NotAShelf/flipoff
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detector: convert BGR frame to RGB before passing to MediaPipe

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Signed-off-by: NotAShelf <raf@notashelf.dev>
Change-Id: Id1b4bb7e9f34b13952f92c639ef0ba986a6a6964
This commit is contained in:
raf 2026-04-07 16:18:36 +03:00
commit f217384332
Signed by: NotAShelf
GPG key ID: 29D95B64378DB4BF
1 changed files with 5 additions and 21 deletions
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26
src/flipoff/detector.py
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@ -1,5 +1,4 @@
import time import time
from typing import Callable
import cv2 import cv2
import mediapipe as mp import mediapipe as mp
@ -30,7 +29,9 @@ class HandDetector:
) -> list[list[NormalizedLandmark]]: ) -> list[list[NormalizedLandmark]]:
if timestamp_ms is None: if timestamp_ms is None:
timestamp_ms = int(time.time() * 1000) timestamp_ms = int(time.time() * 1000)
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame) # OpenCV captures in BGR; MediaPipe expects RGB.
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame_rgb)
result = self._detector.detect_for_video(mp_image, timestamp_ms) result = self._detector.detect_for_video(mp_image, timestamp_ms)
return result.hand_landmarks if result.hand_landmarks else [] return result.hand_landmarks if result.hand_landmarks else []
@ -41,6 +42,8 @@ class HandDetector:
class Camera: class Camera:
def __init__(self, index: int = 0) -> None: def __init__(self, index: int = 0) -> None:
self._cap = cv2.VideoCapture(index) self._cap = cv2.VideoCapture(index)
if not self._cap.isOpened():
raise RuntimeError(f"Cannot open camera at index {index}")
def read(self) -> tuple[bool, np.ndarray]: def read(self) -> tuple[bool, np.ndarray]:
ret, frame = self._cap.read() ret, frame = self._cap.read()
@ -48,22 +51,3 @@ class Camera:
def release(self) -> None: def release(self) -> None:
self._cap.release() self._cap.release()
class GestureDetector:
def __init__(
self,
detector: HandDetector,
gesture_callback: Callable[[list[NormalizedLandmark]], bool],
) -> None:
self._detector = detector
self._callback = gesture_callback
def process_frame(self, frame: np.ndarray) -> bool | None:
hands = self._detector.detect(frame)
if hands:
return self._callback(hands[0])
return None
def close(self) -> None:
self._detector.close()