Upload guided_filter.py

guided_filter.py

@@ -0,0 +1,281 @@
+
+# -*- coding: utf-8 -*-
+## @package guided_filter.core.filters
+#
+#  Implementation of guided filter.
+#  * GuidedFilter: Original guided filter.
+#  * FastGuidedFilter: Fast version of the guided filter.
+#  @author      tody
+#  @date        2015/08/26
+
+import numpy as np
+import cv2
+
+## Convert image into float32 type.
+def to32F(img):
+    if img.dtype == np.float32:
+        return img
+    return (1.0 / 255.0) * np.float32(img)
+
+## Convert image into uint8 type.
+def to8U(img):
+    if img.dtype == np.uint8:
+        return img
+    return np.clip(np.uint8(255.0 * img), 0, 255)
+
+## Return if the input image is gray or not.
+def _isGray(I):
+    return len(I.shape) == 2
+
+
+## Return down sampled image.
+#  @param scale (w/s, h/s) image will be created.
+#  @param shape I.shape[:2]=(h, w). numpy friendly size parameter.
+def _downSample(I, scale=4, shape=None):
+    if shape is not None:
+        h, w = shape
+        return cv2.resize(I, (w, h), interpolation=cv2.INTER_NEAREST)
+
+    h, w = I.shape[:2]
+    return cv2.resize(I, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_NEAREST)
+
+
+## Return up sampled image.
+#  @param scale (w*s, h*s) image will be created.
+#  @param shape I.shape[:2]=(h, w). numpy friendly size parameter.
+def _upSample(I, scale=2, shape=None):
+    if shape is not None:
+        h, w = shape
+        return cv2.resize(I, (w, h), interpolation=cv2.INTER_LINEAR)
+
+    h, w = I.shape[:2]
+    return cv2.resize(I, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_LINEAR)
+
+## Fast guide filter.
+class FastGuidedFilter:
+    ## Constructor.
+    #  @param I Input guidance image. Color or gray.
+    #  @param radius Radius of Guided Filter.
+    #  @param epsilon Regularization term of Guided Filter.
+    #  @param scale Down sampled scale.
+    def __init__(self, I, radius=5, epsilon=0.4, scale=4):
+        I_32F = to32F(I)
+        self._I = I_32F
+        h, w = I.shape[:2]
+
+        I_sub = _downSample(I_32F, scale)
+
+        self._I_sub = I_sub
+        radius = int(radius / scale)
+
+        if _isGray(I):
+            self._guided_filter = GuidedFilterGray(I_sub, radius, epsilon)
+        else:
+            self._guided_filter = GuidedFilterColor(I_sub, radius, epsilon)
+
+    ## Apply filter for the input image.
+    #  @param p Input image for the filtering.
+    def filter(self, p):
+        p_32F = to32F(p)
+        shape_original = p.shape[:2]
+
+        p_sub = _downSample(p_32F, shape=self._I_sub.shape[:2])
+
+        if _isGray(p_sub):
+            return self._filterGray(p_sub, shape_original)
+
+        cs = p.shape[2]
+        q = np.array(p_32F)
+
+        for ci in range(cs):
+            q[:, :, ci] = self._filterGray(p_sub[:, :, ci], shape_original)
+        return to8U(q)
+
+    def _filterGray(self, p_sub, shape_original):
+        ab_sub = self._guided_filter._computeCoefficients(p_sub)
+        ab = [_upSample(abi, shape=shape_original) for abi in ab_sub]
+        return self._guided_filter._computeOutput(ab, self._I)
+
+
+## Guide filter.
+class GuidedFilter:
+    ## Constructor.
+    #  @param I Input guidance image. Color or gray.
+    #  @param radius Radius of Guided Filter.
+    #  @param epsilon Regularization term of Guided Filter.
+    def __init__(self, I, radius=5, epsilon=0.4):
+        I_32F = to32F(I)
+
+        if _isGray(I):
+            self._guided_filter = GuidedFilterGray(I_32F, radius, epsilon)
+        else:
+            self._guided_filter = GuidedFilterColor(I_32F, radius, epsilon)
+
+    ## Apply filter for the input image.
+    #  @param p Input image for the filtering.
+    def filter(self, p):
+        return to8U(self._guided_filter.filter(p))
+
+
+## Common parts of guided filter.
+#
+#  This class is used by guided_filter class. GuidedFilterGray and GuidedFilterColor.
+#  Based on guided_filter._computeCoefficients, guided_filter._computeOutput,
+#  GuidedFilterCommon.filter computes filtered image for color and gray.
+class GuidedFilterCommon:
+    def __init__(self, guided_filter):
+        self._guided_filter = guided_filter
+
+    ## Apply filter for the input image.
+    #  @param p Input image for the filtering.
+    def filter(self, p):
+        p_32F = to32F(p)
+        if _isGray(p_32F):
+            return self._filterGray(p_32F)
+
+        cs = p.shape[2]
+        q = np.array(p_32F)
+
+        for ci in range(cs):
+            q[:, :, ci] = self._filterGray(p_32F[:, :, ci])
+        return q
+
+    def _filterGray(self, p):
+        ab = self._guided_filter._computeCoefficients(p)
+        return self._guided_filter._computeOutput(ab, self._guided_filter._I)
+
+
+## Guided filter for gray guidance image.
+class GuidedFilterGray:
+    #  @param I Input gray guidance image.
+    #  @param radius Radius of Guided Filter.
+    #  @param epsilon Regularization term of Guided Filter.
+    def __init__(self, I, radius=5, epsilon=0.4):
+        self._radius = 2 * radius + 1
+        self._epsilon = epsilon
+        self._I = to32F(I)
+        self._initFilter()
+        self._filter_common = GuidedFilterCommon(self)
+
+    ## Apply filter for the input image.
