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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
# ----------------------------------------------------------------------
#
# 2D landmarks: 5pt and 68pt
#
# ----------------------------------------------------------------------
class Landmarks2D:
# Abstract class
def __init__(self):
self.log = logger_utils.Logger.getLogger()
def landmarks(self, im, bbox):
# override
self.log.warn('Define landmarks() function')
pass
def flatten(self, points):
'''Converts list of point-tupes into a flattened list for CSV
:param points: (list) of x,y points
:returns dict item for each point (eg {'x1':100, 'y1':200})
'''
points_formatted = {}
for idx, pt in enumerate(points, 1):
for j, d in enumerate('xy'):
points_formatted[f'{d}{idx}'] = pt[j]
return points_formatted
def normalize(self, points, dim):
return [np.array(p)/dim for p in points] # divides each point by w,h dim
import face_alignment
class FaceAlignment2D_68(Landmarks2D):
# https://github.com/1adrianb/face-alignment
# Estimates 2D facial landmarks
def __init__(self, gpu=0, flip_input=False):
t = face_alignment.LandmarksType._2D
device = f'cuda:{gpu}' if gpu > -1 else 'cpu'
self.fa = face_alignment.FaceAlignment(t, device=device, flip_input=flip_input)
super().__init__()
self.log.debug(f'{device}')
self.log.debug(f'{t}')
def landmarks(self, im):
'''Calculates the 2D facial landmarks
:param im: (numpy.ndarray) BGR image
:returns (list) of 68 (int) (tuples) as (x,y)
'''
# predict landmarks
points = self.fa.get_landmarks(im) # returns array of arrays of 68 2D pts/face
# convert to data type
points = [list(map(int, p)) for p in points[0]]
return points
class Dlib2D(Landmarks2D):
def __init__(self, model):
super().__init__()
# init dlib
import dlib
self.predictor = dlib.shape_predictor(model)
self.log.info(f'loaded predictor model: {model}')
def landmarks(self, im, bbox):
# Draw high-confidence faces
dim_wh = im.shape[:2][::-1]
bbox = bbox.to_dlib()
im_gray = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
points = [[p.x, p.y] for p in self.predictor(im_gray, bbox).parts()]
return points
class Dlib2D_68(Dlib2D):
def __init__(self):
# Get 68-point landmarks using DLIB
super().__init__(cfg.DIR_MODELS_DLIB_68PT)
class Dlib2D_5(Dlib2D):
def __init__(self):
# Get 5-point landmarks using DLIB
super().__init__(cfg.DIR_MODELS_DLIB_5PT)
class MTCNN2D_5(Landmarks2D):
# Get 5-point landmarks using MTCNN
# https://github.com/ipazc/mtcnn
# pip install mtcnn
def __init__(self):
super().__init__()
self.log.warn('NB: MTCNN runs both face detector and landmark predictor together.')
self.log.warn(' this will use face with most similar ROI')
from mtcnn.mtcnn import MTCNN
self.detector = MTCNN()
def landmarks(self, im, bbox):
'''Detects face using MTCNN and returns (list) of BBox
:param im: (numpy.ndarray) image
:returns list of BBox
'''
results = []
dim_wh = im.shape[:2][::-1] # (w, h)
# run MTCNN to get bbox and landmarks
dets = self.detector.detect_faces(im)
keypoints = []
bboxes = []
#iterate detections and convert to BBox
for det in dets:
#rect = det['box']
points = det['keypoints']
# convert to normalized for contain-comparison
points_norm = [np.array(pt)/dim_wh for pname, pt in points.items()]
contains = False not in [bbox.contains(pn) for pn in points_norm]
if contains:
results.append(points) # append original points
return results
# ----------------------------------------------------------------------
#
# 3D landmarks
#
# ----------------------------------------------------------------------
class Landmarks3D:
def __init__(self):
self.log = logger_utils.Logger.getLogger()
def landmarks(self, im, bbox):
pass
def flatten(self, points):
'''Converts list of point-tupes into a flattened list for CSV
:param points: (list) of x,y points
:returns dict item for each point (eg {'x1':100, 'y1':200})
'''
points_formatted = {}
for idx, pt in enumerate(points, 1):
for j, d in enumerate('xyz'):
points_formatted[f'{d}{idx}'] = pt[j]
return points_formatted
def normalize(self, points, dim):
return [np.array(p)/dim for p in points] # divides each point by w,h dim
class FaceAlignment3D_68(Landmarks3D):
# Estimates 3D facial landmarks
import face_alignment
def __init__(self, gpu=0, flip_input=False):
super().__init__()
device = f'cuda:{gpu}' if gpu > -1 else 'cpu'
self.fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device=device, flip_input=flip_input)
def landmarks(self, im, as_type=str):
'''Calculates the 3D facial landmarks
:param im: (numpy.ndarray) BGR image
:returns (list) of 68 (int) (tuples) as (x,y, z)
'''
# predict landmarks
points = self.fa.get_landmarks(im) # returns array of arrays of 68 3D pts/face
# convert to data type
points = [list(map(int, p)) for p in points[0]]
return points
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