Added more api to display function

tests/test_simple_api.py

@@ -0,0 +1,61 @@
+import unittest as ut
+
+class TestSubWin(ut.TestCase):
+
+    def test_display_numpy(self):
+        from cvpubsubs import display
+        import numpy as np
+
+        display(np.random.normal(0.5, .1, (500,500,3)))
+
+    def test_display_numpy_callback(self):
+        from cvpubsubs import display
+        import numpy as np
+
+        arr = np.random.normal(0.5, .1, (500, 500, 3))
+
+        def fix_arr_cv(arr_in):
+            arr_in[:] += np.random.normal(0.01, .005, (500, 500, 3))
+            arr_in%=1.0
+
+        display(arr, callbacks= fix_arr_cv, blocking=True)
+
+    def test_display_numpy_loop(self):
+        from cvpubsubs import display
+        import numpy as np
+
+        arr = np.random.normal(0.5, .1, (500, 500, 3))
+
+        displayer, ids = display(arr, blocking = False)
+
+        while True:
+            arr[:] += np.random.normal(0.01, .005, (500, 500, 3))
+            arr %= 1.0
+            displayer.update(arr, ids[0])
+        displayer.end()
+
+    def test_display_tensorflow(self):
+        from cvpubsubs import display
+        import numpy as np
+        from tensorflow.keras import layers, models
+        import tensorflow as tf
+
+        for gpu in tf.config.experimental.list_physical_devices("GPU"):
+            tf.compat.v2.config.experimental.set_memory_growth(gpu, True)
+        #tf.keras.backend.set_floatx("float16")
+
+        displayer, ids = display(0, blocking = False)
+        displayer.wait_for_init()
+        autoencoder = models.Sequential()
+        autoencoder.add(
+            layers.Conv2D(20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape)
+        )
+        autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
+
+        autoencoder.compile(loss="mse", optimizer="adam")
+
+        while True:
+            grab = tf.convert_to_tensor(displayer.frame_dict['0frame'][np.newaxis, ...].astype(np.float32)/255.0)
+            autoencoder.fit(grab, grab, steps_per_epoch=1, epochs=1)
+            output_image = autoencoder.predict(grab, steps=1)
+            displayer.update((output_image[0]*255.0).astype(np.uint8), "uid for autoencoder output")