├── labels.txt ├── dataset ├── labels.txt ├── can.class │ ├── 00000.jpg │ ├── 00001.jpg │ ├── 00002.jpg │ ├── 00003.jpg │ ├── 00004.jpg │ ├── 00005.jpg │ ├── 00006.jpg │ ├── 00007.jpg │ ├── 00008.jpg │ ├── 00009.jpg │ ├── 00010.jpg │ ├── 00011.jpg │ ├── 00012.jpg │ ├── 00013.jpg │ ├── 00014.jpg │ ├── 00015.jpg │ ├── 00016.jpg │ ├── 00017.jpg │ ├── 00018.jpg │ ├── 00019.jpg │ ├── 00020.jpg │ ├── 00021.jpg │ ├── 00022.jpg │ ├── 00023.jpg │ ├── 00024.jpg │ ├── 00025.jpg │ ├── 00026.jpg │ ├── 00027.jpg │ ├── 00028.jpg │ ├── 00029.jpg │ ├── 00030.jpg │ ├── 00031.jpg │ ├── 00032.jpg │ ├── 00033.jpg │ ├── 00034.jpg │ ├── 00035.jpg │ ├── 00036.jpg │ ├── 00037.jpg │ ├── 00038.jpg │ ├── 00039.jpg │ ├── 00040.jpg │ ├── 00041.jpg │ ├── 00042.jpg │ ├── 00043.jpg │ ├── 00044.jpg │ ├── 00045.jpg │ ├── 00046.jpg │ ├── 00047.jpg │ ├── 00048.jpg │ ├── 00049.jpg │ ├── 00050.jpg │ ├── 00051.jpg │ ├── 00052.jpg │ ├── 00053.jpg │ ├── 00054.jpg │ ├── 00055.jpg │ ├── 00056.jpg │ ├── 00057.jpg │ ├── 00058.jpg │ ├── 00059.jpg │ ├── 00060.jpg │ ├── 00061.jpg │ ├── 00062.jpg │ ├── 00063.jpg │ ├── 00064.jpg │ ├── 00065.jpg │ ├── 00066.jpg │ ├── 00067.jpg │ ├── 00068.jpg │ ├── 00069.jpg │ ├── 00070.jpg │ ├── 00071.jpg │ ├── 00072.jpg │ ├── 00073.jpg │ ├── 00074.jpg │ ├── 00075.jpg │ ├── 00076.jpg │ ├── 00077.jpg │ ├── 00078.jpg │ ├── 00079.jpg │ ├── 00080.jpg │ ├── 00081.jpg │ ├── 00082.jpg │ ├── 00083.jpg │ ├── 00084.jpg │ ├── 00085.jpg │ ├── 00086.jpg │ ├── 00087.jpg │ ├── 00088.jpg │ ├── 00089.jpg │ ├── 00090.jpg │ ├── 00091.jpg │ ├── 00092.jpg │ ├── 00093.jpg │ ├── 00094.jpg │ ├── 00095.jpg │ ├── 00096.jpg │ ├── 00097.jpg │ ├── 00098.jpg │ ├── 00099.jpg │ ├── 00100.jpg │ ├── 00101.jpg │ ├── 00102.jpg │ ├── 00103.jpg │ ├── 00104.jpg │ ├── 00105.jpg │ ├── 00106.jpg │ ├── 00107.jpg │ ├── 00108.jpg │ ├── 00109.jpg │ ├── 00110.jpg │ ├── 00111.jpg │ ├── 00112.jpg │ ├── 00113.jpg │ ├── 00114.jpg │ ├── 00115.jpg │ ├── 00116.jpg │ ├── 00117.jpg │ ├── 00118.jpg │ ├── 00119.jpg │ ├── 00120.jpg │ ├── 00121.jpg │ ├── 00122.jpg │ ├── 00123.jpg │ ├── 00124.jpg │ ├── 00125.jpg │ ├── 00126.jpg │ ├── 00127.jpg │ ├── 00128.jpg │ ├── 00129.jpg │ ├── 00130.jpg │ ├── 00131.jpg │ ├── 00132.jpg │ ├── 00133.jpg │ ├── 00134.jpg │ ├── 00135.jpg │ ├── 00136.jpg │ ├── 00137.jpg │ ├── 00138.jpg │ ├── 00139.jpg │ ├── 00140.jpg │ ├── 00141.jpg │ ├── 00142.jpg │ ├── 00143.jpg │ ├── 00144.jpg │ ├── 00145.jpg │ ├── 00146.jpg │ ├── 00147.jpg │ ├── 00148.jpg │ ├── 00149.jpg │ ├── 00150.jpg │ ├── 00151.jpg │ ├── 00152.jpg │ ├── 00153.jpg │ ├── 00154.jpg │ ├── 00155.jpg │ ├── 00156.jpg │ ├── 00157.jpg │ ├── 00158.jpg │ ├── 00159.jpg │ ├── 00160.jpg │ ├── 00161.jpg │ ├── 00162.jpg │ ├── 00163.jpg │ ├── 00164.jpg │ ├── 00165.jpg │ ├── 00166.jpg │ ├── 00167.jpg │ ├── 00168.jpg │ ├── 00169.jpg │ ├── 00170.jpg │ ├── 00171.jpg │ ├── 00172.jpg │ ├── 00173.jpg │ ├── 00174.jpg │ ├── 00175.jpg │ ├── 00176.jpg │ ├── 00177.jpg │ ├── 00178.jpg │ ├── 00179.jpg │ ├── 00180.jpg │ ├── 00181.jpg │ ├── 00182.jpg │ ├── 00183.jpg │ ├── 00184.jpg │ └── 00185.jpg ├── bottle.class │ ├── 00000.jpg │ ├── 00001.jpg │ ├── 00002.jpg │ ├── 00003.jpg │ ├── 00004.jpg │ ├── 00005.jpg │ ├── 00006.jpg │ ├── 00007.jpg │ ├── 00008.jpg │ ├── 00009.jpg │ ├── 00010.jpg │ ├── 00011.jpg │ ├── 00012.jpg │ ├── 00013.jpg │ ├── 00014.jpg │ ├── 00015.jpg │ ├── 00016.jpg │ ├── 00017.jpg │ ├── 00018.jpg │ ├── 00019.jpg │ ├── 00020.jpg │ ├── 00021.jpg │ ├── 00022.jpg │ ├── 00023.jpg │ ├── 00024.jpg │ ├── 00025.jpg │ ├── 00026.jpg │ ├── 00027.jpg │ ├── 00028.jpg │ ├── 00029.jpg │ ├── 00030.jpg │ ├── 00031.jpg │ ├── 00032.jpg │ ├── 00033.jpg │ ├── 00034.jpg │ ├── 00035.jpg │ ├── 00036.jpg │ ├── 00037.jpg │ ├── 00038.jpg │ ├── 00039.jpg │ ├── 00040.jpg │ ├── 00041.jpg │ ├── 00042.jpg │ ├── 00043.jpg │ ├── 00044.jpg │ ├── 00045.jpg │ ├── 00046.jpg │ ├── 00047.jpg │ ├── 00048.jpg │ ├── 00049.jpg │ ├── 00050.jpg │ ├── 00051.jpg │ ├── 00052.jpg │ ├── 00053.jpg │ ├── 00054.jpg │ ├── 00055.jpg │ ├── 00056.jpg │ ├── 00057.jpg │ ├── 00058.jpg │ ├── 00059.jpg │ ├── 00060.jpg │ ├── 00061.jpg │ ├── 00062.jpg │ ├── 00063.jpg │ ├── 00064.jpg │ ├── 00065.jpg │ ├── 00066.jpg │ ├── 00067.jpg │ ├── 00068.jpg │ ├── 00069.jpg │ ├── 00070.jpg │ ├── 00071.jpg │ ├── 00072.jpg │ ├── 00073.jpg │ ├── 00074.jpg │ ├── 00075.jpg │ ├── 00076.jpg │ ├── 00077.jpg │ ├── 00078.jpg │ ├── 00079.jpg │ ├── 00080.jpg │ ├── 00081.jpg │ ├── 00082.jpg │ ├── 00083.jpg │ ├── 00084.jpg │ ├── 00085.jpg │ ├── 00086.jpg │ ├── 00087.jpg │ ├── 00088.jpg │ ├── 00089.jpg │ ├── 00090.jpg │ ├── 00091.jpg │ ├── 00092.jpg │ ├── 00093.jpg │ ├── 00094.jpg │ ├── 00095.jpg │ ├── 00096.jpg │ ├── 00097.jpg │ ├── 00098.jpg │ ├── 00099.jpg │ ├── 00100.jpg │ ├── 00101.jpg │ ├── 00102.jpg │ ├── 00103.jpg │ ├── 00104.jpg │ ├── 00105.jpg │ ├── 00106.jpg │ ├── 00107.jpg │ ├── 00108.jpg │ ├── 00109.jpg │ ├── 00110.jpg │ ├── 00111.jpg │ ├── 00112.jpg │ ├── 00113.jpg │ ├── 00114.jpg │ ├── 00115.jpg │ ├── 00116.jpg │ ├── 00117.jpg │ ├── 00118.jpg │ ├── 00119.jpg │ ├── 00120.jpg │ ├── 00121.jpg │ ├── 00122.jpg │ ├── 00123.jpg │ ├── 00124.jpg │ ├── 00125.jpg │ ├── 00126.jpg │ ├── 00127.jpg │ ├── 00128.jpg │ ├── 00129.jpg │ ├── 00130.jpg │ ├── 00131.jpg │ ├── 00132.jpg │ ├── 00133.jpg │ ├── 00134.jpg │ ├── 00135.jpg │ ├── 00136.jpg │ ├── 00137.jpg │ ├── 00138.jpg │ ├── 00139.jpg │ ├── 00140.jpg │ ├── 00141.jpg │ ├── 00142.jpg │ ├── 00143.jpg │ ├── 00144.jpg │ ├── 00145.jpg │ ├── 00146.jpg │ ├── 00147.jpg │ ├── 00148.jpg │ ├── 00149.jpg │ ├── 00150.jpg │ ├── 00151.jpg │ ├── 00152.jpg │ ├── 00153.jpg │ ├── 00154.jpg │ ├── 00155.jpg │ ├── 00156.jpg │ ├── 00157.jpg │ ├── 00158.jpg │ ├── 00159.jpg │ ├── 00160.jpg │ └── 00161.jpg ├── battery.class │ ├── 00000.jpg │ ├── 00001.jpg │ ├── 00002.jpg │ ├── 00003.jpg │ ├── 00004.jpg │ ├── 00005.jpg │ ├── 00006.jpg │ ├── 00007.jpg │ ├── 00008.jpg │ ├── 00009.jpg │ ├── 00010.jpg │ ├── 00011.jpg │ ├── 00012.jpg │ ├── 00013.jpg │ ├── 00014.jpg │ ├── 00015.jpg │ ├── 00016.jpg │ ├── 00017.jpg │ ├── 00018.jpg │ ├── 00019.jpg │ ├── 00020.jpg │ ├── 00021.jpg │ ├── 00022.jpg │ ├── 00023.jpg │ ├── 00024.jpg │ ├── 00025.jpg │ ├── 00026.jpg │ ├── 00027.jpg │ ├── 00028.jpg │ ├── 00029.jpg │ ├── 00030.jpg │ ├── 00031.jpg │ ├── 00032.jpg │ ├── 00033.jpg │ ├── 00034.jpg │ ├── 00035.jpg │ ├── 00036.jpg │ ├── 00037.jpg │ ├── 00038.jpg │ ├── 00039.jpg │ ├── 00040.jpg │ ├── 00041.jpg │ ├── 00042.jpg │ ├── 00043.jpg │ ├── 00044.jpg │ ├── 00045.jpg │ ├── 00046.jpg │ ├── 00047.jpg │ ├── 00048.jpg │ ├── 00049.jpg │ ├── 00050.jpg │ ├── 00051.jpg │ ├── 00052.jpg │ ├── 00053.jpg │ ├── 00054.jpg │ ├── 00055.jpg │ ├── 00056.jpg │ ├── 00057.jpg │ ├── 00058.jpg │ ├── 00059.jpg │ ├── 00060.jpg │ ├── 00061.jpg │ ├── 00062.jpg │ ├── 00063.jpg │ ├── 00064.jpg │ ├── 00065.jpg │ ├── 00066.jpg │ ├── 00067.jpg │ ├── 00068.jpg │ ├── 00069.jpg │ ├── 00070.jpg │ ├── 00071.jpg │ ├── 00072.jpg │ ├── 00073.jpg │ ├── 00074.jpg │ ├── 00075.jpg │ ├── 00076.jpg │ ├── 00077.jpg │ ├── 00078.jpg │ ├── 00079.jpg │ ├── 00080.jpg │ ├── 00081.jpg │ ├── 00082.jpg │ ├── 00083.jpg │ ├── 00084.jpg │ ├── 00085.jpg │ ├── 00086.jpg │ ├── 00087.jpg │ ├── 00088.jpg │ ├── 00089.jpg │ ├── 00090.jpg │ ├── 00091.jpg │ ├── 00092.jpg │ ├── 00093.jpg │ ├── 00094.jpg │ ├── 00095.jpg │ ├── 00096.jpg │ ├── 00097.jpg │ ├── 00098.jpg │ ├── 00099.jpg │ ├── 00100.jpg │ ├── 00101.jpg │ ├── 00102.jpg │ ├── 00103.jpg │ ├── 00104.jpg │ ├── 00105.jpg │ ├── 00106.jpg │ ├── 00107.jpg │ ├── 00108.jpg │ ├── 00109.jpg │ ├── 00110.jpg │ ├── 00111.jpg │ ├── 00112.jpg │ ├── 00113.jpg │ ├── 00114.jpg │ ├── 00115.jpg │ ├── 00116.jpg │ ├── 00117.jpg │ ├── 00118.jpg │ ├── 00119.jpg │ ├── 00120.jpg │ ├── 00121.jpg │ ├── 00122.jpg │ ├── 00123.jpg │ ├── 00124.jpg │ ├── 00125.jpg │ ├── 00126.jpg │ ├── 00127.jpg │ ├── 00128.jpg │ ├── 00129.jpg │ ├── 00130.jpg │ ├── 00131.jpg │ ├── 00132.jpg │ ├── 00133.jpg │ ├── 00134.jpg │ ├── 00135.jpg │ ├── 00136.jpg │ ├── 00137.jpg │ ├── 00138.jpg │ ├── 00139.jpg │ ├── 00140.jpg │ ├── 00141.jpg │ ├── 00142.jpg │ ├── 00143.jpg │ ├── 00144.jpg │ ├── 00145.jpg │ ├── 00146.jpg │ ├── 00147.jpg │ ├── 00148.jpg │ ├── 00149.jpg │ ├── 00150.jpg │ ├── 00151.jpg │ ├── 00152.jpg │ ├── 00153.jpg │ ├── 00154.jpg │ ├── 00155.jpg │ ├── 00156.jpg │ ├── 00157.jpg │ ├── 00158.jpg │ ├── 00159.jpg │ ├── 00160.jpg │ ├── 00161.jpg │ ├── 00162.jpg │ ├── 00163.jpg │ ├── 00164.jpg │ ├── 00165.jpg │ ├── 00166.jpg │ ├── 00167.jpg │ ├── 00168.jpg │ ├── 00169.jpg │ ├── 00170.jpg │ ├── 00171.jpg │ ├── 00172.jpg │ ├── 