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exposure_fusion.py
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exposure_fusion.py
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import numpy as np
import cv2
import sys
import logging
from builtins import isinstance
#import dataclass
from dataclasses import dataclass
logging.basicConfig(level=logging.INFO, filename="out.log", filemode="w",
format="hdr-fusion - %(asctime)s - %(levelname)s - %(message)s",
datefmt="%d-%b-%y %H:%M:%S")
logging.getLogger().addHandler(logging.StreamHandler())
@dataclass
class Exponents():
"""Exponents structure wrapping the three RGB exponents for the HDR Fusion algorithm.
"""
e_contrast: float
e_saturation: float
e_exposedness: float
class ExposureFusion():
"""ExposureFusion Functor for the HDR Fusion algorithm.
"""
def __init__(self, perform_alignment: bool = True, use_softmax: bool = True,
exponents: Exponents = Exponents(1., 1., 1.), sigma: float = 0.2,
matches_to_consider: int = 32, pyramid_levels: int = 5):
"""__init__ Sets the parameters for the ExposureFusion functor.
Parameters
----------
perform_alignment : bool, optional
Whether to perform image alignment between the input LDR images as a
preprocessing step, by default True
use_softmax : bool, optional
Whether to utilize softmax as a non-linearity on the weights when
normalizing them to a distribution, by default True
exponents : Exponents, optional
The three exponents for the (R,G,B) channels, by default Exponents(1., 1., 1.),
as suggested in the paper
sigma : float, optional
The standard deviation of the Gaussian used to calculate
well-exposedness, gets squared to variance internally, by default 0.2
matches_to_consider : int, optional
The number of matches to consider when performing image alignment,
by default 32
pyramid_levels : int, optional
The number of levels to use in the Gaussian and Laplacian pyramids, by default 5
deeper pyramids take longer but can provide better results.
"""
self.perform_alignment: bool = perform_alignment
self.use_softmax: bool = use_softmax
self.exponents: Exponents = exponents
self.sigma: float = sigma ** 2
self.matches_to_consider: int = 32
self.pyramid_levels: int = pyramid_levels
if self.perform_alignment:
import cv2
"""
ORB is open source, fast and generally performs better than SIFT on
this task.
"""
self.ORB_detector = cv2.ORB_create()
# create a Brute Force Matcher object, using Hamming distance since
# We are using ORB.
self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
def __repr__(self) -> str:
"""__repr__ Returns a string representation of the ExposureFusion functor.
Returns
-------
str
A string representation of the ExposureFusion functor.
"""
return f"""ExposureFusion(perform_alignment={self.perform_alignment},
use_softmax={self.use_softmax}, exponents={self.exponents},
sigma={self.sigma}, matches_to_consider={self.matches_to_consider})"""
def __call__(self, images: "list[np.ndarray]") -> np.ndarray:
"""__call__ Perform exposure fusion
Parameters
----------
images : list[np.ndarray]
A list of numpy arrays, all of the same shape and with RGB channels last.
the individual images should be of the same scene at different levels of exposure.
The images should be at least 2.
Returns
-------
np.ndarray
The resulting HDR image obtained by applying Exposure Fusion on the LDR inputs
"""
# Safety checks
try:
assert len(images) >= 2
assert all([isinstance(image, np.ndarray) for image in images])
assert all([image.shape == images[0].shape for image in images])
assert all([image.shape[-1] == 3 for image in images])
except AssertionError as e:
logging.exception("Invalid input to ExposureFusion functor")
return None
logging.info("Input images are valid, proceeding with Exposure Fusion")
if self.perform_alignment:
logging.info("Performing image alignment")
try:
images = self.align_images(images)
except Exception as e:
logging.exception("Image alignment failed")
return None
else:
logging.info("Skipping image alignment")
logging.info("Calculating weights")
try:
weights = self.calculate_weights(images)
except Exception as e:
logging.exception("Failed to calculate weights")
return None
logging.info("Creating image pyramids")
try:
gaussians, laplacians = self.create_image_pyramids(images, weights)
except Exception as e:
logging.exception("Failed to create image pyramids")
return None
logging.info("Blending pyramids into final Laplacian")
try:
final_laplacian = self.blend_pyramids(gaussians, laplacians)
except Exception as e:
logging.exception("Failed to blend pyramids")
return None
logging.info("Reconstructing final HDR image")
try:
hdr_image = self.reconstruct_image(final_laplacian)
except Exception as e:
logging.exception("Failed to reconstruct HDR image")
return None
return hdr_image
def align_images(self, images: "list[np.ndarray]") -> "list[np.ndarray]":
"""align_images Performs image alignment on the input images
This method is not meant to be called directly, but rather as a preprocessing step
in the functor's __call__ method pipeline.
