v1.1 code added.

Data/.gitignore

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+Predicted_Videos/*.mp4
+MOS.csv

VideoNaturalnessEvaluator/.gitignore

@@ -1,2 +1,6 @@
-Data/Naturalness_Features/*.npy
+Data/VINE_Features/*/*.npy
+Data/SSA_Features/*/*.npy
+Data/Predicted_Videos
+Data/MOS.csv
 Runs/*
+Trained_Models/

VideoNaturalnessEvaluator/EnvironmentData/C3D.yml

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+name: RM012_C3D2
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - c-ares=1.17.1=h36c2ea0_0
+  - ca-certificates=2020.12.5=ha878542_0
+  - cached-property=1.5.1=py_0
+  - certifi=2020.12.5=py36h5fab9bb_1
+  - h5py=3.1.0=nompi_py36hc1bc4f5_100
+  - hdf5=1.10.6=nompi_h7c3c948_1111
+  - joblib=1.0.1=pyhd8ed1ab_0
+  - krb5=1.17.2=h926e7f8_0
+  - ld_impl_linux-64=2.33.1=h53a641e_7
+  - libblas=3.9.0=3_openblas
+  - libcblas=3.9.0=3_openblas
+  - libcurl=7.71.1=hcdd3856_8
+  - libedit=3.1.20191231=h14c3975_1
+  - libev=4.33=h516909a_1
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=9.1.0=hdf63c60_0
+  - libgfortran-ng=7.5.0=hae1eefd_17
+  - libgfortran4=7.5.0=hae1eefd_17
+  - liblapack=3.9.0=3_openblas
+  - libnghttp2=1.41.0=h8cfc5f6_2
+  - libopenblas=0.3.12=pthreads_hb3c22a3_1
+  - libssh2=1.9.0=hab1572f_5
+  - libstdcxx-ng=9.1.0=hdf63c60_0
+  - ncurses=6.2=he6710b0_1
+  - numpy=1.19.4=py36h8732dcd_1
+  - openssl=1.1.1j=h27cfd23_0
+  - pandas=1.0.1=py36hb3f55d8_0
+  - pip=20.3.1=py36h06a4308_0
+  - python=3.6.12=hcff3b4d_2
+  - python-dateutil=2.8.1=py_0
+  - python_abi=3.6=1_cp36m
+  - pytz=2021.1=pyhd8ed1ab_0
+  - readline=8.0=h7b6447c_0
+  - scikit-learn=0.23.2=py36hb6e6923_3
+  - scikit-video=1.1.11=pyh24bf2e0_0
+  - setuptools=51.0.0=py36h06a4308_2
+  - simplejson=3.17.2=py36h8c4c3a4_1
+  - six=1.15.0=pyh9f0ad1d_0
+  - sqlite=3.33.0=h62c20be_0
+  - threadpoolctl=2.1.0=pyh5ca1d4c_0
+  - tk=8.6.10=hbc83047_0
+  - tqdm=4.59.0=pyhd8ed1ab_0
+  - wheel=0.36.1=pyhd3eb1b0_0
+  - xz=5.2.5=h7b6447c_0
+  - zlib=1.2.11=h7b6447c_3
+  - pip:
+    - cycler==0.10.0
+    - dask==2020.12.0
+    - decorator==4.4.2
+    - keras==0.3.3
+    - kiwisolver==1.3.1
+    - matplotlib==3.3.3
+    - networkx==2.5
+    - pillow==8.0.1
+    - pyparsing==2.4.7
+    - pyyaml==5.3.1
+    - scikit-image==0.12.3
+    - scipy==1.5.4
+    - theano==0.8.1
+    - toolz==0.11.1
+prefix: /media/nagabhushan/Data02/SoftwareFiles/Anaconda/anaconda3/envs/RM012_C3D2
+

VideoNaturalnessEvaluator/src/ssa/C3D_FeatureExtractor.py

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+# Shree KRISHNAya Namaha
+# C3D Conv Net
+# https://gist.github.com/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2
+# Author: Nagabhushan S N
+# Last Modified: 14/03/2021
+
+import datetime
+import os
+import time
+import traceback
+
+from tqdm import tqdm
+
+os.environ['KERAS_BACKEND'] = 'theano'
+
+from pathlib import Path
+import numpy
+import skimage.transform
+import skvideo.io
+from keras import backend
+from keras.models import model_from_json
+
+
+class C3D:
+    def __init__(self, model_path: Path, weights_path: Path):
+        self.model = model_from_json(open(model_path.as_posix(), 'r').read())
+        print(self.model.summary())
+        self.model.load_weights(weights_path.as_posix())
+        self.model.compile(loss='mean_squared_error', optimizer='sgd')
+        self.feature_extractor = backend.function([self.model.layers[0].input],
+                                                  [self.model.layers[11].get_output(train=False)])
+
+    def get_features(self, video: numpy.ndarray):
+        frames = []
+        for frame in video:
+            resized_frame = skimage.transform.resize(frame, (112, 112))
+            frames.append(resized_frame)
+        resized_video = numpy.stack(frames)
+        reshaped_video = numpy.moveaxis(resized_video[None, ...], [0, 1, 2, 3, 4], [0, 2, 3, 4, 1])
+        features = self.feature_extractor([reshaped_video])[0]
+        features = numpy.moveaxis(features[0], [0, 1, 2, 3], [3, 0, 1, 2])
+        return features
+
+
+def save_c3d_features(videos_dirpath: Path, model_path: Path, weights_path: Path, output_dirpath: Path):
+    """
+    C3D features from last conv layer; Dimension: 2x7x7x512
+    """
+
+    output_dirpath = output_dirpath / 'C3D'
+    output_dirpath.mkdir(parents=True, exist_ok=False)
+    c3d_model = C3D(model_path, weights_path)
+    for video_path in tqdm(sorted(videos_dirpath.rglob('*.mp4'))):
+        video = skvideo.io.vread(video_path.as_posix())
+        video_feature_map = c3d_model.get_features(video)
+        output_path = output_dirpath / f'{video_path.stem}.npy'
+        numpy.save(output_path.as_posix(), video_feature_map)
+    return
+
+
+def demo1():
+    root_dirpath = Path('../../')
+    videos_dirpath = root_dirpath / 'Data/Predicted_Videos'
+    model_path = root_dirpath / 'Trained_Models/C3D/sports_1M.json'
+    weights_path = root_dirpath / 'Trained_Models/C3D/sports1M_weights.h5'
+    output_dirpath = root_dirpath / 'Data/SSA_Features'
+    save_c3d_features(videos_dirpath, model_path, weights_path, output_dirpath)
+    return
+
+
+def main():
+    demo1()
+    return
+
+
+if __name__ == '__main__':
+    print('Program started at ' + datetime.datetime.now().strftime('%d/%m/%Y %I:%M:%S %p'))
+    start_time = time.time()
+    try:
+        main()
+    except Exception as e:
+        print(e)
+        traceback.print_exc()
+    end_time = time.time()
+    print('Program ended at ' + datetime.datetime.now().strftime('%d/%m/%Y %I:%M:%S %p'))
+    print('Execution time: ' + str(datetime.timedelta(seconds=end_time - start_time)))