diff --git a/final_project/main.py b/final_project/main.py
index 9be031afa7a71fc4a96b084e886ac3d8e0f23cef..cde4c48271153e92ce8bcd0d5670787a03481380 100644
--- a/final_project/main.py
+++ b/final_project/main.py
@@ -6,12 +6,12 @@ import numpy as np
from sklearn.ensemble import RandomForestClassifier
import time
import os
+from tkinter import Tk, Label
+from PIL import Image, ImageTk
from mouse_class import Mouse
from hand_detection import normalise_landmarks
-
-## main: open video and do hand detection
def main():
#define Mouse
mouse = Mouse()
@@ -31,81 +31,99 @@ def main():
# if cannot open video give warning
if not cap.isOpened():
- print("Warning: cannot reach camera")
+ print("Warning: Cannot reach camera")
+ return
else:
- print("Program is running, push 'q' to quit.")
-
+ print("Program is running, press 'q' to quit.")
+
+ # set up Tkinter window
+ root = Tk()
+ root.title("Hand Tracking - Always on Top")
+ root.attributes("-topmost", True)
+ video_label = Label(root)
+ video_label.pack()
+
+ # adjust window geometry
+ # Get the screen width and height
+ screen_width = root.winfo_screenwidth()
+ screen_height = root.winfo_screenheight()
+
+ # Define window size and position (e.g., 320x240 window at bottom-right corner)
+ window_width = 160
+ window_height = 120
+ x_position = screen_width - window_width - 10 # 10px margin from the right
+ y_position = screen_height - window_height - 70 # 50px margin from the bottom
+
+ # Set window geometry
+ root.geometry(f"{window_width}x{window_height}+{x_position}+{y_position}")
# mediapipe hand object
- with mp_hands.Hands( max_num_hands=1, model_complexity=1,
+ 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():
+ def update_frame():
ret, frame = cap.read()
-
if not ret:
- print("Warning: cannot read camera input")
- break
-
- # flip frame to appear as a mirror
+ print("Warning: Cannot read camera input")
+ root.destroy()
+ return
+
+ # flip frame and process it
frame = cv2.flip(frame, 1)
frameRGB = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
- ## hand detection
+
+ # Hand detection
results = hands.process(frameRGB)
landmark_list = []
mouse_command = None
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]
+ 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),
- )
+ # 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
+
+ # normalise landmarks for more powerful training
normalised_landmark_list = normalise_landmarks(landmark_list)
-
+
# apply model
pred = model.predict(np.asarray(normalised_landmark_list).reshape(1, -1))
mouse_command = 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)
+ cv2.putText(
+ img=frameRGB, text=pred[0], org=(30, 30),
+ fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=1, color=(255, 0, 0), thickness=1
+ )
mouse.add_prediction(mouse_command)
if mouse_command == "move cursor" or "grab":
mouse.get_hand_pos(landmark_list[8])
-
- # 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
+ # Convert frame to Tkinter-compatible format and display
+ frameRGB_resized = cv2.resize(frameRGB, (root.winfo_width(), root.winfo_height()))
+ img = ImageTk.PhotoImage(Image.fromarray(frameRGB_resized))
+ video_label.config(image=img)
+ video_label.image = img
+
+ # Refresh frame
+ root.after(10, update_frame)
+
+ # Start updating frames
+ update_frame()
+
+ # Quit the program properly
+ root.protocol("WM_DELETE_WINDOW", lambda: (cap.release(), root.destroy()))
+ root.mainloop()
cap.release()
- cv2.destroyAllWindows()
-
print("Program closed")
if __name__ == '__main__':
- main()
\ No newline at end of file
+ main()