qurator-spk
/
eynollah
mirror of https://github.com/qurator-spk/eynollah.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

inference batch size debugged

Browse Source
pull/138/head^2
vahidrezanezhad 4 months ago
parent 7ae6a8776f
commit 93005959e5
1 changed files with 66 additions and 5 deletions
Show Stats Download Patch File Download Diff File

71
qurator/eynollah/eynollah.py
Unescape Escape View File

@ -89,7 +89,7 @@ from .utils.xml import order_and_id_of_texts
from .plot import EynollahPlotter
from .writer import EynollahXmlWriter
MIN_AREA_REGION = 0.0005
MIN_AREA_REGION = 0.00001
SLOPE_THRESHOLD = 0.13
RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45:
DPI_THRESHOLD = 298
@ -182,6 +182,7 @@ class Eynollah:
logger=None,
pcgts=None,
):
self.light_version = light_version
if not dir_in:
if image_pil:
self._imgs = self._cache_images(image_pil=image_pil)
@ -209,7 +210,6 @@ class Eynollah:
self.input_binary = input_binary
self.allow_scaling = allow_scaling
self.headers_off = headers_off
self.light_version = light_version
self.ignore_page_extraction = ignore_page_extraction
self.ocr = do_ocr
self.pcgts = pcgts
@ -828,7 +828,64 @@ class Eynollah:
batch_indexer = batch_indexer + 1
if batch_indexer == n_batch_inference:
label_p_pred = model.predict(img_patch,verbose=0)
seg = np.argmax(label_p_pred, axis=3)
indexer_inside_batch = 0
for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_in = seg[indexer_inside_batch,:,:]
seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
index_y_u_in = list_y_u[indexer_inside_batch]
index_y_d_in = list_y_d[indexer_inside_batch]
index_x_u_in = list_x_u[indexer_inside_batch]
index_x_d_in = list_x_d[indexer_inside_batch]
if i_batch == 0 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
else:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
indexer_inside_batch = indexer_inside_batch +1
list_i_s = []
list_j_s = []
list_x_u = []
list_x_d = []
list_y_u = []
list_y_d = []
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
elif i==(nxf-1) and j==(nyf-1):
label_p_pred = model.predict(img_patch,verbose=0)
seg = np.argmax(label_p_pred, axis=3)
@ -885,6 +942,7 @@ class Eynollah:
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
prediction_true = prediction_true.astype(np.uint8)
#del model
#gc.collect()
@ -1789,9 +1847,9 @@ class Eynollah:
t_bin = time.time()
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=10)
prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=10)
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
#print("inside bin ", time.time()-t_bin)
prediction_bin=prediction_bin[:,:,0]
@ -1808,7 +1866,6 @@ class Eynollah:
textline_mask_tot_ea = self.run_textline(img_bin)
#print("inside 2 ", time.time()-t_in)
#print(img_resized.shape, num_col_classifier, "num_col_classifier")
@ -1839,6 +1896,10 @@ class Eynollah:
mask_texts_only = (prediction_regions_org[:,:] ==1)*1
mask_texts_only = mask_texts_only.astype('uint8')
mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=3)
mask_images_only=(prediction_regions_org[:,:] ==2)*1
polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)

Write Preview
Loading…
Cancel
Save