NanjingForestryUniversity/tobacoo-industry
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连接fifo前进行一次预测
This commit is contained in:
FEIJINTI 2022-06-22 09:20:32 +08:00
parent a42fcb3438
commit 52c4feee09
2 changed files with 17 additions and 7 deletions
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23
02_feature_design&training.ipynb
View File

@ -348,12 +348,11 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 27,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"pycharm": { "pycharm": {
"name": "#%%\n", "name": "#%%\n"
"is_executing": true
} }
}, },
"outputs": [ "outputs": [
@ -406,6 +405,18 @@
"needs_background": "light" "needs_background": "light"
}, },
"output_type": "display_data" "output_type": "display_data"
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
"Input \u001B[1;32mIn [27]\u001B[0m, in \u001B[0;36m<cell line: 3>\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m model \u001B[38;5;241m=\u001B[39m SpecDetector(model_path\u001B[38;5;241m=\u001B[39mmodel_file, blk_sz\u001B[38;5;241m=\u001B[39mblk_sz, channel_num\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mlen\u001B[39m(selected_bands))\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m# 画图验证效果\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m \u001B[43mvisualization_evaluation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdetector\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43mr\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mF:\u001B[39;49m\u001B[38;5;124;43m\\\u001B[39;49m\u001B[38;5;124;43mzhouchao\u001B[39;49m\u001B[38;5;124;43m\\\u001B[39;49m\u001B[38;5;124;43m617\u001B[39;49m\u001B[38;5;124;43m\\\u001B[39;49m\u001B[38;5;124;43m617_pure_tobacco\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mselected_bands\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mselected_bands\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[1;32m~\\PycharmProjects\\tobacco-industry\\utils.py:112\u001B[0m, in \u001B[0;36mvisualization_evaluation\u001B[1;34m(detector, data_path, selected_bands)\u001B[0m\n\u001B[0;32m 110\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m idx, image_path \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(image_paths):\n\u001B[0;32m 111\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mopen\u001B[39m(image_path, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mrb\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01mas\u001B[39;00m f:\n\u001B[1;32m--> 112\u001B[0m data \u001B[38;5;241m=\u001B[39m \u001B[43mf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 113\u001B[0m img \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mfrombuffer(data, dtype\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39mfloat32)\u001B[38;5;241m.\u001B[39mreshape((nrows, \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m, ncols))\u001B[38;5;241m.\u001B[39mtranspose(\u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m 114\u001B[0m nbands \u001B[38;5;241m=\u001B[39m img\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m2\u001B[39m]\n",
"\u001B[1;31mKeyboardInterrupt\u001B[0m: "
]
} }
], ],
"source": [ "source": [
@ -420,8 +431,7 @@
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"pycharm": { "pycharm": {
"name": "#%%\n", "name": "#%%\n"
"is_executing": true
} }
}, },
"outputs": [], "outputs": [],
@ -437,8 +447,7 @@
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"pycharm": { "pycharm": {
"name": "#%%\n", "name": "#%%\n"
"is_executing": true
} }
} }
} }

1
main.py
View File

@ -11,6 +11,7 @@ selected_model = "rf_8x8_c4_400_13.model"
def main(): def main():
model_path = os.path.join(ROOT_DIR, "models", selected_model) model_path = os.path.join(ROOT_DIR, "models", selected_model)
detector = SpecDetector(model_path, blk_sz=8, channel_num=4) detector = SpecDetector(model_path, blk_sz=8, channel_num=4)
_ = detector.predict(np.ones((600, 1024, 4)))
total_len = nrows * ncols * nbands * 4 total_len = nrows * ncols * nbands * 4
if not os.access(img_fifo_path, os.F_OK): if not os.access(img_fifo_path, os.F_OK):
os.mkfifo(img_fifo_path, 0o777) os.mkfifo(img_fifo_path, 0o777)