Update prediction_simulation.py

README.md

@@ -78,6 +78,13 @@ Script `train_set.py` saves the trainset in folder _dataset_ and, script `train_
 
 This step is to be executed on Raspberry Pi. Please refer to [baby_cry_rpi](https://github.com/giulbia/baby_cry_rpi.git)
 
+##### PREDICTION ON PC
+
+this added step to test the same functionality on the pc using s shell, use sh baby_cry-sh.sh,
+it uses the sox utility for macOS  to use the microphone 
+brew install sox
+
+
 ##### SIMULATION
 
 This step allows you to test the model on your computer. It uses scripts from `rpi_methods` folder.
@@ -94,4 +101,4 @@ Log files are created for each step, they are saved in folder `baby_cry_detectio
 
 
 >Part of the data used for training comes from
-[ESC-50: Dataset for environmental sound classification](https://github.com/karoldvl/ESC-50)
+[ESC-50: Dataset for environmental sound classification](https://github.com/karoldvl/ESC-50)

baby-cry-sh.sh

@@ -0,0 +1,70 @@
+#!/usr/bin/env bash
+
+function recording() {
+    echo "Start Recording..."
+sox -t coreaudio default "$filename" trim 0 9 rate 44100 channels 1 
+}
+
+filename="/Users/MT/Desktop/stg.wav"
+PREDICTION_SCRIPT="/Users/MT/Desktop/baby_cry_detection/baby_cry_detection/prediction_simulation/prediction_simulation.py"
+lullaby_file="/Users/MT/Desktop/baby_cry_detection/baby_cry_detection/prediction_simulation/strange-lullaby-28691.mp3"
+log_file="/Users/MT/Desktop/output/prediction/prediction.txt"
+PREDICTION=1
+PLAYING=0
+CPT=0
+result=0
+
+function predict() {
+    echo "Predicting..."
+    PREDICTION=$(python3 "$PREDICTION_SCRIPT" --file_name "$filename")
+    if grep -q "1" "$log_file"; then
+    result=1
+    elif grep -q "0" "$log_file"; then
+    result=0
+    fi
+}
+
+function start_playing() {
+    if [[ $PLAYING == 0 ]]; then
+        echo "Start playing"
+        play -q "$lullaby_file"
+        PLAYING=1
+    fi
+}
+
+function stop_playing(){
+    if [[ $PLAYING == 1 ]]; then
+        echo "Stop playing"
+        PLAYING=0
+    fi
+}
+
+function clean_up {
+    # Perform program exit housekeeping
+    echo ""
+    echo "Thank you for using parenting 2.0"
+    echo "Good Bye."
+    stop_playing
+    exit
+}
+
+trap clean_up SIGHUP SIGINT SIGTERM
+
+echo "Welcome to Parenting 2.0"
+echo ""
+
+recording
+predict
+
+if [[ $result == 0 ]]; then
+    stop_playing
+    echo "Your Baby is relaxing"
+else
+    CPT=$(expr $CPT + 1)
+    start_playing
+fi
+
+echo "Majority Vote Prediction: $PREDICTION"
+echo "State of the Process PREDICTION=$PREDICTION, PLAYING=$PLAYING, COMPTEUR=$CPT"
+
+clean_up

baby_cry_detection/pc_main/baby-cry-sh.sh

@@ -0,0 +1,70 @@
+#!/usr/bin/env bash
+
+function recording() {
+    echo "Start Recording..."
+sox -t coreaudio default "$filename" trim 0 9 rate 44100 channels 1 
+}
+
+filename="/Users/MT/Desktop/stg.wav"
+PREDICTION_SCRIPT="/Users/MT/Desktop/baby_cry_detection/baby_cry_detection/prediction_simulation/prediction_simulation.py"
+lullaby_file="/Users/MT/Desktop/baby_cry_detection/baby_cry_detection/prediction_simulation/strange-lullaby-28691.mp3"
+log_file="/Users/MT/Desktop/output/prediction/prediction.txt"
+PREDICTION=1
+PLAYING=0
+CPT=0
+result=0
+
+function predict() {
+    echo "Predicting..."
+    PREDICTION=$(python3 "$PREDICTION_SCRIPT" --file_name "$filename")
+    if grep -q "1" "$log_file"; then
+    result=1
+    elif grep -q "0" "$log_file"; then
+    result=0
+    fi
+}
+
+function start_playing() {
+    if [[ $PLAYING == 0 ]]; then
+        echo "Start playing"
+        play -q "$lullaby_file"
+        PLAYING=1
+    fi
+}
+
+function stop_playing(){
+    if [[ $PLAYING == 1 ]]; then
+        echo "Stop playing"
+        PLAYING=0
+    fi
+}
+
+function clean_up {
+    # Perform program exit housekeeping
+    echo ""
+    echo "Thank you for using parenting 2.0"
+    echo "Good Bye."
+    stop_playing
+    exit
+}
+
+trap clean_up SIGHUP SIGINT SIGTERM
+
+echo "Welcome to Parenting 2.0"
+echo ""
+
+recording
+predict
+
+if [[ $result == 0 ]]; then
+    stop_playing
+    echo "Your Baby is relaxing"
+else
+    CPT=$(expr $CPT + 1)
+    start_playing
+fi
+
+echo "Majority Vote Prediction: $PREDICTION"
+echo "State of the Process PREDICTION=$PREDICTION, PLAYING=$PLAYING, COMPTEUR=$CPT"
+
+clean_up

