refine manul parser (#131)

deepdoc/parser/pdf_parser.py

@@ -247,7 +247,7 @@ class HuParser:
                 b["SP"] = ii
 
     def __ocr(self, pagenum, img, chars, ZM=3):
-        bxs = self.ocr(np.array(img))
+        bxs = self.ocr.detect(np.array(img))
         if not bxs:
             self.boxes.append([])
             return
@@ -278,8 +278,10 @@ class HuParser:
 
         for b in bxs:
             if not b["text"]:
-                b["text"] = b["txt"]
+                left, right, top, bott = b["x0"]*ZM, b["x1"]*ZM, b["top"]*ZM, b["bottom"]*ZM
+                b["text"] = self.ocr.recognize(np.array(img), np.array([[left, top], [right, top], [right, bott], [left, bott]], dtype=np.float32))
             del b["txt"]
+        bxs = [b for b in bxs if b["text"]]
         if self.mean_height[-1] == 0:
             self.mean_height[-1] = np.median([b["bottom"] - b["top"]
                                               for b in bxs])

deepdoc/vision/ocr.py

@@ -69,7 +69,7 @@ def load_model(model_dir, nm):
     options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
     options.intra_op_num_threads = 2
     options.inter_op_num_threads = 2
-    if ort.get_device() == "GPU":
+    if False and ort.get_device() == "GPU":
         sess = ort.InferenceSession(model_file_path, options=options, providers=['CUDAExecutionProvider'])
     else:
         sess = ort.InferenceSession(model_file_path, options=options, providers=['CPUExecutionProvider'])
@@ -366,7 +366,7 @@ class TextDetector(object):
                 'keep_keys': ['image', 'shape']
             }
         }]
-        postprocess_params = {"name": "DBPostProcess", "thresh": 0.3, "box_thresh": 0.6, "max_candidates": 1000,
+        postprocess_params = {"name": "DBPostProcess", "thresh": 0.3, "box_thresh": 0.5, "max_candidates": 1000,
                               "unclip_ratio": 1.5, "use_dilation": False, "score_mode": "fast", "box_type": "quad"}
 
         self.postprocess_op = build_post_process(postprocess_params)
@@ -534,6 +534,34 @@ class OCR(object):
                     break
         return _boxes
 
+    def detect(self, img):
+        time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
+
+        if img is None:
+            return None, None, time_dict
+
+        start = time.time()
+        dt_boxes, elapse = self.text_detector(img)
+        time_dict['det'] = elapse
+
+        if dt_boxes is None:
+            end = time.time()
+            time_dict['all'] = end - start
+            return None, None, time_dict
+        else:
+            cron_logger.debug("dt_boxes num : {}, elapsed : {}".format(
+                len(dt_boxes), elapse))
+
+        return zip(self.sorted_boxes(dt_boxes), [("",0) for _ in range(len(dt_boxes))])
+
+    def recognize(self, ori_im, box):
+        img_crop = self.get_rotate_crop_image(ori_im, box)
+
+        rec_res, elapse = self.text_recognizer([img_crop])
+        text, score = rec_res[0]
+        if score < self.drop_score:return ""
+        return text
+
     def __call__(self, img, cls=True):
         time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
 
@@ -562,6 +590,7 @@ class OCR(object):
             img_crop_list.append(img_crop)
 
         rec_res, elapse = self.text_recognizer(img_crop_list)
+
         time_dict['rec'] = elapse
         cron_logger.debug("rec_res num  : {}, elapsed : {}".format(
             len(rec_res), elapse))
@@ -575,6 +604,7 @@ class OCR(object):
         end = time.time()
         time_dict['all'] = end - start
 
+
         #for bno in range(len(img_crop_list)):
         #    print(f"{bno}, {rec_res[bno]}")
 

deepdoc/vision/recognizer.py

@@ -41,7 +41,7 @@ class Recognizer(object):
         if not os.path.exists(model_file_path):
             raise ValueError("not find model file path {}".format(
                 model_file_path))
-        if ort.get_device() == "GPU":
+        if False and ort.get_device() == "GPU":
             options = ort.SessionOptions()
             options.enable_cpu_mem_arena = False
             self.ort_sess = ort.InferenceSession(model_file_path, options=options, providers=[('CUDAExecutionProvider')])