Parameters
----------
images : list[np.ndarray]
A list of numpy arrays, all of the same shape and with RGB channels last.
the individual images should be of the same scene at different levels of exposure.
The images should be at least 2.
Returns
-------
list[np.ndarray]
The aligned images
"""
center_indx = len(images) // 2
# Find the center image, which will be used to align the other images to
reference_img = images[center_indx]
kp_ref, des_ref = self.ORB_detector.detectAndCompute(
reference_img, None)
for idx, img in enumerate(images):
if idx == center_indx:
continue
kp, des = self.ORB_detector.detectAndCompute(img, None)
matches = self.matcher.match(des_ref, des)
matches = sorted(matches, key=lambda x: x.distance)
# only consider the K best matches, to reduce the effect of outliers.
matches = matches[:self.matches_to_consider]
src_pts = np.float32(
[kp_ref[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
dst_pts = np.float32(
[kp[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
img = cv2.warpPerspective(img, M, img.shape[:2][::-1])
images[idx] = img
return images
def calculate_weights(self, images: "list[np.ndarray]") -> "list[np.ndarray]":
"""calculate_weights Calculates the weights for each image in the input list
This method is not meant to be called directly, but rather as a preprocessing step
in the functor's __call__ method pipeline.
Parameters
----------
images : list[np.ndarray]
A list of numpy arrays, all of the same shape and with RGB channels last.
the individual images should be of the same scene at different levels of exposure.
The images should be at least 2.
Returns
-------
list[np.ndarray]
The weights for each image in the input list
"""
weights = []
weights_sum = np.zeros(images[0].shape[:2], dtype=np.float32)
# Calculate contrast, saturation and exposure weights
for image in images:
image = image.astype(np.float32) / 255.0
w_c = self.calculate_contrast_weight(image)
w_s = self.calculate_saturation_weight(image)
w_e = self.calculate_exposure_weight(image)
w = (w_c ** self.exponents.e_contrast) * \
(w_s ** self.exponents.e_contrast) * \
(w_e ** self.exponents.e_exposedness)
# Apply e^x to the weights so that normalization comes out to be a softmax
if self.use_softmax:
w = np.exp(w)
weights.append(w)
weights_sum += w
# Normalize weights
weights = [np.uint8(255 * w / weights_sum) for w in weights]
return weights
def calculate_contrast_weight(self, image: np.ndarray) -> np.ndarray:
"""calculate_contrast_weight Calculates the contrast weight for the input image
Parameters
----------
image : np.ndarray
The image for which the contrast weight is to be calculated
Returns
-------
np.ndarray
The contrast weight for the input image
"""
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply a soft Gaussian blur to the image to reduce noise
gray = cv2.GaussianBlur(gray, (0, 0), self.sigma)
# Calculate Laplacian
laplacian = cv2.Laplacian(gray, cv2.CV_32F)
# Calculate contrast
contrast = np.abs(laplacian)
return contrast
def calculate_saturation_weight(self, image: np.ndarray) -> np.ndarray:
"""calculate_saturation_weight Calculates the saturation weight for the input image
Parameters
----------
image : np.ndarray
The image for which the saturation weight is to be calculated
Returns
-------
np.ndarray
The saturation weight for the input image
"""
# Convert to HSV
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Calculate saturation
saturation = hsv[:, :, 1]
return saturation
def calculate_exposure_weight(self, image: np.ndarray) -> np.ndarray:
"""calculate_exposure_weight Calculates the exposure weight for the input image
Parameters
----------
image : np.ndarray
The image for which the exposure weight is to be calculated
Returns
-------
np.ndarray
