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finished the lut tables

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sccolbert
2009-11-10 05:12:23 +01:00
parent 30048f2414
commit 152c6209c8
1 changed files with 70 additions and 52 deletions
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scikits/image/io/_plugins/_colormixer.pyx
+70 -52
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@@ -45,17 +45,25 @@ def add(np.ndarray[np.uint8_t, ndim=3] img,
cdef np.int16_t op_result
cdef int i, j
cdef np.uint8_t lut[256]
cdef int i, j, l
with nogil:
for l from 0 <= l < 256:
op_result = <np.int16_t>(l + n)
if op_result > 255:
op_result = 255
elif op_result < 0:
op_result = 0
else:
pass
lut[l] = <np.uint8_t>op_result
for i from 0 <= i < height:
for j from 0 <= j < width:
op_result = <np.int16_t>(stateimg[i,j,k] + n)
if op_result > 255:
img[i, j, k] = 255
elif op_result < 0:
img[i, j, k] = 0
else:
img[i, j, k] = <np.uint8_t>op_result
img[i, j, k] = lut[stateimg[i,j,k]]
@cython.boundscheck(False)
@@ -84,17 +92,25 @@ def multiply(np.ndarray[np.uint8_t, ndim=3] img,
cdef float op_result
cdef int i, j
cdef np.uint8_t lut[256]
cdef int i, j, l
with nogil:
for l from 0 <= l < 256:
op_result = l * n
if op_result > 255:
op_result = 255
elif op_result < 0:
op_result = 0
else:
pass
lut[l] = <np.uint8_t>op_result
for i from 0 <= i < height:
for j from 0 <= j < width:
op_result = <float>(stateimg[i,j,k] * n)
if op_result > 255:
img[i, j, k] = 255
elif op_result < 0:
img[i, j, k] = 0
else:
img[i, j, k] = <np.uint8_t>op_result
img[i,j,k] = lut[stateimg[i,j,k]]
@cython.boundscheck(False)
@@ -122,20 +138,26 @@ def brightness(np.ndarray[np.uint8_t, ndim=3] img,
cdef int width = img.shape[1]
cdef float op_result
cdef np.uint8_t lut[256]
cdef int i, j, k
with nogil:
for k from 0 <= k < 256:
op_result = k * factor + offset
if op_result > 255:
op_result = 255
elif op_result < 0:
op_result = 0
else:
pass
lut[k] = <np.uint8_t>op_result
for i from 0 <= i < height:
for j from 0 <= j < width:
for k from 0 <= k < 3:
op_result = <float>((stateimg[i,j,k] * factor + offset))
if op_result > 255:
img[i, j, k] = 255
elif op_result < 0:
img[i, j, k] = 0
else:
img[i, j, k] = <np.uint8_t>op_result
img[i,j,0] = lut[stateimg[i,j,0]]
img[i,j,1] = lut[stateimg[i,j,1]]
img[i,j,2] = lut[stateimg[i,j,2]]
@cython.boundscheck(False)
@@ -147,31 +169,25 @@ def sigmoid_gamma(np.ndarray[np.uint8_t, ndim=3] img,
cdef int height = img.shape[0]
cdef int width = img.shape[1]
cdef float c1, c2, r, g, b
cdef int i, j, k
cdef float c1 = 1 / (1 + exp(beta))
cdef float c2 = 1 / (1 + exp(beta - alpha)) - c1
cdef np.uint8_t lut[256]
with nogil:
# compute the lut
for k from 0 <= k < 256:
lut[k] = <np.uint8_t>(((1 / (1 + exp(beta - (k / 255.) * alpha)))
- c1) * 255 / c2)
for i from 0 <= i < height:
for j from 0 <= j < width:
r = <float>stateimg[i,j,0] / 255.
g = <float>stateimg[i,j,1] / 255.
b = <float>stateimg[i,j,2] / 255.
img[i,j,0] = lut[stateimg[i,j,0]]
img[i,j,1] = lut[stateimg[i,j,1]]
img[i,j,2] = lut[stateimg[i,j,2]]
c1 = 1 / (1 + exp(beta))
c2 = 1 / (1 + exp(beta - alpha)) - c1
r = 1 / (1 + exp(beta - r * alpha))
r = (r - c1) / c2
g = 1 / (1 + exp(beta - g * alpha))
g = (g - c1) / c2
b = 1 / (1 + exp(beta - b * alpha))
b = (b - c1) / c2
img[i,j,0] = <np.uint8_t>(r * 255)
img[i,j,1] = <np.uint8_t>(g * 255)
img[i,j,2] = <np.uint8_t>(b * 255)
@cython.boundscheck(False)
@@ -182,24 +198,26 @@ def gamma(np.ndarray[np.uint8_t, ndim=3] img,
cdef int height = img.shape[0]
cdef int width = img.shape[1]
cdef float r, g, b
cdef np.uint8_t lut[256]
cdef int i, j
cdef int i, j, k
if gamma == 0:
gamma = 0.00000000000000000001
gamma = 1./gamma
with nogil:
# compute the lut
for k from 0 <= k < 256:
lut[k] = <np.uint8_t>((pow((k / 255.), gamma) * 255))
for i from 0 <= i < height:
for j from 0 <= j < width:
r = <float>stateimg[i,j,0] / 255.
g = <float>stateimg[i,j,1] / 255.
b = <float>stateimg[i,j,2] / 255.
img[i,j,0] = lut[stateimg[i,j,0]]
img[i,j,1] = lut[stateimg[i,j,1]]
img[i,j,2] = lut[stateimg[i,j,2]]
img[i,j,0] = <np.uint8_t>(pow(r, gamma) * 255)
img[i,j,1] = <np.uint8_t>(pow(g, gamma) * 255)
img[i,j,2] = <np.uint8_t>(pow(b, gamma) * 255)
@cython.cdivision(True)