hand-detection/main2.py

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Python
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2024-05-29 03:18:12 +00:00
import cv2
import numpy as np
import mediapipe as mp
# Initialize MediaPipe hand detection
mp_hands = mp.solutions.hands
hands = mp_hands.Hands()
# Initialize MediaPipe drawing utilities
mp_drawing = mp.solutions.drawing_utils
# Initialize camera capture
cap = cv2.VideoCapture(0)
# Landmark names
landmark_names = [str(landmark) for landmark in mp_hands.HandLandmark]
# Loop through camera frames
while True:
# Read frame from camera
ret, frame = cap.read()
# Convert frame to RGB for MediaPipe
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Detect hand landmarks with MediaPipe
results = hands.process(frame_rgb)
# List to store the landmarks of hand 1 and hand 2
landmarks_hand1 = []
landmarks_hand2 = []
# Loop through detected hands
if results.multi_hand_landmarks:
for idx, hand_landmarks in enumerate(results.multi_hand_landmarks):
wrist_y = hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].y
middle_finger_y = hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_MCP].y
# Determine the text position based on hand index and orientation
if wrist_y < middle_finger_y:
# Draw landmarks for the detected hand
mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
# Calculate the bounding box around the hand landmarks
x_min = int(min(hand_landmarks.landmark, key=lambda lm: lm.x).x * frame.shape[1])
y_min = int(min(hand_landmarks.landmark, key=lambda lm: lm.y).y * frame.shape[0])
x_max = int(max(hand_landmarks.landmark, key=lambda lm: lm.x).x * frame.shape[1])
y_max = int(max(hand_landmarks.landmark, key=lambda lm: lm.y).y * frame.shape[0])
# Display "Hand 1" or "Hand 2" text on top of the detected hand
hand_text = f'Hand {idx + 1}'
# if hand_text == 'Hand 1': hand_name = "Tangan untuk dipijat" else: hand_name = "Tangan yang melakukan pijat"
if hand_text == 'Hand 1':
hand_name = "Tangan untuk dipijat"
else:
hand_name = "Tangan yang melakukan pijat"
text_x = x_min
text_y = y_min - 10
cv2.putText(frame, hand_name, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# Extract landmarks for each hand
if idx == 0:
landmarks_hand1 = hand_landmarks.landmark
# hand1_text = f'Hand {idx + 1}'
elif idx == 1:
landmarks_hand2 = hand_landmarks.landmark
# hand2_text = f'Hand {idx + 1}'
# Check if landmarks are available for both hands
if landmarks_hand1 and landmarks_hand2:
# Get the thumb tip of hand 2
thumb_tip_hand2 = landmarks_hand2[mp_hands.HandLandmark.THUMB_TIP]
# Calculate distances between thumb tip of hand 2 and all landmarks of hand 1
distances = [np.sqrt((landmark.x - thumb_tip_hand2.x)**2 +
(landmark.y - thumb_tip_hand2.y)**2) for landmark in landmarks_hand1]
# print(distances)
# # Find the index of the closest landmark
# closest_landmark_idx = np.argmin(distances)
# # Display the index of the closest landmark
# cv2.putText(frame, f'Thumb Tip of Hand 2 closest to Landmark {closest_landmark_idx}',
# (int(thumb_tip_hand2.x * frame.shape[1]), int(thumb_tip_hand2.y * frame.shape[0])),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# Find the index of the closest landmark
closest_landmark_idx = np.argmin(distances)
print(np.min(distances))
# Get the name of the closest landmark
closest_landmark_name = landmark_names[closest_landmark_idx]
# Display the name of the closest landmark
cv2.putText(frame, f'Thumb Tip of Hand 2 closest to Landmark: {closest_landmark_name}',
(int(thumb_tip_hand2.x * frame.shape[1]), int(thumb_tip_hand2.y * frame.shape[0])),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# Show frame with detected hands and messages
cv2.imshow('Hand Detection with Proximity Check', frame)
# Exit on 'q' key press
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release camera and destroy windows
cap.release()
cv2.destroyAllWindows()