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"""
Enhanced eye state detection to avoid half-closed eyes in frames
"""
import cv2
import numpy as np
from typing import Dict, Tuple, List
import os
class EyeStateDetector:
"""Detect eye states (open, closed, half-closed) in images"""
def __init__(self):
# Load cascade classifiers
self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
self.eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
# Eye aspect ratio thresholds
self.EAR_THRESHOLD_CLOSED = 0.2
self.EAR_THRESHOLD_HALF = 0.25
self.EAR_THRESHOLD_OPEN = 0.3
def check_eyes_state(self, image_path: str) -> Dict[str, any]:
"""
Check the state of eyes in an image
Returns:
dict: {
'state': 'open'|'closed'|'half_closed'|'unknown',
'confidence': float (0-1),
'suitable_for_comic': bool,
'eye_aspect_ratio': float
}
"""
img = cv2.imread(image_path)
if img is None:
return {
'state': 'unknown',
'confidence': 0.0,
'suitable_for_comic': False,
'eye_aspect_ratio': 0.0
}
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = self.face_cascade.detectMultiScale(gray, 1.1, 4)
if len(faces) == 0:
return {
'state': 'unknown',
'confidence': 0.0,
'suitable_for_comic': True, # No face, might be background
'eye_aspect_ratio': 0.0
}
# Process the largest face
x, y, w, h = max(faces, key=lambda f: f[2] * f[3])
face_roi = gray[y:y+h, x:x+w]
# Detect eyes in face region
eyes = self.eye_cascade.detectMultiScale(face_roi, 1.05, 5)
if len(eyes) < 2:
# Less than 2 eyes detected - might be closed or profile view
return {
'state': 'possibly_closed',
'confidence': 0.5,
'suitable_for_comic': False,
'eye_aspect_ratio': 0.0
}
# Calculate eye metrics
eye_metrics = self._analyze_eye_openness(face_roi, eyes)
# Determine state
state, confidence, suitable = self._determine_eye_state(eye_metrics)
return {
'state': state,
'confidence': confidence,
'suitable_for_comic': suitable,
'eye_aspect_ratio': eye_metrics['average_ear']
}
def _analyze_eye_openness(self, face_roi, eyes) -> Dict[str, float]:
"""Analyze how open the eyes are"""
eye_aspects = []
for (ex, ey, ew, eh) in eyes[:2]: # Process first two eyes
eye_roi = face_roi[ey:ey+eh, ex:ex+ew]
# Calculate eye aspect ratio (simplified)
# In a real implementation, we'd use facial landmarks
# Here we use a simpler approach based on eye region intensity
# Check vertical gradient (open eyes have more gradient)
gradient = cv2.Sobel(eye_roi, cv2.CV_64F, 0, 1, ksize=3)
gradient_magnitude = np.abs(gradient).mean()
# Check darkness ratio (closed eyes are darker)
mean_intensity = eye_roi.mean()
# Estimate eye aspect ratio
ear = self._estimate_ear(gradient_magnitude, mean_intensity, eh)
eye_aspects.append(ear)
return {
'average_ear': np.mean(eye_aspects) if eye_aspects else 0.0,
'min_ear': min(eye_aspects) if eye_aspects else 0.0,
'max_ear': max(eye_aspects) if eye_aspects else 0.0
}
def _estimate_ear(self, gradient, intensity, height) -> float:
"""Estimate eye aspect ratio from simple features"""
# Normalize features
gradient_score = min(gradient / 50.0, 1.0)
intensity_score = min(intensity / 150.0, 1.0)
height_score = min(height / 30.0, 1.0)
# Combine scores (higher = more open)
ear = (gradient_score * 0.5 + intensity_score * 0.3 + height_score * 0.2)
return ear
def _determine_eye_state(self, metrics: Dict[str, float]) -> Tuple[str, float, bool]:
"""Determine eye state from metrics"""
ear = metrics['average_ear']
if ear < self.EAR_THRESHOLD_CLOSED:
return 'closed', 0.8, False
elif ear < self.EAR_THRESHOLD_HALF:
return 'half_closed', 0.7, False
elif ear < self.EAR_THRESHOLD_OPEN:
return 'partially_open', 0.6, True # Acceptable but not ideal
else:
return 'open', 0.9, True
def select_best_frame(self, frame_paths: List[str], target_emotion: str = None) -> str:
"""
Select the best frame from a list, avoiding half-closed eyes
Args:
frame_paths: List of frame file paths
target_emotion: Optional emotion to match
Returns:
Path to the best frame
"""
frame_scores = []
for frame_path in frame_paths:
eye_state = self.check_eyes_state(frame_path)
# Calculate score
score = 0.0
# Eye state scoring
if eye_state['state'] == 'open':
score += 1.0
elif eye_state['state'] == 'partially_open':
score += 0.7
elif eye_state['state'] == 'half_closed':
score += 0.2
else:
score += 0.1
# Confidence bonus
score += eye_state['confidence'] * 0.3
# Suitability check
if not eye_state['suitable_for_comic']:
score *= 0.5 # Penalize unsuitable frames
frame_scores.append((frame_path, score, eye_state))
# Sort by score and return best
frame_scores.sort(key=lambda x: x[1], reverse=True)
if frame_scores:
best_frame, best_score, best_state = frame_scores[0]
print(f" 👁️ Selected frame with {best_state['state']} eyes (score: {best_score:.2f})")
return best_frame
return frame_paths[0] if frame_paths else None
def enhance_frame_selection(video_path: str, subtitle, output_dir: str, frames_to_extract: int = 5):
"""
Extract multiple frames and select the best one (no half-closed eyes)
Args:
video_path: Path to video file
subtitle: Subtitle object with start/end times
output_dir: Directory to save the selected frame
frames_to_extract: Number of candidate frames to extract
Returns:
Path to the selected frame
"""
import tempfile
detector = EyeStateDetector()
# Create temp directory for candidate frames
temp_dir = tempfile.mkdtemp()
try:
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
# Calculate time range
start_time = subtitle.start.total_seconds()
end_time = subtitle.end.total_seconds()
duration = end_time - start_time
# Extract multiple frames across the subtitle duration
candidate_frames = []
for i in range(frames_to_extract):
# Distribute frames evenly across the duration
time_offset = (i + 1) / (frames_to_extract + 1) * duration
timestamp = start_time + time_offset
frame_num = int(timestamp * fps)
# Extract frame
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
ret, frame = cap.read()
if ret:
temp_path = os.path.join(temp_dir, f"candidate_{i}.png")
cv2.imwrite(temp_path, frame)
candidate_frames.append(temp_path)
cap.release()
# Select best frame
if candidate_frames:
best_frame_path = detector.select_best_frame(candidate_frames)
# Copy best frame to output
if best_frame_path:
output_path = os.path.join(output_dir, f"frame_{subtitle.index:03d}.png")
img = cv2.imread(best_frame_path)
cv2.imwrite(output_path, img)
return output_path
finally:
# Clean up temp files
import shutil
shutil.rmtree(temp_dir, ignore_errors=True)
return None |