Actualizar README.md

Read Shape Mesas

.gitignore

@@ -8,6 +8,7 @@ Train&Test_4/
 Train&Test_D/
 Train&Test_C/
 Train&Test_A/
+Train&Test_A2/
 Train&Test_S/
 result_ssd7_panel_cell/
 Thermal/

GPS_Panel/README.md

@@ -21,7 +21,9 @@ Enumeration_KML.ipynb
 This algorithm uses the division of the panels to classify them individually if they are with any fault.
 
 ## Train Model 
-In the Classifier folder there is the training of the model `` `Train_Classifier_DEEP_SVC.ipynb``` which is based on the union of a deep learning model pre-trained with Imagenet together with an SVM model.
+In the Classifier folder there is the training of the model 
+[Train_Classifier_DEEP_SVC](Classifier/Train_Classifier_DEEP_SVC.ipynb)
+which is based on the union of a deep learning model pre-trained with Imagenet together with an SVM model.
 
 ## Example
 

GPS_Panel/Utils.py

@@ -239,7 +239,7 @@ def rgb2hsv(rgb):
 
 def doubleMADsfromMedian(y,thresh=3.5):
     # warning: this function does not check for NAs
-    # nor does it address issues when 
+    # nor does it address issues when
     # more than 50% of your data have identical values
     m = np.median(y)
     abs_dev = np.abs(y - m)
@@ -255,7 +255,7 @@ def doubleMADsfromMedian(y,thresh=3.5):
 def watershed_marked(thresh, min_Area = 100, threshold_median_Area = 3):
     ## Thresh is the segmentation image use to watershed
     ##
-    
+
     # Perform the distance transform algorithm
     dist = cv2.distanceTransform(thresh, cv2.DIST_L2, 3)
     # Normalize the distance image for range = {0.0, 1.0}
@@ -300,14 +300,14 @@ def watershed_marked(thresh, min_Area = 100, threshold_median_Area = 3):
     for i,Logic in zip(Areas[:,0], L_Areas) :
         if Logic:
             markers[markers == i] = 0
-            
+
     return Areas[L_Areas,:], dist_8u,markers
 
 def pixel2gps(points, geot):
     # transform pixel to gps coordinate
     return np.vstack(gr.map_pixel_inv(points[:,1], points[:,0],geot[1],geot[-1], geot[0],geot[3])).T
 
-    
+
 
 def gps2pixel(points_coord, geot):
     # transform gps coordinate to pixel

GPS_Panel/requirements.txt

@@ -9,5 +9,8 @@ matplotlib==3.2.2
 scipy==1.4.1
 ipython==7.19.0
 scikit_learn==0.23.2
-skimage==0.0
+geopandas
-
+rtree==0.9.3
+simplekml
+urbanaccess pandana  
+cenpy 

README.md

@@ -1,3 +1,4 @@
+original: https://github.com/RentadroneCL/Photovoltaic_Fault_Detector
 
 # Photovoltaic Fault Detector
 
@@ -6,11 +7,11 @@
 [![Open Source Helpers](https://www.codetriage.com/rentadronecl/photovoltaic_fault_detector/badges/users.svg)](https://www.codetriage.com/rentadronecl/photovoltaic_fault_detector)
 [![Coverage Status](https://coveralls.io/repos/github/RentadroneCL/Photovoltaic_Fault_Detector/badge.svg)](https://coveralls.io/github/RentadroneCL/Photovoltaic_Fault_Detector)
 
-[Rentadrone.cl](https://rentadronecl.github.io)
+[SimpleMap.io](https://simplemap.io/)
 
 ## Forum
 
-This project is part of the [UNICEF Innovation Fund Discourse community](https://unicef-if.discourse.group/c/projects/rentadrone/10). You can post comments or questions about each category of [Rentadrone Developers](https://rentadrone.cl/developers/) algorithms. We encourage users to participate in the forum and to engage with fellow users.
+This project is part of the [UNICEF Innovation Fund Discourse community](https://unicef-if.discourse.group/c/projects/rentadrone/10). You can post comments or questions about each category of [SimpleMap.io Open-Source Initiative](https://rentadronecl.github.io) algorithms. We encourage users to participate in the forum and to engage with fellow users.
 
 ## Summary
 
@@ -209,7 +210,7 @@ The configuration file for SSD300 is a json file, which looks like this  (exampl
 `python train_ssd.py -c config.json -o /path/to/result`
 
 or
-`python train_ssd.py -c config.json -o /path/to/result`
+`python train_yolo.py -c config.json -o /path/to/result`
 
 By the end of this process, the code will write the weights of the best model to file best_weights.h5 (or whatever name specified in the setting "saved_weights_name" in the config.json file). The training process stops when the loss on the validation set is not improved in 20 consecutive epoches.
 
