diff --git a/bob/ip/qualitymeasure/galbally_iqm_features.py b/bob/ip/qualitymeasure/galbally_iqm_features.py
index 10776a7ee0fe314171e09c1a0ae7c7743a74fb2b..64a4ca574a5e1b7a870de2822d34088de1403164 100644
--- a/bob/ip/qualitymeasure/galbally_iqm_features.py
+++ b/bob/ip/qualitymeasure/galbally_iqm_features.py
@@ -727,36 +727,26 @@ def edge_thinning(bx, by, thinning=1):
# np.spacing(1) is the same as eps in matlab.
myEps = np.spacing(1) * 100.0
# compute the edge-map a la Matlab
- for r in range(m):
- for c in range(n):
- if thinning:
- if r < 0 or r > (m - 1) or (c - 1) < 0:
- b1 = True
- else:
- b1 = (b[r, c - 1] < b[r, c])
-
- if(r < 0) or r > (m - 1) or (c + 1) > (n - 1):
- b2 = True
- else:
- b2 = (b[r, c] > b[r, c + 1])
-
- if (c < 0) or c > (n - 1) or (r - 1) < 0:
- b3 = True
- else:
- b3 = (b[r, c] > b[r - 1, c])
-
- if(c < 1) or c > (n - 1) or (r + 1) > (m - 1):
- b4 = True
- else:
- b4 = (b[r, c] > b[r + 1, c])
-
- c1 = (b[r, c] > cutoff)
- c2 = ((bx[r, c] >= (by[r, c] - myEps)) & b1 & b2)
- c3 = ((by[r, c] >= (bx[r, c] - myEps)) & b3 & b4)
-
- e[r, c] = c1 & (c2 | c3)
- else:
- e[r, c] = (b[r, c] > cutoff)
+
+ if not thinning:
+ e = b > cutoff
+ else:
+ b1 = np.ones_like(b, dtype=bool)
+ b2 = np.ones_like(b, dtype=bool)
+ b3 = np.ones_like(b, dtype=bool)
+ b4 = np.ones_like(b, dtype=bool)
+
+ c1 = b > cutoff
+
+ b1[:, 1:] = (np.roll(b, 1, axis=1) < b)[:, 1:]
+ b2[:, :-1] = (np.roll(b, -1, axis=1) < b)[:, :-1]
+ c2 = (bx >= (by - myEps)) & b1 & b2
+
+ b3[1:, :] = (np.roll(b, 1, axis=0) < b)[1:, :]
+ b4[:-1, 1:] = (np.roll(b, -1, axis=0) < b)[:-1, 1:]
+ c3 = (by >= (bx - myEps)) & b3 & b4
+
+ e = c1 & (c2 | c3)
return e
@@ -813,33 +803,17 @@ def angle_similarity(refImage, testImage, diffImage):
refNorm = np.linalg.norm(refImage, axis=1)
testNorm = np.linalg.norm(testImage, axis=1)
- thetaVec = np.zeros([refImage.shape[0], 1])
diffNorm = np.linalg.norm(diffImage, axis=1)
magnitVec = diffNorm / 255.0 # Galbally divides by sqrt(255**2)
magnitVec = np.reshape(magnitVec, (refImage.shape[0], 1))
- for i in range(refImage.shape[0]):
- refR = refImage[i, :]
- testR = testImage[i, :]
- cosTheta = np.dot(refR, testR) / (refNorm[i] * testNorm[i])
- if(cosTheta < -1.0):
- cosTheta = -1.0
- if(cosTheta > 1.0):
- cosTheta = 1.0
- theta = np.arccos(cosTheta)
- thetaVec[i] = theta
-
- # the following (commented out) code should be more efficient than the for-
- # loop above, but seems to be buggy.
