def computeMeanValues(args):
outfile = open("ImageMeanValues1.txt", 'w')
goodMeans = []
yellowedMeans = []
for i in range(3118,4116):
image = []
try:
if i < 10:
image = dia.openImage("DJI0000" + str(i) + ".JPG")
print "DJI0000" + str(i) + ".JPG"
elif i >= 10 and i < 100:
image = dia.openImage("DJI000" + str(i) + ".JPG")
print "DJI000" + str(i) + ".JPG"
elif i >= 100 and i < 1000 :
image = dia.openImage("DJI00" + str(i) + ".JPG")
print "DJI00" + str(i) + ".JPG"
else:
image = dia.openImage("DJI0" + str(i) + ".JPG")
print "DJI0" + str(i) + ".JPG"
meanVector = dia.meanVector(image)
if meanVector[2] > 80:
goodMeans.append(meanVector[2])
else:
yellowedMeans.append(meanVector[2])
outfile.write("Image Name: DJI00" + str(i) + ".JPG\n")
outfile.write("Image Mean Vector\n")
outfile.write(str(meanVector))
outfile.write('\n\n')
except AttributeError:
print "DJI00" + str(i) + ".JPG not found"
goodMeans = np.array(goodMeans, dtype='uint64')
yellowedMeans = np.array(yellowedMeans, dtype='uint64')
goodMeanGlobal = np.sum(1.0*goodMeans/goodMeans.shape[0])
yellowedMeanGlobal = np.sum(1.0*yellowedMeans/yellowedMeans.shape[0])
def shiftImages(args):
outfile = open("ImageMeanValues1.txt", 'a')
yellowedMeans = []
goodMeans = []
redMeans = []
redGoodMeans = []
means = []
for i in range(3118,4116):
image = []
try:
if i < 10:
image = dia.openImage("DJI0000" + str(i) + ".JPG")
print "DJI0000" + str(i) + ".JPG"
elif i >= 10 and i < 100:
image = dia.openImage("DJI000" + str(i) + ".JPG")
print "DJI000" + str(i) + ".JPG"
elif i >= 100 and i < 1000 :
image = dia.openImage("DJI00" + str(i) + ".JPG")
print "DJI00" + str(i) + ".JPG"
else:
image = dia.openImage("DJI0" + str(i) + ".JPG")
print "DJI0" + str(i) + ".JPG"
meanVector = dia.meanVector(image)
means.append(meanVector)
if meanVector[2] > 80:
goodMeans.append(meanVector[2])
else:
yellowedMeans.append(meanVector[2])
if meanVector[0] < 145:
redGoodMeans.append(meanVector[0])
else:
redMeans.append(meanVector[0])
except AttributeError:
print "DJI00" + str(i) + ".JPG not found"
means = np.array(means)
plt.plot(range(0,means.shape[0]), means[:,0], c='red')
plt.plot(range(0,means.shape[0]), means[:,1], c='green')
plt.plot(range(0,means.shape[0]), means[:,2], c='blue')
plt.title('Image Mean Values')
plt.xlabel('Image Number')
plt.ylabel('Mean DN')
plt.show()
goodMeans = np.array(goodMeans, dtype='uint64')
yellowedMeans = np.array(yellowedMeans, dtype='uint64')
goodMeanGlobal = np.sum(1.0*goodMeans/goodMeans.shape[0])
yellowedMeanGlobal = np.sum(1.0*yellowedMeans/yellowedMeans.shape[0])
meanDifference = goodMeanGlobal - yellowedMeanGlobal
outfile.write("\n\n\n\n These are the global statistics of the blue band using a value of 80 to separate the \"good images\" from the \"yellowed images\". The value was obtained by inspection of the output mean values.\n\n")
outfile.write('\nGood Mean = ' + str(goodMeanGlobal))
outfile.write('\nYellow Mean = ' + str(yellowedMeanGlobal))
goodVar = np.sum(np.power(goodMeans - goodMeanGlobal,2))/(goodMeans.shape[0] - 1)
yellowedVar = np.sum(np.power(yellowedMeans - yellowedMeanGlobal,2))/(yellowedMeans.shape[0] - 1)
outfile.write('\nGood Standard Deviation = ' + str(np.sqrt(goodVar)))
outfile.write('\nYellowed Standard Deviaiton = ' + str(np.sqrt(yellowedVar)))
outfile.write('\nYellowed Range = ' + str(np.amax(yellowedMeans) - np.amin(yellowedMeans)))
outfile.write('\nGood Range = ' + str(np.amax(goodMeans) - np.amin(goodMeans)))
