mirror of
https://github.com/AmanIbrash-brsh/emotional_damage.git
synced 2026-11-09 10:15:09 +00:00
229 lines
9.7 KiB
Python
229 lines
9.7 KiB
Python
import cv2
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import mediapipe as mp
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from mediapipe.tasks import python
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from mediapipe.tasks.python import vision
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import numpy as np
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import time
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import math
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def overlay_image_alpha(img, img_overlay, x, y, alpha_mask):
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"""Наложение изображения img_overlay на img с учетом альфа-канала."""
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y1, y2 = max(0, y), min(img.shape[0], y + img_overlay.shape[0])
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x1, x2 = max(0, x), min(img.shape[1], x + img_overlay.shape[1])
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y1o, y2o = max(0, -y), min(img_overlay.shape[0], img.shape[0] - y)
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x1o, x2o = max(0, -x), min(img_overlay.shape[1], img.shape[1] - x)
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if y1 >= y2 or x1 >= x2 or y1o >= y2o or x1o >= x2o:
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return
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alpha = alpha_mask[y1o:y2o, x1o:x2o]
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alpha_inv = 1.0 - alpha
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for c in range(3):
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img[y1:y2, x1:x2, c] = (alpha * img_overlay[y1o:y2o, x1o:x2o, c] +
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alpha_inv * img[y1:y2, x1:x2, c])
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def check_gestures(pose_landmarks, gesture_result):
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"""Проверяет позы пользователя и возвращает 'timeout', 'pointing', 'shocked', 'cinema', 'fist', 'bye' или None."""
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is_fist = False
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is_open_palm = False
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# 1. Жесты кисти
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if gesture_result and gesture_result.gestures:
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for hand_gestures in gesture_result.gestures:
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for gesture in hand_gestures:
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if gesture.category_name == 'Pointing_Up' and gesture.score > 0.5:
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return 'pointing'
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if gesture.category_name == 'Closed_Fist' and gesture.score > 0.5:
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is_fist = True
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if gesture.category_name == 'Open_Palm' and gesture.score > 0.5:
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is_open_palm = True
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# 2. Поза тела
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if pose_landmarks:
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landmarks = pose_landmarks[0]
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nose = landmarks[0]
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left_ear = landmarks[7]
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right_ear = landmarks[8]
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mouth_l = landmarks[9]
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mouth_r = landmarks[10]
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left_wrist = landmarks[15]
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right_wrist = landmarks[16]
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mouth_x = (mouth_l.x + mouth_r.x) / 2.0
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mouth_y = (mouth_l.y + mouth_r.y) / 2.0
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# Мем "Кулак ко рту"
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if is_fist:
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dist_l_mouth = math.hypot(left_wrist.x - mouth_x, left_wrist.y - mouth_y) if left_wrist.visibility > 0.15 else 999
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dist_r_mouth = math.hypot(right_wrist.x - mouth_x, right_wrist.y - mouth_y) if right_wrist.visibility > 0.15 else 999
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if dist_l_mouth < 0.25 or dist_r_mouth < 0.25:
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return 'fist'
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if left_wrist.visibility > 0.15 and right_wrist.visibility > 0.15:
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# Мем "Absolute Cinema" (обе руки высоко подняты)
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if left_wrist.y < nose.y and right_wrist.y < nose.y:
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return 'cinema'
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# Мем с Шаком
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dist_wrists = math.hypot(left_wrist.x - right_wrist.x, left_wrist.y - right_wrist.y)
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if dist_wrists < 0.15 and left_wrist.y > nose.y and right_wrist.y > nose.y:
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return 'timeout'
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# Мем "Шок"
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dist_l = math.hypot(left_wrist.x - left_ear.x, left_wrist.y - left_ear.y)
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dist_r = math.hypot(right_wrist.x - right_ear.x, right_wrist.y - right_ear.y)
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if dist_l < 0.25 and dist_r < 0.25:
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return 'shocked'
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# Мем "Пока" (одна открытая ладонь на уровне лица)
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# Проверяем после Cinema, чтобы не перебивало двумя руками
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if is_open_palm:
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# Если хотя бы одно запястье находится примерно на уровне носа (по вертикали)
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if (left_wrist.visibility > 0.15 and abs(left_wrist.y - nose.y) < 0.25) or \
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(right_wrist.visibility > 0.15 and abs(right_wrist.y - nose.y) < 0.25):
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return 'bye'
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return None
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def load_mask(path):
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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if img is None:
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return None
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if img.shape[2] == 3:
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b, g, r = cv2.split(img)
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alpha = np.ones(b.shape, dtype=b.dtype) * 255
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img = cv2.merge((b, g, r, alpha))
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return img
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def main():
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# Загружаем маски из папки memes
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mask_timeout = load_mask('memes/timeout.png')
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mask_pointing = load_mask('memes/pointing.png')
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mask_cinema = load_mask('memes/scorsese.png')
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mask_shocked = load_mask('memes/shocked.png')
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mask_fist = load_mask('memes/fist.png')
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mask_jarvis = load_mask('memes/jarvis.png')
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mask_bye = load_mask('memes/bye.png')
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if mask_timeout is None or mask_pointing is None or mask_cinema is None or mask_shocked is None or mask_fist is None or mask_jarvis is None or mask_bye is None:
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print("Ошибка: Не удалось загрузить одну из картинок.")