- # rowDP= np.diag(np.dot(refImage,testImage.T))
- # normRefrows= np.linalg.norm(refImage,axis=1)
- # normTestrows= np.linalg.norm(testImage,axis=1)
-
- # cosThetaVec = rowDP/(normRefrows * normTestrows)
- # cosThetaVec = np.nan_to_num(cosThetaVec) #nan occurs when one of the
- # # norms is 0, ie. vector is all 0s. In that case set cosTheta to 0
-
- # thetaVec = np.arccos(cosThetaVec)
+ # np.einsum('ij,ij->i',a,b) is equivalent to np.diag(np.dot(a,b.T))
+ cosTheta = np.einsum('ij,ij->i', refImage, testImage) / \
+ (refNorm * testNorm)
+ cosTheta[cosTheta < -1.0] = -1.0
+ cosTheta[cosTheta > 1.0] = 1.0
+ cosTheta = np.nan_to_num(cosTheta)
+ thetaVec = np.arccos(cosTheta).reshape((refImage.shape[0], 1))
tmp2 = thetaVec * 2.0 / np.pi
diff --git a/bob/ip/qualitymeasure/msu_iqa_features.py b/bob/ip/qualitymeasure/msu_iqa_features.py
index 29b15bfafda3436fa490a741668441c33bf5b36f..da8bd56b8209f43ad1b53571c685d2fc913a30a1 100644
--- a/bob/ip/qualitymeasure/msu_iqa_features.py
+++ b/bob/ip/qualitymeasure/msu_iqa_features.py
@@ -399,13 +399,17 @@ def rgbhist(image, maxval, nBins, normType=0):
im = image.reshape(3, numPix).copy()
im = im.T
- for i in range(0, numPix): # for i=1:size(I,1)*size(I,2)
- p = (im[i, :]).astype(float) # p = double(im(i,:));
- # p = floor(p/(maxval/nBins))+1;
- p = (np.floor(p / decimator)).astype(np.uint32)
- # H(p(1),p(2),p(3)) = H(p(1),p(2),p(3)) + 1;
- H[p[0], p[1], p[2]] += 1
- # end
+ p = np.floor(im.astype(float) / decimator).astype(np.uint32)
+ # in future versions of numpy (1.13 and above) you can replace this with:
+ # unique_p, count = np.unique(p, return_counts=True, axis=0)
+ # the following lines were taken from: https://stackoverflow.com/a/16973510
+ p2 = np.ascontiguousarray(p).view(
+ np.dtype((np.void, p.dtype.itemsize * p.shape[1])))
+ unique_p, count = np.unique(p2, return_counts=True)
+ unique_p = unique_p.view(p.dtype).reshape(-1, p.shape[1])
+ # till here
+ H[unique_p[:, 0], unique_p[:, 1], unique_p[:, 2]] = count
+
H = H.ravel() # H = H(:);
# Un-Normalized histogram
diff --git a/bob/ip/qualitymeasure/tan_specular_highlights.py b/bob/ip/qualitymeasure/tan_specular_highlights.py
index 44d11b0c9cd4a3243274cdaf38e5c2c0c721b49b..ae53bfe21948c37d439943f1c1b3803fb635da3a 100644
--- a/bob/ip/qualitymeasure/tan_specular_highlights.py
+++ b/bob/ip/qualitymeasure/tan_specular_highlights.py
@@ -130,10 +130,7 @@ def specular_free_image(src, srcPixelStatus=None):
":: srcPixelStatus should be None, or should match the " \
"pixel-layout of src."
- for y in range(src.shape[1]):
- for x in range(src.shape[2]):
- if np.all(src[:, y, x] < camDark):
- srcPixelStatus[y, x] = G_CAMERA_DARK
+ srcPixelStatus[np.logical_and.reduce(src < camDark, 0)] = G_CAMERA_DARK
srcMaxChroma, srcMax, srcTotal = max_chroma(src)
@@ -181,10 +178,9 @@ def iteration(src, srcPixelStatus, sfi, epsilon, verboseFlag=True):
count, srcPixelStatus = init_labels(src, sfi, epsilon, srcPixelStatus)
while(1):
+ maxY, maxX = src.shape[-2:]
srcMaxChroma, srcMax, srcTotal = max_chroma(src)
srcChroma, srcTotal = rgb_chroma(src, srcTotal)
- maxY = src.shape[1]
- maxX = src.shape[2]
red_chroma_diff_x = np.diff(srcChroma[0, :, :], axis=1)
red_chroma_diff_y = np.diff(srcChroma[0, :, :], axis=0)
@@ -192,119 +188,248 @@ def iteration(src, srcPixelStatus, sfi, epsilon, verboseFlag=True):
grn_chroma_diff_x = np.diff(srcChroma[1, :, :], axis=1)
grn_chroma_diff_y = np.diff(srcChroma[1, :, :], axis=0)
- # if verboseFlag:
- # print('.')
