Addedd blur for smoother HDR effect
(Doubles processing time, can be switched off)
This commit is contained in:
Thomas
parent
b36944281f
commit
7a92698dfc
@ -33,16 +33,17 @@ if int(select) == 3:
|
||||
stack = file.openImageList(path_list, True)
|
||||
|
||||
if int(select) == 4:
|
||||
path_list = ['test_hdr0.jpg', 'test_hdr1.jpg', 'test_hdr2.jpg']
|
||||
path_list = ['webcam25_3_2023_ev0.jpg','webcam25_3_2023_ev1.jpg','webcam25_3_2023_ev2.jpg']
|
||||
stack = file.openImageList(path_list, True)
|
||||
|
||||
print(path_list)
|
||||
|
||||
#Process HDR with mertens fusion and post effects
|
||||
result = hdr.process(stack, 1, 1, 1, True)
|
||||
#Process HDR with mertens fusion and post effects, blur
|
||||
#Set last value to false for double the speed but lesser blancaed hdr effect.
|
||||
result = hdr.process(stack, 1, 1, 1, True, True)
|
||||
|
||||
#Save Result to File
|
||||
file = file.saveResultToFile(result, 'hdr/result', 75)
|
||||
file = file.saveResultToFile(result, 'hdr/result2', 75)
|
||||
|
||||
|
||||
|
||||
|
||||
100
numpyHDR.py
100
numpyHDR.py
@ -28,42 +28,42 @@ def simple_clip(fused,gamma):
|
||||
|
||||
return fused
|
||||
def convolve2d(image, kernel):
|
||||
"""Perform a 2D convolution on the given image with the given kernel.
|
||||
# Get the dimensions of the input image and kernel
|
||||
image_height, image_width = image.shape
|
||||
kernel_height, kernel_width = kernel.shape
|
||||
|
||||
Args:
|
||||
image: The input image to convolve.
|
||||
kernel: The kernel to convolve the image with.
|
||||
# Compute the padding needed to handle boundary effects
|
||||
pad_height = (kernel_height - 1) // 2
|
||||
pad_width = (kernel_width - 1) // 2
|
||||
padded_image = np.pad(image, ((pad_height, pad_height), (pad_width, pad_width)), mode='constant')
|
||||
|
||||
Returns:
|
||||
The convolved image.
|
||||
"""
|
||||
# Get the dimensions of the image and kernel.
|
||||
image_height, image_width = image.shape[:2]
|
||||
kernel_height, kernel_width = kernel.shape[:2]
|
||||
# Define generators for row and column indices
|
||||
row_indices = range(image_height)
|
||||
col_indices = range(image_width)
|
||||
|
||||
# Compute the amount of padding to add to the image.
|
||||
pad_height = kernel_height // 2
|
||||
pad_width = kernel_width // 2
|
||||
|
||||
# Pad the image with zeros.
|
||||
padded_image = np.zeros(
|
||||
(image_height + 2 * pad_height, image_width + 2 * pad_width),
|
||||
dtype=np.float32,
|
||||
)
|
||||
padded_image[pad_height:-pad_height, pad_width:-pad_width] = image
|
||||
|
||||
# Flip the kernel horizontally and vertically.
|
||||
flipped_kernel = np.flipud(np.fliplr(kernel))
