mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 03:55:21 +00:00
96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
# built-in dependencies
|
|
from typing import Any, Union, List
|
|
|
|
# 3rd party dependencies
|
|
import cv2
|
|
import numpy as np
|
|
|
|
# project dependencies
|
|
from deepface.models.Detector import Detector, FacialAreaRegion
|
|
|
|
|
|
class FastMtCnnClient(Detector):
|
|
"""
|
|
Fast MtCnn Detector from github.com/timesler/facenet-pytorch
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.model = self.build_model()
|
|
|
|
def _process_single_image(self, img: np.ndarray) -> List[FacialAreaRegion]:
|
|
"""
|
|
Helper function to detect faces in a single image.
|
|
|
|
Args:
|
|
img (np.ndarray): pre-loaded image as numpy array
|
|
|
|
Returns:
|
|
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
|
|
"""
|
|
resp = []
|
|
|
|
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # mtcnn expects RGB but OpenCV read BGR
|
|
detections = self.model.detect(
|
|
img_rgb, landmarks=True
|
|
) # returns boundingbox, prob, landmark
|
|
if (
|
|
detections is not None
|
|
and len(detections) > 0
|
|
and not any(detection is None for detection in detections) # issue 1043
|
|
):
|
|
for regions, confidence, eyes in zip(*detections):
|
|
x, y, w, h = xyxy_to_xywh(regions)
|
|
right_eye = eyes[0]
|
|
left_eye = eyes[1]
|
|
|
|
left_eye = tuple(int(i) for i in left_eye)
|
|
right_eye = tuple(int(i) for i in right_eye)
|
|
|
|
facial_area = FacialAreaRegion(
|
|
x=x,
|
|
y=y,
|
|
w=w,
|
|
h=h,
|
|
left_eye=left_eye,
|
|
right_eye=right_eye,
|
|
confidence=confidence,
|
|
)
|
|
resp.append(facial_area)
|
|
|
|
return resp
|
|
|
|
def build_model(self) -> Any:
|
|
"""
|
|
Build a fast mtcnn face detector model
|
|
Returns:
|
|
model (Any)
|
|
"""
|
|
# this is not a must dependency. do not import it in the global level.
|
|
try:
|
|
from facenet_pytorch import MTCNN as fast_mtcnn
|
|
import torch
|
|
except ModuleNotFoundError as e:
|
|
raise ImportError(
|
|
"FastMtcnn is an optional detector, ensure the library is installed. "
|
|
"Please install using 'pip install facenet-pytorch'"
|
|
) from e
|
|
|
|
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
|
face_detector = fast_mtcnn(device=device)
|
|
|
|
return face_detector
|
|
|
|
|
|
def xyxy_to_xywh(regions: Union[list, tuple]) -> tuple:
|
|
"""
|
|
Convert (x1, y1, x2, y2) format to (x, y, w, h) format.
|
|
Args:
|
|
regions (list or tuple): facial area coordinates as x, y, x+w, y+h
|
|
Returns:
|
|
regions (tuple): facial area coordinates as x, y, w, h
|
|
"""
|
|
x, y, x_plus_w, y_plus_h = regions[0], regions[1], regions[2], regions[3]
|
|
w = x_plus_w - x
|
|
h = y_plus_h - y
|
|
return (int(x), int(y), int(w), int(h))
|