def setUp(self):
imageFile = '../test/test.jpg'
self.image = ImageIO.cv2read(imageFile)
self.grayscale = cv2.cvtColor(self.image,cv2.COLOR_BGR2GRAY)
self.stats = stats()
self.fft = fft()
self.feats = Features()
self.lap = Laplacian()
self.edges = Edges()
def imageHandler(imgfile):
retStrings = []
if imgfile in seen:
return
if os.path.isdir(os.path.join(args.directory, imgfile)):
temp = os.listdir(os.path.join(args.directory, imgfile))
for f in temp:
images.append(os.path.join(imgfile, f))
return
# silently skip the bin files that have the gps data
if imgfile.endswith('bin'):
return
# alert to other files that were skipped
if not (imgfile.endswith('png') | imgfile.endswith('jpg')):
sys.stderr.write("Skipped file: " + imgfile + "\n")
return
if args.verbose:
sys.stderr.write("Parsing " + imgfile + "\n")
retStrings.append( imgfile + "\t" )
img = ImageIO.cv2read(os.path.join(args.directory, imgfile))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ngray = Normalization.equalizeHistograms(gray)
ngray = cv2.GaussianBlur(ngray, (3,3), 0)
feats.detect_kp_ORB(ngray)
retStrings.append( str(feats.numberKeyPoints()) + "\t" + str(feats.medianKeyPointSize()) + "\t" + str(feats.meanKeyPointSize()) )
for i in range(15):
retStrings.append("\t" + str(feats.numKeyPoints(i*10)))
retStrings.append("\n")
return retStrings
for f in temp:
images.append(os.path.join(imgfile, f))
continue
# silently skip the bin files that have the gps data
if imgfile.endswith('bin'):
continue
# alert to other files that were skipped
if not (imgfile.endswith('png') | imgfile.endswith('jpg')):
sys.stderr.write("Skipped file: " + imgfile + "\n")
continue
if args.verbose:
sys.stderr.write("Parsing " + imgfile + "\n")
fout.write( imgfile + "\t" )
img = ImageIO.cv2read(os.path.join(args.directory, imgfile))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ngray = Normalization.equalizeHistograms(gray)
ngray = cv2.GaussianBlur(ngray, (3,3), 0)
feats.detect_kp_ORB(ngray)
fout.write( str(feats.numberKeyPoints()) + "\t" + str(feats.medianKeyPointSize()) + "\t" + str(feats.meanKeyPointSize()) )
for i in range(15):
fout.write("\t" + str(feats.numKeyPoints(i*10)))
fout.write("\n")
def imageWriter(images,seen,args,fout,classification,stats,fft,lap,edge):
for imgfile in images:
if imgfile in seen: continue
if os.path.isdir(os.path.join(args.directory, imgfile)):
temp = os.listdir(os.path.join(args.directory, imgfile))
for f in temp:
images.append(os.path.join(imgfile, f))
continue
#rewrite of above if statement
#if os.path.isdir(os.path.join(args.directory,imgfile)):
# temp = os.listdir(os.path.join(args.directory,imgfile))
# pool = Pool()
# pool.map(images.append, os.path.join(imgfile, f))
# pool.close()
# pool.join()
# continue
if not args.all and imgfile not in classification:
continue
# silently skip the bin files that have the gps data
if imgfile.endswith('bin'):
continue
# alert to other files that were skipped
if not (imgfile.endswith('png') | imgfile.endswith('jpg')):
sys.stderr.write("Skipped file: " + imgfile + "\n")
continue
if args.verbose:
sys.stderr.write("Parsing " + imgfile + "\n")
fout.write( imgfile + "\t" )
if imgfile in classification:
fout.write( classification[imgfile] + "\t")
else:
fout.write( "unknown\t" )
img = ImageIO.cv2read(os.path.join(args.directory, imgfile))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
fout.write( ('\t'.join(map(str, [stats.min(gray), stats.max(gray), stats.median(gray), stats.mean(gray)]))) + "\t" )
ngray = Normalization.equalizeHistograms(gray)
# apply a gaussian blur to remove edge effects
ngray = cv2.GaussianBlur(ngray, (3,3), 0)
fout.write( ('\t'.join(map(str, [stats.min(ngray), stats.max(ngray), stats.median(ngray), stats.mean(ngray)]))) + "\t")
