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Change Example orthofoto

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This commit is contained in:
Daniel Saavedra
2020-08-26 00:39:38 -04:00
parent f530ce8b70
commit 0a5f9f87fd
26 changed files with 31018 additions and 567 deletions
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Code_Example/.gitignore vendored Normal file
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Code_Example/.ipynb_checkpoints/Example Detection AllInOne-checkpoint.ipynb Normal file
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Code_Example/.ipynb_checkpoints/Example Detection AllInOne_100Example-checkpoint.ipynb Normal file
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Code_Example/.ipynb_checkpoints/Example_prediction-checkpoint.ipynb Normal file
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Code_Example/.ipynb_checkpoints/Example_prediction_Ortofoto-checkpoint.ipynb Normal file
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Example Detection AllInOne.ipynb → Code_Example/Example Detection AllInOne.ipynb
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@@ -12,7 +12,7 @@
"import json\n",
"import cv2\n",
"import sys\n",
"sys.path += [os.path.abspath('keras-yolo3-master')]\n",
"sys.path += [os.path.abspath('../keras-yolo3-master')]\n",
"\n",
"from utils.utils import get_yolo_boxes, makedirs\n",
"from utils.bbox import draw_boxes\n",
@@ -38,7 +38,7 @@
"metadata": {},
"outputs": [],
"source": [
"input_path = 'images/Mision 23_DJI_0061.jpg' \n",
"input_path = '../images/Mision 23_DJI_0061.jpg' \n",
"#output_path = 'Result_Complete_Example/'\n",
"#makedirs(output_path)"
]

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Example Detection AllInOne_100Example.ipynb → Code_Example/Example Detection AllInOne_100Example.ipynb
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@@ -12,7 +12,7 @@
"import json\n",
"import cv2\n",
"import sys\n",
"sys.path += [os.path.abspath('keras-yolo3-master')]\n",
"sys.path += [os.path.abspath('../keras-yolo3-master')]\n",
"\n",
"from utils.utils import get_yolo_boxes, makedirs\n",
"from utils.bbox import draw_boxes\n",
@@ -38,8 +38,8 @@
"metadata": {},
"outputs": [],
"source": [
"input_path = '100_Example/'\n",
"output_path = 'Result_Complete_Example/'\n",
"input_path = '../100_Example/'\n",
"output_path = '../Result_Complete_Example/'\n",
"makedirs(output_path)"
]
},

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Code_Example/Example_2_Ortofoto.html Normal file
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Example_prediction.ipynb → Code_Example/Example_prediction.ipynb
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@@ -20,7 +20,7 @@
"import json\n",
"import cv2\n",
"import sys\n",
"sys.path += [os.path.abspath('keras-yolo3-master')]\n",
"sys.path += [os.path.abspath('../keras-yolo3-master')]\n",
"\n",
"from utils.utils import get_yolo_boxes, makedirs\n",
"from utils.bbox import draw_boxes\n",
@@ -129,7 +129,7 @@
}
],
"source": [
"image_path = 'images/Mision 23_DJI_0061.jpg' \n",
"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
"\n",
"\n",
"image = cv2.imread(image_path)\n",
@@ -251,7 +251,7 @@
}
],
"source": [
"image_path = 'images/Mision 23_DJI_0061.jpg' \n",
"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
"\n",
"\n",
"image = cv2.imread(image_path)\n",
@@ -374,7 +374,7 @@
}
],
"source": [
"image_path = 'images/Mision 23_DJI_0061.jpg' \n",
"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
"\n",
"\n",
"image = cv2.imread(image_path)\n",
@@ -489,7 +489,7 @@
}
],
"source": [
"image_path = 'images/Mision 23_DJI_0061.jpg' \n",
"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
"\n",
"\n",
"image = cv2.imread(image_path)\n",
@@ -571,7 +571,7 @@
}
],
"source": [
"image_path = 'images/Mision 23_DJI_0061.jpg' \n",
"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
"\n",
"\n",
"image = cv2.imread(image_path)\n",