|
||||
|
||||
# Convolve the padded image with the flipped kernel.
|
||||
convolved_image = np.zeros_like(image, dtype=np.float32)
|
||||
for row in range(image_height):
|
||||
for col in range(image_width):
|
||||
patch = padded_image[
|
||||
# Define a generator expression to generate patches centered at each pixel
|
||||
patches = (
|
||||
padded_image[
|
||||
row : row + kernel_height, col : col + kernel_width
|
||||
]
|
||||
product = patch * flipped_kernel
|
||||
convolved_image[row, col] = product.sum()
|
||||
for row in row_indices
|
||||
for col in col_indices
|
||||
)
|
||||
|
||||
# Define a generator expression to generate element-wise products of patches and flipped kernels
|
||||
products = (
|
||||
patch * np.flip(kernel, axis=(0, 1))
|
||||
for patch in patches
|
||||
)
|
||||
|
||||
# Define a generator expression to generate convolved values
|
||||
convolved_values = (
|
||||
product.sum()
|
||||
for product in products
|
||||
)
|
||||
|
||||
# Reshape the convolved values into an output image
|
||||
convolved_image = np.array(list(convolved_values)).reshape((image_height, image_width))
|
||||
|
||||
return convolved_image
|
||||
|
||||
@ -96,14 +96,35 @@ def shadowlift(img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
|
||||
img_adjusted = np.clip(img_adjusted, 0, 1)
|
||||
|
||||
return img_adjusted
|
||||
def blur(image, amount=1):
|
||||
# Define a kernel for sharpening
|
||||
kernel = np.array([[0, -1, 0],
|
||||
[-1, 4, -1],
|
||||
[0, -1, 0]])
|
||||
|
||||
def mertens_fusion(stack, gamma=1, contrast_weight=1):
|
||||
# Apply the kernel to each channel of the image using convolution
|
||||
blurred = convolve2d(image, kernel)
|
||||
|
||||
# Add the original image to the sharpened image with a weight of the sharpening amount
|
||||
sharpened = image + amount * (image - blurred)
|
||||
|
||||
sharpened = np.clip(sharpened, 0, 1)
|
||||
|
||||
# Crop the output image to match the input size
|
||||
#sharpened = sharpened.reshape(image.shape)
|
||||
|
||||
return sharpened
|
||||
|
||||
|
||||
|
||||
def mertens_fusion(stack, gamma:float =1, contrast_weight:float =1 ,blurred: bool = False) -> bytearray:
|
||||
"""Fuse multiple exposures into a single HDR image using the Mertens algorithm.
|
||||
|
||||
Args:
|
||||
image_paths: A list of paths to input images.
|
||||
gamma: The gamma correction value to apply to the input images.
|
||||
contrast_weight: The weight of the local contrast term in the weight map computation.
|
||||
blurred: Helps making transitions for the weights smoother but increases provessing time x2
|
||||
|
||||
Returns:
|
||||
The fused HDR image.
|
||||
@ -111,6 +132,7 @@ def mertens_fusion(stack, gamma=1, contrast_weight=1):
|
||||
|
||||
images = []
|
||||
for array in stack:
|
||||
#Incoming arrays in 255 er range
|
||||
img = np.array(array).astype(np.float32) / 255.0
|
||||
img = np.power(img, gamma)
|
||||
images.append(img)
|
||||
@ -119,8 +141,15 @@ def mertens_fusion(stack, gamma=1, contrast_weight=1):
|
||||
weight_maps = []
|
||||
|
||||
for img in images:
|
||||
threshold_h = .99
|
||||
threshold_l = .1
|
||||
# Apply thresholding to filter out overexposed portions of the image
|
||||
img = np.where(img > threshold_h, 0.99, img)
|
||||
gray = np.dot(img, [0.2989, 0.5870, 0.1140])
|
||||
kernel = np.array([[-1, -1, -1], [-1, 7, -1], [-1, -1, -1]])
|
||||
if blurred:
|
||||
gray = blur(gray, 1)
|
||||
#kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
|
||||
kernel = np.array([[1, 2, 1], [2, -11, 2], [1, 2, 1]])
|
||||
laplacian = np.abs(convolve2d(gray, kernel))
|
||||
weight = np.power(laplacian, contrast_weight)
|
||||
weight_maps.append(weight)
|
||||
@ -133,7 +162,6 @@ def mertens_fusion(stack, gamma=1, contrast_weight=1):
|
||||
fused = np.zeros(images[0].shape, dtype=np.float32)
|
||||
for i, img in enumerate(images):
|
||||
fused += weight_maps[i][:, :, np.newaxis] * img
|
||||
#print(fused)
|
||||
|
||||
return fused
|
||||
|
||||
@ -178,7 +206,7 @@ def compress_dynamic_range_histo(image, new_min=0.01, new_max=0.99):
|
||||
|
||||
return new_image
|
||||
|
||||
def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: bool = True):
|
||||
def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: bool = True, blurred: bool = True):
|
||||
'''Processes the stack that contains a list of arrays form the camera into a PIL compatible clipped output array
|
||||
Args:
|
||||
stack : input list with arrays
|
||||
@ -202,7 +230,7 @@ def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: b
|
||||
|
||||
'''
|
||||
|
||||
hdr_image = mertens_fusion(stack ,gain, weight)
|
||||
hdr_image = mertens_fusion(stack ,gain, weight, blurred)
|
||||
if post == True:
|
||||
#hdr_image = self.highlightsdrop(hdr_image)
|
||||
hdr_image = shadowlift(hdr_image)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user