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- Tuned Values

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- Added Contrast(compress) with Slight shadow lift in effect chain
This commit is contained in:
Thomas 2023-03-20 23:23:21 +01:00
parent 2d3f4f05be
commit 3df2d4aa09
3 changed files with 80 additions and 29 deletions
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77
NumpyHDR.py
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@ -1,5 +1,6 @@
from PIL import Image
import numpy as np
from numba import njit
#import matplotlib.pyplot as plt
class NumpyHDR:
@ -101,23 +102,19 @@ class NumpyHDR:
#plot_histogram(mask, title="mask")
return mask
def shadowlift(self, img, center=200, width=20, threshold=0.2, amount=1):
'''Mask with sigmoid smooth targets dark sections'''
def highlightsdrop(self, img, center=0.7, width=0.15, threshold=0.6, amount=0.15):
'''Mask with sigmoid smooth targets dark sections. Creates artefacts'''
mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
print(mask)
print(img)
mask = np.where(img < threshold, mask, 1) # Apply threshold to the mask
print(mask)
#plot_histogram(mask, title= "maskin")
img_adjusted = img * mask * amount # Adjust the image with a user-specified amount
img_adjusted = img - mask * (amount) # Adjust the image with a user-specified amount
return img_adjusted
def highlightdrop(self, img, center=200, width=20, threshold=0.8, amount: float=1):
def shadowlift(self, img, center=0.2, width=0.1, threshold=0.2, amount= 0.06):
'''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
img_adjusted = img + mask * (amount) # Adjust the image with a user-specified amount
return img_adjusted
@ -137,13 +134,14 @@ class NumpyHDR:
for path in image_paths:
#print(path)
img = Image.open(path).convert('RGB')
img = img.resize((1920, 1080))
img = img.resize((1280, 720))
img = np.array(img).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:
gray = np.dot(img, [0.2989, 0.5870, 0.1140])
#kernel = np.array([[-1, 1, -1], [1, 7, 1], [-1, 1, -1]])
@ -163,18 +161,7 @@ class NumpyHDR:
return fused
def sequence(self):
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(f"{self.output_path}_hdr.jpg", quality=self.compress_quality)
class NumpyUtility:
def __init__(self):
'''Utilitys made with chatGPT for experimentation, few are working'''
def compress_dynamic_range(image):
def compress_dynamic_range(self, image):
# Find the 1st and 99th percentiles of the image
p1, p99 = np.percentile(image, (1, 99))
@ -182,17 +169,14 @@ class NumpyUtility:
img_range = p99 - p1
# Calculate the compression factor required to fit the image into 8-bit range
c = 255.0 / img_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, 255)
# Convert the image to uint8 format
new_image = new_image.astype(np.uint8)
new_image = np.clip((image - p1) * c, 0, 1)
return new_image
def compress_dynamic_range_histo(image, new_min=0, new_max=255):
def compress_dynamic_range_histo(self, image, new_min=0.01, new_max=0.99):
"""Compress the dynamic range of an image using histogram stretching.
Args:
@ -218,6 +202,41 @@ class NumpyUtility:
return fused
def sequence(self, gain, weight):
print(self.input_image)
hdr_image = self.mertens_fusion(self.input_image ,gain, weight)
hdr_image = self.compress_dynamic_range(hdr_image)
#hdr_image = self.compress_dynamic_range_histo(hdr_image)
#hdr_image = self.highlightsdrop(hdr_image)
hdr_image = self.shadowlift(hdr_image)
result = self.simple_clip(hdr_image,1.2)
image = Image.fromarray(result)
image.save(f"{self.output_path}_hdr.jpg", quality=self.compress_quality)
class NumpyUtility:
def __init__(self):
'''Utilitys made with chatGPT for experimentation, few are working'''
def compress_dynamic_range(image):
# Find the 1st and 99th percentiles of the image
p1, p99 = np.percentile(image, (1, 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)
# Convert the image to uint8 format
new_image = new_image.astype(np.uint8)
return new_image
def adjust_luminance(image, mask, amount):
# Convert the image to LAB color space
rgb_image = image.astype(np.float32) / 255.0
@ -523,7 +542,7 @@ class NumpyUtility:
The tonemapped output image as a NumPy array.
"""
# Convert the image to floating point RGB.
image = image.astype(np.float32)
#image = image.astype(np.float32)
# Compute the log average luminance.
lum = np.exp(np.mean(np.log(0.0001 + image)))

9
main.py Normal file
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@ -0,0 +1,9 @@
import numpyHDR
#Testfile
hdr = numpyHDR.NumpyHDR()
liste = ['hdr/webcam20_3_2023_ev0.jpg','hdr/webcam20_3_2023_ev1.jpg','hdr/webcam20_3_2023_ev-2.jpg']
hdr.input_image = liste
hdr.output_path = 'hdr/fused_merten7'
hdr.compress_quality = 75
hdr.sequence(0.8, 0.1)

23
requirements.txt Normal file
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@ -0,0 +1,23 @@
contourpy==1.0.7
cycler==0.11.0
fonttools==4.39.2
imageio==2.26.0
importlib-resources==5.12.0
kiwisolver==1.4.4
lazy_loader==0.1
llvmlite==0.39.1
matplotlib==3.7.1
networkx==3.0
numba==0.56.4
numpy==1.23.5
opencv-python-headless==4.7.0.72
packaging==23.0
Pillow==9.4.0
pyparsing==3.0.9
python-dateutil==2.8.2
PyWavelets==1.4.1
scikit-image==0.20.0
scipy==1.9.1
six==1.16.0
tifffile==2023.3.15
zipp==3.15.0