class HDR(Filter):
"""High-dynamic-range_imaging with python PIL - works with files"""
def __init__(self):
Filter.__init__(self)
self.case = 'test'
self.resize = False
self.strength = Param("strength", 0.0, min_v=0.0, max_v=3.0)
self.naturalness = Param("naturalness", 10, min_v=0, max_v=10)
# self.sub_lum = Param("sub_lum", 100, min_v=0, max_v=255)
# self.shift_x = Param("shift_x", 0, min_v=-800, max_v=800)
# self.shift_y = Param("shift_y", 0, min_v=-600, max_v=600)
self.show_image = Param("show_images", 1, min_v=1, max_v=10)
self.limit_image = Param("limit_image", 4, min_v=1, max_v=10)
self.debug_show = Param(
"show_debug",
"show_normal",
lst_value=[
"show_normal",
"show_sat",
"show_con"])
self.show_hdr = Param("show_hdr", False)
self.index = 0
self.images = []
self.imgs = []
self.first_time = True
def execute(self, image):
show_hdr = self.show_hdr.get()
limit_image = self.limit_image.get()
len_image = len(self.images)
if len_image == limit_image:
self.images[self.index - 1] = image
self.index += 1
if self.index > limit_image:
self.index = 1
elif len_image < limit_image:
self.images.append(image)
self.index += 1
else:
self.images.pop()
if self.index > limit_image:
self.index = limit_image
len_image = len(self.images)
show_no = self.show_image.get()
show_no = len_image if show_no > len_image else show_no
if not show_hdr:
return self.images[show_no - 1]
nature = self.naturalness.get()
if nature:
nature /= 10.0
return self.get_hdr(self.images, strength=self.strength.get(),
naturalness=nature)
"""
SOURCE: https://sites.google.com/site/bpowah/hdrandpythonpil
a collection of images to merge into HDR
blend an arbitrary number of photos into a single HDR image
or several images with various combinations of HDR parameters
it is assumed that project folders contain all the images you want to merge
case = folder name
cur_dir = folder where the project folders are located
images are auto-sorted by degree of exposure (image brightness)
"""
def get_masks(self, imgs, cur_str):
"""
create a set of masks from a list of images
(one mask for every adjacent pair of images
"""
masks = []
mask_ct = len(imgs) - 1
imgs = [self.bal(img.convert(mode='L'), cur_str) for img in imgs]
for i in range(mask_ct):
blend_fraction = .5 # 1. - (float(i)+.5)/float(mask_ct)
m = Image.blend(imgs[i], imgs[i + 1], blend_fraction)
masks.append(m)
return masks
def bal(self, im, cur_str):
"""
adjust the balance of the mask
(re-distribute the histogram so that there are more
extreme blacks and whites)
like increasing the contrast, but without clipping
and maintains overall average image brightness
"""
h = im.histogram()
ln = range(len(h))
up = [sum(h[0: i]) for i in ln]
lo = [sum(h[i:-1]) for i in ln]
ct = sum(h)
st = int(cur_str * 255.)
lut = [i + st * up[i] * lo[i] * (up[i] - lo[i]) / ct ** 3 for i in ln]
for i in ln:
if lut[i] < 1:
lut[i] = 1
if lut[i] > 255:
lut[i] = 255
return im.point(lut)
def merge(self, imgs, cur_str):
"""
combine a set images into a smaller set by combinding all
adjacent images
"""
masks = self.get_masks(imgs, cur_str)
imx = lambda i: Image.composite(imgs[i], imgs[i + 1], masks[i])
return [imx(i) for i in range(len(masks))]
def merge_all(self, imgs, cur_str):
"""
iteratively merge a set of images until only one remains
"""
while len(imgs) > 1:
imgs = self.merge(imgs, cur_str)
return imgs[0]
def get_hdr(self, images, strength=0.0, naturalness=1.0):
"""
process the hdr image(s)
strength - a float that defines how strong the hdr
effect should be
- a value of zero will combine images by using a
greyscale image average
- a value greater than zero will use higher contrast
versions of those greyscale images
- suggest you a value between 0.0 and 2.0
naturalness - values between zero and one
- zero will be a very high-contrast image
- 1.0 will be a very flat image
- 0.7 to 0.9 tend to give the best results
"""
imgs = copy([Image.fromarray(img) for img in images])
sat_img = self.merge_all(imgs, strength)
if self.debug_show.get_pos_list() == 1:
return np.array(sat_img)
imgs.reverse()
con_img = self.merge_all(imgs, strength)
if self.debug_show.get_pos_list() == 2:
return np.array(con_img)
"""
combines a saturated image with a contrast image
and puts them in a dictionary of completed images
"""
images = Image.blend(con_img, sat_img, naturalness)
images = np.array(images)
return images
