- Tuned Values
- Added Contrast(compress) with Slight shadow lift in effect chain
This commit is contained in:
Thomas
parent
2d3f4f05be
commit
3df2d4aa09
77
NumpyHDR.py
77
NumpyHDR.py
|
|
@ -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)))
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
@ -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
|
||||
Loading…
Reference in New Issue