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