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reflectanceauto2.py
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reflectanceauto2.py
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#
# This program ...
#
# TODO: description of the program
# Purpose: automate reflectance calculation
# The cal values are in "reflectance units"
# Doing the reflectance takes a lot of time
# Correlation between reflectance and red channel
#
import numpy as np
import cv2
import math
from matplotlib import pyplot as plt
from scipy import stats
import sys
from detector import ManualDetector
from PyQt5.QtWidgets import QApplication
import csv
def reflectancecalculator(app, target, target_filename, csv_filename):
#Stuff for QML detector
# This needs to be done first. Couldn't find a way to put it in the ManualDetector
#app = QApplication(sys.argv)
# Create the ManualDetector which will be used for successive images
detector = ManualDetector(app)
#End QML
#
# The extent of the x and y axes on the output graph
#
X_AXIS_MAX = 125
Y_AXIS_MAX = 255
# The reference template
template_filename = 'reference-images/Reflectance-Template-2.3.png'
# The target file to examine
# Open the target image in grayscale and color
img_gray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
img_color = target
#print(img_gray.dtype)
#print(img_color.dtype)
#cv2.imshow('Current Image',img_gray)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
#cv2.imshow('Current Image',img_color)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
# Open the template in grayscale and color
ref_gray = cv2.imread(template_filename, 0)
ref_color = cv2.imread(template_filename)
#
# A class to represent a square on the target image
#
SIDE = 112
class Square:
def __init__(self, name, parent, x, y, reflectance=0):
self.name = name
self.matrix = parent[y:y+SIDE,x:x+SIDE]
self.shape = self.matrix.shape
self.reflectance = reflectance
def mode(self):
return stats.mode(self.matrix, axis=None)[0][0]
def show(self):
cv2.imshow(self.name, self.matrix)
#
# White correction (optional for now)
#
# To what extent is the image evently lit?
# Beginning of each session
# Use a white sheet of same material
# Same camera setup
# Create a mask to use for subtraction
#
## TODO
#
# Correct for camera distorion
#
# yml file, camera intrinsics, OpenCV
#
## TODO
#
# Image warping using SIFT feature matching and homography
#
# Create a SIFT feature detector
sift = cv2.xfeatures2d.SIFT_create()
# Find the keypoints and descriptors using SIFT
kp1,des1 = sift.detectAndCompute(img_gray, None)
kp2,des2 = sift.detectAndCompute(ref_gray, None)
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1,des2,k=2)
good = []
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
# Find the homography and perform a warp of the target image to match the reference
# image perspective
MIN_MATCH_COUNT = 10
if len(good)>MIN_MATCH_COUNT:
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
M,mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
h,w = img_gray.shape
template_h,template_w = ref_gray.shape
result = cv2.warpPerspective(img_color,M,(w,h))[0:template_h,0:template_w]
#
# Get the squares in the target and reference images
#
# Get the red channel for the target image and the reference image
img_rchannel = result[:, :, 2]
ref_rchannel = ref_color[:, :, 2]
# The calibration squares include their associated reflectance values
cal_01 = Square("cal_01", ref_rchannel, 169, 600, 3.4)
cal_02 = Square("cal_02", ref_rchannel, 169, 450, 4.2)
cal_03 = Square("cal_03", ref_rchannel, 169, 300, 8.9)
cal_05 = Square("cal_05", ref_rchannel, 319, 150, 11.0)
cal_06 = Square("cal_06", ref_rchannel, 469, 150, 20.2)
cal_07 = Square("cal_07", ref_rchannel, 619, 150, 36.8)
cal_09 = Square("cal_09", ref_rchannel, 769, 300, 44.3)
cal_10 = Square("cal_10", ref_rchannel, 769, 450, 70.0)
cal_11 = Square("cal_11", ref_rchannel, 769, 600, 100.2)
# The target image will use detected red channel values
img_01 = Square("img_01", img_rchannel, 169, 600)
img_02 = Square("img_02", img_rchannel, 169, 450)
