🔒 Automating a rhythm game (Repost 2)

[storm wolf]

@covert magnet i don't think getting the pixel colo is gonna work

[unborn quail]

@storm wolf

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[storm wolf]

for referance this is the old post

import time
import mss
import numpy as np
import pyautogui

# Define the region to capture the specific area based on the given coordinates
region = {"top": 766, "left": 1503, "width": 59, "height": 65}

# Define the color to action mapping
color_to_action = {
    (4, 0, 255): 'left_click',     # Color to left mouse click
    (255, 0, 157): 'right_click'   # Color to right mouse click
}

# Initialize screen capture
sct = mss.mss()

def get_pixel_color(region):
    # Capture the screen region
    sct_img = sct.grab(region)
    # Convert to a numpy array
    img = np.array(sct_img)
    # Get the color of the center pixel
    center_pixel = img[region['height']//2, region['width']//2, :3]
    return tuple(center_pixel)

while True:
    try:
        # Get the color of the pixel
        color = get_pixel_color(region)
        
        # Check if the color is in our mapping
        if color in color_to_action:
            # Perform the corresponding action
            action = color_to_action[color]
            if action == 'left_click':
                pyautogui.click(button='left')
                print(f"Detected color: {color}, Performed action: {action}")
            elif action == 'right_click':
                pyautogui.click(button='right')
                print(f"Detected color: {color}, Performed action: {action}")

        # Small delay to avoid overloading the CPU
        time.sleep(0.01)
    except KeyboardInterrupt:
        break

import pyautogui
import time
import numpy as np
import cv2

# Define the coordinates and expected colors for 'E' and 'Q'
pixels_e = [
    ((1504, 900), (65, 65, 65)),
    ((1504, 910), (74, 74, 74)),
    ((1504, 922), (65, 65, 65)),
    ((1495, 910), (64, 64, 64)),
]

pixels_q = [
    ((1504, 904), (66, 66, 66)),
    ((1508, 924), (68, 68, 68)),
    ((1486, 911), (69, 69, 69)),
    ((1504, 900), (76, 76, 76)),
]

def match_pixels(pixels, screenshot):
    for (x, y), color in pixels:
        if not np.array_equal(screenshot[y, x], color):
            return False
    return True

def press_key(key):
    pyautogui.press(key)
    print(f"Pressed key: {key}")

def main():
    time.sleep(2)  # Delay to switch to the target application

    while True:
        # Capture the screen
        screenshot = np.array(pyautogui.screenshot())

        # Convert the screenshot to BGR format for OpenCV compatibility
        screenshot = cv2.cvtColor(screenshot, cv2.COLOR_RGB2BGR)

        # Check if 'E' is detected
        if match_pixels(pixels_e, screenshot):
            press_key('e')

        # Check if 'Q' is detected
        if match_pixels(pixels_q, screenshot):
            press_key('q')

        # Add a small delay to avoid excessive CPU usage
        time.sleep(0.01)

if __name__ == "__main__":
    main()

import pyautogui
import time
import numpy as np
import cv2

# Define the coordinates and expected colors for 'E' and 'Q'
pixels_e = [
    ((1504, 900), (65, 65, 65)),
    ((1504, 910), (74, 74, 74)),
    ((1504, 922), (65, 65, 65)),
    ((1495, 910), (64, 64, 64)),
]

pixels_q = [
    ((1504, 904), (66, 66, 66)),
    ((1508, 924), (68, 68, 68)),
    ((1486, 911), (69, 69, 69)),
    ((1504, 900), (76, 76, 76)),
]

def match_pixels(pixels, screenshot):
    for (x, y), color in pixels:
        if not np.array_equal(screenshot[y, x], color):
            print(f"Pixel mismatch at ({x}, {y})")
            print(f"Expected color: {color}, Actual color: {screenshot[y, x]}")
            return False
    return True

def press_key(key):
    pyautogui.press(key)
    print(f"Pressed key: {key}")

def main():
    time.sleep(2)  # Delay to switch to the target application

    while True:
        # Capture the screen
        screenshot = np.array(pyautogui.screenshot())

        # Convert the screenshot to BGR format for OpenCV compatibility
        screenshot = cv2.cvtColor(screenshot, cv2.COLOR_RGB2BGR)

        # Check if 'E' is detected
        if match_pixels(pixels_e, screenshot):
            press_key('e')
            print("Detected 'E'")

        # Check if 'Q' is detected
        if match_pixels(pixels_q, screenshot):
            press_key('q')
            print("Detected 'Q'")

        # Add a small delay to avoid excessive CPU usage
        time.sleep(0.01)

if __name__ == "__main__":
    main()

[storm wolf]

the colors don't seem consistent

so that strat is out the window

[covert magnet]

that's the point

its about finding a threshold for colors that allows detection

from dataclasses import dataclass


@dataclass
class Color:
    red: int
    green: int
    blue: int

    def is_similar_to(self, other: "Color", tolerance: int = 40) -> bool:
        return (
            abs(self.red - other.red) <= tolerance
            and abs(self.green - other.green) <= tolerance
            and abs(self.blue - other.blue) <= tolerance
        )

something like that

could help

[storm wolf]

mhm so if the color is in the range of 40 it counts it as good?

[covert magnet]

yeah well you can modify the tolerance

its about finding the right threshold

my mistake

are you familiar with object oriented programming?

[storm wolf]

no?

[unborn quail]

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