displayiing all statistics

1 files changed, 41 insertions, 31 deletions

diff --git a/megapixels/app/server/tasks/demo.py b/megapixels/app/server/tasks/demo.py
index 5143dd56..c27b08b5 100644
--- a/megapixels/app/server/tasks/demo.py
+++ b/megapixels/app/server/tasks/demo.py
@@ -24,7 +24,8 @@ def demo_task(self, uuid_name, fn):
 
   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, face_age_gender
+  from app.processors import face_detector, face_landmarks, face_age_gender, face_beauty
+  # , face_emotion
   from app.models.data_store import DataStore
 
   # TODO add selective testing
@@ -39,16 +40,16 @@ def demo_task(self, uuid_name, fn):
 
   meta = {
     'step': 0,
-    'total': 3,
+    'total': 10,
     'message': 'Starting',
     'uuid': uuid_name,
-    'data': {},
+    'data': { 'statistics': {} },
   }
   paths = []
 
-  def step(msg, step=0):
-    meta['step'] += step
+  def step(msg, step=1):
     meta['message'] = msg
+    meta['step'] += step
     log.debug('> {}'.format(msg))
     self.update_state(state='PROCESSING', meta=meta)
 
@@ -178,56 +179,65 @@ def demo_task(self, uuid_name, fn):
   # age
 
   # real
+  step('Running age predictor')
   age_real_predictor = face_age_gender.FaceAgeReal()
   st = time.time()
   age_real = age_real_predictor.predict(im_resized, bbox_dim)
   log.info(f'age real took: {(time.time()-st)/1000:.5f}s')
+  meta['data']['statistics']['age_real'] = f'{(age_real):.2f}'
   
   # apparent
   age_apparent_predictor = face_age_gender.FaceAgeApparent()
   st = time.time()
   age_apparent = age_apparent_predictor.predict(im_resized, bbox_dim)
   log.info(f'age apparent took: {(time.time()-st)/1000:.5f}s')
+  meta['data']['statistics']['age_apparent'] = f'{(age_apparent):.2f}'
 
   # gender
+  step('Running gender predictor')
   gender_predictor = face_age_gender.FaceGender()
   st = time.time()
   gender = gender_predictor.predict(im_resized, bbox_dim)
   log.info(f'gender took: {(time.time()-st)/1000:.5f}s')
+  meta['data']['statistics']['gender'] = f"M: {gender['m']:.2f}, F: {gender['f']:.2f}"
 
-  # ----------------------------------------------------------------------------
-  # output
-  
-  log.info(f'Face coords: {bbox_dim} face')
-  log.info(f'Age (real): {(age_real):.2f}')
-  log.info(f'Age (apparent): {(age_apparent):.2f}')
-  log.info(f'gender: {gender}')
+  # # ----------------------------------------------------------------------------
+  # # emotion
+
+  # emotion_predictor = face_emotion.FaceEmotion(gpu=opt_gpu)
+  # emotion_score = emotion_predictor.emotion(im_resized, bbox_dim)
+  # log.info(f'emotion score: {(100*emotion_score):.2f}')
+
+  # im_emotion = im_resized.copy()
+  # draw_utils.draw_bbox(im_emotion, bbox_dim)
+  # txt = f'emotion score: {(100*emotion_score):.2f}'
+  # draw_utils.draw_text(im_emotion, bbox_dim.pt_tl, txt)
+  # save_image('emotion', 'Emotion', im_emotion)
 
 
   # ----------------------------------------------------------------------------
-  # draw
+  # beauty
 
-  # draw real age
-  im_age_real = im_resized.copy()
-  draw_utils.draw_bbox(im_age_real, bbox_dim)
-  txt = f'{(age_real):.2f}'
-  draw_utils.draw_text(im_age_real, bbox_dim.pt_tl, txt)
+  # TODO fix Keras CPU/GPU device selection issue
+  # NB: GPU visibility issues with dlib/keras
+  # Wrap this with cuda toggle and run before init dlib GPU
 
-  # apparent age
-  im_age_apparent = im_resized.copy()
-  draw_utils.draw_bbox(im_age_apparent, bbox_dim)
-  txt = f'{(age_apparent):.2f}'
-  draw_utils.draw_text(im_age_apparent, bbox_dim.pt_tl, txt)
+  step('Running beauty predictor')
+  device_cur = os.getenv('CUDA_VISIBLE_DEVICES', '')
+  os.environ['CUDA_VISIBLE_DEVICES'] = ''
+  beauty_predictor = face_beauty.FaceBeauty()
+  os.environ['CUDA_VISIBLE_DEVICES'] = device_cur
 
-  # gender
-  im_gender = im_resized.copy()
-  draw_utils.draw_bbox(im_age_apparent, bbox_dim)
-  txt = f"M: {gender['m']:.2f}, F: {gender['f']:.2f}"
-  draw_utils.draw_text(im_gender, (10, dim[1]-20), txt)
+  beauty_score = beauty_predictor.beauty(im_resized, bbox_dim)
+  log.info(f'beauty score: {(100*beauty_score):.2f}')
 
-  save_image('age_real', 'Age (Real)', im_age_real)
-  save_image('age_apparent', 'Age (Apparent)', im_age_apparent)
-  save_image('gender', 'Gender', im_gender)
+  # # draw 2d landmarks
+  # im_beauty = im_resized.copy()
+  # draw_utils.draw_bbox(im_beauty, bbox_dim)
+  # txt = f'Beauty score: {(100*beauty_score):.2f}'
+  # draw_utils.draw_text(im_beauty, bbox_dim.pt_tl, txt)
+  # save_image('beauty', 'Beauty', im_beauty)
+  meta['data']['statistics']['beauty'] = f'{(100*beauty_score):.2f}'
 
   step('Done')