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Code_Example/Example_prediction_Ortofoto.ipynb Normal file
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Panel_Detector_Fault_1_SSD.ipynb → Code_Example/Panel_Detector_Fault_1_SSD.ipynb
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Panel_Detector_Fault_C_SSD.ipynb → Code_Example/Panel_Detector_Fault_C_SSD.ipynb
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Panel_Detector_Fault_SSD.ipynb → Code_Example/Panel_Detector_Fault_SSD.ipynb
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Panel_Detector_SSD.ipynb → Code_Example/Panel_Detector_SSD.ipynb
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Code_Example/config_full_yolo_fault_1_infer.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["1"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_S/Train/images/",
"train_annot_folder": "../Train&Test_S/Train/anns/",
"cache_name": "../Result_yolo3_fault_1/experimento_fault_1_gpu.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 200,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../log_experimento_fault_gpu",
"saved_weights_name": "../Result_yolo3_fault_1/yolo3_full_fault_1.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_S/Test/images/",
"valid_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Result_yolo3_fault_1/val_fault_1.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_S/Test/images/",
"test_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Result_yolo3_fault_1/test_fault_1.pkl",
"test_times": 1
}
}

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Code_Example/config_full_yolo_fault_1_train.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["1"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_S/Train/images/",
"train_annot_folder": "../Train&Test_S/Train/anns/",
"cache_name": "../Result_yolo3_fault_1/experimento_fault_1_gpu.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 200,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../log_experimento_fault_gpu",
"saved_weights_name": "../Result_yolo3_fault_1/yolo3_full_fault_1.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_S/Test/images/",
"valid_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Result_yolo3_fault_1/val_fault_1.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_S/Test/images/",
"test_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Result_yolo3_fault_1/test_fault_1.pkl",
"test_times": 1
}
}

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Code_Example/config_full_yolo_fault_2_infer.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["2"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_H/Train/images/",
"train_annot_folder": "../Train&Test_H/Train/anns/",
"cache_name": "../Result_yolo3_fault_2/experimento_fault_2_gpu.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 200,
"warmup_epochs": 10,
"ignore_thresh": 0.5,
"gpus": "0",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../Result_yolo3_fault_2/log_experimento_fault_gpu_2",
"saved_weights_name": "../Result_yolo3_fault_2/yolo3_full_fault_2.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_H/Test/images/",
"valid_annot_folder": "../Train&Test_H/Test/anns/",
"cache_name": "../Result_yolo3_fault_2/val_fault_2.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_H/Test/images/",
"test_annot_folder": "../Train&Test_H/Test/anns/",
"cache_name": "../Result_yolo3_fault_2/test_fault_2.pkl",
"test_times": 1
}
}

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Code_Example/config_full_yolo_fault_2_train.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["2"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_H/Train/images/",
"train_annot_folder": "../Train&Test_H/Train/anns/",
"cache_name": "../Result_yolo3_fault_2/experimento_fault_2_gpu.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 200,
"warmup_epochs": 10,
"ignore_thresh": 0.5,
"gpus": "0",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../Result_yolo3_fault_2/log_experimento_fault_gpu_2",
"saved_weights_name": "../Result_yolo3_fault_2/yolo3_full_fault_2.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_H/Test/images/",
"valid_annot_folder": "../Train&Test_H/Test/anns/",
"cache_name": "../Result_yolo3_fault_2/val_fault_2.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_H/Test/images/",
"test_annot_folder": "../Train&Test_H/Test/anns/",
"cache_name": "../Result_yolo3_fault_2/test_fault_2.pkl",
"test_times": 1
}
}