The exposure weight for the input image
"""
# Calculate exposure
exposure = np.prod(
np.exp(-((image - 0.5)**2)/(2*self.sigma)), axis=2, dtype=np.float32)
return exposure
def create_image_pyramids(self, images: "list[np.ndarray]",
weights: "list[np.ndarray]") -> "list[list[np.ndarray]]":
"""create_image_pyramids Generate gaussian and laplacian pyramids for the input images
Parameters
----------
images : list[np.ndarray]
A list of input LDR images
weights : list[np.ndarray]
A list of weights for each image in the input list
Returns
-------
(list[np.ndarray], list[np.ndarray])
A list of gaussian and laplacian pyramids for the input images
"""
g_pyramids = []
l_pyramids = []
for image, weight in zip(images, weights):
gaussian_pyramid = []
image_gaussians = []
laplacian_pyramid = []
# Create gaussian pyramid
for i in range(self.pyramid_levels):
if i == 0:
gaussian_pyramid.append(weight)
else:
gaussian_pyramid.append(cv2.pyrDown(gaussian_pyramid[-1]))
# Create gaussian pyramid for image
for i in range(self.pyramid_levels):
if i == 0:
image_gaussians.append(image)
else:
image_gaussians.append(cv2.pyrDown(image_gaussians[-1]))
# Create laplacian pyramid
for i in range(self.pyramid_levels - 1, -1, -1):
if i == self.pyramid_levels - 1:
laplacian_pyramid.append(image_gaussians[i])
else:
size = (image_gaussians[i].shape[1],
image_gaussians[i].shape[0])
gaussian_expanded = cv2.pyrUp(
image_gaussians[i+1], dstsize=size)
laplacian_pyramid.append(cv2.subtract(
image_gaussians[i], gaussian_expanded))
g_pyramids.append(gaussian_pyramid)
l_pyramids.append(laplacian_pyramid)
return g_pyramids, l_pyramids
def blend_pyramids(self, gaussian_pyramids: "list[list[np.ndarray]]",
laplacian_pyramids: "list[list[np.ndarray]]") -> any:
"""blend_pyramids Blends the Gaussian and laplacian pyramids
Parameters
----------
gaussian_pyramids : list[np.ndarray]
A list of gaussian pyramids, each of which has dimensions (pyramid_levels, height, width, channels)
laplacian_pyramids : list[np.ndarray]
A list of laplacian pyramids, each of which has dimensions (pyramid_levels, height, width, channels)
Returns
-------
np.ndarray
_description_
"""
res_laplacian = []
for level in range(self.pyramid_levels):
reverse_level = self.pyramid_levels - (1 + level)
res_plevel = np.zeros(laplacian_pyramids[0][reverse_level].shape,
dtype=np.uint8)
for img_idx in range(len(gaussian_pyramids)):
gaussian = gaussian_pyramids[img_idx][level]
laplacian = laplacian_pyramids[img_idx][reverse_level]
gaussian = np.float32(gaussian/255)
gaussian = np.repeat(gaussian[:, :, np.newaxis], 3, axis=2)
combination = cv2.multiply(
gaussian, laplacian, dtype=cv2.CV_8UC3)
res_plevel = cv2.add(res_plevel, combination)
res_laplacian.append(res_plevel)
return res_laplacian
def reconstruct_image(self, laplacian_pyramid: "list[np.ndarray]") -> np.ndarray:
"""reconstruct_image Retrieves the final HDR image from the laplacian pyramid
that was generated by the blend_pyramids function
Parameters
----------
laplacian_pyramid : list[np.ndarray]
A list of images, each of which has dimensions (height, width, channels)
Returns
-------
np.ndarray
The final HDR image
"""
laplacian_pyramid = laplacian_pyramid[::-1]
res = laplacian_pyramid[0]
for i in range(1, len(laplacian_pyramid)):
size = (laplacian_pyramid[i].shape[1],
laplacian_pyramid[i].shape[0])
print("Shapes: ", res.shape, size)
res = cv2.pyrUp(res, dstsize=size)
res = cv2.add(res, laplacian_pyramid[i])
return res
if __name__ == "__main__":
fuser = ExposureFusion(perform_alignment=False, use_softmax=False, pyramid_levels=5, sigma=0.2)
i = 1
images = [cv2.imread(
f"data/pictures/HDR_test_scene_{i}__1.{i}.{j}.png") for j in range(1, 6)]
HDR = fuser(images)
if HDR is not None:
cv2.imwrite("data/pictures/HDR_test_scene_1.png", HDR)