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102 lines
2.9 KiB
102 lines
2.9 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import paddle |
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from enum import Enum |
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import numpy as np |
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import cv2 |
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from .utils import * |
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import sys |
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class LandmarksType(Enum): |
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"""Enum class defining the type of landmarks to detect. |
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``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face |
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``_2halfD`` - this points represent the projection of the 3D points into 3D |
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``_3D`` - detect the points ``(x,y,z)``` in a 3D space |
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""" |
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_2D = 1 |
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_2halfD = 2 |
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_3D = 3 |
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class NetworkSize(Enum): |
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# TINY = 1 |
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# SMALL = 2 |
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# MEDIUM = 3 |
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LARGE = 4 |
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def __new__(cls, value): |
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member = object.__new__(cls) |
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member._value_ = value |
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return member |
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def __int__(self): |
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return self.value |
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class FaceAlignment: |
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def __init__(self, |
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landmarks_type, |
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network_size=NetworkSize.LARGE, |
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flip_input=False, |
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face_detector='sfd', |
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verbose=False): |
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self.flip_input = flip_input |
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self.landmarks_type = landmarks_type |
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self.verbose = verbose |
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network_size = int(network_size) |
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# Get the face detector |
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face_detector_module = __import__( |
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'ppgan.faceutils.face_detection.detection.' + face_detector, |
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globals(), locals(), [face_detector], 0) |
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self.face_detector = face_detector_module.FaceDetector(verbose=verbose) |
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def get_detections_for_batch(self, images): |
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images = images[..., ::-1] |
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detected_faces = self.face_detector.detect_from_batch(images.copy()) |
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results = [] |
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for i, d in enumerate(detected_faces): |
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if len(d) == 0: |
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results.append(None) |
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continue |
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d = d[0] |
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d = np.clip(d, 0, None) |
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x1, y1, x2, y2 = map(int, d[:4]) |
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results.append((x1, y1, x2, y2)) |
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return results |
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def get_detections_for_image(self, images): |
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images = images[..., ::-1] |
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detected_faces = self.face_detector.detect_from_batch(images.copy()) |
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results = [] |
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for i, d in enumerate(detected_faces[0]): |
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if len(d) == 0: |
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results.append(None) |
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continue |
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d = np.clip(d, 0, None) |
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x1, y1, x2, y2 = map(int, d[:-1]) |
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results.append((x1, y1, x2, y2)) |
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return results
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