def augment(img):
np.seterr(over='ignore')
blockSize = 9
height = len(img)
width = len(img[0])
gimfiltR = 50 # Radius size of guided filter
eps = 10**-3 # Epsilon value of guided filter
Nrer = [0.95, 0.93,
0.85] # Normalized residual energy ratio of G-B-R channels
AtomsphericLight = np.zeros(3)
AtomsphericLight[0] = (
1.13 * np.mean(img[:, :, 0])) + 1.11 * np.std(img[:, :, 0]) - 25.6
AtomsphericLight[1] = (
1.13 * np.mean(img[:, :, 1])) + 1.11 * np.std(img[:, :, 1]) - 25.6
AtomsphericLight[2] = 140 / (
1 + 14.4 * np.exp(-0.034 * np.median(img[:, :, 2])))
AtomsphericLight = np.clip(AtomsphericLight, 5, 250)
transmissionR = getTransmission(img, AtomsphericLight, blockSize)
TM_R_modified = Depth_TM(img, AtomsphericLight)
TM_R_modified_Art = Sat_max(img)
transmissionR_new = np.copy(transmissionR)
for i in range(0, img.shape[0]):
for j in range(0, img.shape[1]):
if (transmissionR_new[i, j] > TM_R_modified[i, j]):
transmissionR_new[i, j] = TM_R_modified[i, j]
if (transmissionR_new[i, j] < TM_R_modified_Art[i, j]):
transmissionR_new[i, j] = TM_R_modified_Art[i, j]
transmissionR_Stretched = stretching(transmissionR_new, height, width)
transmissionB, transmissionG, depth_map = getGBTransmissionESt(
transmissionR_Stretched, AtomsphericLight)
transmission = Refinedtransmission(transmissionB, transmissionG,
transmissionR_Stretched, img)
sceneRadiance = sceneRadianceRGB(img, transmission, AtomsphericLight)
sceneRadiance = OptimalParameter(sceneRadiance)
return sceneRadiance
transmissionR_Stretched = stretching(transmissionR_new, height, width)
transmissionB, transmissionG, depth_map = getGBTransmissionESt(
transmissionR_Stretched, AtomsphericLight)
transmission = Refinedtransmission(transmissionB, transmissionG,
transmissionR_Stretched, img)
transmissionR_Stretched = transmission[:, :, 2]
# cv2.imwrite('Results_TMs/' + Num + 'TM_R.jpg', np.uint8(transmissionR * 255))
# cv2.imwrite('Results_temps/' + prefix + 'TM_R_Refi_fusion_Sat_lamba.jpg', np.uint8(transmissionR_Stretched * 255))
# cv2.imwrite('OutputImages/' + prefix + '_SMBLOTMOP_TM.jpg', np.uint8(transmissionR_Stretched * 255))
cv2.imwrite('Results_Enhance_fusion/' + prefix + '_SMBLOTMOP_TM_R.jpg',
np.uint8(transmissionR_Stretched * 255))
# cv2.imwrite('Results_Enhance_fusion/' + prefix + '_SMBLOTMOP_TM_G.jpg', np.uint8(transmission[:, :, 1] * 255))
# cv2.imwrite('Results_Enhance_fusion/' + prefix + '_SMBLOTMOP_TM_B.jpg', np.uint8(transmission[:, :, 0] * 255))
sceneRadiance = sceneRadianceRGB(img, transmission, AtomsphericLight)
# cv2.imwrite('OutputImages/' + prefix + '_SMBLOTM.jpg', sceneRadiance)
cv2.imwrite('Results_Enhance_fusion/' + prefix + '_SMBLOTM.jpg',
sceneRadiance)
sceneRadiance = OptimalParameter(sceneRadiance)
# cv2.imwrite('Results_temps/' + prefix + '_SMBLOTMOptimalParameters.jpg', sceneRadiance)
# cv2.imwrite('OutputImages/' + prefix + '_SMBLOTMOP.jpg', sceneRadiance)
cv2.imwrite('Results_Enhance_fusion/' + prefix + '_SMBLOTMOP.jpg',
sceneRadiance)
Endtime = datetime.datetime.now()
Time = Endtime - starttime
print('Time', Time)
np.seterr(over='ignore')
if __name__ == '__main__':
pass
# folder = "C:/Users/Administrator/Desktop/UnderwaterImageEnhancement/NonPhysical/ICM"
folder = r"\\cylog.local\rede\Usuarios\Documentos\Luiz\Documents\PROJETO_DIP"
path = folder + "/InputImages"
files = os.listdir(path)
files = natsort.natsorted(files)
for i in range(len(files)):
file = files[i]
filepath = path + "/" + file
prefix = file.split('.')[0]
if os.path.isfile(filepath):
print('******** file ********',file)
# img = cv2.imread('InputImages/' + file)
img = cv2.imread(folder + '/InputImages/' + file)
img = stretching(img)
sceneRadiance = sceneRadianceRGB(img)
# cv2.imwrite(folder + '/OutputImages/' + Number + 'Stretched.jpg', sceneRadiance)
sceneRadiance = HSVStretching(sceneRadiance)
sceneRadiance = sceneRadianceRGB(sceneRadiance)
cv2.imwrite('ICM.jpg', sceneRadiance)
plt.subplot(111),plt.imshow(sceneRadiance)
plt.show()
for i in files:
img = cv2.cv2.imread("D:/Single-Underwater-Image-Enhancement-and-Color-Restoration-master/underwater-test-dataset/upload/DATABASE 1./"+str(c)+".jpg")
height = len(img)
width = len(img[0])
sceneRadiance = RGB_equalisation(img, height, width)
# sceneRadiance = stretching(img)
sceneRadiance = stretching(sceneRadiance)
sceneRadiance_Lower, sceneRadiance_Upper = rayleighStretching(sceneRadiance, height, width)
sceneRadiance = (np.float64(sceneRadiance_Lower) + np.float64(sceneRadiance_Upper)) / 2
# cv2.imwrite('OutputImages/' + prefix + 'Lower0.jpg', sceneRadiance_Lower)
# cv2.imwrite('OutputImages/' + prefix + 'Upper0.jpg', sceneRadiance_Upper)
sceneRadiance = HSVStretching(sceneRadiance)
sceneRadiance = sceneRadianceRGB(sceneRadiance)
cv2.cv2.imwrite( str(c)+'_out.jpg', sceneRadiance)
entropy = skimage.measure.shannon_entropy(sceneRadiance)
print(str(entropy) + '\t')
mse = skimage.measure.compare_mse(img,sceneRadiance)
psnr = skimage.measure.compare_psnr(img, sceneRadiance)
print(str(mse) + '\t' + str(psnr)+'\n')
c=c+1
avg_entropy+=entropy
avg_psnr+=psnr
avg_mse+=mse
endtime = datetime.datetime.now()
time = endtime-starttime