def main():
# Read and Demosaicing image
filepath = "dataset/dragon.png"
img_raw = cv2.imread(filepath, 0)
img_demosaiced = pa.demosaicing(img_raw)
# Calculate Stokes vector
radians = np.array([0, np.pi/4, np.pi/2, np.pi*3/4])
img_stokes = pa.calcStokes(img_demosaiced, radians)
# Convert Stokes vector to DoLP and AoLP
img_DoLP = pa.cvtStokesToDoLP(img_stokes) # 0~1
img_AoLP = pa.cvtStokesToAoLP(img_stokes) # 0~pi
name, ext = os.path.splitext(filepath)
# Apply the HSV color map on the AoLP image
img_AoLP_color = pa.applyColorToAoLP(img_AoLP)
cv2.imwrite(name+"_AoLP"+".png", img_AoLP_color)
# Set saturation to DoLP
# As a result, the image is lighter and has color only at the polarized area
img_AoLP_light = pa.applyColorToAoLP(img_AoLP, saturation=img_DoLP)
cv2.imwrite(name+"_AoLP_saturation"+".png", img_AoLP_light)
# Set value to DoLP
# As a result, the image is darker and has color only at the polarized area
img_AoLP_dark = pa.applyColorToAoLP(img_AoLP, value=img_DoLP)
cv2.imwrite(name+"_AoLP_value"+".png", img_AoLP_dark)
def main():
# Read polarization image
filepath = "dataset/dragon.png"
img_raw = cv2.imread(filepath, -1)
# Demosaicing
img_demosaiced = pa.demosaicing(img_raw)
img_0, img_45, img_90, img_135 = cv2.split(img_demosaiced)
print("Export demosaicing images : {}".format(filepath))
name, ext = os.path.splitext(filepath)
cv2.imwrite(name + "-0" + ext, img_0)
cv2.imwrite(name + "-45" + ext, img_45)
cv2.imwrite(name + "-90" + ext, img_90)
cv2.imwrite(name + "-135" + ext, img_135)
def pol2color(image):
img_demosaiced = pa.demosaicing(image)
# Calculate the Stokes vector per-pixel
angles = np.deg2rad([0, 45, 90, 135])
img_stokes = pa.calcStokes(img_demosaiced, angles)
# Decompose the Stokes vector into its components
img_S0, img_S1, img_S2 = cv2.split(img_stokes)
# Convert the Stokes vector to Intensity, DoLP and AoLP
img_intensity = pa.cvtStokesToIntensity(img_stokes)
img_DoLP = pa.cvtStokesToDoLP(img_stokes)
img_AoLP = pa.cvtStokesToAoLP(img_stokes)
# https://github.com/elerac/polanalyser/wiki/How-to-Visualizing-the-AoLP-Image
# img_AoLP_cmapped = pa.applyColorToAoLP(img_AoLP, value=img_DoLP)
img_AoLP_cmapped = pa.applyColorToAoLP(img_AoLP, saturation=img_DoLP)
return img_AoLP_cmapped
def main():
# Read image and demosaicing
filepath = "dataset/dragon.png"
img_raw = cv2.imread(filepath, 0)
img_demosaiced = pa.demosaicing(img_raw)
# Calculate the Stokes vector per-pixel
radians = np.array([0, np.pi / 4, np.pi / 2, np.pi * 3 / 4])
img_stokes = pa.calcStokes(img_demosaiced, radians)
# Decomposition into 3 components (S0, S1, S2)
img_S0, img_S1, img_S2 = cv2.split(img_stokes)
# Convert Stokes vector to meaningful values
img_intensity = pa.cvtStokesToIntensity(img_stokes)
img_Imax = pa.cvtStokesToImax(img_stokes)
img_Imin = pa.cvtStokesToImin(img_stokes)
img_DoLP = pa.cvtStokesToDoLP(img_stokes) # 0~1
img_AoLP = pa.cvtStokesToAoLP(img_stokes) # 0~pi
# Normarize and save images
name, ext = os.path.splitext(filepath)
dtype_raw = img_raw.dtype
img_intensity_norm = normarize(img_intensity, dtype_raw)
cv2.imwrite(name + "_intensity" + ext, img_intensity_norm)
img_Imax_norm = normarize(img_Imax, dtype_raw)
cv2.imwrite(name + "_Imax" + ext, img_Imax_norm)
img_Imin_norm = normarize(img_Imin, dtype_raw)
cv2.imwrite(name + "_Imin" + ext, img_Imin_norm)
img_DoLP_norm = normarize(img_DoLP * 255, np.uint8, gamma=1.0)
cv2.imwrite(name + "_DoLP" + ".png", img_DoLP_norm)
img_AoLP_color = pa.applyColorToAoLP(img_AoLP) # apply pseudo-color
cv2.imwrite(name + "_AoLP" + ".png", img_AoLP_color)
# hist_HDR = plt.hist(img_HDR.ravel(), 256)
# hist_HDR = plt.hist(np.log10(img_HDR.ravel()+1))
# plt.imshow(img_HDR)
# plt.imshow(np.log10(img_HDR+1))
# plt.imshow(np.log10(np.float64(img_mid)+1))
hist_HDR = plt.hist(np.log10(img_HDR.ravel() + 1), 1000)
plt.title('HDR image')
plt.xlabel("log10 pixel byte values / s")
plt.ylabel("Frequency")
plt.savefig(os.path.dirname(imfile) + '/HDR_histogram.png')
plt.close()
"""
## Process for polarization
"""
img_demosaiced = pa.demosaicing(img_HDR)
img_000, img_045, img_090, img_135 = cv2.split(img_demosaiced)
plt.imshow(img_000)
# Calculate the Stokes vector per-pixel
radians = np.array([0, np.pi / 4, np.pi / 2, np.pi * 3 / 4])
img_stokes = pa.calcStokes(img_demosaiced, radians)
plt.imshow(img_stokes)
# Decompose the Stokes vector into its components
img_S0, img_S1, img_S2 = cv2.split(img_stokes)
# Convert the Stokes vector to Intensity, DoLP and AoLP
# pip install numba import numpy as np from matplotlib import pyplot as plt from matplotlib.pylab import cm import cv2 import polanalyser as pa from tkinter.filedialog import askopenfilename import os imfile = askopenfilename() img_raw = cv2.imread(imfile, 0) #0 means greyscale img_demosaiced = pa.demosaicing(img_raw) img_000, img_045, img_090, img_135 = cv2.split(img_demosaiced) plt.imshow(img_000) # Calculate the Stokes vector per-pixel radians = np.array([0, np.pi / 4, np.pi / 2, np.pi * 3 / 4]) img_stokes = pa.calcStokes(img_demosaiced, radians) plt.imshow(img_stokes) # Decompose the Stokes vector into its components img_S0, img_S1, img_S2 = cv2.split(img_stokes) # Convert the Stokes vector to Intensity, DoLP and AoLP