Update of prediction scripts path used

README.md

@@ -84,7 +84,7 @@ Expected values for the **choice** parameter are ['svm_model', 'ensemble_model',
 Now we have a model trained, we can use it with an image as input:
 
 ```bash
-python predict_noisy_image_svd.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --metric 'lab' --mode 'svdn' --custom 'min_max_filename'
+python prediction/predict_noisy_image_svd.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --metric 'lab' --mode 'svdn' --custom 'min_max_filename'
 ```
 
 - **metric**: metric choice need to be one of the listed above.
@@ -103,7 +103,7 @@ All SVD metrics developed need:
 Now we have a model trained, we can use it with an image as input:
 
 ```bash
-python prediction_scene.py --data path/to/xxxx.csv --model saved_model/xxxx.joblib --output xxxxx --scene xxxx
+python prediction/prediction_scene.py --data path/to/xxxx.csv --model saved_model/xxxx.joblib --output xxxxx --scene xxxx
 ```
 **Remark**: *scene* parameter expected need to be the correct name of the Scene.
 
@@ -126,7 +126,7 @@ Once you have simulation done. Checkout your **threshold_map/%MODEL_NAME%/simula
 In order to see if a model well generalized, a bash script is available:
 
 ```bash
-bash testModelByScene.sh '100' '110' 'saved_models/xxxx.joblib' 'svdne' 'lab'
+bash others/testModelByScene.sh '100' '110' 'saved_models/xxxx.joblib' 'svdne' 'lab'
 ```
 
 Parameters list:
@@ -142,7 +142,7 @@ Parameters list:
 Main objective of this project is to predict as well as a human the noise perception on a photo realistic image. Human threshold is available from training data. So a script was developed to give the predicted treshold from model and compare predicted treshold from the expected one.
 
 ```bash
-python predict_seuil_expe.py --interval "x,x" --model 'saved_models/xxxx.joblib' --mode ["svd", "svdn", "svdne"] --metric ['lab', 'mscn', ...] --limit_detection xx --custom 'custom_min_max_filename'
+python prediction/predict_seuil_expe.py --interval "x,x" --model 'saved_models/xxxx.joblib' --mode ["svd", "svdn", "svdne"] --metric ['lab', 'mscn', ...] --limit_detection xx --custom 'custom_min_max_filename'
 ```
 
 Parameters list:

data_processing/generateAndTrain_maxwell.sh

@@ -57,7 +57,7 @@ for counter in {0..4}; do
                     python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --renderer "maxwell" --step 40 --random 1 --percent 1
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'
                     python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
                 fi
             done

data_processing/generateAndTrain_maxwell_custom.sh

@@ -58,7 +58,7 @@ for counter in {0..4}; do
                     python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
                 fi
             done

data_processing/generateAndTrain_maxwell_custom_center.sh

@@ -58,7 +58,7 @@ for counter in {0..4}; do
                     python generate/generate_data_model_random_center.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
                 fi
             done

data_processing/generateAndTrain_maxwell_custom_split.sh

@@ -58,7 +58,7 @@ for counter in {0..4}; do
                     python generate/generate_data_model_random_split.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
                 fi
             done

others/testModelByScene.sh

@@ -57,6 +57,6 @@ for scene in {"A","B","C","D","E","F","G","H","I"}; do
 
   python generate/generate_data_model.py --output ${FILENAME} --interval "${INPUT_BEGIN},${INPUT_END}" --kind ${INPUT_MODE} --metric ${INPUT_METRIC} --scenes "${scene}" --zones "${zones}" --percent 1 --sep ";" --rowindex "0"
 
-  python prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_metric_${INPUT_METRIC}.prediction" --scene ${scene}
+  python prediction/prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_metric_${INPUT_METRIC}.prediction" --scene ${scene}
 
 done

others/testModelByScene_maxwell.sh

@@ -65,6 +65,6 @@ for scene in {"A","D","G","H"}; do
 
   python generate/generate_data_model.py --output ${FILENAME} --interval "${INPUT_BEGIN},${INPUT_END}" --kind ${INPUT_MODE} --metric ${INPUT_METRIC} --scenes "${scene}" --zones "${zones}" --percent 1
 