+ # a non-complete attempt to remove the for loop below
+ # # find non G_CAMERA_DARK pixels
+ # non_g_camera_dark = (srcPixelStatus != G_CAMERA_DARK)
+ # non_g_camera_dark[-1, :] = False
+ # non_g_camera_dark[:, -1] = False
+ # g_specularx = (non_g_camera_dark) & \
+ # (srcPixelStatus == G_SPECULARX)
+ # g_speculary = (non_g_camera_dark) & \
+ # (srcPixelStatus == G_SPECULARY)
+
+ # '''for specular y'''
+
+ # # if it is a boundary in x
+ # boundary_x = np.zeros_like(g_specularx)
+ # boundary_x[:-1, :-1] = g_specularx[:-1, :-1] & \
+ # (np.fabs(red_chroma_diff_x[:-1, :]) > thR) & \
+ # (np.fabs(grn_chroma_diff_x[:-1, :]) > thG)
+ # srcPixelStatus[boundary_x] = G_BOUNDARY
+
+ # # if it is noise in x
+ # srcMaxChromaDiffX = np.diff(srcMaxChroma, axis=1)
+ # srcMaxChromaAbsDiffX = np.fabs(srcMaxChromaDiffX)
+ # noise_mask_x = g_specularx & (~boundary_x)
+ # noise_mask_x[:-1, :-1] = noise_mask_x[:-1, :-1] & \
+ # (srcMaxChromaAbsDiffX[:-1, :] < 0.01)
+ # srcPixelStatus[noise_mask_x] = G_NOISE
+
+ # # if it is a boundary in x
+ # boundary_y = np.zeros_like(g_speculary)
+ # boundary_y[:-1, :-1] = g_speculary[:-1, :-1] & \
+ # (np.fabs(red_chroma_diff_y[:, :-1]) > thR) & \
+ # (np.fabs(grn_chroma_diff_y[:, :-1]) > thG)
+ # srcPixelStatus[boundary_y] = G_BOUNDARY
+
+ # # if it is noise in x
+ # srcMaxChromaDiffY = np.diff(srcMaxChroma, axis=0)
+ # srcMaxChromaAbsDiffY = np.fabs(srcMaxChromaDiffY)
+ # noise_mask_y = g_speculary & (~boundary_y)
+ # noise_mask_y[:-1, :-1] = noise_mask_y[:-1, :-1] & \
+ # (srcMaxChromaAbsDiffY[:, :-1] < 0.01)
+ # srcPixelStatus[noise_mask_y] = G_NOISE
+
+ # # if it is not noise or boundary
+ # boundary_x = np.zeros_like(g_specularx)
+ # boundary_x[:-1, :-1] = g_specularx[:-1, :-1] & \
+ # (np.fabs(red_chroma_diff_x[:-1, :]) > thR) & \
+ # (np.fabs(grn_chroma_diff_x[:-1, :]) > thG)
+ # srcMaxChromaDiffX = np.diff(srcMaxChroma, axis=1)
+ # srcMaxChromaAbsDiffX = np.fabs(srcMaxChromaDiffX)
+ # noise_mask_x = g_specularx & (~boundary_x)
+ # noise_mask_x[:-1, :-1] = noise_mask_x[:-1, :-1] & \
+ # (srcMaxChromaAbsDiffX[:-1, :] < 0.01)
+ # non_noise_mask_x = g_specularx & (~boundary_x) & (~noise_mask_x)
+ # srcPixelStatus[non_noise_mask_x] = G_DIFFUSE
+ # non_noise_mask_next_x = np.roll(non_noise_mask_x, 1, axis=1)
+ # srcPixelStatus[non_noise_mask_next_x] = G_DIFFUSE
+
+ # boundary_y = np.zeros_like(g_speculary)
+ # boundary_y[:-1, :-1] = g_speculary[:-1, :-1] & \
+ # (np.fabs(red_chroma_diff_y[:, :-1]) > thR) & \
+ # (np.fabs(grn_chroma_diff_y[:, :-1]) > thG)
+ # srcMaxChromaDiffY = np.diff(srcMaxChroma, axis=0)
+ # srcMaxChromaAbsDiffY = np.fabs(srcMaxChromaDiffY)
+ # noise_mask_y = g_speculary & (~boundary_y)
+ # noise_mask_y[:-1, :-1] = noise_mask_y[:-1, :-1] & \
+ # (srcMaxChromaAbsDiffY[:, :-1] < 0.01)
