extracting 3d facial vectors

6 files changed, 271 insertions, 217 deletions

diff --git a/megapixels/app/server/api.py b/megapixels/app/server/api.py
index 5ad454d8..b3bce9bc 100644
--- a/megapixels/app/server/api.py
+++ b/megapixels/app/server/api.py
@@ -1,5 +1,3 @@
-import logging
-import logging.handlers
 import os
 import re
 import time
diff --git a/megapixels/app/server/api_task.py b/megapixels/app/server/api_task.py
index c9bc19ed..57ae9f7d 100644
--- a/megapixels/app/server/api_task.py
+++ b/megapixels/app/server/api_task.py
@@ -27,7 +27,8 @@ def task_status(task_name, task_id):
   if task_name in task_lookup:
     task = task_lookup[task_name]['task'].AsyncResult(task_id)
     # task = AsyncResult(task_id, app=celery)
-  else:
+
+  if task_name not in task_lookup or task.info is None:
     return jsonify({
       'state': 'error',
       'percent': 100,
@@ -75,10 +76,16 @@ def sleep_test():
 
 @api_task.route('/upload/blur', methods=['POST'])
 def upload():
+  return process('blur')
+
+@api_task.route('/upload/demo', methods=['POST'])
+def demo():
+  return process('demo')
+
+def process(style):
   """
-  Process a images in a particular style
+  Process an image in a particular style
   """
-  style = 'blur'
   print('style: {}'.format(style))
   if style in task_lookup:
     task = task_lookup[style]['task']
diff --git a/megapixels/app/server/tasks/__init__.py b/megapixels/app/server/tasks/__init__.py
index fd6e398a..c0db0be5 100644
--- a/megapixels/app/server/tasks/__init__.py
+++ b/megapixels/app/server/tasks/__init__.py
@@ -6,6 +6,7 @@ celery = Celery(__name__, backend=cfg.CELERY_RESULT_BACKEND, broker=cfg.CELERY_B
 
 from app.server.tasks.sleep import sleep_task
 from app.server.tasks.blur import blur_task
+from app.server.tasks.demo import demo_task
 
 def list_active_tasks():
     dropdown = {}
@@ -35,12 +36,12 @@ task_lookup = {
     'blur': {
         'title': 'Blur',
         'task': blur_task,
-        'active': False,
+        'active': True,
     },
-    # 'fullmonte': {
-    #     'title': 'TIA facial processing pipeline',
-    #     'task': fullmonte,
-    #     'active': True,
-    #     'default': True,
-    # }
+    'demo': {
+        'title': 'Facial processing pipeline',
+        'task': demo_task,
+        'active': True,
+        'default': True,
+    }
 }
diff --git a/megapixels/app/server/tasks/blur.py b/megapixels/app/server/tasks/blur.py
index 42977097..74798cee 100644
--- a/megapixels/app/server/tasks/blur.py
+++ b/megapixels/app/server/tasks/blur.py
@@ -14,14 +14,14 @@ import app.settings.app_cfg as cfg
 from app.server.tasks import celery
 
 from celery.utils.log import get_task_logger
-celery_logger = get_task_logger(__name__)
+log = get_task_logger(__name__)
 import imutils
 
 @celery.task(bind=True)
 def blur_task(self, uuid_name, fn):
   """Process image and update during"""
-  celery_logger.debug('process_image_task, uuid: {}'.format(uuid_name))
-  celery_logger.debug('fn: {}'.format(fn))
+  log.debug('process_image_task, uuid: {}'.format(uuid_name))
+  log.debug('fn: {}'.format(fn))
 
   files = []
 
@@ -48,7 +48,7 @@ def blur_task(self, uuid_name, fn):
   fn = uuid_name + '_blur.jpg'
   fpath = os.path.join(cfg.DIR_SITE_USER_CONTENT, fn)
   im_blur_pil.save(fpath, 'JPEG', quality=80)
-  celery_logger.debug('fpath: {}'.format(fpath))
+  log.debug('fpath: {}'.format(fpath))
   print('fpath: {}'.format(fpath))
 
   # files.append({
@@ -58,7 +58,10 @@ def blur_task(self, uuid_name, fn):
 
   meta['step'] += 1
   meta['message'] = 'Applying blur'
-  meta['data']['blur_fn'] = os.path.join('/user_content/', fn)
+  meta['data']['blur_fn'] = {
+    'title': 'Blurred image',
+    'url': os.path.join('/user_content/', fn)
+  }
   self.update_state(state='PROCESSING', meta=meta)
   time.sleep(3)
 
@@ -70,7 +73,7 @@ def blur_task(self, uuid_name, fn):
   self.update_state(state='PROCESSING', meta=meta)
   time.sleep(2)
 
-  celery_logger.debug('done!!')
+  log.debug('done!!')
   
   meta['step'] = meta['total']
   meta['state'] = 'complete'
diff --git a/megapixels/app/server/tasks/demo.py b/megapixels/app/server/tasks/demo.py
new file mode 100644
index 00000000..acc5dbac
--- /dev/null
+++ b/megapixels/app/server/tasks/demo.py
@@ -0,0 +1,244 @@
+
+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
diff --git a/megapixels/app/server/tasks/fullmonte.py b/megapixels/app/server/tasks/fullmonte.py
deleted file mode 100644
index 8215656a..00000000
--- a/megapixels/app/server/tasks/fullmonte.py
+++ /dev/null
@@ -1,199 +0,0 @@
-
-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
-  
-@celery.task(bind=True)
-def fullmonte_task(self, uuid_name, fn):
-  # TOOD add selective testing
-  opt_run_pose = True
-  opt_run_2d_68 = True
-  opt_run_3d_68 = True
-  opt_run_3d_68 = True
-
-  return
-
-  # -------------------------------------------------
-  # init here
-
-
-  log = logger_utils.Logger.getLogger()
-
-  # load image
-  im = cv.imread(opt_fp_in)
-  im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
-
-
-  # ----------------------------------------------------------------------------
-  # detect face
-
-  face_detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu)  # -1 for CPU
-  log.info('detecting face...')
-  st = time.time()
-  bboxes = face_detector.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')
-    return
-  else:
-    log.info(f'Detected face in {(time.time() - st):.2f}s')
-  log.info('')
-
-
-  # ----------------------------------------------------------------------------
-  # detect 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('')
-
-
-  # ----------------------------------------------------------------------------
-  # generate 3D 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
-  
-  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
-  
-  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:
-    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
-
-  # 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
-  if opt_display:
-    
-    # draw bbox
-    
-    # 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)
-
-    # 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
-
-    while True:
-      # 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])
-      frame_number = (frame_number + 1) % n_frames
-      k = cv.waitKey(duration) & 0xFF
-      if k == 27 or k == ord('q'):  # ESC
-        cv.destroyAllWindows()
-        sys.exit()
-      elif k != 255:
-        # any key to continue
-        break
\ No newline at end of file