@@ -264,6 +265,5 @@ Before sending your pull requests, make sure you followed this list.
 - Read [Code of Conduct](CODE_OF_CONDUCT.md).
 - Check if my changes are consistent with the [guidelines](https://github.com/RentadroneCL/model-definition/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
 - Changes are consistent with the [Coding Style](https://github.com/RentadroneCL/model-definition/blob/master/CONTRIBUTING.md#c-coding-style).
-- Run [Unit Tests](https://github.com/RentadroneCL/model-definition/CONTRIBUTING.md#running-unit-tests).
 
 

Result_yolo3_panel2/time.txt

@@ -0,0 +1 @@
+Tiempo promedio:nan

Result_yolo3_panel2/yolo3_full_yolo.err

@@ -0,0 +1,29 @@
+2020-12-14 22:27:04.267611: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
+2020-12-14 22:27:04.267749: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
+2020-12-14 22:27:04.267767: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+2020-12-14 22:27:05.268036: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
+2020-12-14 22:27:05.268076: E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303)
+2020-12-14 22:27:05.268102: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (dlsaavedra-X406UAR): /proc/driver/nvidia/version does not exist
+2020-12-14 22:27:05.268334: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
+2020-12-14 22:27:05.311492: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1800000000 Hz
+2020-12-14 22:27:05.313101: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f4be3033d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2020-12-14 22:27:05.313143: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+WARNING:tensorflow:AutoGraph could not transform <bound method YoloLayer.call of <yolo.YoloLayer object at 0x7fb92423a8d0>> and will run it as-is.
+Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
+Cause: unexpected indent (<unknown>, line 144)
+WARNING:tensorflow:AutoGraph could not transform <bound method YoloLayer.call of <yolo.YoloLayer object at 0x7fb904726710>> and will run it as-is.
+Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
+Cause: unexpected indent (<unknown>, line 144)
+WARNING:tensorflow:AutoGraph could not transform <bound method YoloLayer.call of <yolo.YoloLayer object at 0x7fb9044f2c10>> and will run it as-is.
+Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
+Cause: unexpected indent (<unknown>, line 144)
+WARNING:tensorflow:ModelCheckpoint mode 1 is unknown, fallback to auto mode.
+WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Layers with arguments in `__init__` must override `get_config`.
+2020-12-14 22:27:16.470550: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started.
+2020-12-14 22:27:16.470928: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcupti.so.10.1'; dlerror: libcupti.so.10.1: cannot open shared object file: No such file or directory
+2020-12-14 22:27:16.470972: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI could not be loaded or symbol could not be found.
+2020-12-14 22:27:16.470988: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer)failed with error CUPTI could not be loaded or symbol could not be found.
+2020-12-14 22:27:20.300351: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1329] function cupti_interface_->EnableCallback( 0 , subscriber_, CUPTI_CB_DOMAIN_DRIVER_API, cbid)failed with error CUPTI could not be loaded or symbol could not be found.
+2020-12-14 22:27:20.300441: I tensorflow/core/profiler/internal/gpu/device_tracer.cc:88]  GpuTracer has collected 0 callback api events and 0 activity events.
+2020-12-14 22:28:18.604124: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
+2020-12-14 22:29:29.713989: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled

Result_yolo3_panel2/yolo3_full_yolo.output

@@ -0,0 +1,22 @@
+Seen labels: 	{'panel': 854}
+
+Given labels: 	['panel']
+
+Training on: 	['panel']
+
+multi_gpu:1
+
+Loading pretrained weights.
+
+Train for 12 steps, validate for 1 steps
+Epoch 1/2
+
+Epoch 00001: loss improved from inf to 40.76296, saving model to Result_yolo3_panel2/yolo3_full_panel.h5
+12/12 - 68s - loss: 40.7630 - yolo_layer_loss: 12.8884 - yolo_layer_1_loss: 27.8505 - yolo_layer_2_loss: 0.0241 - val_loss: 3793.7336 - val_yolo_layer_loss: 1322.7670 - val_yolo_layer_1_loss: 2470.9607 - val_yolo_layer_2_loss: 0.0060
+Epoch 2/2
+
+Epoch 00002: loss did not improve from 40.76296
+12/12 - 71s - loss: 46.9910 - yolo_layer_loss: 20.9780 - yolo_layer_1_loss: 26.0097 - yolo_layer_2_loss: 0.0033 - val_loss: 72523.8125 - val_yolo_layer_loss: 4534.5332 - val_yolo_layer_1_loss: 67989.2812 - val_yolo_layer_2_loss: 0.0000e+00
+94 instances of class panel with average precision: 0.0000
+mAP using the weighted average of precisions among classes: 0.0000
+mAP: 0.0000

Result_yolo3_panel2/yolo3_full_yolo_test.err

@@ -0,0 +1,11 @@
+2020-12-14 22:29:36.919265: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
+2020-12-14 22:29:36.919408: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
+2020-12-14 22:29:36.919425: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+2020-12-14 22:29:38.653634: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
+2020-12-14 22:29:38.653676: E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303)
+2020-12-14 22:29:38.653711: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (dlsaavedra-X406UAR): /proc/driver/nvidia/version does not exist
+2020-12-14 22:29:38.653939: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
+2020-12-14 22:29:38.678825: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1800000000 Hz
+2020-12-14 22:29:38.679672: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5569ee6e0e00 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2020-12-14 22:29:38.679731: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.

