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PaddleOCR
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Adapt to windows

This commit is contained in:
WenmuZhou 2020-11-30 16:54:18 +08:00
parent a44c2a699f
commit 1c43e8bbb4
3 changed files with 14 additions and 12 deletions
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10
tools/infer/predict_cls.py
View File

@ -132,7 +132,7 @@ def main(args):
valid_image_file_list.append(image_file)
img_list.append(img)
try:
img_list, cls_res, elapse = text_classifier(img_list)
img_list, cls_res, predict_time = text_classifier(img_list)
except Exception as e:
print(e)
logger.info(
@ -143,10 +143,10 @@ def main(args):
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
exit()
for ino in range(len(img_list)):
print("Predicts of %s:%s" % (valid_image_file_list[ino], cls_res[ino]))
print("Total predict time for %d images, cost: %.3f" %
(len(img_list), elapse))
print("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
ino]))
print("Total predict time for {} images, cost: {:.3f}".format(
len(img_list), predict_time))
if __name__ == "__main__":
main(utility.parse_args())

5
tools/infer/predict_det.py
View File

@ -178,10 +178,11 @@ if __name__ == "__main__":
if count > 0:
total_time += elapse
count += 1
print("Predict time of %s:" % image_file, elapse)
print("Predict time of {}: {}".format(image_file, elapse))
src_im = utility.draw_text_det_res(dt_boxes, image_file)
img_name_pure = os.path.split(image_file)[-1]
img_path = os.path.join(draw_img_save, "det_res_%s" % img_name_pure)
img_path = os.path.join(draw_img_save,
"det_res_{}".format(img_name_pure))
cv2.imwrite(img_path, src_im)
if count > 1:
print("Avg Time:", total_time / (count - 1))

7
tools/infer/predict_rec.py
View File

@ -145,9 +145,10 @@ def main(args):
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
exit()
for ino in range(len(img_list)):
print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
print("Total predict time for %d images, cost: %.3f" %
(len(img_list), predict_time))
print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
ino]))
print("Total predict time for {} images, cost: {:.3f}".format(
len(img_list), predict_time))
if __name__ == "__main__":