def show(img):
"""展示图片
"""
if isinstance(img, bytes):
img = IPyImage(img)
elif isinstance(img, np.ndarray):
if img.max() <= 1:
img = img * 255
data = cv.imencode(".png", img)[1].tobytes()
img = IPyImage(data)
elif isinstance(img, str) and os.path.isfile(img):
with open(img, "rb") as f:
img = IPyImage(f.read())
display(img)
def predict(test_image_np, detection_model):
label_id_offset = 1
for i in range(len(test_images_np)):
input_tensor = tf.convert_to_tensor(test_images_np[i],
dtype=tf.float32)
detections = detect(input_tensor, detection_model)
plot_detections(
test_images_np[i][0],
detections['detection_boxes'][0].numpy(),
detections['detection_classes'][0].numpy().astype(np.uint32) +
label_id_offset,
detections['detection_scores'][0].numpy(),
category_index,
figsize=(15, 20),
image_name="gif_frame_" + ('%02d' % i) + ".jpg")
imageio.plugins.freeimage.download()
anim_file = 'duckies_test.gif'
filenames = glob.glob('gif_frame_*.jpg')
filenames = sorted(filenames)
last = -1
images = []
for filename in filenames:
image = imageio.imread(filename)
images.append(image)
imageio.mimsave(anim_file, images, 'GIF-FI', fps=5)
display(IPyImage(open(anim_file, 'rb').read()))
def test_show_remote_ref():
from IPython.display import Image as IPyImage
uri = "https://octodex.github.com/images/original.png"
img = Image(uri)
# TODO check the actual content
assert img._repr_html_() == img.display()._repr_html_()
assert img.display()._repr_html_() == IPyImage(uri)._repr_html_()
def pil2gif(file_name, img_list, file_dir='gif', fps=10, show=True):
if not os.path.exists(file_dir):
os.makedirs(file_dir)
file_path = os.path.join(file_dir, file_name)
img_arr = np.array([np.array(img) for img in img_list])
imageio.mimwrite(file_path, img_arr, fps=fps)
if show:
with open(file_path, 'rb') as file:
display(IPyImage(file.read()))
def screenshot():
def peer(a):
print(type(a).__name__)
print(type(a).__module__)
print(dir(a))
#display(self.pil_image_test)
# print(self.core_image.value)
#peer(self.figure)
#self.figure.save_png("test.png")
#display(IPyImage("test.png"))
#display(self.core.image)
clear_output(wait=True)
self.plot.model.plot.show()
display(IPyImage(self.core_image.value))
def ipy_image(self) -> IPyImage:
"""
Converts a clip to an image.
"""
if self._ipy_image_cache is not None:
return self._ipy_image_cache
size = self.first_frame.size()
raw = self.environment.parent.output.bytes_of(self.first_frame.to_pil())
self._ipy_image_cache = IPyImage(
data=raw,
format="png",
embed=True,
unconfined=True,
width=size.width,
height=size.height
)
return self._ipy_image_cache
def on_epoch_end(self, epoch, logs=None):
# Randomly sample data
indexes = np.random.choice(len(self.inputs), size=self.n_samples)
X_test, y_test = self.inputs[indexes], self.ground_truth[indexes]
predictions = np.argmax(self.model.predict(X_test), axis=1)
# Plot the digits
display_digits(X_test, predictions, y_test, epoch, n=self.display_freq)
# Save the figure
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
image = Image.open(buf)
self.images.append(np.array(image))
# Display the digits every 'display_freq' number of epochs
if epoch % self.display_freq == 0:
plt.show()
def on_train_end(self, logs=None):
imageio.mimsave(GIF_PATH, self.images, fps=1)
callbacks = [VisCallback(x_test, y_test)]
SCALE = 60
# FYI, the format is set to PNG here to bypass checks for acceptable embeddings
IPyImage(GIF_PATH, format='png', width=15 * SCALE, height=3 * SCALE)
for i in range(len(test_images_np)):
input_tensor = tf.convert_to_tensor(test_images_np[i],
dtype=tf.float32)
detections = detect(input_tensor)
plot_detections(
test_images_np[i][0],
detections['detection_boxes'][0].numpy(),
detections['detection_classes'][0].numpy().astype(np.uint32) +
label_id_offset,
detections['detection_scores'][0].numpy(),
category_index,
figsize=(15, 20),
image_name="gif_frame_" + ('%02d' % i) + ".jpg")
imageio.plugins.freeimage.download()
anim_file = 'duckies_test.gif'
filenames = glob.glob('gif_frame_*.jpg')
filenames = sorted(filenames)
last = -1
images = []
for filename in filenames:
image = imageio.imread(filename)
images.append(image)
imageio.mimsave(anim_file, images, 'GIF-FI', fps=5)
display(IPyImage(open(anim_file, 'rb').read()))