Retina encoder: biological encoder for vision WIP

.github/workflows/build.yml

@@ -65,8 +65,17 @@ jobs:
     - name: build htmcore with setup.py
       run: python setup.py install --user --force
 
-    - name: C++ & Python Tests
-      run: python setup.py test
+    - name: C++ Tests
+      shell: bash
+      run: ./build/Release/bin/unit_tests
+
+    - name: Python Bindings Tests
+      shell: bash
+      run: pytest bindings/py/tests
+
+    - name: Python (pure) Tests
+      shell: bash
+      run: pytest py/tests
 
     - name: Memory leaks check (valgrind)
       if: matrix.os == 'ubuntu-18.04'

py/htm/examples/mnist.py

@@ -24,6 +24,7 @@
 
 from htm.bindings.algorithms import SpatialPooler, Classifier
 from htm.bindings.sdr import SDR, Metrics
+from htm.encoders.eye import Eye
 
 
 def load_ds(name, num_test, shape=None):
@@ -90,9 +91,13 @@ def main(parameters=default_parameters, argv=None, verbose=True):
     random.shuffle(training_data)
 
     # Setup the AI.
-    enc = SDR(train_images[0].shape)
+    encoder = Eye(output_diameter=train_images[0].shape[0],
+                  sparsityParvo = 0.2,
+                  sparsityMagno = 0.0, 
+                  color = True)
+
     sp = SpatialPooler(
-        inputDimensions            = enc.dimensions,
+        inputDimensions            = encoder.dimensions,
         columnDimensions           = parameters['columnDimensions'],
         potentialRadius            = parameters['potentialRadius'],
         potentialPct               = parameters['potentialPct'],
@@ -115,7 +120,10 @@ def main(parameters=default_parameters, argv=None, verbose=True):
     # Training Loop
     for i in range(len(train_images)):
         img, lbl = random.choice(training_data)
-        encode(img, enc)
+        encoder.new_image(img)
+        (enc, _) = encoder.compute()
+        if i == 1:
+          encoder.plot()
         sp.compute( enc, True, columns )
         sdrc.learn( columns, lbl ) #TODO SDRClassifier could accept string as a label, currently must be int
 
@@ -125,7 +133,7 @@ def main(parameters=default_parameters, argv=None, verbose=True):
     # Testing Loop
     score = 0
     for img, lbl in test_data:
-        encode(img, enc)
+        enc = encode(img)
         sp.compute( enc, False, columns )
         if lbl == np.argmax( sdrc.infer( columns ) ):
             score += 1

py/tests/encoders/eye_test.py

@@ -0,0 +1,29 @@
+
+""" Unit tests for retina encoder. """
+
+import unittest
+import os
+import numpy as np
+
+from htm.encoders.eye import Eye
+
+class EyeEncoderTest(unittest.TestCase):
+  """ Unit tests for Eye encoder class. """
+
+  def testBasicUsage(self):
+    eye = Eye()
+    eye.reset()
+    FILE=os.path.join('py','tests','encoders','ronja_the_cat.jpg')
+    eye.new_image(FILE)
+    eye.scale = 0.5
+    #eye.center_view()
+    eye.position = (400,400)
+    for _ in range(10):
+      pos,rot,sc = eye.small_random_movement()
+      (sdrParvo, sdrMagno) = eye.compute(pos,rot,sc)
+      #eye.plot(delay=500)
+    print("Sparsity parvo: {}".format(len(eye.parvo_sdr.sparse)/np.product(eye.parvo_sdr.dimensions)))
+    print("Sparsity magno: {}".format(len(eye.magno_sdr.sparse)/np.product(eye.magno_sdr.dimensions)))
+    assert(eye.dimensions == (1,1))
+
+

requirements.txt

@@ -9,3 +9,4 @@ prettytable>=0.7.2 # for monitor-mixin in htm.advanced (+its tests)
 ## optional dependencies, such as for visualizations, running examples
 # should be placed in setup.py section extras_require. Install those by
 # pip install htm.core[examples] 
+opencv-contrib-python #for cv2.bioinspired for RetinaEncoder

setup.py

@@ -399,9 +399,9 @@ def configure(platform, build_type):
     extras_require={'scikit-image>0.15.0':'examples',
                     'sklearn':'examples',
                     'matplotlib':'examples',
-                    'PIL':'examples',
-                    'scipy':'examples'
-                   },
+                    'scipy':'examples',
+                    'opencv-contrib-python': 'examples', #for cv2.bioinspired for RetinaEncoder
+    },
     zip_safe=False,
     cmdclass={
       "clean": CleanCommand,