numpyHDR/numpyHDR.py

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import numpy as np
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'''Numpy and PIL implementation of a Mertens Fusion alghoritm
Usage: Instantiate then set attributes:
input_image = List containing path strings including .jpg Extension
output_path = String ot Output without jpg ending
compress_quality = 0-100 Jpeg compression level defaults to 75
Run function sequence() to start processing.
Example:
hdr = numpyHDR.NumpyHDR()
hdr.input_image = photos/EV- stages/
hdr.compress_quality = 50
hdr.output_path = photos/result/
hdr.sequence()
returns: Nothing
'''
def simple_clip(fused,gamma):
# Apply gamma correction
#fused = np.clip(fused, 0, 1)
fused = np.power(fused, 1.0 / gamma)
#hdr_8bit = np.clip(res_mertens * 255, 0, 255).astype('uint8')
fused = (255.0 * fused).astype(np.uint8)
#fused = Image.fromarray(fused)
return fused
def convolve2d(image, kernel):
# Get the dimensions of the input image and kernel
image_height, image_width = image.shape
kernel_height, kernel_width = kernel.shape
# 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')
# Define generators for row and column indices
row_indices = range(image_height)
col_indices = range(image_width)
# Define a generator expression to generate patches centered at each pixel
patches = (
padded_image[
row : row + kernel_height, col : col + kernel_width
]
for row in row_indices
for col in col_indices
)
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# 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
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)
# Define a generator expression to generate convolved values
convolved_values = (
product.sum()
for product in products
)
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# Reshape the convolved values into an output image
convolved_image = np.array(list(convolved_values)).reshape((image_height, image_width))
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return convolved_image
def mask(img, center=50, width=20, threshold=0.2):
'''Mask with sigmoid smooth'''
mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
mask = np.where(img > threshold, mask, 1) # Apply threshold to the mask
mask = img * mask
#plot_histogram(mask, title="mask")
return mask
def highlightsdrop(img, center=0.7, width=0.2, threshold=0.6, amount=0.08):
'''Mask with sigmoid smooth targets bright sections'''
mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
mask = np.where(img > threshold, mask, 0) # Apply threshold to the mask
mask = mask.reshape((img.shape))
print(np.max(mask))
img_adjusted = img - (mask * amount) # Adjust the image with a user-specified amount
img_adjusted = np.clip(img_adjusted, 0, 1)
return img_adjusted
def shadowlift(img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
'''Mask with sigmoid smooth targets bright sections'''
mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
mask = np.where(img < threshold, mask, 0) # Apply threshold to the mask
mask = mask.reshape((img.shape))
print(np.max(mask))
img_adjusted = (mask * amount) + img # Adjust the image with a user-specified amount
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]])
# 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
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def mertens_fusion(stack, gamma:float =1, contrast_weight:float =1 ,blurred: bool = False) -> bytearray:
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"""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
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Returns:
The fused HDR image.
"""
images = []
for array in stack:
#Incoming arrays in 255 er range
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img = np.array(array).astype(np.float32) / 255.0
img = np.power(img, gamma)
images.append(img)
# Compute the weight maps for each input image based on the local contrast.
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)
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gray = np.dot(img, [0.2989, 0.5870, 0.1140])
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]])
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laplacian = np.abs(convolve2d(gray, kernel))
weight = np.power(laplacian, contrast_weight)
weight_maps.append(weight)
# Normalize the weight maps.
total_weight = sum(weight_maps)
weight_maps = [w / total_weight for w in weight_maps]
# Compute the fused HDR image by computing a weighted sum of the input images.
fused = np.zeros(images[0].shape, dtype=np.float32)
for i, img in enumerate(images):
fused += weight_maps[i][:, :, np.newaxis] * img
return fused
def compress_dynamic_range(image):
'''Compress dynamic range based on percentile'''
# Find the 1st and 99th percentiles of the image
p1, p99 = np.percentile(image, (0, 99))
# Calculate the range of the image
img_range = p99 - p1
# Calculate the compression factor required to fit the image into 8-bit range
c = 1 / img_range
# Subtract the 1st percentile from the image and clip it to the [0, 1] range
new_image = np.clip((image - p1) * c, 0, 1)
return new_image
def compress_dynamic_range_histo(image, new_min=0.01, new_max=0.99):
"""Compress the dynamic range of an image using histogram stretching.
Args:
image: A numpy array representing an image.
new_min: The minimum value of the new range.
new_max: The maximum value of the new range.
Returns:
The compressed image.
"""
# Calculate the histogram of the image.
hist, bins = np.histogram(image.ravel(), bins=256, range=(0, 1))
# Calculate the cumulative distribution function (CDF) of the histogram.
cdf = hist.cumsum()
cdf = (cdf - cdf.min()) / (cdf.max() - cdf.min()) # normalize to [0, 1]
# Interpolate the CDF to get the new pixel values.
new_pixels = np.interp(image.ravel(), bins[:-1], cdf * (new_max - new_min) + new_min)
# Reshape the new pixel values to the shape of the original image.
new_image = new_pixels.reshape((image.shape[0], image.shape[1], image.shape[2]))
return new_image
def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: bool = True, blurred: bool = True):
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'''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
gain : low value low contrast, high value high contrast and brightness
weight: How much the extracted portions of each image gets allpied to to the result "HDR effect intensity"
gamma: Post fusion adjustment of the gamma.
post: Enable or disable effects applied after the fusion True or False, default True
shadowlift = slightly lifts the shadows
Args:
center: position of the filter dropoff
width: range of the gradient, softness
threshold: sets the threshhold form 0 to 1 0.1= lowest blacks....
amount: How much the shadows should be lifted. Values under 0.1 seem to be good.
returns:
Hdr image with lifted blacks clipped to 0,1 range
compress dynamic range:
Tries to fit the image better into the available range. Less loggy image.
Returns:
HDR Image as PIL compatible array.
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'''
hdr_image = mertens_fusion(stack ,gain, weight, blurred)
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if post == True:
#hdr_image = self.highlightsdrop(hdr_image)
hdr_image = shadowlift(hdr_image)
hdr_image = compress_dynamic_range(hdr_image)
#hdr_image = self.compress_dynamic_range_histo(hdr_image)
hdr_image = simple_clip(hdr_image,gamma)
return hdr_image
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