fix cv exception

deploy/cpp_infer/src/postprocess_op.cpp

@@ -219,7 +219,7 @@ PostProcessor::BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
   std::vector<std::vector<std::vector<int>>> boxes;
 
   for (int _i = 0; _i < num_contours; _i++) {
-    if (contours[_i].size() <= 0) {
+    if (contours[_i].size() <= 2) {
       continue;
     }
     float ssid;

tools/eval.py

@@ -82,7 +82,7 @@ def main():
             'fetch_name_list':eval_fetch_name_list,\
             'fetch_varname_list':eval_fetch_varname_list}
         metrics = eval_det_run(exe, config, eval_info_dict, "eval")
-        print("Eval result", metrics)
+        logger.info("Eval result: {}".format(metrics))
     else:
         reader_type = config['Global']['reader_yml']
         if "benchmark" not in reader_type:
@@ -92,7 +92,7 @@ def main():
                               'fetch_name_list': eval_fetch_name_list, \
                               'fetch_varname_list': eval_fetch_varname_list}
             metrics = eval_rec_run(exe, config, eval_info_dict, "eval")
-            print("Eval result:", metrics)
+            logger.info("Eval result: {}".format(metrics))
         else:
             eval_info_dict = {'program':eval_program,\
                 'fetch_name_list':eval_fetch_name_list,\

tools/eval_utils/eval_rec_utils.py

@@ -75,6 +75,7 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
             char_ops, preds, preds_lod, labels, labels_lod, is_remove_duplicate)
         total_acc_num += acc_num
         total_sample_num += sample_num
+        logger.info("eval batch id: {}, acc: {}".format(total_batch_num, acc))
         total_batch_num += 1
     avg_acc = total_acc_num * 1.0 / total_sample_num
     metrics = {'avg_acc': avg_acc, "total_acc_num": total_acc_num, \

tools/infer_det.py

@@ -70,7 +70,7 @@ def draw_det_res(dt_boxes, config, img, img_name):
 def main():
     config = program.load_config(FLAGS.config)
     program.merge_config(FLAGS.opt)
-    print(config)
+    logger.info(config)
 
     # check if set use_gpu=True in paddlepaddle cpu version
     use_gpu = config['Global']['use_gpu']

tools/infer_rec.py

@@ -84,7 +84,7 @@ def main():
     if len(infer_list) == 0:
         logger.info("Can not find img in infer_img dir.")
     for i in range(max_img_num):
-        print("infer_img:%s" % infer_list[i])
+        logger.info("infer_img:%s" % infer_list[i])
         img = next(blobs)
         predict = exe.run(program=eval_prog,
                           feed={"image": img},
@@ -115,9 +115,9 @@ def main():
             preds = preds.reshape(-1)
             preds_text = char_ops.decode(preds)
 
-        print("\t index:", preds)
-        print("\t word :", preds_text)
-        print("\t score :", score)
+        logger.info("\t index: {}".format(preds))
+        logger.info("\t word : {}".format(preds_text))
+        logger.info("\t score: {}".format(score))
 
     # save for inference model
     target_var = []

tools/test_hubserving.py

@@ -41,19 +41,19 @@ def draw_server_result(image_file, res):
     if len(res) == 0:
         return np.array(image)
     keys = res[0].keys()
-    if 'text_region' not in keys: # for ocr_rec, draw function is invalid 
-        print("draw function is invalid for ocr_rec!")
+    if 'text_region' not in keys:  # for ocr_rec, draw function is invalid 
+        logger.info("draw function is invalid for ocr_rec!")
         return None
-    elif 'text' not in keys: # for ocr_det
-        print("draw text boxes only!")
+    elif 'text' not in keys:  # for ocr_det
+        logger.info("draw text boxes only!")
         boxes = []
         for dno in range(len(res)):
             boxes.append(res[dno]['text_region'])
         boxes = np.array(boxes)
         draw_img = draw_boxes(image, boxes)
         return draw_img
-    else: # for ocr_system
-        print("draw boxes and texts!")
+    else:  # for ocr_system
+        logger.info("draw boxes and texts!")
         boxes = []
         texts = []
         scores = []
@@ -63,7 +63,8 @@ def draw_server_result(image_file, res):
             scores.append(res[dno]['confidence'])
         boxes = np.array(boxes)
         scores = np.array(scores)
-        draw_img = draw_ocr(image, boxes, texts, scores, draw_txt=True, drop_score=0.5)       
+        draw_img = draw_ocr(
+            image, boxes, texts, scores, draw_txt=True, drop_score=0.5)
         return draw_img
 
 
@@ -81,13 +82,13 @@ def main(url, image_path):
 
         # 发送HTTP请求
         starttime = time.time()
-        data = {'images':[cv2_to_base64(img)]}
+        data = {'images': [cv2_to_base64(img)]}
         r = requests.post(url=url, headers=headers, data=json.dumps(data))
         elapse = time.time() - starttime
         total_time += elapse
-        print("Predict time of %s: %.3fs" % (image_file, elapse))
+        logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
         res = r.json()["results"][0]
-        print(res)
+        logger.info(res)
 
         if is_visualize:
             draw_img = draw_server_result(image_file, res)
@@ -98,16 +99,17 @@ def main(url, image_path):
                 cv2.imwrite(
                     os.path.join(draw_img_save, os.path.basename(image_file)),
                     draw_img[:, :, ::-1])
-                print("The visualized image saved in {}".format(
+                logger.info("The visualized image saved in {}".format(
                     os.path.join(draw_img_save, os.path.basename(image_file))))
         cnt += 1
         if cnt % 100 == 0:
-            print(cnt, "processed")
-    print("avg time cost: ", float(total_time)/cnt)
+            logger.info("{} processed".format(cnt))
+    logger.info("avg time cost: {}".format(float(total_time) / cnt))
 
-if __name__  == '__main__':
+
+if __name__ == '__main__':
     if len(sys.argv) != 3:
-        print("Usage: %s server_url image_path" % sys.argv[0])
+        logger.info("Usage: %s server_url image_path" % sys.argv[0])
     else:
         server_url = sys.argv[1]
         image_path = sys.argv[2]

tools/train.py

@@ -118,7 +118,7 @@ def main():
 def test_reader():
     config = program.load_config(FLAGS.config)
     program.merge_config(FLAGS.opt)
-    print(config)
+    logger.info(config)
     train_reader = reader_main(config=config, mode="train")
     import time
     starttime = time.time()
@@ -129,7 +129,7 @@ def test_reader():
             if count % 1 == 0:
                 batch_time = time.time() - starttime
                 starttime = time.time()
-                print("reader:", count, len(data), batch_time)
+                logger.info("reader:", count, len(data), batch_time)
     except Exception as e:
         logger.info(e)
     logger.info("finish reader: {}, Success!".format(count))