Added docstring

This commit is contained in:
Thomas 2023-03-20 18:10:12 +01:00
parent 374195cc41
commit 2d3f4f05be

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@ -3,13 +3,31 @@ import numpy as np
#import matplotlib.pyplot as plt
class NumpyHDR:
'''Numpy and PIL implementation of a Mertens Fusion alghoritm'''
def __init__(self):
self.input_image: list
self.output_path: str = '/'
self.compress_quality: int = 50
'''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
def plot_histogram(image, title="Histogram", bins=256):
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 __init__(self):
self.input_image: list = []
self.output_path: str = '/'
self.compress_quality: int = 75
def plot_histogram(self, image, title="Histogram", bins=256):
"""Plot the histogram of an image.
Args:
@ -25,7 +43,7 @@ class NumpyHDR:
plt.show()
### Experimental functions above this line. chatGPT sketches
def simple_clip(fused,gamma):
def simple_clip(self, fused,gamma):
# Apply gamma correction
fused = np.clip(fused, 0, 1)
fused = np.power(fused, 1.0 / gamma)
@ -33,7 +51,9 @@ class NumpyHDR:
fused = (255.0 * fused).astype(np.uint8)
#fused = Image.fromarray(fused)
def convolve2d(image, kernel):
return fused
def convolve2d(self, image, kernel):
"""Perform a 2D convolution on the given image with the given kernel.
Args:
@ -73,14 +93,16 @@ class NumpyHDR:
return convolved_image
def mask(img, center=50, width=20, threshold=0.2):
def mask(self, 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 shadowlift(img, center=200, width=20, threshold=0.2, amount=1):
def shadowlift(self, img, center=200, width=20, threshold=0.2, amount=1):
'''Mask with sigmoid smooth targets dark sections'''
mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
print(mask)
print(img)
@ -91,14 +113,15 @@ class NumpyHDR:
return img_adjusted
def highlightdrop(img, center=200, width=20, threshold=0.8, amount: float=1):
def highlightdrop(self, img, center=200, width=20, threshold=0.8, amount: float=1):
'''Mask with sigmoid smooth targets bright sections'''
mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
mask = np.where(img > threshold, mask, 1) # Apply threshold to the mask
img_adjusted = img + mask * (-amount) # Adjust the image with a user-specified amount
return img_adjusted
def mertens_fusion(image_paths, gamma=2.2, contrast_weight=0.2):
def mertens_fusion(self, image_paths, gamma=2.2, contrast_weight=0.2):
"""Fuse multiple exposures into a single HDR image using the Mertens algorithm.
Args:
@ -112,6 +135,7 @@ class NumpyHDR:
# Load the input images and convert them to floating-point format.
images = []
for path in image_paths:
#print(path)
img = Image.open(path).convert('RGB')
img = img.resize((1920, 1080))
img = np.array(img).astype(np.float32) / 255.0
@ -124,7 +148,7 @@ class NumpyHDR:
gray = np.dot(img, [0.2989, 0.5870, 0.1140])
#kernel = np.array([[-1, 1, -1], [1, 7, 1], [-1, 1, -1]])
kernel = np.array([[-1, -1, -1], [-1, 7, -1], [-1, -1, -1]])
laplacian = np.abs(convolve2d(gray, kernel))
laplacian = np.abs(self.convolve2d(gray, kernel))
weight = np.power(laplacian, contrast_weight)
weight_maps.append(weight)
@ -140,12 +164,11 @@ class NumpyHDR:
return fused
def sequence(self):
#list = ['hdr/webcam20_3_2023_ev1.jpg','hdr/webcam20_3_2023_ev-1.jpg']
hdr_image = self.mertens_fusion(self.input_image ,0.7, 0.01)
result = self.simple_clip(hdr_image,1)
image = Image.fromarray(result)
#output_path = f'hdr/webcam_hdr10.jpg'
image.save(self.output_path, quality=self.compress_quality)
image.save(f"{self.output_path}_hdr.jpg", quality=self.compress_quality)
class NumpyUtility:
def __init__(self):