Result_yolo3_panel2/yolo3_full_yolo_test.output

@@ -0,0 +1,4 @@
+dict_items([(0, (0.0, 94.0))])
+94 instances of class panel with average precision: 0.0000
+mAP using the weighted average of precisions among classes: 0.0000
+mAP: 0.0000

config_full_yolo_panel_infer2.json

@@ -0,0 +1,49 @@
+{
+    "model" : {
+        "min_input_size":       400,
+        "max_input_size":       400,
+        "anchors":              [0,0, 0,0, 0,0, 4,175, 4,92, 5,92, 6,190, 7,87, 30,5],
+        "labels":               ["panel"],
+	"backend": 		"keras-yolo3-master/full_yolo_backend.h5"
+    },
+
+    "train": {
+        "train_image_folder":   "Train&Test_A2/Train/images/",
+        "train_annot_folder":   "Train&Test_A2/Train/anns/",
+	"cache_name":           "Result_yolo3_panel2/train_panel.pkl",
+
+        "train_times":          3,
+
+        "batch_size":           2,
+        "learning_rate":        1e-3,
+        "nb_epochs":            500,
+        "warmup_epochs":        15,
+        "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_panel2/log_experimento_panel_gpu",
+	"saved_weights_name":   "Result_yolo3_panel2/yolo3_full_panel.h5",
+        "debug":                true
+    },
+
+    "valid": {
+        "valid_image_folder":   "Train&Test_A2/Train/images/",
+        "valid_annot_folder":   "Train&Test_A2/Train/anns/",
+        "cache_name":           "Result_yolo3_panel2/val_panel_2.pkl",
+
+        "valid_times":          1
+    },
+   "test": {
+        "test_image_folder":   "Train&Test_A2/Train/images/",
+        "test_annot_folder":   "Train&Test_A2/Train/anns/",
+        "cache_name":          "Result_yolo3_panel2/test_panel_2.pkl",
+
+        "test_times":          1
+    }
+}

config_full_yolo_panel_train2.json

@@ -0,0 +1,49 @@
+{
+    "model" : {
+        "min_input_size":       400,
+        "max_input_size":       400,
+        "anchors":              [0,0, 0,0, 0,0, 4,175, 4,92, 5,92, 6,190, 7,87, 30,5],
+        "labels":               ["panel"],
+	"backend": 		"keras-yolo3-master/full_yolo_backend.h5"
+    },
+
+    "train": {
+        "train_image_folder":   "Train&Test_A2/Train/images/",
+        "train_annot_folder":   "Train&Test_A2/Train/anns/",
+	"cache_name":           "Result_yolo3_panel2/train_panel.pkl",
+
+        "train_times":          3,
+
+        "batch_size":           2,
+        "learning_rate":        1e-3,
+        "nb_epochs":            2,
+        "warmup_epochs":        15,
+        "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_panel2/log_experimento_panel_gpu",
+	"saved_weights_name":   "Result_yolo3_panel2/yolo3_full_panel.h5",
+        "debug":                true
+    },
+
+    "valid": {
+        "valid_image_folder":   "Train&Test_A2/Test/images/",
+        "valid_annot_folder":   "Train&Test_A2/Test/anns/",
+        "cache_name":           "Result_yolo3_panel2/val_panel.pkl",
+
+        "valid_times":          1
+    },
+   "test": {
+        "test_image_folder":   "Train&Test_A2/Test/images/",
+        "test_annot_folder":   "Train&Test_A2/Test/anns/",
+        "cache_name":          "Result_yolo3_panel2/test_panel.pkl",
+
+        "test_times":          1
+    }
+}

keras-yolo3-master/train.py

@@ -272,11 +272,7 @@ def _main_(args):
         callbacks        = callbacks
     )
 
-
+    train_model.load_weights(config['train']['saved_weights_name'])
-    # make a GPU version of infer_model for evaluation
-    #if multi_gpu > 1:
-    #    infer_model = load_model(config['train']['saved_weights_name'])
-    infer_model.load_weights(config['train']['saved_weights_name'])
     infer_model.save(config['train']['saved_weights_name'])
     ###############################
     #   Run the evaluation

requirements.txt

@@ -4,14 +4,14 @@ gast==0.2.2
 grpcio
 h5py
 Markdown
-numpy
+numpy==1.22
 opencv-contrib-python
 opt-einsum
 protobuf
 PyYAML
 scipy
 six
-tensorflow==2.1
+tensorflow==2.7.2
 termcolor
 tqdm
 Werkzeug