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Diffstat (limited to 'megapixels/app/processors/face_landmarks_2d.py')
| -rw-r--r-- | megapixels/app/processors/face_landmarks_2d.py | 87 |
1 files changed, 87 insertions, 0 deletions
diff --git a/megapixels/app/processors/face_landmarks_2d.py b/megapixels/app/processors/face_landmarks_2d.py new file mode 100644 index 00000000..e8ce93c1 --- /dev/null +++ b/megapixels/app/processors/face_landmarks_2d.py @@ -0,0 +1,87 @@ +import os +from os.path import join +from pathlib import Path + +import cv2 as cv +import numpy as np +import imutils +from app.utils import im_utils, logger_utils +from app.models.bbox import BBox +from app.settings import app_cfg as cfg +from app.settings import types +from app.models.bbox import BBox + +class LandmarksFaceAlignment: + + # Estimates 2D facial landmarks + import face_alignment + + def __init__(self, gpu=0): + self.log = logger_utils.Logger.getLogger() + device = f'cuda:{gpu}' if gpu > -1 else 'cpu' + self.fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device=device, flip_input=True) + + def landmarks(self, im, as_type=str): + '''Calculates the 3D facial landmarks + :param im: (numpy.ndarray) image + :param as_type: (str) or (list) type to return data + ''' + preds = self.fa.get_landmarks(im) + # convert to comma separated ints + # storing data as "[1,2], [3,4]" is larger file size than storing as "1,2,3,4" + # storing a list object in Pandas seems to result in 30% larger CSV files + # TODO optimize this + preds_int = [list(map(int, x)) for x in preds[0]] # list of ints + if as_type is str: + return ','.join([','.join(list(map(str,[x,y]))) for x,y in preds_int]) + else: + return preds_int + + +class LandmarksDLIB: + + def __init__(self): + # init dlib + import dlib + self.log = logger_utils.Logger.getLogger() + self.predictor = dlib.shape_predictor(cfg.DIR_MODELS_DLIB_68PT) + + def landmarks(self, im, bbox): + # Draw high-confidence faces + dim = im.shape[:2][::-1] + bbox = bbox.to_dlib() + im_gray = cv.cvtColor(im, cv.COLOR_BGR2GRAY) + landmarks = [[p.x, p.y] for p in self.predictor(im_gray, bbox).parts()] + return landmarks + + +class LandmarksMTCNN: + + # https://github.com/ipazc/mtcnn + # pip install mtcnn + + dnn_size = (400, 400) + + def __init__(self, size=(400,400)): + from mtcnn.mtcnn import MTCNN + self.detector = MTCNN() + + def landmarks(self, im, opt_size=None, opt_conf_thresh=None, opt_pyramids=None): + '''Detects face using MTCNN and returns (list) of BBox + :param im: (numpy.ndarray) image + :returns list of BBox + ''' + rois = [] + dnn_size = self.dnn_size if opt_size is None else opt_size + im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1]) + dim = im.shape[:2][::-1] + + # run MTCNN + dets = self.detector.detect_faces(im) + + for det in dets: + rect = det['box'] + keypoints = det['keypoints'] # not using here. see 'face_landmarks.py' + bbox = BBox.from_xywh_dim(*rect, dim) + rois.append(bbox) + return rois
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