使用 OpenVINO™ 实现飞桨版 PGNet 推理程序 | 开发者实战

pip install openvino-dev[onnx]

# 如果想使用CPU进行预测，需设置use_gpu参数为Falsepython3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_valid_set="totaltext" --use_gpu=False

from openvino.runtime import Core
# 指定PGNet模型路径pgnet_path = ".\e2e_server_pgnetA_infer\inference.pdmodel"
# 创建Core对象core = Core()# 载入并编译PGNet模型sess = core.compile_model(model=pgnet_path, device_name="CPU")# 输出PGNet模型输入&输出信息print(sess.input)

# OpenVINO Support Here        elif self.use_openvino:            outputs = self.predictor([img])            out_layers = self.predictor.output            preds = {}            preds['f_border'] = outputs[out_layers(0)]            preds['f_char'] = outputs[out_layers(1)]            preds['f_direction'] = outputs[out_layers(2)]            preds['f_score'] = outputs[out_layers(3)]

python tools\infer\predict_e2e.py --e2e_algorithm="PGNet" --image_dir=.\doc\imgs_en --e2e_model_dir=ov_infer\e2e_server_pgnetA_infer\inference.pdmodel --e2e_pgnet_valid_set="totaltext" --use_gpu=False --use_openvino=True

from openvino.runtime import Corefrom ppocr.data import create_operators, transformfrom ppocr.postprocess import build_post_processimport cv2import numpy as npimport time
# 指定PGNet模型路径pgnet_path = "./e2e_server_pgnetA_infer/inference.pdmodel"
# 创建Core对象core = Core()# 载入并编译PGNet模型pgnet = core.compile_model(model=pgnet_path, device_name="CPU")
# 创建preprocess_oppre_process_list = [{            'E2EResizeForTest': {        'max_side_len': 768,        'valid_set': 'totaltext'}        }, {            'NormalizeImage': {                'std': [0.229, 0.224, 0.225],                'mean': [0.485, 0.456, 0.406],                'scale': '1./255.',                'order': 'hwc'            }        }, {            'ToCHWImage': None        }, {            'KeepKeys': {                'keep_keys': ['image', 'shape']            }        }]preprocess_op = create_operators(pre_process_list)
# 创建postprocess_oppostprocess_params = {}postprocess_params['name'] = 'PGPostProcess'postprocess_params["score_thresh"] = 0.5postprocess_params["character_dict_path"] = "./ic15_dict.txt"postprocess_params["valid_set"] = 'totaltext'postprocess_params["mode"] = 'fast'postprocess_op = build_post_process(postprocess_params)
def clip_det_res(points, img_height, img_width):    for pno in range(points.shape[0]):        points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))        points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))    return points# 定义filter_tag_det_res_only_clip函数def filter_tag_det_res_only_clip(dt_boxes, image_shape):        img_height, img_width = image_shape[0:2]        dt_boxes_new = []        for box in dt_boxes:            box = clip_det_res(box, img_height, img_width)            dt_boxes_new.append(box)        dt_boxes = np.array(dt_boxes_new)        return dt_boxes
# 载入图像数据并实现预处理image_path = 'img623.jpg'image = cv2.imread(image_path)data = {'image': image}data = transform(data, preprocess_op)img, shape_list = dataimg = np.expand_dims(img, axis=0)shape_list = np.expand_dims(shape_list, axis=0)starttime = time.time()
# Do the inference by OpenVINOoutputs = pgnet([img])out_layers = pgnet.outputpreds = {}preds['f_border'] = outputs[out_layers(0)]preds['f_char'] = outputs[out_layers(1)]preds['f_direction'] = outputs[out_layers(2)]preds['f_score'] = outputs[out_layers(3)]
post_result = postprocess_op(preds, shape_list)points, strs = post_result['points'], post_result['texts']dt_boxes = filter_tag_det_res_only_clip(points, image.shape)elapse = time.time() - starttimeprint(f"Predict time: {elapse}s")import utilitysrc_im = utility.draw_e2e_res(points, strs, image_path)cv2.imshow("PGNet infer by OpenVINO", src_im)cv2.waitKey(0)cv2.destroyAllWindows()