externalize drawing, cleanup

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