Added just getting frames and not displaying. Added publishing to ros and zeromq.

examples/callbacks/black_and_white.py

@@ -6,4 +6,4 @@ def black_and_white(arr):
     return (np.sum(arr, axis=-1) / 3).astype(np.uint8)
 
 
-display(0, callbacks=black_and_white, blocking=True)
+display(0, callbacks=black_and_white, blocking=True)

examples/callbacks/psychadelic_effect.py

@@ -5,9 +5,15 @@ from examples.videos import test_video
 
 def forest_color(arr):
     forest_color.i += 1
-    arr[..., 0] = (m.sin(forest_color.i * (2 * m.pi) * .4 / 360) * 255 + arr[..., 0]) % 255
-    arr[..., 1] = (m.sin((forest_color.i * (2 * m.pi) * .5 + 45) / 360) * 255 + arr[..., 1]) % 255
-    arr[..., 2] = (m.cos(forest_color.i * (2 * m.pi) * .3 / 360) * 255 + arr[..., 2]) % 255
+    arr[..., 0] = (
+        m.sin(forest_color.i * (2 * m.pi) * 0.4 / 360) * 255 + arr[..., 0]
+    ) % 255
+    arr[..., 1] = (
+        m.sin((forest_color.i * (2 * m.pi) * 0.5 + 45) / 360) * 255 + arr[..., 1]
+    ) % 255
+    arr[..., 2] = (
+        m.cos(forest_color.i * (2 * m.pi) * 0.3 / 360) * 255 + arr[..., 2]
+    ) % 255
 
 
 forest_color.i = 0

examples/callbacks/random_display.py

@@ -9,4 +9,4 @@ def fix_arr_cv(arr_in):
     arr_in %= 1.0
 
 
-display(arr, callbacks=fix_arr_cv, blocking=True)
+display(arr, callbacks=fix_arr_cv, blocking=True)

examples/debugging/breakpoint_display.py

@@ -2,7 +2,7 @@ from displayarray import breakpoint_display
 import numpy as np
 
 center = (75, 450)
-zoom = .5
+zoom = 0.5
 zoom_out = 1.0 / zoom
 
 arr = np.random.uniform(0, 1, (300, 600, 3))
@@ -13,8 +13,8 @@ x = np.arange(arr.shape[1])
 y_ = (y - center[0]) * zoom_out / arr.shape[0]
 x_ = (x - center[1]) * zoom_out / arr.shape[1]
 p = np.array(np.meshgrid(x_, y_))
-breakpoint_display(p[0] + .5)
-breakpoint_display(p[1] + .5)
+breakpoint_display(p[0] + 0.5)
+breakpoint_display(p[1] + 0.5)
 
 barrel_power = 1.5
 

examples/debugging/random_display.py

@@ -1,4 +1,4 @@
 from displayarray import display
 import numpy as np
 
-display(np.random.normal(0.5, 0.1, (500, 500, 3))).block()
+display(np.random.normal(0.5, 0.1, (500, 500, 3))).block()

examples/documentation/test_fractal.py

@@ -2,7 +2,9 @@ import numpy as np
 from displayarray import display
 
 
-def mandel(height=240, width=320, itermax=255, y_min=-1.8, y_max=.6, x_min=-1.6, x_max=1.6):
+def mandel(
+    height=240, width=320, itermax=255, y_min=-1.8, y_max=0.6, x_min=-1.6, x_max=1.6
+):
     """
     Generate a view of the mandlebrot fractal
 
@@ -43,13 +45,13 @@ def mandel(height=240, width=320, itermax=255, y_min=-1.8, y_max=.6, x_min=-1.6,
     return img / 255.0
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     img = mandel()
-    center = (0, -.6)
+    center = (0, -0.6)
     length = 3.2
     d = display(img)
     while d:
-        length *= .9
+        length *= 0.9
         y_min = center[1] - length / 2.0
         y_max = center[1] + length / 2.0
         x_min = center[0] - length / 2.0

examples/effects/lens_crop.py

@@ -4,10 +4,12 @@ from examples.videos import test_video
 
 # Move the mouse to center the image, scroll to increase/decrease barrel, ctrl+scroll to increase/decrease zoom
 
-d = display(test_video) \
-    .add_callback(crop.Crop()) \
-    .add_callback(lens.Barrel().enable_mouse_control()) \
+d = (
+    display(test_video)
+    .add_callback(crop.Crop())
+    .add_callback(lens.Barrel().enable_mouse_control())
     .wait_for_init()
+)
 
 while d:
     print(d.frames[0].shape)

examples/effects/lens_crop_blocking.py

@@ -4,7 +4,6 @@ from examples.videos import test_video
 
 # Move the mouse to move where the crop is from on the original image
 
-display(test_video) \
-    .add_callback(crop.Crop()) \
-    .add_callback(lens.Barrel().enable_mouse_control()) \
-    .block()
+display(test_video).add_callback(crop.Crop()).add_callback(
+    lens.Barrel().enable_mouse_control()
+).block()

examples/tensorflow/denoising_autoencoder.py

@@ -28,20 +28,33 @@ autoencoder.compile(loss="mse", optimizer="adam")
 while displayer:
     displayer.update()
     grab = tf.convert_to_tensor(
-        next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(np.float32) / 255.0
-    )
-    grab_noise = tf.convert_to_tensor(
-        ((next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(
-            np.float32) + np.random.uniform(0, 255, grab.shape)) / 2)
+        next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(np.float32)
         / 255.0
     )
-    displayer.update((grab_noise.numpy()[0] * 255.0).astype(np.uint8), "uid for grab noise")
+    grab_noise = tf.convert_to_tensor(
+        (
+            (
+                next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(
+                    np.float32
+                )
+                + np.random.uniform(0, 255, grab.shape)
+            )
+            / 2
+        )
+        / 255.0
+    )
+    displayer.update(
+        (grab_noise.numpy()[0] * 255.0).astype(np.uint8), "uid for grab noise"
+    )
     autoencoder.fit(grab_noise, 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")
+    displayer.update(
+        (output_image[0] * 255.0).astype(np.uint8), "uid for autoencoder output"
+    )
 
-    get_3rd_layer_output = tf.keras.backend.function([autoencoder.layers[0].input],
-                                                     [autoencoder.layers[1].output])
+    get_3rd_layer_output = tf.keras.backend.function(
+        [autoencoder.layers[0].input], [autoencoder.layers[1].output]
+    )
     layer_output = get_3rd_layer_output([grab_noise])[0]
 
-    displayer.update(layer_output[0], "conv 1")
+    displayer.update(layer_output[0], "conv 1")

examples/videos/__init__.py

@@ -2,5 +2,5 @@
 
 import os
 
-test_video = os.path.join(os.path.dirname(__file__), 'fractal test.mp4')
-test_video_2 = os.path.join(os.path.dirname(__file__), 'fractal test 2.mp4')
+test_video = os.path.join(os.path.dirname(__file__), "fractal test.mp4")
+test_video_2 = os.path.join(os.path.dirname(__file__), "fractal test 2.mp4")