fix infer

ppocr/modeling/architectures/rec_model.py

@@ -109,6 +109,8 @@ class RecModel(object):
                 decoded_out, 'label':label}
             return loader, outputs
         elif mode == "export":
-            return [image, {'decoded_out': decoded_out}]
+            predict = predicts['predict']
+            predict = fluid.layers.softmax(predict)
+            return [image, {'decoded_out': decoded_out, 'predicts': predict}]
         else:
             return loader, {'decoded_out': decoded_out}

ppocr/postprocess/db_postprocess.py

@@ -81,7 +81,7 @@ class DBPostProcess(object):
             scores[index] = score
         return boxes, scores
 
-    def unclip(self, box, unclip_ratio=1.5):
+    def unclip(self, box, unclip_ratio=2.0):
         poly = Polygon(box)
         distance = poly.area * unclip_ratio / poly.length
         offset = pyclipper.PyclipperOffset()

tools/export_model.py

@@ -52,7 +52,8 @@ def main():
 
     # check if set use_gpu=True in paddlepaddle cpu version
     use_gpu = config['Global']['use_gpu']
-    program.check_gpu(True)
+    # program.check_gpu(True)
+    use_gpu = False
 
     alg = config['Global']['algorithm']
     assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']

tools/infer/predict_det.py

@@ -116,10 +116,10 @@ class TextDetector(object):
             rect_height = int(np.linalg.norm(box[0] - box[3]))
             if rect_width <= 10 or rect_height <= 10:
                 continue
-            if diffh <= 10 and diffw <= 10:
-                box = self.expand_det_res(
-                    copy.deepcopy(box), bbox_height, bbox_width, img_height,
-                    img_width)
+            # if diffh <= 10 and diffw <= 10:
+            #     box = self.expand_det_res(
+            #         copy.deepcopy(box), bbox_height, bbox_width, img_height,
+            #         img_width)
             dt_boxes_new.append(box)
         dt_boxes = np.array(dt_boxes_new)
         return dt_boxes

tools/infer/predict_eval.py

@@ -38,6 +38,8 @@ if __name__ == "__main__":
     total_time_all = 0
     count = 0
     save_path = "./inference_output/predict.txt"
+    if not os.path.exists(os.path.dirname(save_path)):
+        os.makedirs(os.path.dirname(save_path))
     fout = open(save_path, "wb")
     for image_name in image_file_list:
         image_file = image_name
@@ -77,7 +79,10 @@ if __name__ == "__main__":
         draw_img_save = os.path.join(
             os.path.dirname(save_path), "inference_draw",
             os.path.basename(image_file))
-        cv2.imwrite(draw_img_save, new_img)
+        if not os.path.exists(os.path.dirname(draw_img_save)):
+            os.makedirs(os.path.dirname(draw_img_save))
+        cv2.imwrite(draw_img_save, new_img[:, :, ::-1])
+        print("drawed img saved in {}".format(draw_img_save))
         # save predicted results in txt file
         otstr = image_name + "\t" + json.dumps(bbox_list) + "\n"
         fout.write(otstr.encode('utf-8'))

tools/infer/utility.py

@@ -138,7 +138,7 @@ def draw_ocr(image, boxes, txts, scores, draw_txt):
 
         for i, txt in enumerate(txts):
             font = ImageFont.truetype(
-                "/simfang.TTF", font_size, encoding="utf-8")
+                "./doc/simfang.TTF", font_size, encoding="utf-8")
             new_txt = str(i) + ':  ' + txt + '    ' + str(scores[i])
             draw_txt.text((20, gap * (i + 1)), new_txt, txt_color, font=font)