-  python prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_metric_${INPUT_METRIC}.prediction" --scene ${scene}
+  python prediction/prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_metric_${INPUT_METRIC}.prediction" --scene ${scene}
 
 done

prediction/predict_seuil_expe.py

@@ -121,7 +121,7 @@ def main():
                     tmp_file_path = tmp_filename.replace('__model__',  p_model_file.split('/')[-1].replace('.joblib', '_'))
                     block.save(tmp_file_path)
 
-                    python_cmd = "python predict_noisy_image_svd.py --image " + tmp_file_path + \
+                    python_cmd = "python prediction/predict_noisy_image_svd.py --image " + tmp_file_path + \
                                     " --interval '" + p_interval + \
                                     "' --model " + p_model_file  + \
                                     " --mode " + p_mode + \

prediction/predict_seuil_expe_maxwell.py

@@ -124,7 +124,7 @@ def main():
                         tmp_file_path = tmp_filename.replace('__model__',  p_model_file.split('/')[-1].replace('.joblib', '_'))
                         block.save(tmp_file_path)
 
-                        python_cmd = "python predict_noisy_image_svd.py --image " + tmp_file_path + \
+                        python_cmd = "python prediction/predict_noisy_image_svd.py --image " + tmp_file_path + \
                                         " --interval '" + p_interval + \
                                         "' --model " + p_model_file  + \
                                         " --mode " + p_mode + \

prediction/predict_seuil_expe_maxwell_curve.py

@@ -122,7 +122,7 @@ def main():
                         tmp_file_path = tmp_filename.replace('__model__',  p_model_file.split('/')[-1].replace('.joblib', '_'))
                         block.save(tmp_file_path)
 
-                        python_cmd_line = "python predict_noisy_image_svd.py --image {0} --interval '{1}' --model {2} --mode {3} --feature {4}"
+                        python_cmd_line = "python prediction/predict_noisy_image_svd.py --image {0} --interval '{1}' --model {2} --mode {3} --feature {4}"
                         python_cmd = python_cmd_line.format(tmp_file_path, p_interval, p_model_file, p_mode, p_feature) 
 
                         # specify use of custom file for min max normalization

simulation/run_maxwell_simulation.sh

@@ -42,7 +42,7 @@ for size in {"4","8","16","26","32","40"}; do
 
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                            python predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'
+                            python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'
 
                             python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
 

simulation/run_maxwell_simulation_corr_custom.sh

@@ -30,7 +30,7 @@ for label in {"0","1"}; do
 
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                            python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                            python prediction/prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                             python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
 

simulation/run_maxwell_simulation_custom.sh

@@ -43,7 +43,7 @@ for size in {"4","8","16","26","32","40"}; do
 
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                            python predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                            python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                             python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
 

simulation/run_maxwell_simulation_custom_filters.sh

@@ -34,7 +34,7 @@ for size in {"4","8","16","26","32","40"}; do
                         # python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                         # python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                        python predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                        python prediction/predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                         python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
                     fi

simulation/run_maxwell_simulation_filters_statistics.sh

@@ -30,7 +30,7 @@ for nb_zones in {4,6,8,10,12}; do
                 # python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                 # python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --custom ${CUSTOM_MIN_MAX_FILENAME}
+                python prediction/predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                 python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
             fi

simulation/run_maxwell_simulation_keras_corr_custom.sh

@@ -31,7 +31,7 @@ for label in {"0","1"}; do
 
                         python deep_network_keras_svd.py --data ${FILENAME} --output ${MODEL_NAME} --size ${size}
 
-                        python predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                        python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                         python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric}
 

simulation/run_maxwell_simulation_keras_custom.sh

@@ -28,7 +28,7 @@ for metric in {"sub_blocks_stats","sub_blocks_stats_reduced","sub_blocks_area","
 
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python predict_seuil_expe_maxwell_curve.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                 python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric}