diff options
Diffstat (limited to 'megapixels/app/processors/face_landmarks_2d.py')
| -rw-r--r-- | megapixels/app/processors/face_landmarks_2d.py | 87 |
1 files changed, 0 insertions, 87 deletions
diff --git a/megapixels/app/processors/face_landmarks_2d.py b/megapixels/app/processors/face_landmarks_2d.py deleted file mode 100644 index e8ce93c1..00000000 --- a/megapixels/app/processors/face_landmarks_2d.py +++ /dev/null @@ -1,87 +0,0 @@ -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
\ No newline at end of file |