pythonopencvimage-processingcomputer-visionwebcam
Webcam color calibration using OpenCV
Having ordered half a dozen webcams online for a project I notice that the colors on the output are not consistent.
In order to compensate for this I have attempted to take a template image and extract the R,G and B histograms and tried to match the target images's RGB histograms based on this.
This was inspired from the description of the solution for a very similar problem Comparative color calibration
The perfect solution will look like this :
In order to try to solve this I wrote the following script which performed poorly:
EDIT (Thanks to @DanMašek and @api55)
import numpy as np
def show_image(title, image, width = 300):
# resize the image to have a constant width, just to
# make displaying the images take up less screen real
# estate
r = width / float(image.shape[1])
dim = (width, int(image.shape[0] * r))
resized = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
# show the resized image
cv2.imshow(title, resized)
def hist_match(source, template):
"""
Adjust the pixel values of a grayscale image such that its histogram
matches that of a target image
Arguments:
-----------
source: np.ndarray
Image to transform; the histogram is computed over the flattened
array
template: np.ndarray
Template image; can have different dimensions to source
Returns:
-----------
matched: np.ndarray
The transformed output image
"""
oldshape = source.shape
source = source.ravel()
template = template.ravel()
# get the set of unique pixel values and their corresponding indices and
# counts
s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
return_counts=True)
t_values, t_counts = np.unique(template, return_counts=True)
# take the cumsum of the counts and normalize by the number of pixels to
# get the empirical cumulative distribution functions for the source and
# template images (maps pixel value --> quantile)
s_quantiles = np.cumsum(s_counts).astype(np.float64)
s_quantiles /= s_quantiles[-1]
t_quantiles = np.cumsum(t_counts).astype(np.float64)
t_quantiles /= t_quantiles[-1]
# interpolate linearly to find the pixel values in the template image
# that correspond most closely to the quantiles in the source image
interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
return interp_t_values[bin_idx].reshape(oldshape)
from matplotlib import pyplot as plt
from scipy.misc import lena, ascent
import cv2
source = cv2.imread('/media/somadetect/Lexar/color_transfer_data/1/frame10.png')
s_b = source[:,:,0]
s_g = source[:,:,1]
s_r = source[:,:,2]
template = cv2.imread('/media/somadetect/Lexar/color_transfer_data/5/frame6.png')
t_b = source[:,:,0]
t_r = source[:,:,1]
t_g = source[:,:,2]
matched_b = hist_match(s_b, t_b)
matched_g = hist_match(s_g, t_g)
matched_r = hist_match(s_r, t_r)
y,x,c = source.shape
transfer = np.empty((y,x,c), dtype=np.uint8)
transfer[:,:,0] = matched_r
transfer[:,:,1] = matched_g
transfer[:,:,2] = matched_b
show_image("Template", template)
show_image("Target", source)
show_image("Transfer", transfer)
cv2.waitKey(0)
Template image :
Target Image:
The Matched Image:
Then I found Adrian's (pyimagesearch) attempt to solve a very similar problem in the following link
The results seem to be fairly good with some saturation defects. I would welcome any suggestions or pointers on how to address this issue so all web cam outputs could be calibrated to output similar colors based on one template image.
Solution
I have attempted a white patch based calibration routine. Here is the link https://theiszm.wordpress.com/tag/white-balance/.
The code snippet follows:
import cv2
import math
import numpy as np
import sys
from matplotlib import pyplot as plt
def hist_match(source, template):
"""
Adjust the pixel values of a grayscale image such that its histogram
matches that of a target image
Arguments:
-----------
source: np.ndarray
Image to transform; the histogram is computed over the flattened
array
template: np.ndarray
Template image; can have different dimensions to source
Returns:
-----------
matched: np.ndarray
The transformed output image
"""
oldshape = source.shape
source = source.ravel()
template = template.ravel()
# get the set of unique pixel values and their corresponding indices and
# counts
s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
return_counts=True)
t_values, t_counts = np.unique(template, return_counts=True)
# take the cumsum of the counts and normalize by the number of pixels to
# get the empirical cumulative distribution functions for the source and
# template images (maps pixel value --> quantile)
s_quantiles = np.cumsum(s_counts).astype(np.float64)
s_quantiles /= s_quantiles[-1]
t_quantiles = np.cumsum(t_counts).astype(np.float64)
t_quantiles /= t_quantiles[-1]
# interpolate linearly to find the pixel values in the template image
# that correspond most closely to the quantiles in the source image
interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
return interp_t_values[bin_idx].reshape(oldshape)
# Read original image
im_o = cv2.imread('/media/Lexar/color_transfer_data/5/frame10.png')
im = im_o
cv2.imshow('Org',im)
cv2.waitKey()
B = im[:,:, 0]
G = im[:,:, 1]
R = im[:,:, 2]
R= np.array(R).astype('float')
G= np.array(G).astype('float')
B= np.array(B).astype('float')
# Extract pixels that correspond to pure white R = 255,G = 255,B = 255
B_white = R[168, 351]
G_white = G[168, 351]
R_white = B[168, 351]
print B_white
print G_white
print R_white
# Compensate for the bias using normalization statistics
R_balanced = R / R_white
G_balanced = G / G_white
B_balanced = B / B_white
R_balanced[np.where(R_balanced > 1)] = 1
G_balanced[np.where(G_balanced > 1)] = 1
B_balanced[np.where(B_balanced > 1)] = 1
B_balanced=B_balanced * 255
G_balanced=G_balanced * 255
R_balanced=R_balanced * 255
B_balanced= np.array(B_balanced).astype('uint8')
G_balanced= np.array(G_balanced).astype('uint8')
R_balanced= np.array(R_balanced).astype('uint8')
im[:,:, 0] = (B_balanced)
im[:,:, 1] = (G_balanced)
im[:,:, 2] = (R_balanced)
# Notice saturation artifacts
cv2.imshow('frame',im)
cv2.waitKey()
# Extract the Y plane in original image and match it to the transformed image
im_o = cv2.cvtColor(im_o, cv2.COLOR_BGR2YCR_CB)
im_o_Y = im_o[:,:,0]
im = cv2.cvtColor(im, cv2.COLOR_BGR2YCR_CB)
im_Y = im[:,:,0]
matched_y = hist_match(im_o_Y, im_Y)
matched_y= np.array(matched_y).astype('uint8')
im[:,:,0] = matched_y
im_final = cv2.cvtColor(im, cv2.COLOR_YCR_CB2BGR)
cv2.imshow('frame',im_final)
cv2.waitKey()
The input image is:
The result of the script is:
Thank you all for suggestions and pointers!!