00173.jpg │ ├── 00174.jpg │ ├── 00175.jpg │ ├── 00176.jpg │ ├── 00177.jpg │ ├── 00178.jpg │ ├── 00179.jpg │ ├── 00180.jpg │ ├── 00181.jpg │ ├── 00182.jpg │ ├── 00183.jpg │ ├── 00184.jpg │ ├── 00185.jpg │ ├── 00186.jpg │ ├── 00187.jpg │ ├── 00188.jpg │ ├── 00189.jpg │ ├── 00190.jpg │ ├── 00191.jpg │ ├── 00192.jpg │ └── 00193.jpg ├── cigarette.class │ ├── 00000.jpg │ ├── 00001.jpg │ ├── 00002.jpg │ ├── 00003.jpg │ ├── 00004.jpg │ ├── 00005.jpg │ ├── 00006.jpg │ ├── 00007.jpg │ ├── 00008.jpg │ ├── 00009.jpg │ ├── 00010.jpg │ ├── 00011.jpg │ ├── 00012.jpg │ ├── 00013.jpg │ ├── 00014.jpg │ ├── 00015.jpg │ ├── 00016.jpg │ ├── 00017.jpg │ ├── 00018.jpg │ ├── 00019.jpg │ ├── 00020.jpg │ ├── 00021.jpg │ ├── 00022.jpg │ ├── 00023.jpg │ ├── 00024.jpg │ ├── 00025.jpg │ ├── 00026.jpg │ ├── 00027.jpg │ ├── 00028.jpg │ ├── 00029.jpg │ ├── 00030.jpg │ ├── 00031.jpg │ ├── 00032.jpg │ ├── 00033.jpg │ ├── 00034.jpg │ ├── 00035.jpg │ ├── 00036.jpg │ ├── 00037.jpg │ ├── 00038.jpg │ ├── 00039.jpg │ ├── 00040.jpg │ ├── 00041.jpg │ ├── 00042.jpg │ ├── 00043.jpg │ ├── 00044.jpg │ ├── 00045.jpg │ ├── 00046.jpg │ ├── 00047.jpg │ ├── 00048.jpg │ ├── 00049.jpg │ ├── 00050.jpg │ ├── 00051.jpg │ ├── 00052.jpg │ ├── 00053.jpg │ ├── 00054.jpg │ ├── 00055.jpg │ ├── 00056.jpg │ ├── 00057.jpg │ ├── 00058.jpg │ ├── 00059.jpg │ ├── 00060.jpg │ ├── 00061.jpg │ ├── 00062.jpg │ ├── 00063.jpg │ ├── 00064.jpg │ ├── 00065.jpg │ ├── 00066.jpg │ ├── 00067.jpg │ ├── 00068.jpg │ ├── 00069.jpg │ ├── 00070.jpg │ ├── 00071.jpg │ ├── 00072.jpg │ ├── 00073.jpg │ ├── 00074.jpg │ ├── 00075.jpg │ ├── 00076.jpg │ ├── 00077.jpg │ ├── 00078.jpg │ ├── 00079.jpg │ ├── 00080.jpg │ ├── 00081.jpg │ ├── 00082.jpg │ ├── 00083.jpg │ ├── 00084.jpg │ ├── 00085.jpg │ ├── 00086.jpg │ ├── 00087.jpg │ ├── 00088.jpg │ ├── 00089.jpg │ ├── 00090.jpg │ ├── 00091.jpg │ ├── 00092.jpg │ ├── 00093.jpg │ ├── 00094.jpg │ ├── 00095.jpg │ ├── 00096.jpg │ ├── 00097.jpg │ ├── 00098.jpg │ ├── 00099.jpg │ ├── 00100.jpg │ ├── 00101.jpg │ ├── 00102.jpg │ ├── 00103.jpg │ ├── 00104.jpg │ ├── 00105.jpg │ ├── 00106.jpg │ ├── 00107.jpg │ ├── 00108.jpg │ ├── 00109.jpg │ ├── 00110.jpg │ ├── 00111.jpg │ ├── 00112.jpg │ ├── 00113.jpg │ ├── 00114.jpg │ ├── 00115.jpg │ ├── 00116.jpg │ ├── 00117.jpg │ ├── 00118.jpg │ ├── 00119.jpg │ ├── 00120.jpg │ ├── 00121.jpg │ ├── 00122.jpg │ ├── 00123.jpg │ ├── 00124.jpg │ ├── 00125.jpg │ ├── 00126.jpg │ ├── 00127.jpg │ ├── 00128.jpg │ ├── 00129.jpg │ ├── 00130.jpg │ ├── 00131.jpg │ ├── 00132.jpg │ ├── 00133.jpg │ ├── 00134.jpg │ ├── 00135.jpg │ ├── 00136.jpg │ ├── 00137.jpg │ ├── 00138.jpg │ ├── 00139.jpg │ ├── 00140.jpg │ ├── 00141.jpg │ ├── 00142.jpg │ ├── 00143.jpg │ ├── 00144.jpg │ ├── 00145.jpg │ ├── 00146.jpg │ ├── 00147.jpg │ ├── 00148.jpg │ ├── 00149.jpg │ ├── 00150.jpg │ ├── 00151.jpg │ ├── 00152.jpg │ ├── 00153.jpg │ ├── 00154.jpg │ ├── 00155.jpg │ ├── 00156.jpg │ ├── 00157.jpg │ ├── 00158.jpg │ ├── 00159.jpg │ ├── 00160.jpg │ ├── 00161.jpg │ ├── 00162.jpg │ ├── 00163.jpg │ ├── 00164.jpg │ ├── 00165.jpg │ └── 00166.jpg └── dataset_capture_script.py ├── trained.tflite ├── test ├── test_can.png ├── test_bottle.png ├── test_battery.png └── test_cigarette.png ├── README.md └── ei_image_classification.py /labels.txt: -------------------------------------------------------------------------------- 1 | battery 2 | bottle 3 | can 4 | cigarette -------------------------------------------------------------------------------- /dataset/labels.txt: -------------------------------------------------------------------------------- 1 | battery 2 | bottle 3 | can 4 | cigarette 5 | -------------------------------------------------------------------------------- /trained.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/trained.tflite -------------------------------------------------------------------------------- /test/test_can.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/test/test_can.png -------------------------------------------------------------------------------- /test/test_bottle.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/test/test_bottle.png -------------------------------------------------------------------------------- /test/test_battery.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/test/test_battery.png -------------------------------------------------------------------------------- /test/test_cigarette.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/test/test_cigarette.png -------------------------------------------------------------------------------- /dataset/can.class/00000.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/dataset/can.class/00000.jpg -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/dataset/cigarette.class/00164.jpg -------------------------------------------------------------------------------- /dataset/cigarette.class/00165.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/dataset/cigarette.class/00165.jpg -------------------------------------------------------------------------------- /dataset/cigarette.class/00166.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/H-H1/rubbish-identification-and-classification/HEAD/dataset/cigarette.class/00166.jpg -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 垃圾分类 2 | 用于分类电池，瓶子，易拉罐，和烟头 3 | 这个模型是使用edgeimpulse训练的，教程：https://docs.edgeimpulse.com/docs/openmv-cam-h7-plus 4 | 5 | 这只是一个用于测试的demo。如果你需要更高的结果，请使用自己的图像来训练，使用edgeimpulse非常简单。 6 | 7 | 你可以使用OpenMV的**数据集编辑器**打开**dataset**目录，从而添加和上传训练。 8 | 9 | 10 | ## 如何使用: 11 | 1. 放入文件。 12 | 2. ![d963ada1639e21fd054ef4b6ec0c8e4](https://user-images.githubusercontent.com/67135504/118285616-3a725480-b504-11eb-9e7c-70fb3c963146.png) 13 | 14 | 3. 