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sbb_binarization
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padding the whole image in order to avoid artifacts on the page boundries

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transformer_model_integration
vahid 2 years ago
parent ffdc776192
commit de89e7df12
2 changed files with 32 additions and 27 deletions
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2
requirements.txt
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@ -2,4 +2,4 @@ numpy
setuptools >= 41 setuptools >= 41
opencv-python-headless opencv-python-headless
ocrd >= 2.22.3 ocrd >= 2.22.3
tensorflow == 2.4.* tensorflow-gpu >= 2.6.0

57
sbb_binarize/sbb_binarize.py
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@ -112,8 +112,10 @@ class SbbBinarizer:
def load_model(self, model_name): def load_model(self, model_name):
try: try:
model = load_model(join(self.model_dir, model_name), compile=False) model = load_model(join(self.model_dir, model_name), compile=False)
self.margin_percent = 0.1
except: except:
model = load_model(join(self.model_dir, model_name) , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches}) model = load_model(join(self.model_dir, model_name) , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
self.margin_percent = 0.15
model_height = model.layers[len(model.layers)-1].output_shape[1] model_height = model.layers[len(model.layers)-1].output_shape[1]
model_width = model.layers[len(model.layers)-1].output_shape[2] model_width = model.layers[len(model.layers)-1].output_shape[2]
n_classes = model.layers[len(model.layers)-1].output_shape[3] n_classes = model.layers[len(model.layers)-1].output_shape[3]
@ -156,14 +158,31 @@ class SbbBinarizer:
index_start_w = 0 index_start_w = 0
img_padded = np.copy(img) img_padded = np.copy(img)
img_org_h_pad = img_padded.shape[0]
img_org_w_pad = img_padded.shape[1]
img = np.copy(img_padded) index_start_h_alw = 100
index_start_w_alw = 100
img_padded_alw = np.zeros(( img_padded.shape[0]+2*index_start_h_alw, img.shape[1]+2*index_start_w_alw, img.shape[2] ))
img_padded_alw [ 0: index_start_h_alw, index_start_w_alw: index_start_w_alw+img_padded.shape[1], : ] = img_padded[:index_start_h_alw,:,:]
img_padded_alw [ index_start_h_alw: index_start_h_alw+img_padded.shape[0], 0:index_start_w_alw, : ] = img_padded[:,0:index_start_w_alw,:]
img_padded_alw [ img_padded_alw.shape[0]-index_start_h_alw: img_padded_alw.shape[0], index_start_w_alw: index_start_w_alw+img_padded.shape[1], : ] = img_padded[img_padded.shape[0]-index_start_h_alw:img_padded.shape[0],:,:]
img_padded_alw [ index_start_h_alw: index_start_h_alw+img_padded.shape[0],img_padded_alw.shape[1]-index_start_w_alw: img_padded_alw.shape[1], : ] = img_padded[:,img_padded.shape[1]-index_start_w_alw:img_padded.shape[1],:]
img_padded_alw [ index_start_h_alw: index_start_h_alw+img_padded.shape[0], index_start_w_alw: index_start_w_alw+img_padded.shape[1], : ] = img_padded[:,:,:]
img = np.copy(img_padded_alw)
if use_patches: if use_patches:
margin = int(0.1 * model_width) margin = int(self.margin_percent * model_width)
width_mid = model_width - 2 * margin width_mid = model_width - 2 * margin
height_mid = model_height - 2 * margin height_mid = model_height - 2 * margin
@ -215,20 +234,20 @@ class SbbBinarizer:
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
h_res = int( img_patch.shape[0]/1.05) #h_res = int( img_patch.shape[0]/1.05)
w_res = int( img_patch.shape[1]/1.05) #w_res = int( img_patch.shape[1]/1.05)
img_patch_resize = resize_image(img_patch, h_res, w_res) #img_patch_resize = resize_image(img_patch, h_res, w_res)
img_patch_resized_padded =np.ones((img_patch.shape[0],img_patch.shape[1],img_patch.shape[2])).astype(float)#self.do_padding() #img_patch_resized_padded =np.ones((img_patch.shape[0],img_patch.shape[1],img_patch.shape[2])).astype(float)#self.do_padding()
h_start=int( abs(img_patch.shape[0]-img_patch_resize.shape[0])/2. ) #h_start=int( abs(img_patch.shape[0]-img_patch_resize.shape[0])/2. )
w_start=int( abs(img_patch.shape[1]-img_patch_resize.shape[1])/2. ) #w_start=int( abs(img_patch.shape[1]-img_patch_resize.shape[1])/2. )
img_patch_resized_padded[h_start:h_start+img_patch_resize.shape[0],w_start:w_start+img_patch_resize.shape[1],:]=np.copy(img_patch_resize[:,:,:]) #img_patch_resized_padded[h_start:h_start+img_patch_resize.shape[0],w_start:w_start+img_patch_resize.shape[1],:]=np.copy(img_patch_resize[:,:,:])
label_p_pred_padded = model.predict(img_patch_resized_padded.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) #label_p_pred_padded = model.predict(img_patch_resized_padded.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
@ -237,22 +256,8 @@ class SbbBinarizer:
#label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) #label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_padded = np.argmax(label_p_pred_padded, axis=3)[0]
seg_padded_take_core = seg_padded[h_start:h_start+img_patch_resize.shape[0],w_start:w_start+img_patch_resize.shape[1]]
seg_padded_take_core_org_size= resize_image(seg_padded_take_core, img_patch.shape[0], img_patch.shape[1])
#print(seg_padded_take_core_org_size,'sag padded')
#print(seg,'sag')
seg_tot = seg_padded_take_core_org_size+0#seg
seg_tot[seg_tot>1]=1
seg_color = np.repeat(seg_tot[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
#seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) #seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
@ -320,7 +325,7 @@ class SbbBinarizer:
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
prediction_true = prediction_true[index_start_h_alw: index_start_h_alw+img_org_h_pad, index_start_w_alw: index_start_w_alw+img_org_w_pad,:]
prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:] prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)

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