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25e6afd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import cv2
import numpy as np
import base64
import logging
from deepface import DeepFace
from typing import Dict, Tuple, Optional
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger("EmotionProcessor")
class EmotionProcessor:
def __init__(self):
"""Initialize the emotion processor."""
try:
# Load face cascade classifier
self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
logger.info("Emotion processor initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize emotion processor: {e}")
raise
def process_base64_image(self, base64_image: str) -> Tuple[Optional[str], Optional[Dict]]:
"""Process a base64 encoded image and return the dominant emotion."""
try:
# Remove data URL prefix if present
if ',' in base64_image:
base64_image = base64_image.split(',')[1]
# Decode base64 image
img_data = base64.b64decode(base64_image)
nparr = np.frombuffer(img_data, np.uint8)
frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if frame is None:
logger.error("Failed to decode image")
return None, None
return self.process_frame(frame)
except Exception as e:
logger.error(f"Error processing base64 image: {e}")
return None, None
def process_frame(self, frame) -> Tuple[Optional[str], Optional[Dict]]:
"""Process a frame and return the dominant emotion."""
try:
# Convert frame to grayscale
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Convert grayscale frame to RGB format
rgb_frame = cv2.cvtColor(gray_frame, cv2.COLOR_GRAY2RGB)
# Detect faces in the frame
faces = self.face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(faces) == 0:
logger.info("No faces detected")
return "neutral", None
# Process the first face
x, y, w, h = faces[0]
# Extract the face ROI (Region of Interest)
face_roi = rgb_frame[y:y + h, x:x + w]
# Perform emotion analysis on the face ROI
result = DeepFace.analyze(face_roi, actions=['emotion'], enforce_detection=False)
# Determine the dominant emotion
emotion = result[0]['dominant_emotion']
emotion_scores = result[0]['emotion']
logger.info(f"Detected emotion: {emotion}")
return emotion, emotion_scores
except Exception as e:
logger.error(f"Error processing frame: {e}")
return "neutral", None
# For testing
if __name__ == "__main__":
processor = EmotionProcessor()
# Test with a sample image if available
# result = processor.process_frame(cv2.imread('sample.jpg'))
# print(f"Detected emotion: {result}") |