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Code_Example/config_full_yolo_fault_4_infer.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["4"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_D/Train/images/",
"train_annot_folder": "../Train&Test_D/Train/anns/",
"cache_name": "../Result_yolo3_fault_4/Result_yolo3_fault_4.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 500,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../log_experimento_fault_gpu",
"saved_weights_name": "../Result_yolo3_fault_4/yolo3_full_fault_4.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_D/Test/images/",
"valid_annot_folder": "../Train&Test_D/Test/anns/",
"cache_name": "../Result_yolo3_fault_4/Result_yolo3_fault_4.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_D/Test/images/",
"test_annot_folder": "../Train&Test_D/Test/anns/",
"cache_name": "../Result_yolo3_fault_4/Result_yolo3_fault_4.pkl",
"test_times": 1
}
}

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Code_Example/config_full_yolo_fault_4_train.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["4"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_D/Train/images/",
"train_annot_folder": "../Train&Test_D/Train/anns/",
"cache_name": "../Result_yolo3_fault_4/Result_yolo3_fault_4.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 500,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../log_experimento_fault_gpu",
"saved_weights_name": "../Result_yolo3_fault_4/yolo3_full_fault_4.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_D/Test/images/",
"valid_annot_folder": "../Train&Test_D/Test/anns/",
"cache_name": "../Result_yolo3_fault_4/Result_yolo3_fault_4.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_D/Test/images/",
"test_annot_folder": "../Train&Test_D/Test/anns/",
"cache_name": "../Result_yolo3_fault_4/Result_yolo3_fault_4.pkl",
"test_times": 1
}
}

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Code_Example/config_full_yolo_panel_infer.json Normal file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["panel"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_A/Train/images/",
"train_annot_folder": "../Train&Test_A/Train/anns/",
"cache_name": "../Result_yolo3_panel/train_panel.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-3,
"nb_epochs": 500,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../Result_yolo3_panel/log_experimento_panel_gpu",
"saved_weights_name": "../Result_yolo3_panel/yolo3_full_panel.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_A/Test/images/",
"valid_annot_folder": "../Train&Test_A/Test/anns/",
"cache_name": "../Result_yolo3_panel/val_panel.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_A/Test/images/",
"test_annot_folder": "../Train&Test_A/Test/anns/",
"cache_name": "../Result_yolo3_panel/test_panel.pkl",
"test_times": 1
}
}

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Code_Example/config_full_yolo_panel_train.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["panel"],
"backend": "../keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_A/Train/images/",
"train_annot_folder": "../Train&Test_A/Train/anns/",
"cache_name": "../Result_yolo3_panel/train_panel.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-3,
"nb_epochs": 500,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "../Result_yolo3_panel/log_experimento_panel_gpu",
"saved_weights_name": "../Result_yolo3_panel/yolo3_full_panel.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_A/Test/images/",
"valid_annot_folder": "../Train&Test_A/Test/anns/",
"cache_name": "../Result_yolo3_panel/val_panel.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_A/Test/images/",
"test_annot_folder": "../Train&Test_A/Test/anns/",
"cache_name": "../Result_yolo3_panel/test_panel.pkl",
"test_times": 1
}
}

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Example_prediction_Ortofoto.ipynb
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README.md
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@@ -263,5 +263,5 @@ Before sending your pull requests, make sure you followed this list.
# Example to use trained model
In ['Example_Prediction'](Example_prediction.ipynb) this is the example of how to implement an already trained model, it can be modified to change the model you have to use and the image in which you want to detect faults.
In ['Example_Prediction_AllInOne'](Example Detection AllInOne.ipynb) this is the example of how implement all trained model, you can use this code for predict a folder of images and have a output image with detection boxes.
In ['Example_Prediction'](Code_Example/Example_prediction.ipynb) this is the example of how to implement an already trained model, it can be modified to change the model you have to use and the image in which you want to detect faults.
In ['Example_Prediction_AllInOne'](Code_Example/Example Detection AllInOne.ipynb) this is the example of how implement all trained model, you can use this code for predict a folder of images and have a output image with detection boxes.

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command_train
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@@ -1 +0,0 @@
ls images | sed -e 's/\..*$//' > train.txt