def hashObj(feat, params_sun09):
dim = 25
numClasses = 21
# COMPUTE THE FEATURE REPRESENTATION
temp_pred = feat['max_classes2']
temp_pred = temp_pred[0:-1, 0:-1]
temp_pred = smp.imresize(temp_pred, (dim, dim), interp='nearest', mode='F')
temp_pred = np.int8(temp_pred)
temp_pred = temp_pred.reshape(dim*dim, order='F').copy()
tempMask = np.zeros((numClasses*dim*dim))
for k in range(numClasses):
tempClassMask = np.zeros((dim*dim, 1))
tempClassMask[np.nonzero(temp_pred==k)] = 1
tempMask[k::numClasses] = tempClassMask.squeeze()
# COMPUTE THE HASHCODES FROM THE FEATURE
tempMask = tempMask - np.tile(np.transpose(params_sun09['meanY']), (tempMask.shape[0], 1))
tempMask = np.dot(tempMask, params_sun09['Wy'])
tempMask = np.dot(tempMask, params_sun09['R'])
Y = np.zeros(tempMask.shape)
Y[tempMask>=0] = 1
Z = hl.compactbit(Y>0)
return Z
def hashAttrib(feat, params_imgNet_color, imgNet_texture):
dim = 25
numColorClasses = 11
numTextureClasses = 8
colorIdx = np.array([[0, 1, 2, 7, 10, 11, 13, 14, 15, 16, 18]], dtype='int8')
textureIdx = np.array([[3, 4, 5, 6, 8, 12, 9, 17]], dtype='int8')
# COMPUTE THE COLOR FEATURE REPRESENTATION
tempMask = np.zeros((numColorClasses*dim*dim))
prob_map = feat['all_probs2']
prob_map = prob_map[0:-1, 0:-1, colorIdx]
for k in range(numColorClasses):
p1 = prob_map[:, :, k]
p1 = smp.imresize(p1, (dim, dim), interp='bilinear', mode='F')
p1 = p1.reshape(dim*dim, order='F').copy()
tempMask[k::numColorClasses] = p1.squeeze()
# COMPUTE THE HASHCODES FROM THE FEATURE
tempMask = tempMask - np.tile(np.transpose(params_imgNet_color['meanY']), (tempMask.shape[0], 1))
tempMask = np.dot(tempMask, params_imgNet_color['Wy'])
tempMask = np.dot(tempMask, params_imgNet_color['R'])
Y = np.zeros(tempMask.shape)
Y[tempMask>=0] = 1
Zc = hl.compactbit(Y>0)
# COMPUTE THE TEXTURE FEATURE REPRESENTATION
tempMask = np.zeros((numTextureClasses*dim*dim))
prob_map = feat['all_probs2']
prob_map = prob_map[0:-1, 0:-1, textureIdx]
for k in range(numTextureClasses):
p1 = prob_map[:, :, k]
p1 = smp.imresize(p1, (dim, dim), interp='bilinear', mode='F')
p1 = p1.reshape(dim*dim, order='F').copy()
tempMask[k::numTextureClasses] = p1.squeeze()
# COMPUTE THE HASHCODES FROM THE FEATURE
tempMask = tempMask - np.tile(np.transpose(params_imgNet_texture['meanY']), (tempMask.shape[0], 1))
tempMask = np.dot(tempMask, params_imgNet_texture['Wy'])
tempMask = np.dot(tempMask, params_imgNet_texture['R'])
Y = np.zeros(tempMask.shape)
Y[tempMask>=0] = 1
Zt = hl.compactbit(Y>0)
Z = np.hstack([Zc[0:127], Zt[0:127]])
return Z