redGoodMeans = np.array(redGoodMeans, dtype='uint64')
redMeans = np.array(redMeans, dtype='uint64')
redGoodMeanGlobal = np.sum(1.0*redGoodMeans/redGoodMeans.shape[0])
redMeanGlobal = np.sum(1.0*redMeans/redMeans.shape[0])
redMeanDifference = redGoodMeanGlobal - redMeanGlobal
outfile.write("\n\n\n\n These are the global statistics of the Red band using a value of 145 to separate the \"good images\" from the \"yellowed images\". The value was obtained by inspection of the output mean values.\n\n")
outfile.write('\nGood Mean = ' + str(redGoodMeanGlobal))
outfile.write('\nYellow Mean = ' + str(redMeanGlobal))
goodVar = np.sum(np.power(redGoodMeans - redGoodMeanGlobal,2))/(redGoodMeans.shape[0] - 1)
yellowedVar = np.sum(np.power(redMeans - redMeanGlobal,2))/(redMeans.shape[0] - 1)
outfile.write('\nGood Standard Deviation = ' + str(np.sqrt(goodVar)))
outfile.write('\nYellowed Standard Deviaiton = ' + str(np.sqrt(yellowedVar)))
outfile.write('\nYellowed Range = ' + str(np.amax(redMeans) - np.amin(redMeans)))
outfile.write('\nGood Range = ' + str(np.amax(redGoodMeans) - np.amin(redGoodMeans)))
refImage = dia.openImage("DJI03790.JPG")
for i in range(3118,4116):
image = []
try:
if i < 10:
image = dia.openImage("DJI0000" + str(i) + ".JPG")
print "DJI0000" + str(i) + ".JPG"
elif i >= 10 and i < 100:
image = dia.openImage("DJI000" + str(i) + ".JPG")
print "DJI000" + str(i) + ".JPG"
elif i >= 100 and i < 1000 :
image = dia.openImage("DJI00" + str(i) + ".JPG")
print "DJI00" + str(i) + ".JPG"
else:
image = dia.openImage("DJI0" + str(i) + ".JPG")
print "DJI0" + str(i) + ".JPG"
print "Processing Image" + str(i)
meanVector = dia.meanVector(image)
if meanVector[2] < 80:
print "Correcting Image" + str(i)
newImage = np.array([image[0] + redMeanDifference,image[1],image[2] + meanDifference],dtype='uint8')
x = newImage[0] > image[0]
y = newImage[2] < image[2]
b = newImage[0]
c = newImage[2]
b[x] = 0
c[y] = 255
correctedImage = np.array([b,image[1],c], dtype='uint8')
if i < 10:
dia.outputImage(correctedImage, "corrected/CorDJI0000" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI0000" + str(i) + ".JPG", "DJI0000" + str(i) + ".JPG")
elif i >= 10 and i < 100:
dia.outputImage(correctedImage, "corrected/CorDJI000" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI000" + str(i) + ".JPG", "DJI000" + str(i) + ".JPG")
elif i >= 100 and i < 1000 :
dia.outputImage(correctedImage, "corrected/CorDJI00" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI00" + str(i) + ".JPG", "DJI00" + str(i) + ".JPG")
else:
dia.outputImage(correctedImage, "corrected/CorDJI0" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI0" + str(i) + ".JPG", "DJI0" + str(i) + ".JPG")
else:
if i < 10:
dia.outputImage(image, "corrected/CorDJI0000" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI0000" + str(i) + ".JPG", "DJI0000" + str(i) + ".JPG")
elif i >= 10 and i < 100:
dia.outputImage(image, "corrected/CorDJI000" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI000" + str(i) + ".JPG", "DJI000" + str(i) + ".JPG")
elif i >= 100 and i < 1000 :
dia.outputImage(image, "corrected/CorDJI00" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI00" + str(i) + ".JPG", "DJI00" + str(i) + ".JPG")
else:
dia.outputImage(image, "corrected/CorDJI0" + str(i) + ".JPG")
dia.georefImage("corrected/CorDJI0" + str(i) + ".JPG", "DJI0" + str(i) + ".JPG")
except AttributeError as e:
print e
print "DJI00" + str(i) + ".JPG not found"