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File size: 2,420 Bytes
c01955c | 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 | from PIL import Image
import numpy as np
import mediapipe as mp
from utils.asyncHandler import asyncHandler
import logging
import cv2
class FaceFinder:
def __init__(self):
self.mpFace = mp.solutions.face_detection
self.face = self.mpFace.FaceDetection()
self.mpDraw = mp.solutions.drawing_utils
@asyncHandler
async def find(self, img: Image):
logging.info("Entered in the face finder component")
try:
img_array = np.array(img)
img_rgb = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)
results = self.face.process(img_rgb)
if not results.detections:
return {
"is_error": True,
"error_message": "Face not found",
"face_count": 0,
"data": None
}
detections = results.detections
detections = sorted(detections, key=lambda d: d.score[0], reverse=True)
# Count high-confidence detections
high_conf = [d for d in detections if d.score[0] > 0.6]
face_count = len(high_conf) if len(high_conf) > 0 else 1
if face_count > 1:
return {
"is_error": True,
"error_message": f"{face_count} faces detected",
"face_count": face_count,
"data": None
}
detection = detections[0]
logging.info("Exited from the face finder component")
return {
"is_error": False,
"error_message": None,
"face_count": 1,
"data": {
"score": float(detection.score[0]),
"relative_bounding_box": {
"xmin": detection.location_data.relative_bounding_box.xmin,
"ymin": detection.location_data.relative_bounding_box.ymin,
"width": detection.location_data.relative_bounding_box.width,
"height": detection.location_data.relative_bounding_box.height
}
}
}
except Exception as e:
logging.error(f"Error in FaceFinder: {str(e)}")
return {
"is_error": True,
"error_message": str(e),
"data": None
} |