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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
paths
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)
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)
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, im_landmarks_3d_68)
meta['data']['landmarks_3d_68'] = {
'title': '3D Landmarks',
'url': os.path.join('/user_content/', fn),
}
step('Generated 3D Landmarks', step=0)
# ----------------------------------------------------------------------------
# generate 3D GIF animation
# step('Generating GIF Animation')
# log.info('generating 3D animation...')
# if not opt_fp_out:
# fpp_im = Path(opt_fp_in)
# fp_out = join(fpp_im.parent, f'{fpp_im.stem}_anim.gif')
# else:
# fp_out = opt_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('')
# # ----------------------------------------------------------------------------
# # generate face vectors, only to test if feature extraction works
# step('Generating face vectors')
# log.info('initialize face recognition model...')
# from app.processors import face_recognition
# face_rec = face_recognition.RecognitionDLIB()
# st = time.time()
# log.info('generating face vector...')
# vec = face_rec.vec(im_resized, bbox_dim)
# log.info(f'generated face vector in {(time.time() - st):.2f}s')
# log.info('')
# # ----------------------------------------------------------------------------
# # 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('')
# # ----------------------------------------------------------------------------
# # 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('')
# # ----------------------------------------------------------------------------
# # 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')
# display
# draw bbox
# # 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)
# # draw pose
# if opt_run_pose:
# 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)
# # draw animated GIF
# im = Image.open(fp_out)
# im_frames = []
# duration = im.info['duration']
# try:
# while True:
# im.seek(len(im_frames))
# mypalette = im.getpalette()
# im.putpalette(mypalette)
# im_jpg = Image.new("RGB", im.size)
# im_jpg.paste(im)
# im_np = im_utils.pil2np(im_jpg.copy())
# im_frames.append(im_np)
# except EOFError:
# pass # end of GIF sequence
# n_frames = len(im_frames)
# frame_number = 0
# # show all images here
# cv.imshow('Original', im_resized)
# cv.imshow('2D 68PT Landmarks', im_landmarks_2d_68)
# cv.imshow('3D 68PT Landmarks', im_landmarks_3d_68)
# cv.imshow('Pose', im_pose)
# cv.imshow('3D 68pt GIF', im_frames[frame_number])
log.debug('done!!')
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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