+    #  @param p Input image for the filtering.
+    def filter(self, p):
+        return self._filter_common.filter(p)
+
+    def _initFilter(self):
+        I = self._I
+        r = self._radius
+        self._I_mean = cv2.blur(I, (r, r))
+        I_mean_sq = cv2.blur(I ** 2, (r, r))
+        self._I_var = I_mean_sq - self._I_mean ** 2
+
+    def _computeCoefficients(self, p):
+        r = self._radius
+        p_mean = cv2.blur(p, (r, r))
+        p_cov = p_mean - self._I_mean * p_mean
+        a = p_cov / (self._I_var + self._epsilon)
+        b = p_mean - a * self._I_mean
+        a_mean = cv2.blur(a, (r, r))
+        b_mean = cv2.blur(b, (r, r))
+        return a_mean, b_mean
+
+    def _computeOutput(self, ab, I):
+        a_mean, b_mean = ab
+        return a_mean * I + b_mean
+
+
+## Guided filter for color guidance image.
+class GuidedFilterColor:
+    #  @param I Input color guidance image.
+   #  @param radius Radius of Guided Filter.
+    #  @param epsilon Regularization term of Guided Filter.
+    def __init__(self, I, radius=5, epsilon=0.2):
+        self._radius = 2 * radius + 1
+        self._epsilon = epsilon
+        self._I = to32F(I)
+        self._initFilter()
+        self._filter_common = GuidedFilterCommon(self)
+
+    ## Apply filter for the input image.
+    #  @param p Input image for the filtering.
+    def filter(self, p):
+        return self._filter_common.filter(p)
+
+    def _initFilter(self):
+        I = self._I
+        r = self._radius
+        eps = self._epsilon
+
+        Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2]
+
+        self._Ir_mean = cv2.blur(Ir, (r, r))
+        self._Ig_mean = cv2.blur(Ig, (r, r))
+        self._Ib_mean = cv2.blur(Ib, (r, r))
+
+        Irr_var = cv2.blur(Ir ** 2, (r, r)) - self._Ir_mean ** 2 + eps
+        Irg_var = cv2.blur(Ir * Ig, (r, r)) - self._Ir_mean * self._Ig_mean
+        Irb_var = cv2.blur(Ir * Ib, (r, r)) - self._Ir_mean * self._Ib_mean
+        Igg_var = cv2.blur(Ig * Ig, (r, r)) - self._Ig_mean * self._Ig_mean + eps
+        Igb_var = cv2.blur(Ig * Ib, (r, r)) - self._Ig_mean * self._Ib_mean
+        Ibb_var = cv2.blur(Ib * Ib, (r, r)) - self._Ib_mean * self._Ib_mean + eps
+
+        Irr_inv = Igg_var * Ibb_var - Igb_var * Igb_var
+        Irg_inv = Igb_var * Irb_var - Irg_var * Ibb_var
+        Irb_inv = Irg_var * Igb_var - Igg_var * Irb_var
+        Igg_inv = Irr_var * Ibb_var - Irb_var * Irb_var
+        Igb_inv = Irb_var * Irg_var - Irr_var * Igb_var
+        Ibb_inv = Irr_var * Igg_var - Irg_var * Irg_var
+
+        I_cov = Irr_inv * Irr_var + Irg_inv * Irg_var + Irb_inv * Irb_var
+        Irr_inv /= I_cov
+        Irg_inv /= I_cov
+        Irb_inv /= I_cov
+        Igg_inv /= I_cov
+        Igb_inv /= I_cov
+        Ibb_inv /= I_cov
+
+        self._Irr_inv = Irr_inv
+        self._Irg_inv = Irg_inv
+        self._Irb_inv = Irb_inv
+        self._Igg_inv = Igg_inv
+        self._Igb_inv = Igb_inv
+        self._Ibb_inv = Ibb_inv
+
+    def _computeCoefficients(self, p):
+        r = self._radius
+        I = self._I
+        Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2]
+
+        p_mean = cv2.blur(p, (r, r))
+
+        Ipr_mean = cv2.blur(Ir * p, (r, r))
+        Ipg_mean = cv2.blur(Ig * p, (r, r))
+        Ipb_mean = cv2.blur(Ib * p, (r, r))
+
+        Ipr_cov = Ipr_mean - self._Ir_mean * p_mean
+        Ipg_cov = Ipg_mean - self._Ig_mean * p_mean
+        Ipb_cov = Ipb_mean - self._Ib_mean * p_mean
+
+        ar = self._Irr_inv * Ipr_cov + self._Irg_inv * Ipg_cov + self._Irb_inv * Ipb_cov
+        ag = self._Irg_inv * Ipr_cov + self._Igg_inv * Ipg_cov + self._Igb_inv * Ipb_cov
+        ab = self._Irb_inv * Ipr_cov + self._Igb_inv * Ipg_cov + self._Ibb_inv * Ipb_cov
+        b = p_mean - ar * self._Ir_mean - ag * self._Ig_mean - ab * self._Ib_mean
+
+        ar_mean = cv2.blur(ar, (r, r))
+        ag_mean = cv2.blur(ag, (r, r))
+        ab_mean = cv2.blur(ab, (r, r))
+        b_mean = cv2.blur(b, (r, r))
+
+        return ar_mean, ag_mean, ab_mean, b_mean
+
+    def _computeOutput(self, ab, I):
+        ar_mean, ag_mean, ab_mean, b_mean = ab
+
+        Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2]
+
+        q = (ar_mean * Ir +
+             ag_mean * Ig +
+             ab_mean * Ib +
+             b_mean)
+
+        return q