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function of patch-wise inference with scatter_nd is added

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vahidrezanezhad 2024-12-14 02:50:17 +01:00
parent 0e8c561618
commit f93c6c288d
1 changed files with 106 additions and 1 deletions
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src/eynollah/eynollah.py
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@ -1047,6 +1047,110 @@ class Eynollah:
#label_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = label_res[:,:,:]
return img_scaled_padded#, label_scaled_padded
def do_prediction_new_concept_scatter_nd(self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False, thresholding_for_artificial_class_in_light_version=False):
self.logger.debug("enter do_prediction_new_concept")
img_height_model = model.layers[-1].output_shape[1]
img_width_model = model.layers[-1].output_shape[2]
if not patches:
img_h_page = img.shape[0]
img_w_page = img.shape[1]
img = img / 255.0
img = resize_image(img, img_height_model, img_width_model)
label_p_pred = model.predict(img[np.newaxis], verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0]
if thresholding_for_artificial_class_in_light_version:
#seg_text = label_p_pred[0,:,:,1]
#seg_text[seg_text<0.2] =0
#seg_text[seg_text>0] =1
#seg[seg_text==1]=1
seg_art = label_p_pred[0,:,:,4]
seg_art[seg_art<0.2] =0
seg_art[seg_art>0] =1
seg[seg_art==1]=4
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8)
return prediction_true
if img.shape[0] < img_height_model:
img = resize_image(img, img_height_model, img.shape[1])
if img.shape[1] < img_width_model:
img = resize_image(img, img.shape[0], img_width_model)
self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model)
##margin = int(marginal_of_patch_percent * img_height_model)
#width_mid = img_width_model - 2 * margin
#height_mid = img_height_model - 2 * margin
img = img / float(255.0)
img = img.astype(np.float16)
img_h = img.shape[0]
img_w = img.shape[1]
stride_x = img_width_model - 100
stride_y = img_height_model - 100
one_tensor = tf.ones_like(img)
img_patches = tf.image.extract_patches(images=[img,one_tensor],
sizes=[1, img_height_model, img_width_model, 1],
strides=[1, stride_y, stride_x, 1],
rates=[1, 1, 1, 1],
padding='SAME')
one_patches = img_patches[1]
img_patches = img_patches[0]
img_patches = tf.squeeze(img_patches)
img_patches_resh = tf.reshape(img_patches, shape = (img_patches.shape[0]*img_patches.shape[1], img_height_model, img_width_model, 3))
pred_patches = model.predict(img_patches_resh, batch_size=n_batch_inference)
one_patches = tf.squeeze(one_patches)
one_patches = tf.reshape(one_patches, [img_patches.shape[0]*img_patches.shape[1],img_height_model,img_width_model,3])
x = tf.range(img.shape[1])
y = tf.range(img.shape[0])
x, y = tf.meshgrid(x, y)
indices = tf.stack([y, x], axis=-1)
indices_patches = tf.image.extract_patches(images=tf.expand_dims(indices, axis=0), sizes=[1, img_height_model, img_width_model, 1], strides=[1, stride_y, stride_x, 1], rates=[1, 1, 1, 1], padding='SAME')
indices_patches = tf.squeeze(indices_patches)
indices_patches = tf.reshape(indices_patches, [img_patches.shape[0]*img_patches.shape[1],img_height_model, img_width_model,2])
margin_y = int( (img_height_model - stride_y)/2. )
margin_x = int( (img_width_model - stride_x)/2. )
mask_margin = np.zeros((img_height_model, img_width_model))
mask_margin[margin_y:img_height_model-margin_y, margin_x:img_width_model-margin_x] = 1
indices_patches_array = indices_patches.numpy()
for i in range(indices_patches_array.shape[0]):
indices_patches_array[i,:,:,0] = indices_patches_array[i,:,:,0]*mask_margin
indices_patches_array[i,:,:,1] = indices_patches_array[i,:,:,1]*mask_margin
reconstructed = tf.scatter_nd(indices=indices_patches_array, updates=pred_patches, shape=(img.shape[0],img.shape[1],pred_patches.shape[-1]))
reconstructed_argmax = reconstructed.numpy()
prediction_true = np.argmax(reconstructed_argmax, axis=2)
prediction_true = prediction_true.astype(np.uint8)
gc.collect()
return np.repeat(prediction_true[:, :, np.newaxis], 3, axis=2)
def do_prediction_new_concept(self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False, thresholding_for_artificial_class_in_light_version=False):
self.logger.debug("enter do_prediction_new_concept")
@ -4891,7 +4995,7 @@ class Eynollah:
all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals,
cont_page, polygons_lines_xml, ocr_all_textlines)
self.logger.info("Job done in %.1fs", time.time() - t0)
print("Job done in %.1fs", time.time() - t0)
#print("Job done in %.1fs", time.time() - t0)
if self.dir_in:
self.writer.write_pagexml(pcgts)
continue
@ -4975,6 +5079,7 @@ class Eynollah:
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals,
all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals,
cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines)
#print("Job done in %.1fs" % (time.time() - t0))
self.logger.info("Job done in %.1fs", time.time() - t0)
if not self.dir_in:
return pcgts