Improve performance (#15)

* Add deeper nima profiling

* Switch to bmp format

* Switch NIMA to GPU

* Update tensorflow-gpu version

* Remove NIMA profiling

* Remove unused imports

dockerfile

@@ -12,7 +12,7 @@ RUN apt-get update && apt-get install -y apt-utils software-properties-common &&
     libavformat-dev libswscale-dev unzip && \
     python3.6 -m pip install --upgrade pip && \
     python3.6 -m pip install jupyter ipywidgets jupyterlab && \
-    python3.6 -m pip install tensorflow-gpu h5py keras && \
+    python3.6 -m pip install h5py keras && \
     python3.6 -m pip install scikit-image opencv-contrib-python pyyaml
 
 RUN mkdir /comixify

keyframes/keyframes.py

@@ -61,7 +61,7 @@ class KeyFramesExtractor:
                   jj(f"{settings.TMP_DIR}", f"{all_frames_tmp_dir}", "%06d.jpeg")])
         else:
             call(["ffmpeg", "-i", video.file.path, "-vf", "select=not(mod(n\\,15))", "-vsync", "vfr", "-q:v", "2",
-                  jj(settings.TMP_DIR, all_frames_tmp_dir, "%06d.jpeg")])
+                  jj(settings.TMP_DIR, all_frames_tmp_dir, "%06d.bmp")])
         frames_paths = []
         for dirname, dirnames, filenames in os.walk(jj(settings.TMP_DIR, all_frames_tmp_dir)):
             for filename in filenames:

neural_image_assessment/models.py

@@ -2,11 +2,12 @@ import os
 import errno
 import numpy as np
 from keras.models import load_model
-from keras.preprocessing.image import load_img, img_to_array
+from keras.preprocessing.image import img_to_array
 from keras.applications.nasnet import preprocess_input
 import tensorflow as tf
 from PIL import Image
 
+
 MODEL_PATH = 'neural_image_assessment/pretrained_model/nima_model.h5'
 
 
@@ -16,12 +17,11 @@ class NeuralImageAssessment:
             raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), MODEL_PATH)
         self.graph = tf.Graph()
         config = tf.ConfigProto()
-        config.gpu_options.per_process_gpu_memory_fraction = 0.001
+        config.gpu_options.per_process_gpu_memory_fraction = 0.1
         config.gpu_options.allow_growth = True
         self.session = tf.Session(graph=self.graph, config=config)
         with self.graph.as_default():
-            with tf.device('/CPU:0'):
-                self.model = load_model(MODEL_PATH)
+            self.model = load_model(MODEL_PATH)
 
     @staticmethod
     def resize_image(bgr_img_array, target_size=(224, 224), interpolation='nearest'):

requirements.txt

@@ -13,3 +13,4 @@ torch==0.4.1
 torchvision==0.2.1
 scikit-learn==0.19.2
 youtube-dl==2018.9.18
+tensorflow-gpu==1.10.1