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utils.py
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utils.py
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import os
import cv2
import math
import random
import numpy as np
def weight(I):
I_min, I_max = 0., 255.
if I <= (I_min + I_max) /2:
return I - I_min
return I_max - I
def sample_intensity(stack):
I_min, I_max = 0., 255.
num_intensities = int(I_max - I_min + 1)
num_images = len(stack)
sample = np.zeros((num_intensities, num_images), dtype=np.uint8)
mid_img = stack[num_images // 2]
for i in range(int(I_min), int(I_max + 1)):
rows, cols = np.where(mid_img == i)
if len(rows) != 0:
idx = random.randrange(len(rows))
for j in range(len(stack)):
sample[i, j] = stack[j][rows[idx], cols[idx]]
return sample
def estimate_curve(sample, exps, l):
I_min, I_max = 0., 255.
n = 255
A = np.zeros((sample.shape[0] * sample.shape[1] + n, n + sample.shape[0] + 1), dtype=np.float64)
b = np.zeros((A.shape[0], 1), dtype=np.float64)
k = 0
#1. data fitting
for i in range(sample.shape[0]):
for j in range(sample.shape[1]):
I_ij = sample[i,j]
w_ij = weight(I_ij)
A[k, I_ij] = w_ij
A[k, n + 1 + i] = -w_ij
b[k, 0] = w_ij * exps[j]
k += 1
#2. smoothing
for I_k in range(int(I_min + 1), int(I_max)):
w_k = weight(I_k)
A[k, I_k-1] = w_k * l
A[k, I_k] = -2 * w_k * l
A[k, I_k+1] = w_k * l
k += 1
#3. Color centering
A[k, int((I_max - I_min) // 2)] = 1
inv_A = np.linalg.pinv(A)
x = np.dot(inv_A, b)
g = x[0 : n + 1]
return g[:,0]
def computeRadiance(stack, exps, curve):
stack_shape = stack.shape
img_rad = np.zeros(stack_shape[1:], dtype=np.float64)
num_imgs = stack_shape[0]
for i in range(stack_shape[1]):
for j in range(stack_shape[2]):
g = np.array([curve[int(stack[k][i, j])] for k in range(num_imgs)])
w = np.array([weight(stack[k][i, j]) for k in range(num_imgs)])
sumW = np.sum(w)
if sumW > 0:
img_rad[i,j] = np.sum(w * (g - exps) / sumW)
else:
img_rad[i,j] = g[num_imgs // 2] - exps[num_imgs //2]
return img_rad
def globalTonemap(img, l):
return cv2.pow(img/255., 1.0/l)
def intensityAdjustment(image, template):
m, n, channel = image.shape
output = np.zeros((m, n, channel))
for ch in range(channel):
image_avg, template_avg = np.average(image[:, :, ch]), np.average(template[:, :, ch])
output[..., ch] = image[..., ch] * (template_avg / image_avg)
return output
def load(path_test):
filenames = []
exposure_times = []
f = open(os.path.join(path_test, 'image_list.txt'))
for line in f:
if (line[0] == '#'):
continue
# (filename, exposure, *rest) = line.split()
(filename, exposure) = line.split()
filenames += [os.path.join(path_test,filename)]
# exposure_times += [math.log(float(exposure),2)]
exposure_times += [float(exposure)]
return filenames, exposure_times
def read(path_list):
shape = cv2.imread(path_list[0]).shape
stack = np.zeros((len(path_list), shape[0], shape[1], shape[2]))
for i in path_list:
im = cv2.imread(i)
stack[path_list.index(i), :, :, :] = im
return stack