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rename: image_{dir,filename}, {f_name,image_filename_stem}

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This commit is contained in:
Konstantin Baierer 2021-01-27 12:52:51 +01:00
parent f5e11a1056
commit 52df6972ad
2 changed files with 10 additions and 123 deletions
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34
sbb_newspapers_org_image/eynollah.py
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@ -1,3 +1,4 @@
# pylint: disable=no-member
"""
tool to extract table form data from alto xml data
"""
@ -200,15 +201,8 @@ class eynollah:
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
for i in range(nxf):
for j in range(nyf):
@ -295,7 +289,6 @@ class eynollah:
return int(float(dpi))
def resize_image_with_column_classifier(self, is_image_enhanced):
dpi = self.check_dpi()
img = cv2.imread(self.image_filename)
img = img.astype(np.uint8)
@ -540,11 +533,6 @@ class eynollah:
image_res = self.predict_enhancement(img_new)
# cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem) + ".tif",self.image)
# self.image=self.image.astype(np.uint16)
# self.scale_x=1
# self.scale_y=1
# self.height_org = self.image.shape[0]
# self.width_org = self.image.shape[1]
is_image_enhanced = True
else:
is_image_enhanced = False
@ -570,8 +558,6 @@ class eynollah:
def get_image_and_scales_after_enhancing(self, img_org, img_res):
# self.image = cv2.imread(self.image_filename)
self.image = np.copy(img_res)
self.image = self.image.astype(np.uint8)
self.image_org = np.copy(img_org)
@ -630,15 +616,8 @@ class eynollah:
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
for i in range(nxf):
for j in range(nyf):
@ -665,11 +644,8 @@ class eynollah:
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
if i == 0 and j == 0: