uploaded site and api data

api and aux scripts/TrainAI.py

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+import os
+import mediapipe as mp
+import cv2
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+import numpy as np
+
+mp_hands = mp.solutions.hands
+hands = mp_hands.Hands(static_image_mode=True, min_detection_confidence=0.5)
+data_dir = "./data"
+data = []
+labels = []
+
+for dir_ in os.listdir(data_dir):
+    print(dir_)
+    for img_path in os.listdir(os.path.join(data_dir, dir_)):
+        data_aux = []
+        img = cv2.imread(os.path.join(data_dir, dir_, img_path))
+
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        results = hands.process(img_rgb)
+        if results.multi_hand_landmarks:
+            for hand_landmarks in results.multi_hand_landmarks:
+                for i in range(len(hand_landmarks.landmark)):
+                    x = hand_landmarks.landmark[i].x
+                    y = hand_landmarks.landmark[i].y
+                    data_aux.append(x)
+                    data_aux.append(y)
+                    # print(f"Poza {img_path} cu ", end=' ')
+                    # print(x, y)
+            data.append(data_aux)
+            labels.append(dir_)
+
+
+data = np.asarray(data)
+labels = np.asarray(labels)
+x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, shuffle=True, stratify=labels)
+model = RandomForestClassifier()
+model.fit(x_train, y_train)
+y_predict = model.predict(x_test)
+score = accuracy_score(y_predict, y_test)
+
+print(f"{score * 100}% classified correctly")
+f = open("model.p", "wb")
+pickle.dump({"model": model}, f)
+f.close()
+
+