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HenryAI
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KerasCodeExamples.txt

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def get_model(upscale_factor=3, channels=1):
conv_args = {
\"activation\": \"relu\",
\"kernel_initializer\": \"Orthogonal\",
\"padding\": \"same\",
}
inputs = keras.Input(shape=(None, None, channels))
x = layers.Conv2D(64, 5, **conv_args)(inputs)
x = layers.Conv2D(64, 3, **conv_args)(x)
x = layers.Conv2D(32, 3, **conv_args)(x)
x = layers.Conv2D(channels * (upscale_factor ** 2), 3, **conv_args)(x)
outputs = tf.nn.depth_to_space(x, upscale_factor)
return keras.Model(inputs, outputs)
Define utility functions
We need to define several utility functions to monitor our results:
plot_results to plot an save an image.
get_lowres_image to convert an image to its low-resolution version.
upscale_image to turn a low-resolution image to a high-resolution version reconstructed by the model. In this function, we use the y channel from the YUV color space as input to the model and then combine the output with the other channels to obtain an RGB image.
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
import PIL
def plot_results(img, prefix, title):
\"\"\"Plot the result with zoom-in area.\"\"\"
img_array = img_to_array(img)
img_array = img_array.astype(\"float32\") / 255.0
# Create a new figure with a default 111 subplot.
fig, ax = plt.subplots()
im = ax.imshow(img_array[::-1], origin=\"lower\")
plt.title(title)
# zoom-factor: 2.0, location: upper-left
axins = zoomed_inset_axes(ax, 2, loc=2)
axins.imshow(img_array[::-1], origin=\"lower\")
# Specify the limits.
x1, x2, y1, y2 = 200, 300, 100, 200
# Apply the x-limits.
axins.set_xlim(x1, x2)
# Apply the y-limits.
axins.set_ylim(y1, y2)
plt.yticks(visible=False)
plt.xticks(visible=False)
# Make the line.
mark_inset(ax, axins, loc1=1, loc2=3, fc=\"none\", ec=\"blue\")
plt.savefig(str(prefix) + \"-\" + title + \".png\")
plt.show()
def get_lowres_image(img, upscale_factor):
\"\"\"Return low-resolution image to use as model input.\"\"\"
return img.resize(
(img.size[0] // upscale_factor, img.size[1] // upscale_factor),
PIL.Image.BICUBIC,
)
def upscale_image(model, img):
\"\"\"Predict the result based on input image and restore the image as RGB.\"\"\"
ycbcr = img.convert(\"YCbCr\")
y, cb, cr = ycbcr.split()
y = img_to_array(y)
y = y.astype(\"float32\") / 255.0
input = np.expand_dims(y, axis=0)
out = model.predict(input)
out_img_y = out[0]
out_img_y *= 255.0
# Restore the image in RGB color space.
out_img_y = out_img_y.clip(0, 255)
out_img_y = out_img_y.reshape((np.shape(out_img_y)[0], np.shape(out_img_y)[1]))
out_img_y = PIL.Image.fromarray(np.uint8(out_img_y), mode=\"L\")
out_img_cb = cb.resize(out_img_y.size, PIL.Image.BICUBIC)
out_img_cr = cr.resize(out_img_y.size, PIL.Image.BICUBIC)
out_img = PIL.Image.merge(\"YCbCr\", (out_img_y, out_img_cb, out_img_cr)).convert(
\"RGB\"
)
return out_img
Define callbacks to monitor training
The ESPCNCallback object will compute and display the PSNR metric. This is the main metric we use to evaluate super-resolution performance.
class ESPCNCallback(keras.callbacks.Callback):
def __init__(self):
super(ESPCNCallback, self).__init__()
self.test_img = get_lowres_image(load_img(test_img_paths[0]), upscale_factor)
# Store PSNR value in each epoch.
def on_epoch_begin(self, epoch, logs=None):
self.psnr = []