qurator-spk/sbb_pixelwise_segmentation
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integrating first working classification training model

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vahidrezanezhad 2024-04-29 20:59:36 +02:00
parent d27647a0f1
commit dbb84507ed
5 changed files with 419 additions and 158 deletions
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113
utils.py
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@ -8,6 +8,119 @@ import random
from tqdm import tqdm
import imutils
import math
from tensorflow.keras.utils import to_categorical
def return_number_of_total_training_data(path_classes):
sub_classes = os.listdir(path_classes)
n_tot = 0
for sub_c in sub_classes:
sub_files = os.listdir(os.path.join(path_classes,sub_c))
n_tot = n_tot + len(sub_files)
return n_tot
def generate_data_from_folder_evaluation(path_classes, height, width, n_classes):
sub_classes = os.listdir(path_classes)
#n_classes = len(sub_classes)
all_imgs = []
labels = []
dicts =dict()
indexer= 0
for sub_c in sub_classes:
sub_files = os.listdir(os.path.join(path_classes,sub_c ))
sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files]
#print( os.listdir(os.path.join(path_classes,sub_c )) )
all_imgs = all_imgs + sub_files
sub_labels = list( np.zeros( len(sub_files) ) +indexer )
#print( len(sub_labels) )
labels = labels + sub_labels
dicts[sub_c] = indexer
indexer +=1
categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ]
ret_x= np.zeros((len(labels), height,width, 3)).astype(np.int16)
ret_y= np.zeros((len(labels), n_classes)).astype(np.int16)
#print(all_imgs)
for i in range(len(all_imgs)):
row = all_imgs[i]
#####img = cv2.imread(row, 0)
#####img= resize_image (img, height, width)
#####img = img.astype(np.uint16)
#####ret_x[i, :,:,0] = img[:,:]
#####ret_x[i, :,:,1] = img[:,:]
#####ret_x[i, :,:,2] = img[:,:]
img = cv2.imread(row)
img= resize_image (img, height, width)
img = img.astype(np.uint16)
ret_x[i, :,:] = img[:,:,:]
ret_y[i, :] = categories[ int( labels[i] ) ][:]
return ret_x/255., ret_y
def generate_data_from_folder_training(path_classes, batchsize, height, width, n_classes):
sub_classes = os.listdir(path_classes)
n_classes = len(sub_classes)
all_imgs = []
labels = []
dicts =dict()
indexer= 0
for sub_c in sub_classes:
sub_files = os.listdir(os.path.join(path_classes,sub_c ))
sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files]
#print( os.listdir(os.path.join(path_classes,sub_c )) )
all_imgs = all_imgs + sub_files
sub_labels = list( np.zeros( len(sub_files) ) +indexer )
#print( len(sub_labels) )
labels = labels + sub_labels
dicts[sub_c] = indexer
indexer +=1
ids = np.array(range(len(labels)))
random.shuffle(ids)
shuffled_labels = np.array(labels)[ids]
shuffled_files = np.array(all_imgs)[ids]
categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ]
ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
batchcount = 0
while True:
for i in range(len(shuffled_files)):
row = shuffled_files[i]
#print(row)
###img = cv2.imread(row, 0)
###img= resize_image (img, height, width)
###img = img.astype(np.uint16)
###ret_x[batchcount, :,:,0] = img[:,:]
###ret_x[batchcount, :,:,1] = img[:,:]
###ret_x[batchcount, :,:,2] = img[:,:]
img = cv2.imread(row)
img= resize_image (img, height, width)
img = img.astype(np.uint16)
ret_x[batchcount, :,:,:] = img[:,:,:]
#print(int(shuffled_labels[i]) )
#print( categories[int(shuffled_labels[i])] )
ret_y[batchcount, :] = categories[ int( shuffled_labels[i] ) ][:]
batchcount+=1
if batchcount>=batchsize:
ret_x = ret_x/255.
yield (ret_x, ret_y)
ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
batchcount = 0
def do_brightening(img_in_dir, factor):
im = Image.open(img_in_dir)