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return
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# Инициализация моделей Tasks API
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base_options_face = python.BaseOptions(model_asset_path='face_detector.tflite')
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options_face = vision.FaceDetectorOptions(base_options=base_options_face,
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running_mode=vision.RunningMode.VIDEO)
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face_detector = vision.FaceDetector.create_from_options(options_face)
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base_options_pose = python.BaseOptions(model_asset_path='pose_landmarker.task')
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options_pose = vision.PoseLandmarkerOptions(base_options=base_options_pose,
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running_mode=vision.RunningMode.VIDEO,
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output_segmentation_masks=False)
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pose_landmarker = vision.PoseLandmarker.create_from_options(options_pose)
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base_options_gesture = python.BaseOptions(model_asset_path='gesture_recognizer.task')
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options_gesture = vision.GestureRecognizerOptions(base_options=base_options_gesture,
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running_mode=vision.RunningMode.VIDEO,
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num_hands=2)
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gesture_recognizer = vision.GestureRecognizer.create_from_options(options_gesture)
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cap = cv2.VideoCapture(0)
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if not cap.isOpened():
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print("Ошибка: Не удалось открыть камеру")
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return
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print("Камера запущена!")
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print("Управление размером: клавиша '=' (или '+') - увеличить, '-' - уменьшить.")
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scale_factor = 2.5 # Базовый размер
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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continue
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image = cv2.flip(image, 1)
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image_rgb)
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timestamp_ms = int(time.time() * 1000)
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# Обрабатываем кадр
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face_result = face_detector.detect_for_video(mp_image, timestamp_ms)
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pose_result = pose_landmarker.detect_for_video(mp_image, timestamp_ms)
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gesture_result = gesture_recognizer.recognize_for_video(mp_image, timestamp_ms)
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pose_lms = pose_result.pose_landmarks if (pose_result and hasattr(pose_result, 'pose_landmarks')) else None
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detected_gesture = check_gestures(pose_lms, gesture_result)
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if face_result and hasattr(face_result, 'detections') and face_result.detections:
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detection = face_result.detections[0]
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bbox = detection.bounding_box
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ih, iw, _ = image.shape
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x = int(bbox.origin_x)
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y = int(bbox.origin_y)
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w = int(bbox.width)
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h = int(bbox.height)
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if w > iw * 0.25:
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detected_gesture = 'jarvis'
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if detected_gesture:
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if detected_gesture == 'timeout':
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current_mask = mask_timeout
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elif detected_gesture == 'pointing':
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current_mask = mask_pointing
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elif detected_gesture == 'shocked':
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current_mask = mask_shocked
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elif detected_gesture == 'fist':
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current_mask = mask_fist
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elif detected_gesture == 'jarvis':
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current_mask = mask_jarvis
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elif detected_gesture == 'bye':
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current_mask = mask_bye
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else:
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current_mask = mask_cinema
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mask_w = int(w * scale_factor)
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mask_h = int((mask_w / current_mask.shape[1]) * current_mask.shape[0])
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mask_x = x - int((mask_w - w) / 2)
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mask_y = y - int((mask_h - h) / 1.5)
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if mask_w > 0 and mask_h > 0:
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resized_mask = cv2.resize(current_mask, (mask_w, mask_h))
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alpha_mask = resized_mask[:, :, 3] / 255.0
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overlay_image_alpha(image, resized_mask[:, :, :3], mask_x, mask_y, alpha_mask)
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cv2.putText(image, f"Size: {scale_factor:.1f}x (+/- to change)", (10, 20),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 200), 1, cv2.LINE_AA)
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# Вывод на экран
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cv2.imshow('Meme Detector', image)
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key = cv2.waitKey(5) & 0xFF
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if key == ord('q'):
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break
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elif key == ord('=') or key == ord('+'):
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scale_factor += 0.1
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elif key == ord('-'):
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scale_factor -= 0.1
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scale_factor = max(0.5, min(scale_factor, 10.0))
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cap.release()
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cv2.destroyAllWindows()
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face_detector.close()
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pose_landmarker.close()
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gesture_recognizer.close()
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if __name__ == "__main__":
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main()
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