+ # non_noise_mask_y = g_speculary & (~boundary_y) & (~noise_mask_y)
+ # srcPixelStatus[non_noise_mask_y] = G_DIFFUSE
+ # non_noise_mask_next_y = np.roll(non_noise_mask_y, 1, axis=0)
+ # srcPixelStatus[non_noise_mask_next_y] = G_DIFFUSE
+
+ # # if it is not boundary or noise
+ # pos_chroma_maskx = (srcMaxChromaDiffX[:-1, :] > 0)
+ # reduce_mask_pos_x = non_noise_mask_x.copy()
+ # reduce_mask_pos_x[:-1, :-1] = reduce_mask_pos_x[:-1, :-1] & \
+ # pos_chroma_maskx
+ # reduce_mask_pos_next_x = np.roll(reduce_mask_pos_x, 1, axis=1)
+ # iro = np.moveaxis(np.moveaxis(src, 0, -1)[reduce_mask_pos_x], -1, 0)
+ # refMaxChroma = srcMaxChroma[reduce_mask_pos_next_x]
+ # iroTotal = srcTotal[reduce_mask_pos_x]
+ # iroMax = srcMax[reduce_mask_pos_x]
+ # iroMaxChroma = srcMaxChroma[reduce_mask_pos_x]
+ # pStat, iroRes = specular_to_diffuse_vectorized(
+ # iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
+ # iro[:, pStat != G_NOISE] = iroRes[:, pStat != G_NOISE]
+
+ # pos_chroma_maskx = ~pos_chroma_maskx
+ # reduce_mask_pos_x = non_noise_mask_x.copy()
+ # reduce_mask_pos_x[:-1, :-1] = reduce_mask_pos_x[:-1, :-1] & \
+ # pos_chroma_maskx
+ # reduce_mask_pos_next_x = np.roll(reduce_mask_pos_x, 1, axis=1)
+ # iro = np.moveaxis(np.moveaxis(src, 0, -1)[reduce_mask_pos_next_x],
+ # -1, 0)
+ # refMaxChroma = srcMaxChroma[reduce_mask_pos_x]
+ # iroTotal = srcTotal[reduce_mask_pos_next_x]
+ # iroMax = srcMax[reduce_mask_pos_next_x]
+ # iroMaxChroma = srcMaxChroma[reduce_mask_pos_next_x]
+ # pStat, iroRes = specular_to_diffuse_vectorized(
+ # iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
+ # iro[:, pStat != G_NOISE] = iroRes[:, pStat != G_NOISE]
+
+ # # if it is not boundary or noise
+ # pos_chroma_masky = (srcMaxChromaDiffY[:, :-1] > 0)
+ # reduce_mask_pos_y = non_noise_mask_y.copy()
+ # reduce_mask_pos_y[:-1, :-1] = reduce_mask_pos_y[:-1, :-1] & \
+ # pos_chroma_masky
+ # reduce_mask_pos_next_y = np.roll(reduce_mask_pos_y, 1, axis=1)
+ # iro = np.moveaxis(np.moveaxis(src, 0, -1)[reduce_mask_pos_y], -1, 0)
+ # refMaxChroma = srcMaxChroma[reduce_mask_pos_next_y]
+ # iroTotal = srcTotal[reduce_mask_pos_y]
+ # iroMax = srcMax[reduce_mask_pos_y]
+ # iroMaxChroma = srcMaxChroma[reduce_mask_pos_y]
+ # pStat, iroRes = specular_to_diffuse_vectorized(
+ # iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
+ # iro[:, pStat != G_NOISE] = iroRes[:, pStat != G_NOISE]
+
+ # pos_chroma_masky = ~pos_chroma_masky
+ # reduce_mask_pos_y = non_noise_mask_y.copy()
+ # reduce_mask_pos_y[:-1, :-1] = reduce_mask_pos_y[:-1, :-1] & \
+ # pos_chroma_masky
+ # reduce_mask_pos_next_y = np.roll(reduce_mask_pos_y, 1, axis=1)
+ # iro = np.moveaxis(np.moveaxis(src, 0, -1)[reduce_mask_pos_next_y],
+ # -1, 0)
+ # refMaxChroma = srcMaxChroma[reduce_mask_pos_y]
+ # iroTotal = srcTotal[reduce_mask_pos_next_y]
