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fff6481431
some libraries are optional. if an user tries to import these before installation, then not legible message was gotten. now, we will throw legible message.
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
from deepface.detectors import FaceDetector
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from deepface.commons.logger import Logger
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logger = Logger()
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# Model's weights paths
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PATH = "/.deepface/weights/yolov8n-face.pt"
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# Google Drive URL
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WEIGHT_URL = "https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb"
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# Confidence thresholds for landmarks detection
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# used in alignment_procedure function
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LANDMARKS_CONFIDENCE_THRESHOLD = 0.5
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def build_model():
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"""Build YOLO (yolov8n-face) model"""
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import gdown
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import os
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# Import the Ultralytics YOLO model
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try:
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from ultralytics import YOLO
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except ModuleNotFoundError as e:
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raise ImportError(
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"Yolo is an optional detector, ensure the library is installed. \
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Please install using 'pip install ultralytics' "
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) from e
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from deepface.commons.functions import get_deepface_home
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weight_path = f"{get_deepface_home()}{PATH}"
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# Download the model's weights if they don't exist
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if not os.path.isfile(weight_path):
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gdown.download(WEIGHT_URL, weight_path, quiet=False)
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logger.info(f"Downloaded YOLO model {os.path.basename(weight_path)}")
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# Return face_detector
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return YOLO(weight_path)
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def detect_face(face_detector, img, align=False):
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resp = []
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# Detect faces
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results = face_detector.predict(img, verbose=False, show=False, conf=0.25)[0]
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# For each face, extract the bounding box, the landmarks and confidence
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for result in results:
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# Extract the bounding box and the confidence
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x, y, w, h = result.boxes.xywh.tolist()[0]
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confidence = result.boxes.conf.tolist()[0]
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x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
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detected_face = img[y : y + h, x : x + w].copy()
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if align:
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# Tuple of x,y and confidence for left eye
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left_eye = result.keypoints.xy[0][0], result.keypoints.conf[0][0]
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# Tuple of x,y and confidence for right eye
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right_eye = result.keypoints.xy[0][1], result.keypoints.conf[0][1]
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# Check the landmarks confidence before alignment
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if (
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left_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD
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and right_eye[1] > LANDMARKS_CONFIDENCE_THRESHOLD
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):
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detected_face = FaceDetector.alignment_procedure(
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detected_face, left_eye[0].cpu(), right_eye[0].cpu()
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)
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resp.append((detected_face, [x, y, w, h], confidence))
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return resp
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