def deblur(weight_path, input_dir, output_dir):
g = generator_model()
g.load_weights(weight_path)
for image_name in os.listdir(input_dir):
image = np.array([preprocess_image(load_image(os.path.join(input_dir, image_name)))])
x_test = image
generated_images = g.predict(x=x_test)
generated = np.array([deprocess_image(img) for img in generated_images])
x_test = deprocess_image(x_test)
for i in range(generated_images.shape[0]):
x = x_test[i, :, :, :]
img = generated[i, :, :, :]
output = np.concatenate((x, img), axis=1)
im = Image.fromarray(output.astype(np.uint8))
im.save(os.path.join(output_dir, image_name))
def deblur(image_path):
data = {
'A_paths': [image_path],
'A': np.array([preprocess_image(load_image(image_path))])
}
x_test = data['A']
g = generator_model()
g.load_weights('generator.h5')
generated_images = g.predict(x=x_test)
generated = np.array([deprocess_image(img) for img in generated_images])
x_test = deprocess_image(x_test)
for i in range(generated_images.shape[0]):
x = x_test[i, :, :, :]
img = generated[i, :, :, :]
output = np.concatenate((x, img), axis=1)
im = Image.fromarray(output.astype(np.uint8))
im.save('deblur'+image_path)
def deblurOne(self, imageByte: bytes):
image = np.array([preprocess_image(Image.open(io.BytesIO(imageByte)))])
try:
image = np.delete(image, 3, 3)
except:
pass
x_test = image
with self.graph.as_default():
generated_images = self.g.predict(x=x_test)
generated = np.array([deprocess_image(img) for img in generated_images])
x_test = deprocess_image(x_test)
for i in range(generated_images.shape[0]):
img = generated[i, :, :, :]
im = Image.fromarray(img.astype(np.uint8))
output = io.BytesIO()
im.save(output, format="JPEG")
output.seek(0)
return output
def DeBlur_GAN_Decomposition():
st.subheader("GAN based Blur removal")
g = generator_model()
g.load_weights(dblur_weight_path)
img_file_buffer = st.file_uploader("Upload an image",
type=["jpg"]) #"png", "jpg", "jpeg",
if img_file_buffer is not None:
sample_image = Image.open(img_file_buffer)
image_resize = sample_image.resize((256, 256))
#st.write(type(image_resize), image_resize.size)
image_in = np.array([preprocess_image(image_resize)])
#st.write(image_in.shape)
x_test = image_in
generated_images = g.predict(x=x_test)
generated = np.array(
[deprocess_image(img) for img in generated_images])
x_test = deprocess_image(x_test)
#st.write("generated images size:", generated_images.shape)
for i in range(generated_images.shape[0]):
x = x_test[i, :, :, :]
img = generated[i, :, :, :]
#output = np.concatenate((x, img), axis=1)
im = Image.fromarray(img.astype(np.uint8))
with st.spinner('Enhancing Image...'):
# Display
plt.figure(figsize=(25, 20))
plt.subplot(121)
plt.title('Original Image')
plt.axis('off')
plt.imshow(image_resize)
plt.subplot(122)
plt.title('Final Image')
plt.axis('off')
plt.imshow(im)
st.pyplot()
time.sleep(10)