img_03 = Square("img_03", img_rchannel, 169, 300)
img_05 = Square("img_05", img_rchannel, 319, 150)
img_06 = Square("img_06", img_rchannel, 469, 150)
img_07 = Square("img_07", img_rchannel, 619, 150)
img_09 = Square("img_09", img_rchannel, 769, 300)
img_10 = Square("img_10", img_rchannel, 769, 450)
img_11 = Square("img_11", img_rchannel, 769, 600)
# Print the table of reflectance/R channel values
print('Reflectance R Channel Mode')
print('%8s' % cal_11.reflectance, '%12s' % img_11.mode())
print('%8s' % cal_10.reflectance, '%12s' % img_10.mode())
print('%8s' % cal_09.reflectance, '%12s' % img_09.mode())
print('%8s' % cal_07.reflectance, '%12s' % img_07.mode())
print('%8s' % cal_06.reflectance, '%12s' % img_06.mode())
print('%8s' % cal_05.reflectance, '%12s' % img_05.mode())
print('%8s' % cal_03.reflectance, '%12s' % img_03.mode())
print('%8s' % cal_02.reflectance, '%12s' % img_02.mode())
print('%8s' % cal_01.reflectance, '%12s' % img_01.mode())
# Create a merged image for display
alpha = 0.8
merged = (alpha * ref_color + (1 - alpha) * result).astype(dtype=np.uint8)
#cv2.imshow('img_rchannel', img_rchannel)
#cv2.imshow('merged', merged)
def second_largest(numbers):
count = 0
m1 = m2 = float('-inf')
for x in numbers:
count += 1
if x > m2:
if x >= m1:
m1, m2 = x, m1
else:
m2 = x
return m2 if count >= 2 else None
def find_index(row, value):
count = 0
for val in row:
if val == value:
return count
count += 1
return None
cv2.imwrite('working-images/img_rchannel.png', img_rchannel)
filenames = ['working-images/img_rchannel.png']
# For each filename call getFilterInfo to get and display the filter data
for filename in filenames:
(centerX, centerY, radius) = detector.getFilterInfo(filename)
print("Info for '" + filename + "'")
print(" centerX: ", centerX)
print(" centerY: ", centerY)
print(" radius: ", radius)
mask = np.zeros((merged.shape[0], merged.shape[1]), dtype=np.uint8)
cv2.circle(mask, (centerX,centerY), radius, (1,1,1),-1,8,0)
out = img_rchannel * (mask.astype(merged.dtype))
out[np.where(out==[0])] = [255]
#cv2.imshow('out', out)
#cv2.waitKey(0)
cv2.destroyAllWindows()
histg = cv2.calcHist([out],[0],None,[256],[0,256])
idx = second_largest(histg)
r_channel_val = find_index(histg, idx)
x = [cal_01.reflectance, cal_02.reflectance, cal_03.reflectance, cal_05.reflectance, cal_06.reflectance,
cal_07.reflectance, cal_09.reflectance, cal_10.reflectance, cal_11.reflectance]
xi = np.array(x)
y = [img_01.mode(),img_02.mode(),img_03.mode(),img_05.mode(),img_06.mode(),
img_07.mode(),img_09.mode(),img_10.mode(),img_11.mode()]
plt.axis([0, X_AXIS_MAX, 0, Y_AXIS_MAX])
plt.grid(True)
plt.plot(x, y, 'ro')
coefs = np.polyfit(x, y, 2,w=np.sqrt(y))
polynomial = np.poly1d(coefs)
# Solve the quadratic equation for x
a = coefs[0]
b = coefs[1]
c = coefs[2]
y = float(r_channel_val)
d = (b**2)-(4*a*c)+(4*a*y)
sol1 = (-b - math.sqrt(d))/(2*a)
sol2 = (-b + math.sqrt(d))/(2*a)
reflectance_val = sol2 if (sol1 < 0 or sol1 > X_AXIS_MAX) else sol1
print("Reflectance of sample: {0:0.3f}".format(reflectance_val))
xs = np.arange(0.0, X_AXIS_MAX, 0.1)
ys = polynomial(xs)
# Display the table of results
equation_str = "y = {0:.3f}x^2 + {1:.3f}x + {2:.3f}".format(coefs[0], coefs[1], coefs[2])
str2 = "(x,y) = ({0:0.3f}, {1:0.3f})".format(reflectance_val, r_channel_val)
#title = "Reflectance vs R Channel Mode\n{}\n".format(target_filename)
title = "{}".format(target_filename)
plt.suptitle(title, fontsize=16)
plt.xlabel('Reflectance')
plt.ylabel('R Channel Mode')
plt.text(10, 225, equation_str, fontsize=12)
plt.text(10, 210, str2, fontsize=12)
plt.plot(xs, ys)
plt.axhline(y, 0.0, reflectance_val/X_AXIS_MAX, alpha=1.0, color='red', linestyle='dashed')
plt.axvline(reflectance_val, 0.0, y/Y_AXIS_MAX, alpha=1.0, color='red', linestyle='dashed')
plt.show()
#write values to .csv
with open(str(csv_filename)+".csv", 'a', newline='') as csvfile:
reflectwriter = csv.writer(csvfile, delimiter=',')
reflectwriter.writerow([target_filename] + ["Reflectance: "] + [reflectance_val] +
["X: "] + [centerX] + ["Y: "] + [centerY] + ["radius"] + [radius])