# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import fastdeploy as fd
import cv2
import os
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--det_model", required=True, help="Path of Detection model of PPOCR.")
parser.add_argument(
"--cls_model",
required=True,
help="Path of Classification model of PPOCR.")
parser.add_argument(
"--rec_model",
required=True,
help="Path of Recognization model of PPOCR.")
parser.add_argument(
"--rec_label_file",
required=True,
help="Path of Recognization model of PPOCR.")
parser.add_argument(
"--image", type=str, required=True, help="Path of test image file.")
parser.add_argument(
"--device",
type=str,
default='cpu',
help="Type of inference device, support 'cpu' or 'gpu'.")
parser.add_argument(
"--device_id",
type=int,
default=0,
help="Define which GPU card used to run model.")
parser.add_argument(
"--cls_bs",
type=int,
default=1,
help="Classification model inference batch size.")
parser.add_argument(
"--rec_bs",
type=int,
default=6,
help="Recognition model inference batch size")
parser.add_argument(
"--backend",
type=str,
default="default",
help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
)
return parser.parse_args()
def build_option(args):
det_option = fd.RuntimeOption()
cls_option = fd.RuntimeOption()
rec_option = fd.RuntimeOption()
if args.device.lower() == "gpu":
det_option.use_gpu(args.device_id)
cls_option.use_gpu(args.device_id)
rec_option.use_gpu(args.device_id)
if args.backend.lower() == "trt":
assert args.device.lower(
) == "gpu", "TensorRT backend require inference on device GPU."
det_option.use_trt_backend()
cls_option.use_trt_backend()
rec_option.use_trt_backend()
# If use TRT backend, the dynamic shape will be set as follow.
# We recommend that users set the length and height of the detection model to a multiple of 32.
# We also recommend that users set the Trt input shape as follow.
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
[1, 3, 960, 960])
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
[args.cls_bs, 3, 48, 320],
[args.cls_bs, 3, 48, 1024])
rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
[args.rec_bs, 3, 48, 320],
[args.rec_bs, 3, 48, 2304])
# Users could save TRT cache file to disk as follow.
det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
elif args.backend.lower() == "pptrt":
assert args.device.lower(
) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
det_option.use_paddle_infer_backend()
det_option.paddle_infer_option.collect_trt_shape = True
det_option.paddle_infer_option.enable_trt = True
cls_option.use_paddle_infer_backend()
cls_option.paddle_infer_option.collect_trt_shape = True
cls_option.paddle_infer_option.enable_trt = True
rec_option.use_paddle_infer_backend()
rec_option.paddle_infer_option.collect_trt_shape = True
rec_option.paddle_infer_option.enable_trt = True
# If use TRT backend, the dynamic shape will be set as follow.
# We recommend that users set the length and height of the detection model to a multiple of 32.
# We also recommend that users set the Trt input shape as follow.
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
[1, 3, 960, 960])
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
[args.cls_bs, 3, 48, 320],
[args.cls_bs, 3, 48, 1024])
rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
[args.rec_bs, 3, 48, 320],
[args.rec_bs, 3, 48, 2304])
# Users could save TRT cache file to disk as follow.
det_option.set_trt_cache_file(args.det_model)
cls_option.set_trt_cache_file(args.cls_model)
rec_option.set_trt_cache_file(args.rec_model)
elif args.backend.lower() == "ort":
det_option.use_ort_backend()
cls_option.use_ort_backend()
rec_option.use_ort_backend()
elif args.backend.lower() == "paddle":
det_option.use_paddle_infer_backend()
cls_option.use_paddle_infer_backend()
rec_option.use_paddle_infer_backend()
elif args.backend.lower() == "openvino":
assert args.device.lower(
) == "cpu", "OpenVINO backend require inference on device CPU."
det_option.use_openvino_backend()
cls_option.use_openvino_backend()
rec_option.use_openvino_backend()
elif args.backend.lower() == "pplite":
assert args.device.lower(
) == "cpu", "Paddle Lite backend require inference on device CPU."
det_option.use_lite_backend()
cls_option.use_lite_backend()
rec_option.use_lite_backend()
return det_option, cls_option, rec_option
args = parse_arguments()
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
rec_label_file = args.rec_label_file
det_option, cls_option, rec_option = build_option(args)
det_model = fd.vision.ocr.DBDetector(
det_model_file, det_params_file, runtime_option=det_option)
cls_model = fd.vision.ocr.Classifier(
cls_model_file, cls_params_file, runtime_option=cls_option)
rec_model = fd.vision.ocr.Recognizer(
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
# Parameters settings for pre and post processing of Det/Cls/Rec Models.
# All parameters are set to default values.
det_model.preprocessor.max_side_len = 960
det_model.postprocessor.det_db_thresh = 0.3
det_model.postprocessor.det_db_box_thresh = 0.6
det_model.postprocessor.det_db_unclip_ratio = 1.5
det_model.postprocessor.det_db_score_mode = "slow"
det_model.postprocessor.use_dilation = False
cls_model.postprocessor.cls_thresh = 0.9
# Create PP-OCRv3, if cls_model is not needed, just set cls_model=None .
ppocr_v3 = fd.vision.ocr.PPOCRv3(
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
# Set inference batch size for cls model and rec model, the value could be -1 and 1 to positive infinity.
# When inference batch size is set to -1, it means that the inference batch size
# of the cls and rec models will be the same as the number of boxes detected by the det model.
ppocr_v3.cls_batch_size = args.cls_bs
ppocr_v3.rec_batch_size = args.rec_bs
# Read the input image
im = cv2.imread(args.image)
# Predict and reutrn the results
result = ppocr_v3.predict(im)
print(result)
# Visuliaze the results.
vis_im = fd.vision.vis_ppocr(im, result)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")