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109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
from typing import Any, List, Tuple
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import numpy as np
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from deepface.models.Detector import Detector
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from deepface.modules import detection
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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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class YoloClient(Detector):
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def __init__(self):
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self.model = self.build_model()
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def build_model(self) -> Any:
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"""
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Build a yolo detector model
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Returns:
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model (Any)
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"""
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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_faces(
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self, img: np.ndarray, align: bool = False
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) -> List[Tuple[np.ndarray, List[float], float]]:
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"""
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Detect and align face with yolo
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Args:
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face_detector (Any): yolo face detector object
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img (np.ndarray): pre-loaded image
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align (bool): default is true
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Returns:
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results (List[Tuple[np.ndarray, List[float], float]]): A list of tuples
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where each tuple contains:
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- detected_face (np.ndarray): The detected face as a NumPy array.
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- face_region (List[float]): The image region represented as
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a list of floats e.g. [x, y, w, h]
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- confidence (float): The confidence score associated with the detected face.
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Example:
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results = [
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(array(..., dtype=uint8), [110, 60, 150, 380], 0.99),
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(array(..., dtype=uint8), [150, 50, 299, 375], 0.98),
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(array(..., dtype=uint8), [120, 55, 300, 371], 0.96),
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]
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"""
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resp = []
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# Detect faces
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results = self.model.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 = detection.align_face(
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img=detected_face, left_eye=left_eye[0].cpu(), right_eye=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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