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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):
# TINY = 1
# SMALL = 2
# MEDIUM = 3
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
# Get the face detector
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 |