fix bugs

Author: Yifan Peng

README.rst

@@ -22,7 +22,7 @@ Prerequisites
 
 *  python =3.6
 *  tensorflow >=1.6.0
-*  keras =2.1.5
+*  keras =2.2.4
 *  Linux
 
 Tensorflow can be downloaded from `https://www.tensorflow.org <https://www.tensorflow.org/>`_.
@@ -46,13 +46,17 @@ The easiest way is to run the following command
 
    $ python examples/predict_simplified_score.py data/left_eye.jpg data/right_eye.jpg
    ...
-   INFO:root:Loading the model: drusen
-   INFO:root:Loading the model: advanced_amd
-   INFO:root:Loading the model: pigment
+   Downloading data from https://github.com/ncbi-nlp/DeepSeeNet/releases/download/0.1/drusen_model.h5
+   INFO:root:Loading the model: /tmp/.keras/datasets/drusen_model.h5
+   Downloading data from https://github.com/ncbi-nlp/DeepSeeNet/releases/download/0.1/pigment_model.h5
+   INFO:root:Loading the model: /tmp/.keras/datasets/pigment_model.h5
+   Downloading data from https://github.com/ncbi-nlp/DeepSeeNet/releases/download/0.1/advanced_amd_model.h5
+   INFO:root:Loading the model: /tmp/.keras/datasets/advanced_amd_model.h5
    ...
    INFO:root:Processing: data/left_eye.jpg
    INFO:root:Processing: data/right_eye.jpg
    ...
+   INFO:root:Risk factors: {'pigment': (0, 0), 'advanced_amd': (0, 0), 'drusen': (2, 2)}
    The simplified score: 2
 
 The script will
@@ -76,11 +80,12 @@ Besides grading the simplified score, we also provide individual risk factor mod
 
    $ python examples/predict_drusen.py data/left_eye.jpg
    ...
-   INFO:root:Loading the model: drusen
+   INFO:root:Loading the model: /tmp/.keras/datasets/drusen_model.h5
    ...
    INFO:root:Processing: data/left_eye.jpg
    ...
-   Drusen size: large
+   The drusen score: [[0.21020733 0.2953384  0.49445423]]
+   The drusen size: large
 
 
 All models can be found at ``deepseenet``.

deepseenet/deepseenet_pigment.py

@@ -41,7 +41,7 @@ def DeepSeeNetPigment(model='areds'):
     Returns:
         A Keras model instance.
     """
-    if model == 'areds1':
+    if model == 'areds':
         model = get_file(
             'pigment_model.h5',
             PIGMENT_PATH,

deepseenet/deepseenet_simplified.py

@@ -76,7 +76,7 @@ def predict(self, x_left, x_right, verbose=0):
         Returns:
             Numpy array of scores of 0-5
         """
-        assert x_left.shape[0] == x_right.shape[0]
+        # assert x_left.shape[0] == x_right.shape[0]
         scores = {}
         for model_name, (model, preprocess_image) in self.models.items():
             left_score = np.argmax(model.predict(preprocess_image(x_left)), axis=1)[0]

environment3.6.yml

@@ -5,14 +5,14 @@
     predict_drusen [options] <eye_image>
 
 Options:
-    -d <str>    Drusen model. [default: areds1]
+    -d <str>    Drusen model file. [default: areds]
 """
 import logging
 import sys
 
 import docopt
 
-from deepseenet.eyesnet_drusen import EyesNetDrusen, preprocess_image, get_drusen_size
+from deepseenet.deepseenet_drusen import DeepSeeNetDrusen, preprocess_image, get_drusen_size
 from deepseenet.utils import pick_device
 
 if __name__ == '__main__':
@@ -21,7 +21,7 @@
     logging.debug(argv)
 
     pick_device()
-    clf = EyesNetDrusen(argv['-d'])
+    clf = DeepSeeNetDrusen(argv['-d'])
     x = preprocess_image(argv['<eye_image>'])
     score = clf.predict(x, verbose=1)
     print('The drusen score:', score)

examples/extract_feature.py

@@ -5,9 +5,9 @@
     predict_simplified_score [options] <left_eye_image> <right_eye_image>
 
 Options:
-    -d <str>    Drusen model
-    -p <str>    Pigment model
-    -a <str>    Advanced AMD model
+    -d <str>    Drusen model path [default: areds]
+    -p <str>    Pigment model path [default: areds]
+    -a <str>    Advanced AMD model path [default: areds]
 """
 import logging
 import sys
@@ -44,9 +44,9 @@ def preprocess_image(image_path, target_size=(224, 224)):
     logging.basicConfig(level=logging.DEBUG)
     logging.debug(argv)
 
-    drusen_model = argv['-d'] if '-d' in argv else None
-    pigment_model = argv['-p'] if '-p' in argv else None
-    advanced_amd_model = argv['-a'] if '-a' in argv else None
+    drusen_model = argv['-d']
+    pigment_model = argv['-p']
+    advanced_amd_model = argv['-a']
 
     pick_device()
     clf = deepseenet_simplified.DeepSeeNetSimplifiedScore(drusen_model, pigment_model, advanced_amd_model)