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hill_climb_racing.py
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hill_climb_racing.py
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import numpy as np
import mediapipe as mp
import cv2
import time
import pyautogui
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
model_complexity=0,
min_detection_confidence=0.4,
min_tracking_confidence=0.4) as hands:
while cap.isOpened():
success, image = cap.read()
h, w, c = image.shape
start = time.perf_counter()
# Flip the image horizontally for a later selfie-view display
# Convert the BGR image to RGB.
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
# Process the image and find hands
results = hands.process(image)
image.flags.writeable = True
# Draw the hand annotations on the image.
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())
index_finger_tip = hand_landmarks.landmark[0]
index_finger_tip_x = index_finger_tip.x * w
index_finger_tip_y = index_finger_tip.y * h
if index_finger_tip_x > w/2:
cv2.putText(image, "Gas", (500,70), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0,255,0), 2)
pyautogui.keyDown('right')
pyautogui.keyUp('left')
elif index_finger_tip_x < w/2:
cv2.putText(image, "Brake", (500,70), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255,0,0), 2)
pyautogui.keyDown('left')
pyautogui.keyUp('right')
cv2.line(image, (int(w/2), 0), (int(w/2), h), (0, 255, 0), 2)
end = time.perf_counter()
totalTime = end - start
fps = 1 / totalTime
cv2.putText(image, f'FPS: {int(fps)}', (20,70), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0,255,0), 2)
cv2.imshow('MediaPipe Hands', image)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()