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eynollah
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inference with batch size bigger than 1

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pull/138/head^2
vahidrezanezhad 4 months ago
parent 4f8210de71
commit c10a525675
1 changed files with 100 additions and 72 deletions
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qurator/eynollah/eynollah.py
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@ -548,11 +548,11 @@ class Eynollah:
if self.input_binary: if self.input_binary:
img = self.imread() img = self.imread()
if self.dir_in: if self.dir_in:
prediction_bin = self.do_prediction(True, img, self.model_bin) prediction_bin = self.do_prediction(True, img, self.model_bin, n_batch_inference=5)
else: else:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img, model_bin) prediction_bin = self.do_prediction(True, img, model_bin, n_batch_inference=5)
prediction_bin=prediction_bin[:,:,0] prediction_bin=prediction_bin[:,:,0]
prediction_bin = (prediction_bin[:,:]==0)*1 prediction_bin = (prediction_bin[:,:]==0)*1
@ -703,7 +703,7 @@ class Eynollah:
return model, None return model, None
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1): def do_prediction(self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1):
self.logger.debug("enter do_prediction") self.logger.debug("enter do_prediction")
img_height_model = model.layers[len(model.layers) - 1].output_shape[1] img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
@ -745,7 +745,17 @@ class Eynollah:
nyf = img_h / float(height_mid) nyf = img_h / float(height_mid)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
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))
for i in range(nxf): for i in range(nxf):
for j in range(nyf): for j in range(nyf):
if i == 0: if i == 0:
@ -766,59 +776,77 @@ class Eynollah:
if index_y_u > img_h: if index_y_u > img_h:
index_y_u = img_h index_y_u = img_h
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
list_i_s.append(i)
list_j_s.append(j)
list_x_u.append(index_x_u)
list_x_d.append(index_x_d)
list_y_d.append(index_y_d)
list_y_u.append(index_y_u)
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch[batch_indexer,:,:,:] = 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]),
verbose=0) batch_indexer = batch_indexer + 1
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) if batch_indexer == n_batch_inference:
if i == 0 and j == 0: label_p_pred = model.predict(img_patch,verbose=0)
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
#seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin] seg = np.argmax(label_p_pred, axis=3)
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color indexer_inside_batch = 0
elif i == nxf - 1 and j == nyf - 1: for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :] seg_in = seg[indexer_inside_batch,:,:]
#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0] seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color index_y_u_in = list_y_u[indexer_inside_batch]
elif i == 0 and j == nyf - 1: index_y_d_in = list_y_d[indexer_inside_batch]
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
#seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin] index_x_u_in = list_x_u[indexer_inside_batch]
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg index_x_d_in = list_x_d[indexer_inside_batch]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
elif i == nxf - 1 and j == 0: if i_batch == 0 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0] prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg elif i_batch == nxf - 1 and j_batch == nyf - 1:
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
elif i == 0 and j != 0 and j != nyf - 1: prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] elif i_batch == 0 and j_batch == nyf - 1:
#seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin] seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color elif i_batch == nxf - 1 and j_batch == 0:
elif i == nxf - 1 and j != 0 and j != nyf - 1: seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
seg_color = seg_color[margin : 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
#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0] elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color 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 != 0 and i != nxf - 1 and j == 0: elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin] prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
elif i != 0 and i != nxf - 1 and j == nyf - 1: prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :] elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin] seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color else:
else: seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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
#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg indexer_inside_batch = indexer_inside_batch +1
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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))
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
#del model #del model
#gc.collect() #gc.collect()
@ -835,7 +863,7 @@ class Eynollah:
img = img / float(255.0) img = img / float(255.0)
img = resize_image(img, img_height_model, img_width_model) img = resize_image(img, img_height_model, img_width_model)
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
@ -1147,7 +1175,7 @@ class Eynollah:
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent) prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=marginal_of_patch_percent)
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
self.logger.debug("exit extract_text_regions") self.logger.debug("exit extract_text_regions")
@ -1173,7 +1201,7 @@ class Eynollah:
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7)) img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent=marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
if cols == 2: if cols == 2:
@ -1181,7 +1209,7 @@ class Eynollah:
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4)) img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent=marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
elif cols > 2: elif cols > 2:
@ -1189,7 +1217,7 @@ class Eynollah:
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3)) img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent=marginal_of_patch_percent)
prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
if cols == 2: if cols == 2:
@ -1245,7 +1273,7 @@ class Eynollah:
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)) img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent) prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=marginal_of_patch_percent)
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
self.logger.debug("exit extract_text_regions") self.logger.debug("exit extract_text_regions")
return prediction_regions, prediction_regions2 return prediction_regions, prediction_regions2
@ -1634,9 +1662,9 @@ class Eynollah:
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w)) img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
#print(img.shape,'bin shape') #print(img.shape,'bin shape')
if not self.dir_in: if not self.dir_in:
prediction_textline = self.do_prediction(patches, img, model_textline) prediction_textline = self.do_prediction(patches, img, model_textline, n_batch_inference=4)
else: else:
prediction_textline = self.do_prediction(patches, img, self.model_textline) prediction_textline = self.do_prediction(patches, img, self.model_textline, n_batch_inference=4)
prediction_textline = resize_image(prediction_textline, img_h, img_w) prediction_textline = resize_image(prediction_textline, img_h, img_w)
if not self.dir_in: if not self.dir_in:
prediction_textline_longshot = self.do_prediction(False, img, model_textline) prediction_textline_longshot = self.do_prediction(False, img, model_textline)
@ -1721,9 +1749,9 @@ class Eynollah:
if not self.dir_in: if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) 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) prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
else: else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin) prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
prediction_bin=prediction_bin[:,:,0] prediction_bin=prediction_bin[:,:,0]
prediction_bin = (prediction_bin[:,:]==0)*1 prediction_bin = (prediction_bin[:,:]==0)*1
prediction_bin = prediction_bin*255 prediction_bin = prediction_bin*255
@ -1870,9 +1898,9 @@ class Eynollah:
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1])) img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
if self.dir_in: if self.dir_in:
prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2) prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, marginal_of_patch_percent=0.2)
else: else:
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2) prediction_regions_org2 = self.do_prediction(True, img, model_region, marginal_of_patch_percent=0.2)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h ) prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
@ -1905,9 +1933,9 @@ class Eynollah:
else: else:
if not self.dir_in: if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, model_bin) prediction_bin = self.do_prediction(True, img_org, model_bin, n_batch_inference=5)
else: else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin) prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5)
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h ) prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
prediction_bin=prediction_bin[:,:,0] prediction_bin=prediction_bin[:,:,0]
@ -1958,9 +1986,9 @@ class Eynollah:
if not self.dir_in: if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, model_bin) prediction_bin = self.do_prediction(True, img_org, model_bin, n_batch_inference=5)
else: else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin) prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5)
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h ) prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
prediction_bin=prediction_bin[:,:,0] prediction_bin=prediction_bin[:,:,0]

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