diff --git a/control_mouse.py b/control_mouse.py
new file mode 100644
index 0000000000000000000000000000000000000000..da0ea73857577dafa73b25dc4ee6b7ee7f9c790c
--- /dev/null
+++ b/control_mouse.py
@@ -0,0 +1,209 @@
+import cv2
+import random
+import mediapipe as mp
+import pickle
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+import pyautogui
+import time
+from collections import Counter
+
+class Mouse:
+ def __init__(self) -> None:
+
+ self.predictions = []
+ self.previous_action = None
+ self.freeze_action = False
+
+ self.action_length = 11
+ self.move_distance = 10
+ self.scroll_distance = 10
+ self.time_checking = 0.05
+
+ def get_hand_pos(self, hand_pos_x, hand_pos_y):
+ self.hand_pos_x = hand_pos_x
+ self.hand_pos_y = hand_pos_y
+
+ def add_prediction(self, prediction):
+ self.predictions.append(prediction)
+ if len(self.predictions) == self.action_length:
+ self.make_action()
+
+ def make_action(self):
+ action = self.get_major_element(self.predictions)
+ if self.freeze_action and action == self.previous_action:
+ self.update_init()
+ else:
+ self.mouse_control(action)
+ self.update_init()
+
+ def update_init(self, action):
+ self.predictions = []
+ self.previous_action = action
+
+ self.freeze_action = action in {"left click", "right click", "double click"} # maybe change to keyboard and drops
+
+ def mouse_hand_parameters(self):
+ pass
+
+ def mouse_control(self, prediction):
+ if prediction == "stop execution":
+ pass # Stop movement
+ elif prediction == "move cursor":
+ current_x, current_y = pyautogui.position()
+ delta_x = (self.hand_pos_x - current_x) / self.move_distance
+ delta_y = (self.hand_pos_y - current_y) / self.move_distance
+
+ for i in range(self.move_distance):
+ pyautogui.moveTo(current_x + delta_x * (i + 1), current_y + delta_y * (i + 1))
+ time.sleep(0.01) # Short delay for smooth movement
+ # if current ation is different, change? Or update mouse as well?
+
+ elif prediction == "stop moving":
+ pyautogui.move(0, 0) # Stop cursor
+ elif prediction == "left click":
+ pyautogui.click() # Left click
+ elif prediction == "right click":
+ pyautogui.click(button='right') # Right click
+ elif prediction == "double click":
+ pass # Double click
+ elif prediction == "scrolling up":
+ pyautogui.scroll(self.scroll_distance) # Scroll upp
+ elif prediction == "scrolling down":
+ pyautogui.scroll(-self.scroll_distance) # Scroll down
+ elif prediction == "scrolling right":
+ pass # Scroll right
+ elif prediction == "scrolling left":
+ pass # Scroll left
+ elif prediction == "drag":
+ pass
+ elif prediction == "drop":
+ pass
+ elif prediction == "multiple item selection grab":
+ pass
+ elif prediction == "multiple item selection drop":
+ pass
+ elif prediction == "change to keyboard":
+ pass
+
+
+ time.sleep(self.time_checking) # Adjust speed of movement
+
+ def get_major_element(self, string_list):
+ counts = Counter(string_list)
+ # Find the element with the maximum count
+ major_element, _ = counts.most_common(1)[0]
+
+ return major_element
+
+
+def normalise_landmarks(landmark_list):
+ if len(landmark_list) == 0:
+ return landmark_list
+
+ x = [lm[0] for lm in landmark_list]
+ y = [lm[1] for lm in landmark_list]
+
+ min_x = min(x)
+ max_x = max(x)
+ min_y = min(y)
+ max_y = max(y)
+
+ normalised_landmarks = []
+ for lm in landmark_list:
+ x_norm = (lm[0] - min_x) / (max_x - min_x)
+ y_norm = (lm[1] - min_y) / (max_y - min_y)
+ lm_norm = (x_norm, y_norm)
+
+ normalised_landmarks.append(lm_norm)
+
+ return normalised_landmarks
+
+## main: open video and do hand detection
+def main():
+ # load model
+ model_dict = pickle.load(open('./numbers_model.p', 'rb'))
+ model = model_dict['model']
+
+ # create hand detection object
+ mp_hands = mp.solutions.hands
+ mp_drawing = mp.solutions.drawing_utils
+
+ # open video
+ cap = cv2.VideoCapture(0)
+
+ # if cannot open video give warning
+ if not cap.isOpened():
+ print("Warning: cannot reach camera")
+ else:
+ print("Program is running, push 'q' to quit.")
+
+ # mediapipe hand object
+ with mp_hands.Hands( max_num_hands=1, model_complexity=1,
+ min_detection_confidence=0.9, min_tracking_confidence=0.9) as hands:
+
+ # read frames from webcamera
+ while cap.isOpened():
+ ret, frame = cap.read()
+
+ if not ret:
+ print("Warning: cannot read camera input")
+ break
+
+ # flip frame to appear as a mirror
+ frame = cv2.flip(frame, 1)
+ frameRGB = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+ ## hand detection
+ results = hands.process(frameRGB)
+
+ landmark_list = []
+ if results.multi_hand_landmarks:
+ # multi_hand_landmarks can store two hands, if max_num_hands=2, in which case we have to iterate through the hands with
+ # for num, hand in enumerate(results.multi_hand_landmarks):
+
+ # one hand is detected, because max_num_hands=1
+ hand_landmarks = results.multi_hand_landmarks[0]
+
+ # draw landmarks on frame
+ mp_drawing.draw_landmarks(frameRGB, hand_landmarks, mp_hands.HAND_CONNECTIONS,
+ mp_drawing.DrawingSpec(color=(250, 0, 0), thickness=2, circle_radius=4),
+ mp_drawing.DrawingSpec(color=(0, 250, 0), thickness=2, circle_radius=2),
+ )
+
+ # get landmark list with indices described in https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/python/solutions/hands.py
+ for lm in hand_landmarks.landmark:
+ landmark_list.append((lm.x, lm.y))
+
+ # normalise landmarks for mor powerful training
+ normalised_landmark_list = normalise_landmarks(landmark_list)
+
+ # apply model
+ pred = model.predict(np.asarray(normalised_landmark_list).reshape(1, -1))
+ print(pred[0])
+ cv2.putText(img = frameRGB, text = pred[0], org = (30,30),
+ fontFace = cv2.FONT_HERSHEY_DUPLEX, fontScale = 1, color = (255, 0, 0), thickness = 1)
+
+ # transform back RGB and show frame with annotation
+ frame_annotated = cv2.cvtColor(frameRGB, cv2.COLOR_RGB2BGR)
+ cv2.imshow('Hand tracking', frame_annotated)
+
+ # or show original frame without annotation
+ # cv2.imshow('Hand tracking', frame)
+
+ # Check for key presses
+ key = cv2.waitKey(1) & 0xFF
+
+ if key == ord('n'):
+ label = ""
+ elif key == ord('q'):
+ print("Quit camera")
+ break
+
+ cap.release()
+ cv2.destroyAllWindows()
+
+ print("Program closed")
+
+if __name__ == '__main__':
+ main()
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