baby_cry_detection/pc_main/train_model.py

@@ -30,7 +30,7 @@ def main():
     logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
                         datefmt='%Y-%m-%d %I:%M:%S %p',
                         filename=os.path.join(log_path, 'logs_pc_methods_model.log'),
-                        filemode='w',
+                        filemode='a',
                         level=logging.DEBUG)
 
     # TRAIN MODEL
@@ -56,7 +56,7 @@ def main():
         json.dump(parameters, fp)
 
     # Save model
-    with open(os.path.join(save_path, 'model.pkl'), 'wb') as fp:
+    with open(os.path.join(save_path, 'model_1.pkl'), 'wb') as fp:
         pickle.dump(best_estimator, fp)
 
 if __name__ == '__main__':

baby_cry_detection/pc_main/train_set.py

@@ -72,6 +72,8 @@ def main():
     logging.info('Time taken for reading files and feature engineering: {0}'.format(stop - start))
 
     # Save to numpy binary format
+    os.makedirs(save_path, exist_ok=True)
+
     logging.info('Saving training set...')
     np.save(os.path.join(save_path, 'dataset.npy'), X)
     np.save(os.path.join(save_path, 'labels.npy'), y)

baby_cry_detection/prediction_simulation/prediction_simulation.py

@@ -7,6 +7,7 @@
 import timeit
 import warnings
 
+
 from baby_cry_detection.rpi_methods import Reader
 from baby_cry_detection.rpi_methods.feature_engineer import FeatureEngineer
 from baby_cry_detection.rpi_methods.majority_voter import MajorityVoter
@@ -22,7 +23,7 @@ def main():
                         default='{}/../../../output/model/'.format(os.path.dirname(os.path.abspath(__file__))))
     parser.add_argument('--save_path',
                         default='{}/../../../output/prediction/'.format(os.path.dirname(os.path.abspath(__file__))))
-    parser.add_argument('--file_name', default='V_2017-04-01+08_04_36=0_13.mp3')
+    parser.add_argument('--file_name', default='V_2017-04-01+08_04_36=0_13.mp3') # is mp3 sound whil will generate No Backend error
     parser.add_argument('--log_path',
                         default='{}/../../'.format(os.path.dirname(os.path.abspath(__file__))))
 
@@ -38,7 +39,7 @@ def main():
     logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
                         datefmt='%Y-%m-%d %I:%M:%S %p',
                         filename=os.path.join(log_path, 'logs_prediction_test_test_model.log'),
-                        filemode='w',
+                        filemode='a',
                         level=logging.INFO)
 
     # READ RAW SIGNAL
@@ -59,7 +60,7 @@ def main():
     logging.info('Starting feature engineering')
     start = timeit.default_timer()
 
-    # Feature extraction
+    # Feature extraction    
     engineer = FeatureEngineer()
 
     play_list_processed = list()
@@ -97,18 +98,29 @@ def main():
     majority_vote = majority_voter.vote()
 
     stop = timeit.default_timer()
-    logging.info('Time taken for prediction: {0}. Is it a baby cry?? {1}'.format(stop - start, majority_vote))
+    logging.info('Time taken for prediction: {0}. Is it a baby cry??'.format(stop - start))
+
+    if majority_vote == 1:
+        logging.info('Baby crying')
+        result = 1
+    else:
+        logging.info('Baby is relaxing')
+        result = 0
+
+
+
+
 
     # SAVE
 
     logging.info('Saving prediction...')
 
     # Save prediction result
-    with open(os.path.join(save_path, 'prediction.txt'), 'wb') as text_file:
+    with open(os.path.join  (save_path, 'prediction.txt'), 'w') as text_file:
         text_file.write("{}".format(majority_vote))
 
     logging.info('Saved! {}'.format(os.path.join(save_path, 'prediction.txt')))
-
+    return result
 
 if __name__ == '__main__':
     main()