+ # iroMax = srcMax[reduce_mask_pos_next_y]
+ # iroMaxChroma = srcMaxChroma[reduce_mask_pos_next_y]
+ # pStat, iroRes = specular_to_diffuse_vectorized(
+ # iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
+ # iro[:, pStat != G_NOISE] = iroRes[:, pStat != G_NOISE]
for y in range(maxY - 1):
for x in range(maxX - 1):
current_pixel = srcPixelStatus[y, x]
- if(current_pixel != G_CAMERA_DARK):
-
- drx = red_chroma_diff_x[y, x]
- dgx = grn_chroma_diff_x[y, x]
- dry = red_chroma_diff_y[y, x]
- dgy = grn_chroma_diff_y[y, x]
-
- if(current_pixel == G_SPECULARX):
- # if it is a boundary in the x direction
- if((abs(drx) > thR) and (abs(dgx) > thG)):
- # pixel right
- srcPixelStatus[y, x] = G_BOUNDARY
- continue
- # if it is a noise
- elif (abs(srcMaxChroma[y, x] - srcMaxChroma[y, x + 1]) < 0.01):
- srcPixelStatus[y, x] = G_NOISE
- continue
- else: # reduce the specularity at x direction
- if (srcMaxChroma[y, x] < srcMaxChroma[y, x + 1]):
- iro = src[:, y, x]
- pStat = current_pixel
- refMaxChroma = srcMaxChroma[y, x + 1]
- iroTotal = srcTotal[y, x]
- iroMax = srcMax[y, x]
- iroMaxChroma = srcMaxChroma[y, x]
- pStat, iroRes = specular_to_diffuse(
- iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
-
- # update pixelStatus only if
- # zSpecular2Diffuse() returned pStat as
- # G_NOISE.
- if pStat == G_NOISE:
- srcPixelStatus[y, x] = pStat
- else:
- src[:, y, x] = iroRes
-
- else:
- iro = src[:, y, x + 1]
- pStat = srcPixelStatus[y, x + 1]
- refMaxChroma = srcMaxChroma[y, x]
- iroTotal = srcTotal[y, x + 1]
- iroMax = srcMax[y, x + 1]
- iroMaxChroma = srcMaxChroma[y, x + 1]
- pStat, iroRes = specular_to_diffuse(
- iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
-
- # update pixelStatus only if
- # zSpecular2Diffuse() returned pStat as
- # G_NOISE.
- if pStat == G_NOISE:
- srcPixelStatus[y, x + 1] = pStat
- else:
- src[:, y, x + 1] = iroRes
-
- srcPixelStatus[y, x] = G_DIFFUSE
- srcPixelStatus[y, x + 1] = G_DIFFUSE
-
- if(current_pixel == G_SPECULARY):
- # if it is a boundary in the y direction
- if((abs(dry) > thR) and (abs(dgy) > thG)):
- # pixel right
- srcPixelStatus[y, x] = G_BOUNDARY
- continue
- # if it is a noise
- elif (abs(srcMaxChroma[y, x] - srcMaxChroma[y + 1, x]) < 0.01):
- srcPixelStatus[y, x] = G_NOISE
- continue
- else: # reduce the specularity in y direction
- if(srcMaxChroma[y, x] < srcMaxChroma[y + 1, x]):
- iro = src[:, y, x]
- pStat = current_pixel
- refMaxChroma = srcMaxChroma[y + 1, x]
- iroTotal = srcTotal[y, x]
- iroMax = srcMax[y, x]
- iroMaxChroma = srcMaxChroma[y, x]
- pStat, iroRes = specular_to_diffuse(
- iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
- # C call:
- # zSpecular2Diffuse(src(y,x),src(y+1,x).zMaxChroma())
-
- # update pixelStatus only if
- # zSpecular2Diffuse() returned pStat as
- # G_NOISE.