for i in range(3):
imp = img[:,:,i]
fout.write( ('\t'.join(map(str, [stats.min(imp), stats.max(imp), stats.median(imp), stats.mean(imp)]))) + "\t" )
fout.write( str(fft.energy(gray)) + "\t" + str(fft.energy(ngray)) + "\t")
if args.features:
feats.detect_kp_ORB(ngray)
fout.write( str(feats.numberKeyPoints()) + "\t" + str(feats.medianKeyPointSize()) + "\t" + str(feats.meanKeyPointSize()) + "\t")
for i in range(15):
fout.write( str(feats.numKeyPoints(i*10)) + "\t")
else:
fout.write("0\t0\t0\t");
for i in range(15):
fout.write("0\t")
for i in range(15):
k=2*i+1
fout.write( str(lap.sum(ngray, k)) + "\t")
for i in range(25):
t2 = 10*i
fout.write( str(edge.sumCanny(ngray, 1, t2)) + "\t")
#edge.sumCanny(gray)
# Contour detection
ctr = Contours.contours(ngray)
for i in range(5):
threshold=50*i
ctr.withCanny(1, threshold)
if ctr.numberOfContours() == 0:
fout.write( "0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t" )
else:
try:
fout.write( "\t".join(map(str, [ctr.numberOfContours(), ctr.numberOfClosedContours(),
ctr.numberOfOpenContours(), ctr.totalContourArea(), cv2.contourArea(ctr.largestContourByArea()),
ctr.totalPerimeterLength()])) + "\t")
ctr.linelengths()
fout.write( "\t".join(map(str, [ctr.maxLineLength(), ctr.meanLineLength(), ctr.medianLineLength(), ctr.modeLineLength()])) + "\t")
except Exception as e:
sys.stderr.write("There was an error calculating the contours for " + imgfile +": " + e.message + "\n")
fout.write( "0\t0\t0\t0\t0\t0\t0\t0\t0\t0\t" )
fout.write("\n")
''' A test suite written to test the image analysis code.
All of these tests should pass'''
import sys
sys.path.append('../Modules/')
import ImageIO
from Analysis import *
import os
imageFile = 'test.jpg'
if not os.path.exists(imageFile):
sys.stderr.write("The test image file " + imageFile + " does not exist! Can't complete tests");
sys.exit(-1)
image = ImageIO.cv2read(imageFile)
grayscale = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# test the statistics module
print "The following tests are performed on the grayscale image:"
print "\nTesting the stats class"
s=Stats()
print "Testing minimum: ", s.min(grayscale)
print "Testing maximum: ", s.max(grayscale)
print "Testing mean: ", s.mean(grayscale)
print "Testing median: ", s.median(grayscale)
print "\nTesting the fft class"
f=FFT()
print "Testing FFT: "
def setUp(self):
imageFile = '../test/test.jpg'
self.img = ImageIO.cv2read(imageFile)
def test_cv2read(self):
img = ImageIO.cv2read(self.imageFile)
tmpImg = cv2.imread(self.imageFile)
equalityBool = (img == tmpImg).all()
self.assertTrue(equalityBool)
sys.path.append('../Modules/')
import cv2
import ImageIO
import Normalization
from matplotlib import pyplot as plt
import numpy
'''
A test program to make sure that the Normalization methods work.
This normalizes the images, and displays them.
'''
#test = 'test.jpg'
test = "/home/redwards/Dropbox/ComputerVision/TestCode/test.png"
im = ImageIO.cv2read(test)
print im.shape
print "Testing Tylers normalization"
tn = Normalization.Tyler(im)
print "Testing histogram equalization"
he = Normalization.equalizeHistograms(im)
heo = numpy.ones_like(im)
heo[:,:,0]=he
heo[:,:,1]=he
heo[:,:,2]=he
print "Simple normaliztion"
nh = Normalization.simpleNorm(im)
import sys
sys.path.append('../Modules/')
import ImageIO
from Analysis import *
'''
A simple test whether the bgr_energy calculation works
'''
im = sys.argv[1]
img = ImageIO.cv2read(im)
fft=FFT()
for i in range(3):
print "The energy for the ", i, " channel is ", fft.energy(img[:,:,i])
def setUp(self):
imageFile = '../test/test.jpg'
self.img = ImageIO.cv2read(imageFile)
self.contours = contours(self.img)
