Add log

app.py

@@ -86,6 +86,8 @@ def visualize(image_path, boxes, txts, scores,
 
 def inference(img_path, box_thresh=0.5, unclip_ratio=1.6, text_score=0.5,
               text_det=None, text_rec=None):
+    out_log_list = []
+
     det_model_path = str(Path('models') / 'text_det' / text_det)
     rec_model_path = str(Path('models') / 'text_rec' / text_rec)
     if 'v2' in rec_model_path:
@@ -93,27 +95,35 @@ def inference(img_path, box_thresh=0.5, unclip_ratio=1.6, text_score=0.5,
     else:
         rec_image_shape = [3, 48, 320]
 
-    print('Init Class')
+    out_log_list.append('Init Model')
     s = time.time()
     rapid_ocr = RapidOCR(det_model_path=det_model_path,
                          rec_model_path=rec_model_path,
                          rec_img_shape=rec_image_shape)
-    print(det_model_path, rec_model_path, rec_image_shape)
     elapse = time.time() - s
-    print(elapse)
+
+    out_log_list.append(f'Init Model cost: {elapse:.5f}')
+    out_log_list.extend([f'det_model:{det_model_path}',
+                         f'rec_model: {rec_model_path}',
+                         f'rec_image_shape: {rec_image_shape}'])
 
     img = cv2.imread(img_path)
-    ocr_result, _ = rapid_ocr(img, box_thresh=box_thresh,
-                              unclip_ratio=unclip_ratio,
-                              text_score=text_score)
+    ocr_result, infer_elapse = rapid_ocr(img, box_thresh=box_thresh,
+                                         unclip_ratio=unclip_ratio,
+                                         text_score=text_score)
+    det_cost, cls_cost, rec_cost = infer_elapse
+    out_log_list.extend([f'det cost: {det_cost:.5f}',
+                         f'cls cost: {cls_cost:.5f}',
+                         f'rec cost: {rec_cost:.5f}'])
+    out_log = '\n'.join([str(v) for v in out_log_list])
     if not ocr_result:
-        return img_path, '未识别到有效文本'
+        return img_path, '未识别到有效文本', out_log
 
     dt_boxes, rec_res, scores = list(zip(*ocr_result))
     img_save_path = visualize(img_path, dt_boxes, rec_res, scores)
     output_text = [f'{one_rec} {float(score):.4f}'
                    for one_rec, score in zip(rec_res, scores)]
-    return img_save_path, output_text
+    return img_save_path, output_text, out_log
 
 
 examples = [['images/1.jpg'], ['images/ch_en_num.jpg']]
@@ -163,14 +173,15 @@ with gr.Blocks(title='RapidOCR') as demo:
     with gr.Row():
         input_img = gr.Image(type='filepath', label='Input')
         out_img = gr.Image(type='filepath', label='Output')
+    out_log = gr.outputs.Textbox(type='text', label='Run Log')
     out_txt = gr.outputs.Textbox(type='text', label='RecText')
     button = gr.Button('Submit')
     button.click(fn=inference,
                  inputs=[input_img, box_thresh, unclip_ratio, text_score,
                          text_det, text_rec],
-                 outputs=[out_img, out_txt])
+                 outputs=[out_img, out_txt, out_log])
     gr.Examples(examples=examples,
                 inputs=[input_img, box_thresh, unclip_ratio, text_score,
                         text_det, text_rec],
-                outputs=[out_img, out_txt], fn=inference)
+                outputs=[out_img, out_txt, out_log], fn=inference)
 demo.launch(debug=True, enable_queue=True)