- if pStat == G_NOISE:
- srcPixelStatus[y, x] = pStat
- else:
- src[:, y, x] = iroRes
-
- else:
- iro = src[:, y + 1, x]
- pStat = srcPixelStatus[y + 1, x]
- pMaxChroma = srcMaxChroma[y + 1, x]
- iroTotal = srcTotal[y + 1, x]
- iroMax = srcMax[y + 1, x]
- iroMaxChroma = srcMaxChroma[y + 1, x]
- pStat, iroRes = specular_to_diffuse(
- iro, iroMax, iroMaxChroma, iroTotal, pMaxChroma)
-
- # update pixelStatus only if
- # zSpecular2Diffuse() returned pStat as
- # G_NOISE.
- if pStat == G_NOISE:
- srcPixelStatus[y + 1, x] = pStat
- else:
- src[:, y + 1, x] = iroRes
-
- srcPixelStatus[y, x] = G_DIFFUSE
- srcPixelStatus[y + 1, x] = G_DIFFUSE
+ if(current_pixel == G_CAMERA_DARK):
+ continue
+
+ drx = red_chroma_diff_x[y, x]
+ dgx = grn_chroma_diff_x[y, x]
+ dry = red_chroma_diff_y[y, x]
+ dgy = grn_chroma_diff_y[y, x]
+
+ if(current_pixel == G_SPECULARX):
+ # if it is a boundary in the x direction
+ if (abs(drx) > thR) and (abs(dgx) > thG):
+ # pixel right
+ srcPixelStatus[y, x] = G_BOUNDARY
+ continue
+ # if it is a noise
+ if abs(srcMaxChroma[y, x] - srcMaxChroma[y, x + 1]) < 0.01:
+ srcPixelStatus[y, x] = G_NOISE
+ continue
+ # reduce the specularity at x direction
+ if srcMaxChroma[y, x] < srcMaxChroma[y, x + 1]:
+ iro = src[:, y, x]
+ # pStat = current_pixel
+ refMaxChroma = srcMaxChroma[y, x + 1]
+ iroTotal = srcTotal[y, x]
+ iroMax = srcMax[y, x]
+ iroMaxChroma = srcMaxChroma[y, x]
+ pStat, iroRes = specular_to_diffuse(
+ iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
+
+ # update pixelStatus only if
+ # zSpecular2Diffuse() returned pStat as
+ # G_NOISE.
+ if pStat != G_NOISE:
+ src[:, y, x] = iroRes
+ # else:
+ # srcPixelStatus[y, x] = pStat
+
+ else:
+ iro = src[:, y, x + 1]
+ # pStat = srcPixelStatus[y, x + 1]
+ refMaxChroma = srcMaxChroma[y, x]
+ iroTotal = srcTotal[y, x + 1]
+ iroMax = srcMax[y, x + 1]
+ iroMaxChroma = srcMaxChroma[y, x + 1]
+ pStat, iroRes = specular_to_diffuse(
+ iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
+
+ # update pixelStatus only if
+ # zSpecular2Diffuse() returned pStat as
+ # G_NOISE.
