA/8lflsp5x1TLL5QYu_xBRwE0q2TYd.xml

@@ -0,0 +1,2 @@
+<?xml version='1.0' encoding='UTF-8'?>
+<Info Ref="" Type="Relative"/>

resources/project/EEtUlUb-dLAdf0KpMVivaUlztwA/8lflsp5x1TLL5QYu_xBRwE0q2TYp.xml

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+<?xml version='1.0' encoding='UTF-8'?>
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resources/project/KAXfQgCar2Yb8zOxgvf9hdmLP1E/s35PvPMF5-Te6SCzpQ2vUHpvhTkd.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info File="runme.m" GroupUUID="default" Icon="" Name="runme" Type="StartUp" Visible="0">
+ <Extension Name="StartUpPrev" Value="HEAD"/>
+</Info>

resources/project/KAXfQgCar2Yb8zOxgvf9hdmLP1E/s35PvPMF5-Te6SCzpQ2vUHpvhTkp.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info location="89a2a5cc-1383-4cf9-8298-ba7852a76583" type="EntryPoint"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/2kj09UetkV_lru3gvSPXnY6-nM4d.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info Name="Test" ReadOnly="READ_ONLY"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/2kj09UetkV_lru3gvSPXnY6-nM4p.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info location="test" type="Label"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/KKyDJtbdIBOlaeHmIZd5VX6vqx8d.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info Name="Other" ReadOnly="READ_ONLY"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/KKyDJtbdIBOlaeHmIZd5VX6vqx8p.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info location="other" type="Label"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/QWNDYJD5mGW1bWYvPx9DtKnxzw4d.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info Name="Convenience" ReadOnly="READ_ONLY"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/QWNDYJD5mGW1bWYvPx9DtKnxzw4p.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info location="convenience" type="Label"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/R1RggVhA72agIvELiuhWPRS8F0Id.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info Name="None" ReadOnly="READ_ONLY"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/R1RggVhA72agIvELiuhWPRS8F0Ip.xml

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+<Info location="none" type="Label"/>

resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/aEHSZBIY-yve10yGis12Zr5DLZod.xml

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resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/aEHSZBIY-yve10yGis12Zr5DLZop.xml

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resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/j4xwF_j8iFTVayUMfxLgMnTbencd.xml

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resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/j4xwF_j8iFTVayUMfxLgMnTbencp.xml

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resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/r8LR4nLmg9ai3oHrW1r_-KocQzkd.xml

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resources/project/NjSPEMsIuLUyIpr2u1Js5bVPsOs/r8LR4nLmg9ai3oHrW1r_-KocQzkp.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info location="artifact" type="Label"/>

resources/project/Project.xml

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+<?xml version='1.0' encoding='UTF-8'?>
+<Info MetadataType="fixedPathV2"/>

resources/project/fjRQtWiSIy7hIlj-Kmk87M7s21k/NjSPEMsIuLUyIpr2u1Js5bVPsOsd.xml

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resources/project/fjRQtWiSIy7hIlj-Kmk87M7s21k/NjSPEMsIuLUyIpr2u1Js5bVPsOsp.xml

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resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/2fOmAntE_wk__DLooY1qtBbZsQkp.xml

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+<?xml version='1.0' encoding='UTF-8'?>
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resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/5ALC_AoqkAS9A4KBYk73Yo3TvvUp.xml

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