import time
import numpy as np
import cv2 as cv
import altusi.config as cfg
import altusi.visualizer as vis
from altusi import imgproc, helper
from altusi.logger import Logger
from altusi.objectdetector import ObjectDetector
LOG = Logger('app-face-detector')
def app(video_link, video_name, show, record, flip_hor, flip_ver):
# initialize Face Detection net
object_detector = ObjectDetector()
# initialize Video Capturer
cap = cv.VideoCapture(video_link)
(W, H), FPS = imgproc.cameraCalibrate(cap)
LOG.info('Camera Info: ({}, {}) - {:.3f}'.format(W, H, FPS))
if record:
time_str = time.strftime(cfg.TIME_FM)
writer = cv.VideoWriter(video_name + time_str + '.avi',
cv.VideoWriter_fourcc(*'XVID'), 20,
(1280, 720))
cnt_frm = 0
while cap.isOpened():
import os
import time
import numpy as np
import cv2 as cv
import altusi.config as cfg
from altusi import helper, imgproc
from altusi.logger import Logger
import headvisualizer as hvis
import altusi.visualizer as vis
from altusi.facedetector import FaceDetector
from altusi.headposer import HeadPoser
LOG = Logger(__file__.split('.')[0])
def app(video_path, video_name, show=False, record=False):
detector = FaceDetector()
plugin = detector.getPlugin()
poser = HeadPoser(plugin=plugin)
cap = cv.VideoCapture(video_path)
(W, H), FPS = imgproc.cameraCalibrate(cap)
_, frm = cap.read()
frm = imgproc.resizeByHeight(frm)
H, W = frm.shape[:2]
FRM_MOD = int(1. * FPS / cfg.pFPS + 0.5)
LOG.info('Camera Info: ({}, {}) - {:.3f}'.format(W, H, FPS))
import time
import numpy as np
import cv2 as cv
import altusi.config as cfg
import altusi.visualizer as vis
from altusi import imgproc, helper
from altusi.logger import Logger
from altusi.facedetector import FaceDetector
from altusi.facelandmarker import FaceLandmarker
LOG = Logger('app-face-alignment')
h, w = 112., 96.
ref_lm_norm = [
30.2946 / w, 51.6963 / h, 65.5318 / w, 51.5014 / h, 48.0252 / w,
71.7366 / h, 33.5493 / w, 92.3655 / h, 62.7299 / w, 92.2041 / h]
def getTransform(src, dst):
src = np.array(src, np.float)
col_mean_src = np.mean(src, axis=0)
src -= col_mean_src
dst = np.array(dst, np.float)
col_mean_dst = np.mean(dst, axis=0)
dst -= col_mean_dst
mean, dev_src = cv.meanStdDev(src)
dev_src = max(dev_src[0], 1.192e-7)
src /= dev_src[0]
import os
import time
import numpy as np
import cv2 as cv
from altusi import helper, config as cfg
from altusi.logger import Logger
from altusi.facedetector import FaceDetector
from altusi.facelandmarker import FaceLandmarker
from altusi.facealigner import FaceAligner
from altusi.faceembedder import FaceEmbedder
LOG = Logger('app-face-id')
def getDistance(u, v):
uv = np.dot(u, v)
uu = np.dot(u, u)
vv = np.dot(v, v)
norm = np.sqrt(uu * vv) + 1e-6
return 1 - uv / norm
def app(image1_path, image2_path):
# initialize Face detector net
detector = FaceDetector()
# initialize Face Landmarker net
import time
import numpy as np
import cv2 as cv
import altusi.config as cfg
import altusi.visualizer as vis
from altusi import imgproc, helper
from altusi.logger import Logger
from altusi.facedetector import FaceDetector
from altusi.emotioner import Emotioner
LOG = Logger(__file__.split(',')[0])
EMOTION = ['neutral', 'happy', 'sad', 'surprise', 'anger']
def getPadding(image, bbox):
H, W = image.shape[:2]
x1, y1, x2, y2 = bbox
w, h = x2-x1, y2-y1
if h > w:
d = (h - w)/2
x1 = max(0, int(x1 - d))
x2 = min(W, int(x2 + d))
return x1, y1, x2, y2
def app(video_link, video_name, show, record, flip_hor, flip_ver):
# initialize Face Detection net
import time
import numpy as np
import cv2 as cv
import altusi.config as cfg
import altusi.visualizer as vis
