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113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
# built-in dependencies
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from typing import Any, Union, List
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# 3rd party dependencies
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import cv2
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import numpy as np
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# project dependencies
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from deepface.models.Detector import Detector, FacialAreaRegion
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class FastMtCnnClient(Detector):
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"""
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Fast MtCnn Detector from github.com/timesler/facenet-pytorch
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"""
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def __init__(self):
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self.model = self.build_model()
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def detect_faces(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]:
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"""
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Detect and align face with mtcnn
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Args:
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img (Union[np.ndarray, List[np.ndarray]]): pre-loaded image as numpy array or a list of those
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Returns:
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results (Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]): A list or a list of lists of FacialAreaRegion objects
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"""
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if not isinstance(img, list):
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img = [img]
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results = [self._process_single_image(single_img) for single_img in img]
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if len(results) == 1:
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return results[0]
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return results
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def _process_single_image(self, img: np.ndarray) -> List[FacialAreaRegion]:
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"""
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Helper function to detect faces in a single image.
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Args:
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img (np.ndarray): pre-loaded image as numpy array
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Returns:
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results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
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"""
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resp = []
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # mtcnn expects RGB but OpenCV read BGR
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detections = self.model.detect(
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img_rgb, landmarks=True
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) # returns boundingbox, prob, landmark
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if (
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detections is not None
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and len(detections) > 0
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and not any(detection is None for detection in detections) # issue 1043
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):
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for regions, confidence, eyes in zip(*detections):
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x, y, w, h = xyxy_to_xywh(regions)
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right_eye = eyes[0]
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left_eye = eyes[1]
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left_eye = tuple(int(i) for i in left_eye)
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right_eye = tuple(int(i) for i in right_eye)
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facial_area = FacialAreaRegion(
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x=x,
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y=y,
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w=w,
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h=h,
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left_eye=left_eye,
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right_eye=right_eye,
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confidence=confidence,
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)
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resp.append(facial_area)
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return resp
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def build_model(self) -> Any:
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"""
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Build a fast mtcnn face detector model
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Returns:
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model (Any)
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"""
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# this is not a must dependency. do not import it in the global level.
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try:
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from facenet_pytorch import MTCNN as fast_mtcnn
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import torch
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except ModuleNotFoundError as e:
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raise ImportError(
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"FastMtcnn is an optional detector, ensure the library is installed. "
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"Please install using 'pip install facenet-pytorch'"
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) from e
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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face_detector = fast_mtcnn(device=device)
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return face_detector
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def xyxy_to_xywh(regions: Union[list, tuple]) -> tuple:
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"""
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Convert (x1, y1, x2, y2) format to (x, y, w, h) format.
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Args:
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regions (list or tuple): facial area coordinates as x, y, x+w, y+h
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Returns:
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regions (tuple): facial area coordinates as x, y, w, h
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"""
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x, y, x_plus_w, y_plus_h = regions[0], regions[1], regions[2], regions[3]
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w = x_plus_w - x
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h = y_plus_h - y
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return (int(x), int(y), int(w), int(h))
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