qurator-spk/sbb_pixelwise_segmentation
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machine based reading order training is integrated

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vahidrezanezhad 2024-05-24 16:39:48 +02:00
parent bf1468391a
commit 4e4490d740
3 changed files with 109 additions and 0 deletions
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23
utils.py
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@ -268,6 +268,29 @@ def IoU(Yi, y_predi):
#print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batchsize, height, width, n_classes):
all_labels_files = os.listdir(classes_file_dir)
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 all_labels_files:
file_name = i.split('.')[0]
img = cv2.imread(os.path.join(modal_dir,file_name+'.png'))
label_class = int( np.load(os.path.join(classes_file_dir,i)) )
ret_x[batchcount, :,:,0] = img[:,:,0]/3.0
ret_x[batchcount, :,:,2] = img[:,:,2]/3.0
ret_x[batchcount, :,:,1] = img[:,:,1]/5.0
ret_y[batchcount, :] = label_class
batchcount+=1
if batchcount>=batchsize:
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 data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'):
c = 0