使用OpenMV IDE运行 ei_image_classification.py。 15 | ## 结果 16 | ![e60e7c2954450da5a0a7deea53be3f2](https://user-images.githubusercontent.com/67135504/118284859-79ec7100-b503-11eb-86a4-b265fe0ca908.png) 17 | ![f17d648cc4e0486ec28485c718fdc36](https://user-images.githubusercontent.com/67135504/118284872-7ce76180-b503-11eb-8fa2-2ee5e8e872cc.png) 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /dataset/dataset_capture_script.py: -------------------------------------------------------------------------------- 1 | # Dataset Capture Script - By: wangkaizhi - 周六 10月 24 2020 2 | 3 | # Use this script to control how your OpenMV Cam captures images for your dataset. 4 | # You should apply the same image pre-processing steps you expect to run on images 5 | # that you will feed to your model during run-time. 6 | 7 | import sensor, image, time 8 | 9 | sensor.reset() 10 | sensor.set_pixformat(sensor.RGB565) # Modify as you like. 11 | sensor.set_framesize(sensor.QVGA) # Modify as you like. 12 | sensor.skip_frames(time = 2000) 13 | 14 | clock = time.clock() 15 | 16 | while(True): 17 | clock.tick() 18 | img = sensor.snapshot() 19 | # Apply lens correction if you need it. 20 | # img.lens_corr() 21 | # Apply rotation correction if you need it. 22 | # img.rotation_corr() 23 | # Apply other filters... 24 | # E.g. mean/median/mode/midpoint/etc. 25 | print(clock.fps()) 26 | -------------------------------------------------------------------------------- /ei_image_classification.py: -------------------------------------------------------------------------------- 1 | # Edge Impulse - OpenMV Image Classification Example 2 | 3 | import sensor, image, time, os, tf 4 | 5 | sensor.reset() # Reset and initialize the sensor. 6 | sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) 7 | sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) 8 | sensor.set_windowing((240, 240)) # Set 240x240 window. 9 | sensor.skip_frames(time=2000) # Let the camera adjust. 10 | 11 | net = "trained.tflite" 12 | labels = [line.rstrip('\n') for line in open("labels.txt")] 13 | 14 | clock = time.clock() 15 | while(True): 16 | clock.tick() 17 | 18 | img = sensor.snapshot() 19 | 20 | # default settings just do one detection... change them to search the image... 21 | for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5): 22 | print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect()) 23 | img.draw_rectangle(obj.rect()) 24 | # This combines the labels and confidence values into a list of tuples 25 | predictions_list = list(zip(labels, obj.output())) 26 | 27 | for i in range(len(predictions_list)): 28 | print("%s = %f" % (predictions_list[i][0], predictions_list[i][1])) 29 | 30 | print(clock.fps(), "fps") 31 | --------------------------------------------------------------------------------