+ if pStat != G_NOISE:
+ src[:, y, x + 1] = iroRes
+ # else:
+ # srcPixelStatus[y, x + 1] = pStat
+
+ srcPixelStatus[y, x] = G_DIFFUSE
+ srcPixelStatus[y, x + 1] = G_DIFFUSE
+
+ if(current_pixel == G_SPECULARY):
+ # if it is a boundary in the y direction
+ if (abs(dry) > thR) and (abs(dgy) > thG):
+ # pixel right
+ srcPixelStatus[y, x] = G_BOUNDARY
+ continue
+ # if it is a noise
+ if abs(srcMaxChroma[y, x] - srcMaxChroma[y + 1, x]) < 0.01:
+ srcPixelStatus[y, x] = G_NOISE
+ continue
+ # reduce the specularity in y direction
+ if srcMaxChroma[y, x] < srcMaxChroma[y + 1, x]:
+ iro = src[:, y, x]
+ pStat = current_pixel
+ refMaxChroma = srcMaxChroma[y + 1, x]
+ iroTotal = srcTotal[y, x]
+ iroMax = srcMax[y, x]
+ iroMaxChroma = srcMaxChroma[y, x]
+ pStat, iroRes = specular_to_diffuse(
+ iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma)
+ # C call:
+ # zSpecular2Diffuse(src(y,x),src(y+1,x).zMaxChroma())
+
+ # update pixelStatus only if
+ # zSpecular2Diffuse() returned pStat as
+ # G_NOISE.
+ if pStat == G_NOISE:
+ srcPixelStatus[y, x] = pStat
+ else:
+ src[:, y, x] = iroRes
+
+ else:
+ iro = src[:, y + 1, x]
+ pStat = srcPixelStatus[y + 1, x]
+ pMaxChroma = srcMaxChroma[y + 1, x]
+ iroTotal = srcTotal[y + 1, x]
+ iroMax = srcMax[y + 1, x]
+ iroMaxChroma = srcMaxChroma[y + 1, x]
+ pStat, iroRes = specular_to_diffuse(
+ iro, iroMax, iroMaxChroma, iroTotal, pMaxChroma)
+
+ # update pixelStatus only if
+ # zSpecular2Diffuse() returned pStat as
+ # G_NOISE.
+ if pStat == G_NOISE:
+ srcPixelStatus[y + 1, x] = pStat
+ else:
+ src[:, y + 1, x] = iroRes
+
+ srcPixelStatus[y, x] = G_DIFFUSE
+ srcPixelStatus[y + 1, x] = G_DIFFUSE
pcount = count
count, srcPixelStatus = init_labels(src, sfi, epsilon, srcPixelStatus)
@@ -338,7 +463,6 @@ def init_labels(src, sfi, epsilon, srcPixelStatus):
old_settings = np.seterr(divide='ignore', invalid='ignore')
# to have initial labels
- count = 0
zTotalSrc = np.sum(src, axis=0)
diff_x_src = np.diff(zTotalSrc, axis=1)
diff_y_src = np.diff(zTotalSrc, axis=0)
@@ -347,35 +471,29 @@ def init_labels(src, sfi, epsilon, srcPixelStatus):
diff_x_sfi = np.diff(zTotalSfi, axis=1)
diff_y_sfi = np.diff(zTotalSfi, axis=0)
- dlog_src_x = np.log(abs(diff_x_src))
- dlog_src_y = np.log(abs(diff_y_src))
-
- dlog_sfi_x = np.log(abs(diff_x_sfi))
- dlog_sfi_y = np.log(abs(diff_y_sfi))
-
- dlogx = abs(dlog_src_x - dlog_sfi_x)
- dlogy = abs(dlog_src_y - dlog_sfi_y)
-
- maxY = src.shape[1]
- maxX = src.shape[2]
-
- for y in range(1, maxY - 1):
- for x in range(1, maxX - 1):
- pStat = srcPixelStatus[y, x]
- if pStat not in (G_BOUNDARY, G_NOISE, G_CAMERA_DARK, G_DIFFUSE):
- # Robby Tan's original C++ code doesn't check for
- # pStat!=G_DIFFUSE, but this speeds up the processing a lot,
- # and doesn't seem to affect the results.
