def Spatial_CFIS(configFile, DataFolder):
#ap = argparse.ArgumentParser()
# ap.add_argument("-c", "--config", required = True, help = "path to config")
# ap.add_argument("-d", "--data", required = True, help= "path to Data")
# ap.add_argument("-o", "--output", required = True, help = "path to predict image")
# ap.add_argument("-a", "--accuracy", required = True, help = "path to accuracy folder")
# #ap.add_argument("-id","--userID", required = True, help = "User ID")
# args = vars(ap.parse_args())
try:
with open(configFile, 'r') as f:
data = f.read()
Bs_data = Bs(data, "xml")
except:
print("Please choose *.xml or format config xml file same as tutorial")
t = int(Bs_data.FCM.t.contents[0])
c = int(Bs_data.FCM.c.contents[0])
m = int(Bs_data.FCM.m.contents[0])
eps = float(Bs_data.FCM.eps.contents[0])
lr = float(Bs_data.Adam.LearningRate.contents[0])
iterations = int(Bs_data.Adam.iteration.contents[0])
threshold = float(Bs_data.Adam.threshold.contents[0])
momentum = float(Bs_data.Adam.momentum.contents[0])
beta1 = float(Bs_data.Adam.beta1.contents[0])
beta2 = float(Bs_data.Adam.beta2.contents[0])
all_data_folder = DataFolder
SplitData.splitImg(all_data_folder)
training_data_folder = os.path.join('Data', 'Training Data')
creat_HOD.readImage(training_data_folder)
start_time = time.time()
folderImg = os.path.join('Data', os.path.join('Training Data', 'gray'))
folderHod = os.path.join('Data', os.path.join('Training Data', 'hod'))
HodTraining = FCM.readHodImg(folderHod) / 255
ImgTraining = FCM.readDataImg(folderImg) / 255
for iter in range(0, 30):
print('iter: ', iter)
oPath = "Result" + str(iter)
try:
outputPath = os.mkdir(oPath)
except OSError as error:
print(error)
try:
folderU = 'U' + str(iter)
folderV = 'V' + str(iter)
os.mkdir(folderU)
os.mkdir(folderV)
except OSError as error:
print(error)
FCM.FCM(folderU, folderV, ImgTraining, HodTraining, t, c, m, eps)
rel_a, rel_b, rel_c, img_a, img_b, img_c = MakeRule.readUAndV(
folderU, folderV, ImgTraining, HodTraining, c)
np.savetxt(os.path.join('Rel', 'rel_a.txt'),
rel_a,
fmt="%.4f",
delimiter=',')
np.savetxt(os.path.join('Img', 'img_a.txt'),
img_a,
fmt="%.4f",
delimiter=',')
np.savetxt(os.path.join('Rel', 'rel_b.txt'),
rel_b,
fmt="%.4f",
delimiter=',')
np.savetxt(os.path.join('Img', 'img_b.txt'),
img_b,
fmt="%.4f",
delimiter=',')
np.savetxt(os.path.join('Rel', 'rel_c.txt'),
rel_c,
fmt="%.4f",
delimiter=',')
np.savetxt(os.path.join('Img', 'img_c.txt'),
img_c,
fmt="%.4f",
delimiter=',')
path_rel_a = os.path.join('Rel', 'rel_a.txt')
path_img_a = os.path.join('Img', 'img_a.txt')
path_rel_b = os.path.join('Rel', 'rel_b.txt')
path_img_b = os.path.join('Img', 'img_b.txt')
path_rel_c = os.path.join('Rel', 'rel_c.txt')
path_img_c = os.path.join('Img', 'img_c.txt')
rel_rule, img_rule = TrainingAndPredict.readRule(
path_rel_a, path_rel_b, path_rel_c, path_img_a, path_img_b,
path_img_c)
last_image_name = sorted(
os.listdir(
os.path.join('Data', os.path.join('Training Data',
'gray'))))[-1]
if (not FCM.validImg(last_image_name)):
last_image_name = sorted(
os.listdir(
os.path.join('Data', os.path.join('Training Data',
'gray'))))[-2]
last_image_path = os.path.join(
'Data',
os.path.join('Training Data', os.path.join('gray',
last_image_name)))
last_hod_name = sorted(
os.listdir(
os.path.join('Data', os.path.join('Training Data',
'hod'))))[-1]
if (not FCM.validTxt(last_hod_name)):
last_hod_name = sorted(
os.listdir(
os.path.join('Data', os.path.join('Training Data',
'hod'))))[-2]
last_hod_path = os.path.join(
'Data',
os.path.join('Training Data', os.path.join('hod', last_hod_name)))
U, width, height = TrainingAndPredict.calU(rel_rule, img_rule,
last_image_path,
last_hod_path)
np.savetxt("U_all_rule.txt", U, fmt='%.4f', delimiter=',')
s = np.ones([U.shape[0], 1])
for i in range(U.shape[0]):
if (np.sum(U[i] != 0)):
s[i] = np.sum(U[i])
theta1 = np.ones(3)
first_image_testing = sorted(
os.listdir(os.path.join('Data', 'Testing')))[0]
if (not FCM.validImg(first_image_testing)):
first_image_testing = sorted(
os.listdir(os.path.join('Data', 'Testing')))[1]
first_image_testing_path = os.path.join(
'Data', os.path.join('Testing', first_image_testing))
y = cv2.imread(first_image_testing_path, 0) #first image of testing
y = y.flatten()
y = y.reshape(-1, 1)
w = TrainingAndPredict.Adam_img(U, rel_rule, y, theta1, s, lr,
iterations, threshold, momentum, beta1,
beta2, last_image_path)
for fileName in sorted(os.listdir(os.path.join('Data', 'Testing'))):
if (FCM.validImg(fileName)):
rel_W = np.asarray([[1, 2, 1]])
img_W = w.reshape(1, 3)
rel_def, img_def = TrainingAndPredict.calDEF(
rel_W, img_W, rel_rule, img_rule)
O_rel, O_img = TrainingAndPredict.calO(rel_def, img_def, U)
O_rel = ((O_rel / 4) * 255)
O_img = ((O_img / (np.sum(img_W))) * 255)
#img=cv2.imread('Hawaii_100x100_crop/Testing/rs_10.jpg',0)
img = cv2.imread(
os.path.join('Data', os.path.join('Testing', fileName)), 0)
#img=cv2.imread(os.path.join('Data',os.path.join('Testing',...)),0)
img = img.flatten().reshape(1, -1) / 255
for i in range(U.shape[0]):
if (np.sum(U[i] != 0)):
O_rel[0][i] = O_rel[0][i] / np.sum(U[i])
O_img[0][i] = O_img[0][i] / np.sum(U[i])
O_img[0][i] = img[0][i] * (1 + O_img[0][i])
O_img = O_img.reshape(width, height).astype(int)
cv2.imwrite(os.path.join(oPath, fileName), O_img)
#cv2.imwrite('ver1_'+fileName,O_img)
result = RMSE.Accuracy(os.path.join('Data', 'Testing'), oPath)
np.savetxt("./RMSE" + str(iter), result, fmt='%.4f', delimiter=',')
end_time = time.time()
