def main(): X,Y = get_image_data() X = X.transpose((0,2,3,1)) model = CNN( convpool_layer_sizes=[(20,5,5),(20,5,5)], hidden_layer_sizes=[500,300] ) model.fit(X,Y)
def create_blur_image(image, radius, weight):
img, width, height = open_image(image)
image_data = get_image_data(img)
new_image_data = get_image_data(img)
new_color = []
for x in range(width):
for y in range(height):
image_data_submatrix, height_sub, width_sub = extract_submatrix(x, y, height, width,
image_data.copy(), radius, weight)
new_color = calculate_new_color(image_data_submatrix, weight, height_sub, width_sub)
new_image_data[y][x] = new_color
save_new_image(new_image_data, f'test-image-blur-radius-{radius}-weight-{weight}.png')
print('Image successfully changed.')
def main():
X, Y = get_image_data()
# reshape X for tf: N * W * H *C
# i in the j-th place in the tuple means a‘s i-th axis becomes a.transpose()‘s j-th axis.
X = X.transpose((0, 2, 3, 1))
print('X.shape: ', X.shape)
model = Cnn(
convpool_layer_sizes=[(20, 5, 5), (20, 5, 5)],
hidden_layer_sizes=[500, 300]
)
model.fit(X, Y)
def change_image_color(image):
img, width, height = open_image(image)
image_data = get_image_data(img)
new_color = []
for color in ['red', 'green', 'blue']:
for x in range(width):
for y in range(height):
r, g, b, alpha = img.getpixel((x, y))
if color == 'red':
new_color = [r,0,0, alpha]
elif color== 'green':
new_color = [0,g,0, alpha]
elif color == 'blue':
new_color = [0,0,b, alpha]
image_data[y][x] = new_color
save_new_image(image_data, f"test-image-split-{color}.png")
print('Images successfully saved.')
import h5py
from util import get_image_data
folder = './../data/with_brace/ipad_zach/ring_cont2'
images, data = get_image_data(folder)
out_data = [images, data]
with h5py.File(folder + '/image_data.h5', 'w') as hf:
hf.create_dataset("images", data=images)
hf.create_dataset("data", data=data)
def main():
X_train, labels_train, X_val, labels_val = get_image_data()
model = CNN(convpool_layer_sizes=[(20, 5, 5), (20, 5, 5)],
hidden_layer_sizes=[300, 100])
model.fit(X_train, labels_train, X_val, labels_val, show_fig=True)
def main():
X, Y = get_image_data()
model = CNN(conv_pool_size=[(20, 5, 5), (20, 5, 5)],
hidden_layer_size=[500, 300])
model.fit(X, Y)