fix saving pathes

merge_pickles.py

@@ -3,7 +3,11 @@ import os
 from pprint import pprint
 
 
-FOLDER_PATH = "./Moni_train_data/"
+FOLDER_PATH = "./train_data/"
+OUTPUT_FILE = 'PROBA_PROBA.pickle'  # Output merged file
+
+current_dir = os.path.dirname(__file__)
+OUTPUT_FOLDER = os.path.abspath(os.path.join(current_dir, 'merged_training_data', OUTPUT_FILE))
 #FOLDER_PATH = "./proba_data/"
 
 def change_label_name(input_path, old_label, new_label):
@@ -64,6 +68,5 @@ def merge_pickle_files(input_files, output_file):
 if __name__ == "__main__":
     #change_label_name(FOLDER_PATH + "scrolling_right_Moni.pickle", "sscrolling right", "scrolling right")
     input_files = os.listdir(FOLDER_PATH)  # List of .pickle file paths
-    output_file = 'mouse_train_data_all.pickle'  # Output merged file
 
-    merge_pickle_files(input_files, output_file)
+    merge_pickle_files(input_files, OUTPUT_FOLDER)

training/data_collector.py

@@ -2,6 +2,7 @@ import cv2
 import random
 import mediapipe as mp
 import pickle
+import os
 
 def landmarks2px(frame_sizes, landmark_list):
     if len(landmark_list) == 0:
@@ -162,8 +163,11 @@ def main():
     cv2.destroyAllWindows()
     
     print("Save generated data")
+    current_dir = os.path.dirname(__file__)
+    
     filename = input("give filename: ")
-    f = open(filename + '.pickle', 'wb')
+    folder_path = os.path.abspath(os.path.join(current_dir, os.pardir, 'train_data', filename))
+    f = open(folder_path + '.pickle', 'wb')
     pickle.dump({'data': data, 'label':labels}, f)
     f.close()
     

training/test_model.py

@@ -3,6 +3,9 @@ import random
 import mediapipe as mp
 import pickle
 import numpy as np
+import os
+
+FILENAME = 'PROBA_PROBA.p'
 
 def landmarks2px(frame_sizes, landmark_list):
     if len(landmark_list) == 0:
@@ -68,7 +71,9 @@ def write_landmark_on_frame(frame, landmark_list, landmark_list_px):
 ## main: open video and do hand detection
 def main():
     # load model
-    model_dict = pickle.load(open('./trained_Moni_data.p', 'rb'))
+    current_dir = os.path.dirname(__file__)
+    model_path = os.path.abspath(os.path.join(current_dir, os.pardir, 'trained_models', FILENAME))
+    model_dict = pickle.load(open(model_path, 'rb'))
     model = model_dict['model']
     
     # create hand detection object

training/train_model.py

@@ -4,9 +4,14 @@ import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
+import os
 
-filename = 'mouse_train_data_all'
-data_dict = pickle.load(open(filename + '.pickle', 'rb'))
+filename = 'PROBA_PROBA'
+
+current_dir = os.path.dirname(__file__)
+file_path = os.path.abspath(os.path.join(current_dir, os.pardir, 'merged_training_data', filename))
+    
+data_dict = pickle.load(open(file_path + '.pickle', 'rb'))
 print(data_dict.keys())
 
 coordinates = np.asarray(data_dict['data'])
@@ -25,6 +30,7 @@ score = accuracy_score(y_predict, y_test)
 
 print(f'accuracy: {score*100}%')
 
-f = open('trained_mouse_data.p', 'wb')
+output_path = os.path.abspath(os.path.join(current_dir, os.pardir, 'trained_models', filename))
+f = open(output_path + '.p', 'wb')
 pickle.dump({'model': model}, f)
 f.close()
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