qurator-spk/sbb_pixelwise_segmentation
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Code Issues Releases Wiki Activity

rotation augmentation is provided for machine based reading order

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vahidrezanezhad 2025-04-16 23:36:41 +02:00
parent 872e5b0b3a
commit 22f82c5448
2 changed files with 27 additions and 3 deletions
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5
train.py
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@ -380,6 +380,9 @@ def run(_config, n_classes, n_epochs, input_height,
dir_flow_train_labels = os.path.join(dir_train, 'labels') dir_flow_train_labels = os.path.join(dir_train, 'labels')
classes = os.listdir(dir_flow_train_labels) classes = os.listdir(dir_flow_train_labels)
if augmentation:
num_rows = len(classes)*(len(thetha) + 1)
else:
num_rows = len(classes) num_rows = len(classes)
#ls_test = os.listdir(dir_flow_train_labels) #ls_test = os.listdir(dir_flow_train_labels)
@ -390,7 +393,7 @@ def run(_config, n_classes, n_epochs, input_height,
model.compile(loss="binary_crossentropy", model.compile(loss="binary_crossentropy",
optimizer = opt_adam,metrics=['accuracy']) optimizer = opt_adam,metrics=['accuracy'])
for i in range(n_epochs): for i in range(n_epochs):
history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes), steps_per_epoch=num_rows / n_batch, verbose=1) history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1)
model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) )) model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) ))
with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp: with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:

23
utils.py
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@ -363,6 +363,11 @@ def rotation_not_90_func(img, label, thetha):
return rotate_max_area(img, rotated, rotated_label, thetha) return rotate_max_area(img, rotated, rotated_label, thetha)
def rotation_not_90_func_single_image(img, thetha):
rotated = imutils.rotate(img, thetha)
return rotate_max_area(img, rotated, thetha)
def color_images(seg, n_classes): def color_images(seg, n_classes):
ann_u = range(n_classes) ann_u = range(n_classes)
if len(np.shape(seg)) == 3: if len(np.shape(seg)) == 3:
@ -410,7 +415,7 @@ def IoU(Yi, y_predi):
#print("Mean IoU: {:4.3f}".format(mIoU)) #print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU return mIoU
def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batchsize, height, width, n_classes): def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batchsize, height, width, n_classes, thetha, augmentation=False):
all_labels_files = os.listdir(classes_file_dir) all_labels_files = os.listdir(classes_file_dir)
ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16) ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16) ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
@ -434,6 +439,22 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
ret_y= np.zeros((batchsize, n_classes)).astype(np.int16) ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
batchcount = 0 batchcount = 0
if augmentation:
for thetha_i in thetha:
img_rot = rotation_not_90_func_single_image(img, thetha_i)
ret_x[batchcount, :,:,0] = img_rot[:,:,0]/3.0
ret_x[batchcount, :,:,2] = img_rot[:,:,2]/3.0
ret_x[batchcount, :,:,1] = img_rot[:,:,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'): def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'):
c = 0 c = 0
n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images