fix infer_rec for other input
@@ -349,6 +349,13 @@ class TextRecognizer(object):
for beg_img_no in range(0, img_num, batch_num):
end_img_no = min(img_num, beg_img_no + batch_num)
norm_img_batch = []
+ if self.rec_algorithm == "SRN":
+ encoder_word_pos_list = []
+ gsrm_word_pos_list = []
+ gsrm_slf_attn_bias1_list = []
+ gsrm_slf_attn_bias2_list = []
+ if self.rec_algorithm == "SAR":
+ valid_ratios = []
imgC, imgH, imgW = self.rec_image_shape[:3]
max_wh_ratio = imgW / imgH
# max_wh_ratio = 0
@@ -357,22 +364,16 @@ class TextRecognizer(object):
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
-
if self.rec_algorithm == "SAR":
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
img_list[indices[ino]], self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
valid_ratio = np.expand_dims(valid_ratio, axis=0)
- valid_ratios = []
valid_ratios.append(valid_ratio)
norm_img_batch.append(norm_img)
elif self.rec_algorithm == "SRN":
norm_img = self.process_image_srn(
img_list[indices[ino]], self.rec_image_shape, 8, 25)
- encoder_word_pos_list = []
- gsrm_word_pos_list = []
- gsrm_slf_attn_bias1_list = []
- gsrm_slf_attn_bias2_list = []
encoder_word_pos_list.append(norm_img[1])
gsrm_word_pos_list.append(norm_img[2])
gsrm_slf_attn_bias1_list.append(norm_img[3])
