| | 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 |
