Spaces:
Build error
Build error
| import tensorflow as tf | |
| import numpy as np | |
| def tf_box_filter(x, r): | |
| k_size = int(2*r+1) | |
| ch = x.get_shape().as_list()[-1] | |
| weight = 1/(k_size**2) | |
| box_kernel = weight*np.ones((k_size, k_size, ch, 1)) | |
| box_kernel = np.array(box_kernel).astype(np.float32) | |
| output = tf.nn.depthwise_conv2d(x, box_kernel, [1, 1, 1, 1], 'SAME') | |
| return output | |
| def guided_filter(x, y, r, eps=1e-2): | |
| x_shape = tf.shape(x) | |
| #y_shape = tf.shape(y) | |
| N = tf_box_filter(tf.ones((1, x_shape[1], x_shape[2], 1), dtype=x.dtype), r) | |
| mean_x = tf_box_filter(x, r) / N | |
| mean_y = tf_box_filter(y, r) / N | |
| cov_xy = tf_box_filter(x * y, r) / N - mean_x * mean_y | |
| var_x = tf_box_filter(x * x, r) / N - mean_x * mean_x | |
| A = cov_xy / (var_x + eps) | |
| b = mean_y - A * mean_x | |
| mean_A = tf_box_filter(A, r) / N | |
| mean_b = tf_box_filter(b, r) / N | |
| output = mean_A * x + mean_b | |
| return output | |
| def fast_guided_filter(lr_x, lr_y, hr_x, r=1, eps=1e-8): | |
| #assert lr_x.shape.ndims == 4 and lr_y.shape.ndims == 4 and hr_x.shape.ndims == 4 | |
| lr_x_shape = tf.shape(lr_x) | |
| #lr_y_shape = tf.shape(lr_y) | |
| hr_x_shape = tf.shape(hr_x) | |
| N = tf_box_filter(tf.ones((1, lr_x_shape[1], lr_x_shape[2], 1), dtype=lr_x.dtype), r) | |
| mean_x = tf_box_filter(lr_x, r) / N | |
| mean_y = tf_box_filter(lr_y, r) / N | |
| cov_xy = tf_box_filter(lr_x * lr_y, r) / N - mean_x * mean_y | |
| var_x = tf_box_filter(lr_x * lr_x, r) / N - mean_x * mean_x | |
| A = cov_xy / (var_x + eps) | |
| b = mean_y - A * mean_x | |
| mean_A = tf.image.resize_images(A, hr_x_shape[1: 3]) | |
| mean_b = tf.image.resize_images(b, hr_x_shape[1: 3]) | |
| output = mean_A * hr_x + mean_b | |
| return output | |
| if __name__ == '__main__': | |
| import cv2 | |
| from tqdm import tqdm | |
| input_photo = tf.placeholder(tf.float32, [1, None, None, 3]) | |
| #input_superpixel = tf.placeholder(tf.float32, [16, 256, 256, 3]) | |
| output = guided_filter(input_photo, input_photo, 5, eps=1) | |
| image = cv2.imread('output_figure1/cartoon2.jpg') | |
| image = image/127.5 - 1 | |
| image = np.expand_dims(image, axis=0) | |
| config = tf.ConfigProto() | |
| config.gpu_options.allow_growth = True | |
| sess = tf.Session(config=config) | |
| sess.run(tf.global_variables_initializer()) | |
| out = sess.run(output, feed_dict={input_photo: image}) | |
| out = (np.squeeze(out)+1)*127.5 | |
| out = np.clip(out, 0, 255).astype(np.uint8) | |
| cv2.imwrite('output_figure1/cartoon2_filter.jpg', out) | |