def object_modifier(fill_light, edge_light, diffuse_light, output, mask,
fill=1, edge=1, diffuse=1, count=-1, verbose=False):
"""
This function calculates the per object modifier for an image.
Arguments:
fill_light -- path to the fill light image
edge_light -- path to the edge light image
diffuse_light -- path to the diffuse color light image
mask -- the mask image that identifies the object of interest
fill -- the weight of fill light in the object
edge -- the weight of edge light in the object
diffuse -- the weight of diffuse light in the object
count -- the number of imags to use for this calculation
verbose -- should we print debug info
"""
fill_image = utils.read_image(fill_light, normalize=True)
edge_image = utils.read_image(edge_light, normalize=True)
diffuse_image = utils.read_image(diffuse_light, normalize=True)
mask_image = utils.read_image(mask, normalize=True)
print fill_image
modifier = modifier_lights.ModifierLights(verbose=verbose)
res_image = modifier.per_object(fill_image, edge_image, diffuse_image,
mask_image, fill, edge, diffuse)
cv2.imwrite(output, utils.denormalize_img(res_image))
cv2.imshow('Object modifier', res_image)
cv2.waitKey(0)
def main():
global img_list
global gmap
global weight
img_list = utils.read_images("../../test_data/cafe", N, downsample=3)
full_img_list = utils.read_images("../../test_data/cafe", N)
gray_imgs = utils.read_images("../../test_data/cafe", N, gray=True)
x0 = np.full(N, 1.0/N)
(gmap, weight) = gradient_map(gray_imgs)
bnds = []
for i in range(len(img_list)):
bnds.append((0, 1))
lambdas = minimize(edge_light, x0, method='TNC', jac=False,
bounds=bnds)
ret_image = sum_images(lambdas.x, full_img_list)
print lambdas.message
print "Choice of lambdas = %s" % (lambdas.x)
cv2.imwrite('output_edge.png', utils.denormalize_img(ret_image))
cv2.imshow('image', ret_image)
cv2.waitKey(0)
def avg_light(directory, output, count=-1, verbose=False):
"""
This function calculates the fill light of the images.
Arguments:
directory -- the directory where the images are located
count -- the number of imags to use for this calculation
verbose -- should we print debug info
"""
img_list = utils.read_images(directory, count)
basis = basis_lights.BasisLights(img_list, verbose=verbose)
res_image = basis.avg()
cv2.imwrite(output, utils.denormalize_img(res_image))
cv2.imshow('Average light', res_image)
cv2.waitKey(0)
def main():
# px1 = np.array([0, 126, 255], dtype=float)
# px2 = np.array([0, 0, 0], dtype=float)
global img_list
img_list = utils.read_images("../../test_data/cafe", N, downsample=4)
full_img_list = utils.read_images("../../test_data/cafe", N)
# for i in range(0, N):
# # img_name = "../../../input_image/cafe/images/%03d.png" % (i)
# img_name = "../test_data/cafe/%03d.png" % (i)
# img = cv2.imread(img_name)
# img_list.append(utils.normalize_img(img))
# # img_list.append(img)
# print alpha(10, 10)
# print px_avg(400, 500)
# print px_avg(img_list, 0, 0)
# px1 = np.ndarray(shape=(3, 1), dtype=float,
# buffer=np.array([0, 126, 255]))
# px2 = np.ndarray(shape=(1, 3), dtype=float,
# buffer=np.array([[255], [0], [128]]))
# diffuse_color(1)
x0 = np.full(N, 1.0/N)
# print fprime(x0)
# print x0
# print diffuse_color(x0)
# x0 = np.zeros(N)
# print diffuse_color(x1)
# x0 = np.ones(N)
# print diffuse_color(x2)
# results = pyipopt.fmin_unconstrained(diffuse_color, x0, fprime)
# print results
# nlp = pyipopt.create(diffuse_color, N)
# x, zl, zu, constraint_multipliers, obj, status = nlp.solve(x0)
# nlp.close()
# leastsq(diffuse_color, x0)
bnds = []
for i in range(len(img_list)):
bnds.append((0, 1))
lambdas = minimize(diffuse_color, x0, method='TNC', jac=False,
bounds=bnds)
# lambdas = np.array([ 0.0772488 , 0.07700344, 0.0769977 , 0.07699107, 0.07702644,
# 0.07702589, 0.07702531, 0.07694187, 0.07695671, 0.07701653,
# 0.07696243, 0.07767547, 0.07693373])
# lambdas = np.array([ 0.03336186, 0.03335751, 0.03336186, 0.03336186, 0.03335691,
# 0.03336186, 0.03335623, 0.03335545, 0.03336186, 0.03316012,
# 0.03336186, 0.03332896, 0.03336186, 0.03336186, 0.03335172,
# 0.03335502, 0.03336186, 0.03336186, 0.03335585, 0.03333409,
# 0.03335658, 0.03335828, 0.03335722, 0.03335778, 0.03336186,
# 0.03335804, 0.03336186, 0.03335251, 0.03332677, 0.0333048 ])
# print("lambda: %s" % lambdas)
ret_image = sum_images(lambdas.x, full_img_list)
print lambdas.message
print "Choice of lambdas = %s" % (lambdas.x)
cv2.imwrite('output_diffuse.png', utils.denormalize_img(ret_image))
cv2.imshow('image', ret_image)
cv2.waitKey(0)
