165 lines
4.9 KiB
Python
Executable File
165 lines
4.9 KiB
Python
Executable File
#!/usr/bin/env python3
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import sys
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import random
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import numpy as np
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import cv2
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from cv2 import VideoCapture
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from utils import *
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def get_42_img(
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img_path: str,
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margin_top: int,
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margin_bottom: int,
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margin_left: int,
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margin_right: int,
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) -> np.ndarray:
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global img42_side_len
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img: np.ndarray = cv2.imread(img_path, 0)
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if len(img.shape) in [1, 2]:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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img = cv2.flip(img, 1)
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img_height, img_width = img.shape[:2]
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img = img[
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margin_top:img_height-margin_bottom,
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margin_left:img_width-margin_right,
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]
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b_top, b_bottom, b_left, b_right = [10]*4
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img = cv2.copyMakeBorder(img, b_top, b_bottom, b_left, b_right, cv2.BORDER_CONSTANT, value=(0, 0, 0))
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img = cv2.resize(img, (img42_side_len, img42_side_len))
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return img
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img42_side_len: int = 70
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img42: np.ndarray = get_42_img(
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"./assets/img/42.png",
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margin_top = 100 + 20,
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margin_bottom = 100 + 20,
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margin_left = 100,
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margin_right = 100,
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)
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def touches_42(x: int, y: int, img42_x: int, img42_y: int) -> bool:
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global collected_42
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return (
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img42_x <= x <= img42_x + img42_side_len
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and img42_y <= y <= img42_y + img42_side_len
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)
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def add_directional_triangle(
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frame: np.ndarray,
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x1: int,
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y1: int,
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x2: int,
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y2: int,
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rgb: tuple[int, int, int],
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side_len: int,
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stretch: float,
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) -> tuple[int, int]:
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dir_vector: np.ndarray = np.array([
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x1 - x2, y1 - y2
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]).astype(np.float64)
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# normalize
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dir_vector /= np.linalg.norm(dir_vector)
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triangle_height: float = side_len * (3**0.5) / 2
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half_base: float = side_len / 2
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perp_vector: np.ndarray = np.array([-dir_vector[1], dir_vector[0]])
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apex_vertex = (int(x1 + dir_vector[0] * triangle_height * 2/3 * stretch), int(y1 + dir_vector[1] * triangle_height * 2/3 * stretch))
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left_vertex = (int(x1 - perp_vector[0] * half_base - dir_vector[0] * triangle_height/3),
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int(y1 - perp_vector[1] * half_base - dir_vector[1] * triangle_height/3))
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right_vertex = (int(x1 + perp_vector[0] * half_base - dir_vector[0] * triangle_height/3),
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int(y1 + perp_vector[1] * half_base - dir_vector[1] * triangle_height/3))
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triangle: np.ndarray = np.array([apex_vertex, left_vertex, right_vertex])
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cv2.drawContours(frame, [triangle], 0, rgb, -1)
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return apex_vertex
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def show_frame(frame: np.ndarray, to_stdout: bool=False) -> None:
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if to_stdout:
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sys.stdout.buffer.write(frame.tobytes())
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else:
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cv2.imshow("Image", frame)
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cv2.waitKey(1)
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def main() -> int:
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start_sfx()
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capture: VideoCapture = cv2.VideoCapture(0)
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hands: mp.solutions.hands.Hands = mp_hands.Hands(max_num_hands=2)
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collected_42: bool = True
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img42_x: int = -img42_side_len - 1
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img42_y: int = -img42_side_len - 1
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no_fingers: int = 0
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score: int = 0
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i: int = 0
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while True:
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success: bool
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frame: np.ndarray
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success, frame = capture.read()
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if not success:
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continue
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if i > 30:
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if collected_42:
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collected_42 = False
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frame_height, frame_width = frame.shape[:2]
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img42_x = random.randint(0, frame_width - img42_side_len - 1)
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img42_y = random.randint(0, frame_height - img42_side_len - 1)
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frame[
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img42_y : img42_y+img42_side_len,
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img42_x : img42_x+img42_side_len,
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] = img42
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finger_positions = list(get_finger_positions(frame, hands, add_landmarks=True))
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if finger_positions == []:
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no_fingers += 1
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else:
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no_fingers = 0
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if no_fingers > 70:
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return score
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for positions in finger_positions:
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index_knuckle_1_pos: tuple[int, int] = (-1, -1)
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for finger_id, finger_x, finger_y in positions:
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if finger_id == FingerType.INDEX_KNUCKLE_2:
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index_knuckle_1_pos = (finger_x, finger_y)
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elif finger_id == FingerType.INDEX_TIP and index_knuckle_1_pos != (-1, -1):
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apex_x, apex_y = add_directional_triangle(
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frame,
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finger_x,
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finger_y,
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*index_knuckle_1_pos,
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(0, 0, 0,),
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side_len=70,
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stretch=2.0,
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)
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if not collected_42 and (
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touches_42(apex_x, apex_y, img42_x, img42_y)
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or touches_42(finger_x, finger_y, img42_x, img42_y)
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):
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collected_42 = True
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i = 0
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score += 42
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collect_sfx()
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show_frame(frame, to_stdout=(not sys.stdout.isatty()))
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i += 1
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if __name__ == '__main__':
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save_score(main())
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sys.exit(0)
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