| from __future__ import print_function
|
| import os
|
| import torch
|
| from torch.utils.model_zoo import load_url
|
| from enum import Enum
|
| import numpy as np
|
| import cv2
|
| try:
|
| import urllib.request as request_file
|
| except BaseException:
|
| import urllib as request_file
|
|
|
| from .models import FAN, ResNetDepth
|
| from .utils import *
|
|
|
|
|
| class LandmarksType(Enum):
|
| """Enum class defining the type of landmarks to detect.
|
|
|
| ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face
|
| ``_2halfD`` - this points represent the projection of the 3D points into 3D
|
| ``_3D`` - detect the points ``(x,y,z)``` in a 3D space
|
|
|
| """
|
| _2D = 1
|
| _2halfD = 2
|
| _3D = 3
|
|
|
|
|
| class NetworkSize(Enum):
|
|
|
|
|
|
|
| LARGE = 4
|
|
|
| def __new__(cls, value):
|
| member = object.__new__(cls)
|
| member._value_ = value
|
| return member
|
|
|
| def __int__(self):
|
| return self.value
|
|
|
| ROOT = os.path.dirname(os.path.abspath(__file__))
|
|
|
| class FaceAlignment:
|
| def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
|
| device='cuda', flip_input=False, face_detector='sfd', verbose=False):
|
| self.device = device
|
| self.flip_input = flip_input
|
| self.landmarks_type = landmarks_type
|
| self.verbose = verbose
|
|
|
| network_size = int(network_size)
|
|
|
| if 'cuda' in device:
|
| torch.backends.cudnn.benchmark = True
|
|
|
|
|
| face_detector_module = __import__('face_detection.detection.' + face_detector,
|
| globals(), locals(), [face_detector], 0)
|
| self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose)
|
|
|
| def get_detections_for_batch(self, images):
|
| images = images[..., ::-1]
|
| detected_faces = self.face_detector.detect_from_batch(images.copy())
|
| results = []
|
|
|
| for i, d in enumerate(detected_faces):
|
| if len(d) == 0:
|
| results.append(None)
|
| continue
|
| d = d[0]
|
| d = np.clip(d, 0, None)
|
|
|
| x1, y1, x2, y2 = map(int, d[:-1])
|
| results.append((x1, y1, x2, y2))
|
|
|
| return results |
