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import app.settings.app_cfg as cfg
from app.server.tasks import celery
from celery.utils.log import get_task_logger
log = get_task_logger(__name__)
opt_size = (256, 256,)
@celery.task(bind=True)
def demo_task(self, uuid_name, fn):
import sys
import os
from os.path import join
from pathlib import Path
import time
import numpy as np
import cv2 as cv
import dlib
from PIL import Image
import matplotlib.pyplot as plt
from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
from app.utils import plot_utils
from app.processors import face_detector, face_landmarks
from app.models.data_store import DataStore
# TODO add selective testing
opt_gpu = -1
opt_run_pose = True
opt_run_2d_68 = True
opt_run_3d_68 = True
opt_run_3d_68 = True
opt_gif_size = (256, 256,)
opt_gif_frames = 15
meta = {
'step': 0,
'total': 3,
'message': 'Starting',
'uuid': uuid_name,
'data': {},
}
paths = []
def step(msg, step=0):
meta['step'] += step
meta['message'] = msg
log.debug('> {}'.format(msg))
self.update_state(state='PROCESSING', meta=meta)
def save_image(key, title, data):
fn = '{}_{}.jpg'.format(uuid_name, key)
fpath = os.path.join(cfg.DIR_SITE_USER_CONTENT, fn)
paths.append(fpath)
cv.imwrite(fpath, data)
meta['data'][key] = {
'title': title,
'url': os.path.join('/user_content/', fn),
}
step('Loading image')
self.update_state(state='PROCESSING', meta=meta)
# os.path.join('/user_content/', fn)
# -------------------------------------------------
# init here
# load image
im = cv.imread(fn)
im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
# ----------------------------------------------------------------------------
# detect face
face_detector_instance = face_detector.DetectorDLIBCNN(gpu=opt_gpu) # -1 for CPU
step('Detecting face')
st = time.time()
bboxes = face_detector_instance.detect(im_resized, largest=True)
bbox = bboxes[0]
dim = im_resized.shape[:2][::-1]
bbox_dim = bbox.to_dim(dim)
if not bbox:
log.error('No face detected')
meta['error'] = 'No face detected'
self.update_state(state='FAILURE', meta=meta)
return meta
else:
log.info(f'Detected face in {(time.time() - st):.2f}s')
# ----------------------------------------------------------------------------
# detect 3D landmarks
step('Generating 3D Landmarks')
log.info('loading 3D landmark generator files...')
landmark_detector_3d_68 = face_landmarks.FaceAlignment3D_68(gpu=opt_gpu) # -1 for CPU
log.info('generating 3D landmarks...')
st = time.time()
points_3d_68 = landmark_detector_3d_68.landmarks(im_resized, bbox_dim.to_xyxy())
log.info(f'generated 3D landmarks in {(time.time() - st):.2f}s')
log.info('')
# draw 3d landmarks
im_landmarks_3d_68 = im_resized.copy()
draw_utils.draw_landmarks3D(im_landmarks_3d_68, points_3d_68)
draw_utils.draw_bbox(im_landmarks_3d_68, bbox_dim)
save_image('landmarks_3d_68', '3D Landmarks', im_landmarks_3d_68)
# ----------------------------------------------------------------------------
# generate 3D GIF animation
step('Generating GIF Animation')
log.info('generating 3D animation...')
fn = '{}_{}.gif'.format(uuid_name, '3d')
fp_out = os.path.join(cfg.DIR_SITE_USER_CONTENT, fn)
paths.append(fp_out)
st = time.time()
plot_utils.generate_3d_landmark_anim(np.array(points_3d_68), fp_out,
size=opt_gif_size, num_frames=opt_gif_frames)
log.info(f'Generated animation in {(time.time() - st):.2f}s')
log.info(f'Saved to: {fp_out}')
log.info('')
meta['data']['points_3d_68'] = points_3d_68
meta['data']['points_3d_68'] = {
'title': '3D Animated GIF',
'url': os.path.join('/user_content/', fn),
}
# ----------------------------------------------------------------------------
# generate 68 point landmarks using dlib
step('Generating 2D 68PT landmarks')
log.info('initializing face landmarks 68 dlib...')
from app.processors import face_landmarks
landmark_detector_2d_68 = face_landmarks.Dlib2D_68()
log.info('generating 2D 68PT landmarks...')
st = time.time()
points_2d_68 = landmark_detector_2d_68.landmarks(im_resized, bbox_dim)
log.info(f'generated 2D 68PT face landmarks in {(time.time() - st):.2f}s')
log.info('')
# draw 2d landmarks
im_landmarks_2d_68 = im_resized.copy()
draw_utils.draw_landmarks2D(im_landmarks_2d_68, points_2d_68)
draw_utils.draw_bbox(im_landmarks_2d_68, bbox_dim)
save_image('landmarks_2d_68', '2D Landmarks', im_landmarks_2d_68)
# ----------------------------------------------------------------------------
# generate pose from 68 point 2D landmarks
if opt_run_pose:
step('Generating pose')
log.info('initialize pose...')
from app.processors import face_pose
pose_detector = face_pose.FacePoseDLIB()
log.info('generating pose...')
st = time.time()
pose_data = pose_detector.pose(points_2d_68, dim)
log.info(f'generated pose {(time.time() - st):.2f}s')
log.info('')
im_pose = im_resized.copy()
draw_utils.draw_pose(im_pose, pose_data['point_nose'], pose_data['points'])
draw_utils.draw_degrees(im_pose, pose_data)
save_image('pose', 'Pose', im_pose)
# ----------------------------------------------------------------------------
# generate pose from 68 point 2D landmarks
step('Done')
# done
# self.log.debug('Add age real')
# self.log.debug('Add age apparent')
# self.log.debug('Add gender')
# # 3DDFA
# self.log.debug('Add depth')
# self.log.debug('Add pncc')
# # TODO
# self.log.debug('Add 3D face model')
# self.log.debug('Add face texture flat')
# self.log.debug('Add ethnicity')
log.debug('done!!')
time.sleep(3)
for path in paths:
if os.path.exists(path):
os.remove(path)
meta['step'] = meta['total']
meta['state'] = 'SUCCESS'
return meta
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