from altusi import imgproc, helper
from altusi.logger import Logger
from altusi.facedetector import FaceDetector
from altusi.facelandmarker import FaceLandmarker
LOG = Logger('app-landmark-detector')
def app(video_link, video_name, show, flip_hor, flip_ver):
# initialize Face Detection net
face_detector = FaceDetector()
LOG.info('Face Detector initialization done')
# initialize Face Landmark net
face_landmarker = FaceLandmarker()
LOG.info('Face Landmarker initialization done')
# initialize Video Capturer
cap = cv.VideoCapture(video_link)
(W, H), FPS = imgproc.cameraCalibrate(cap, size=720, by_height=True)
LOG.info('Camera Info: ({}, {}) - {:.3f}'.format(W, H, FPS))
while cap.isOpened():
_, frm = cap.read()
-------------
Generate given data (Images and Annotations) to `rec` format
"""
import os
import time
import argparse
import numpy as np
import xml.etree.ElementTree as ET
import altusi.config as cfg
from altusi.logger import Logger
LOG = Logger(__file__.split('.')[0])
def getArgs():
parser = argparse.ArgumentParser()
parser.add_argument('--data',
'-d',
type=str,
required=True,
help='Path to data folder')
parser.add_argument('--name',
'-n',
type=str,
required=False,
default='data',
help='Name of REC data')
import os
import time
import numpy as np
import mxnet as mx
from mxnet import autograd, gluon
import gluoncv as gcv
from gluoncv.utils import download, viz
import altusi.config as cfg
from altusi.logger import Logger
LOG = Logger(__file__.split('.')[0])
classes = cfg.CLASSES
dataset = gcv.data.RecordFileDetection('train-cow.rec')
BASE_MODEL = 'ssd_512_mobilenet1.0_custom'
TRAINED_MODEL = 'ssd_512_mobilenet1.0_cow.params'
BASE_MODEL = 'ssd_512_resnet50_v1_custom'
TRAINED_MODEL = 'ssd_512_resnet50_v1_cow.params'
#BASE_MODEL = 'ssd_300_vgg16_atrous_custom'
#TRAINED_MODEL = 'ssd_300_vgg16_atrous_gym.params'
#
#BASE_MODEL = 'yolo3_darknet53_custom'
#TRAINED_MODEL = 'yolo3_darknet53_gym.params'
net = gcv.model_zoo.get_model(BASE_MODEL,
classes=classes,
import os
import time
import numpy as np
import cv2 as cv
from altusi.logger import Logger
from altusi import helper, imgproc
import altusi.visualizer as vis
from altusi.facedetector import FaceDetector
from altusi.facelandmarker import FaceLandmarker
LOG = Logger('test-func')
TEMPLATE = np.float32([(0.0792396913815, 0.339223741112),
(0.0829219487236, 0.456955367943),
(0.0967927109165, 0.575648016728),
(0.122141515615, 0.691921601066),
(0.168687863544, 0.800341263616),
(0.239789390707, 0.895732504778),
(0.325662452515, 0.977068762493),
(0.422318282013, 1.04329000149),
(0.531777802068, 1.06080371126),
(0.641296298053, 1.03981924107),
(0.738105872266, 0.972268833998),
(0.824444363295, 0.889624082279),
(0.894792677532, 0.792494155836),
(0.939395486253, 0.681546643421),
(0.96111933829, 0.562238253072),
(0.970579841181, 0.441758925744),
import time
import numpy as np
import cv2 as cv
import altusi.config as cfg
import altusi.visualizer as vis
from altusi import imgproc, helper
from altusi.logger import Logger
from altusi.facedetector import FaceDetector
from altusi.emotioner import Emotioner
LOG = Logger(__file__.split(',')[0])
def app(image_path):
# initialize Face Detection net
face_detector = FaceDetector()
# initialize Emotioner net
emotioner = Emotioner()
frm = cv.imread(image_path)
_start_t = time.time()
scores, bboxes = face_detector.getFaces(frm, def_score=0.5)
for i, bbox in enumerate(bboxes):
x1, y1, x2, y2 = bbox
face_img = frm[y1:y2, x1:x2]
emo_idx = emotioner.getEmotion(face_img)
LOG.info('emotion: {}'.format(emo_idx))
_prx_t = time.time() - _start_t