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best_low_latency.py
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best_low_latency.py
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import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
import pyautogui
screen_width, screen_height = pyautogui.size()
cap = cv2.VideoCapture(2)
with mp_hands.Hands(
model_complexity=0,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
while cap.isOpened():
success, image = cap.read()
resize = cv2.resize(image, (240, 240))
image = cv2.flip(resize,1)
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(
image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style()
)
# Get the top left corner of the detected hand's bounding box.
height, width, _ = image.shape
for id, landmark in enumerate(hand_landmarks.landmark):
text_x = int(landmark.x * width)
text_y = int(landmark.y * height)
# Rest of your code for processing hand landmarks
# ...
if id == 4: # thumb
mouse_x = int(screen_width / width * text_x)
mouse_y = int(screen_height / height * text_y)
cv2.circle(image, (text_x, text_y), 10, (0, 255, 255))
pyautogui.moveTo(mouse_x, mouse_y)
x2 = text_x
y2 = text_y
if id == 8: # pointy finger
x1 = text_x
y1 = text_y
cv2.circle(image, (text_x, text_y), 10, (0, 255, 255))
if id == 16: # pointy finger
x3 = text_x
y3 = text_y
cv2.circle(image, (text_x, text_y), 10, (0, 255, 255))
dist = y2 -y1
print("click : ",dist)
dist_ = y3 - y1
print("esc : ",dist_)
if (dist<30):
pyautogui.click()
if (dist_>5):
pyautogui.press('f')
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Hands', image)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()