def __init__(self, args):
     self.text_detector = TextDetectorHelper(args)
     self.text_recognizer = TextRecognizerHelper(args)
     self.use_angle_cls = args.use_angle_cls
     if self.use_angle_cls:
         self.clas_client = Debugger()
         self.clas_client.load_model_config(
             global_args.cls_model_dir, gpu=True, profile=False)
         self.text_classifier = TextClassifierHelper(args)
     self.det_client = Debugger()
     self.det_client.load_model_config(
         global_args.det_model_dir, gpu=True, profile=False)
     self.fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
Exemple #2
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class TextSystemHelper(TextSystem):
    def __init__(self, args):
        self.text_detector = TextDetectorHelper(args)
        self.text_recognizer = TextRecognizerHelper(args)
        self.use_angle_cls = args.use_angle_cls
        if self.use_angle_cls:
            self.clas_client = Debugger()
            self.clas_client.load_model_config(global_args.cls_server_dir,
                                               gpu=True,
                                               profile=False)
            self.text_classifier = TextClassifierHelper(args)
        self.det_client = Debugger()
        self.det_client.load_model_config(global_args.det_server_dir,
                                          gpu=True,
                                          profile=False)
        self.fetch = [
            "save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"
        ]

    def preprocess(self, img):
        feed, fetch, self.tmp_args = self.text_detector.preprocess(img)
        fetch_map = self.det_client.predict(feed, fetch)
        outputs = [fetch_map[x] for x in fetch]
        dt_boxes = self.text_detector.postprocess(outputs, self.tmp_args)
        if dt_boxes is None:
            return None, None
        img_crop_list = []
        dt_boxes = sorted_boxes(dt_boxes)
        self.dt_boxes = dt_boxes
        for bno in range(len(dt_boxes)):
            tmp_box = copy.deepcopy(dt_boxes[bno])
            img_crop = self.get_rotate_crop_image(img, tmp_box)
            img_crop_list.append(img_crop)
        if self.use_angle_cls:
            feed, fetch, self.tmp_args = self.text_classifier.preprocess(
                img_crop_list)
            fetch_map = self.clas_client.predict(feed, fetch)
            outputs = [fetch_map[x] for x in self.text_classifier.fetch]
            for x in fetch_map.keys():
                if ".lod" in x:
                    self.tmp_args[x] = fetch_map[x]
            img_crop_list, _ = self.text_classifier.postprocess(
                outputs, self.tmp_args)
        feed, fetch, self.tmp_args = self.text_recognizer.preprocess(
            img_crop_list)
        return feed, self.fetch, self.tmp_args

    def postprocess(self, outputs, args):
        return self.text_recognizer.postprocess(outputs, args)
Exemple #3
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 def __init__(self, args):
     self.text_detector = TextDetectorHelper(args)
     self.text_recognizer = TextRecognizerHelper(args)
     self.use_angle_cls = args.use_angle_cls
     if self.use_angle_cls:
         self.clas_client = Debugger()
         self.clas_client.load_model_config(global_args.cls_server_dir,
                                            gpu=True,
                                            profile=False)
         self.text_classifier = TextClassifierHelper(args)
     self.det_client = Debugger()
     self.det_client.load_model_config(global_args.det_server_dir,
                                       gpu=True,
                                       profile=False)
     self.fetch = [
         "save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"
     ]