- if dlogx[y, x] > epsilon:
- pStat = G_SPECULARX
- count += 1
- elif dlogy[y, x] > epsilon:
- pStat = G_SPECULARY
- count += 1
- else:
- pStat = G_DIFFUSE
-
- srcPixelStatus[y, x] = pStat
+ dlog_src_x = np.log(np.fabs(diff_x_src))
+ dlog_src_y = np.log(np.fabs(diff_y_src))
+
+ dlog_sfi_x = np.log(np.fabs(diff_x_sfi))
+ dlog_sfi_y = np.log(np.fabs(diff_y_sfi))
+
+ dlogx = np.fabs(dlog_src_x - dlog_sfi_x)
+ dlogy = np.fabs(dlog_src_y - dlog_sfi_y)
+
+ # maxY = src.shape[1]
+ # maxX = src.shape[2]
+ valid_values = (G_BOUNDARY, G_NOISE, G_CAMERA_DARK, G_DIFFUSE)
+ notvalid_mask = np.in1d(
+ srcPixelStatus[1:-1, 1:-1], valid_values,
+ invert=True).reshape(srcPixelStatus.shape[0] - 2, -1)
+ dlogx_mask = (notvalid_mask) & (dlogx[1:-1, 1:] > epsilon)
+ dlogy_mask = (notvalid_mask) & (dlogy[1:, 1:-1] > epsilon) & \
+ (~(dlogx_mask))
+ diffuse_mask = (notvalid_mask) & (~dlogx_mask) & (~dlogy_mask)
+ count = notvalid_mask.sum()
+ srcPixelStatus[1:-1, 1:-1][dlogx_mask] = G_SPECULARX
+ srcPixelStatus[1:-1, 1:-1][dlogy_mask] = G_SPECULARY
+ srcPixelStatus[1:-1, 1:-1][diffuse_mask] = G_DIFFUSE
np.seterr(**old_settings)
return count, srcPixelStatus # count is the number of specular pixels
@@ -417,16 +535,46 @@ def specular_to_diffuse(iro, iroMax, iroMaxChroma, iroTotal, refMaxChroma):
return pixelStatus, iro
-'''
-reset all pixelStatus labels, except for DARK pixels
-src is numpy-array of shape (3,maxY, maxX)
-pixelLabels is a numpy-array of shape (maxY, maxX)
-Called in zIteration().
-'''
+# def specular_to_diffuse_vectorized(iro, iroMax, iroMaxChroma, iroTotal,
+# refMaxChroma):
+# """Converts a color-pixel from speckled to diffuse, by subtracting a
+# certain amount from its intensity value in each color channel.
+#
+# Args:
+# iro: 3-element column vector containing the rgb-color values of the
+# pixel to be processed.
+# iroMax: max value among the elements of iro
+# iroMaxChroma: max-chroma for this pixel
+# iroTotal: sum of the 3 elements of iro.
+# refMaxChroma: chroma-value of a neighboring pixel for comparison
+#
+# Returns:
+# pixelStatus: a value (marker) indicating whether the pixel is
+# considered as noise or diffuse, after processing.
+# iro: updated pixel-color-values.
+# """
+# c = iroMaxChroma
+# # arbitrary initialization
+# pixelStatus = np.empty((*iro.shape[1:]), dtype=iro.dtype)
+# numr = (iroMax * (3.0 * c - 1.0))
+# denm = (c * (3.0 * refMaxChroma - 1.0))
+#
+# non_zero = np.fabs(denm) > 0.000000001
+# dI = numr[non_zero] / denm[non_zero]
+# sI = (iroTotal[non_zero] - dI) / 3.0
+# nrgb = iro[:, non_zero] - sI
+# negnrgb = np.logical_or.reduce(nrgb < 0, axis=0)
+#
+# pixelStatus[non_zero][negnrgb] = G_NOISE
+# iro[:, non_zero][:, ~negnrgb] = nrgb[:, ~negnrgb]
+# pixelStatus[~non_zero] = G_NOISE
+#
+# return pixelStatus, iro
def reset_labels(pixelLabels):
"""Resets all pixel-markers (except those indicating "dark" pixels, to 0.
+ Called in zIteration().
Args:
pixelLabels: numpy array of shape (maxX, maxY) containing pixel-
@@ -533,8 +681,7 @@ def rgb_chroma(rgbImg, rgbTotal=None):
old_settings = np.seterr(divide='ignore', invalid='ignore')
rgbChroma = np.divide(rgbImg.astype(float), rgbTotal.astype(float))
np.seterr(**old_settings)
- for p in range(rgbChroma.shape[0]):
- rgbChroma[p, (rgbTotal == 0)] = 0
+ rgbChroma[:, rgbTotal == 0] = 0
return rgbChroma, rgbTotal