utility.py

import argparse
import os
import sys
import platform
import cv2
import numpy as np
import paddle
from PIL import Image, ImageDraw, ImageFont
import math
from paddle import inference
import time
from ppocr.utils.logging import get_logger


def str2bool(v):
    return v.lower() in ("true", "t", "1")


def init_args():
    parser = argparse.ArgumentParser()
    # params for prediction engine
    parser.add_argument("--use_gpu", type=str2bool, default=True)
    parser.add_argument("--use_xpu", type=str2bool, default=False)
    parser.add_argument("--ir_optim", type=str2bool, default=True)
    parser.add_argument("--use_tensorrt", type=str2bool, default=False)
    parser.add_argument("--min_subgraph_size", type=int, default=15)
    parser.add_argument("--precision", type=str, default="fp32")
    parser.add_argument("--gpu_mem", type=int, default=500)

    # params for text detector
    parser.add_argument("--image_dir", type=str)
    parser.add_argument("--det_algorithm", type=str, default='DB')
    parser.add_argument("--det_model_dir", type=str)
    parser.add_argument("--det_limit_side_len", type=float, default=960)
    parser.add_argument("--det_limit_type", type=str, default='max')

    # DB parmas
    parser.add_argument("--det_db_thresh", type=float, default=0.3)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
    parser.add_argument("--max_batch_size", type=int, default=10)
    parser.add_argument("--use_dilation", type=str2bool, default=False)
    parser.add_argument("--det_db_score_mode", type=str, default="fast")
    parser.add_argument("--vis_seg_map", type=str2bool, default=False)

    # params for text recognizer
    parser.add_argument("--rec_algorithm", type=str, default='SVTR_LCNet')
    parser.add_argument("--rec_model_dir", type=str)
    parser.add_argument("--rec_image_shape", type=str, default="3, 48, 320")
    parser.add_argument("--rec_batch_num", type=int, default=6)
    parser.add_argument("--max_text_length", type=int, default=25)
    parser.add_argument(
        "--rec_char_dict_path",
        type=str,
        default="./ppocr/utils/ppocr_keys_v1.txt")
    parser.add_argument("--use_space_char", type=str2bool, default=True)
    parser.add_argument(
        "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
    parser.add_argument("--drop_score", type=float, default=0.5)

    # params for text classifier
    parser.add_argument("--use_angle_cls", type=str2bool, default=False)
    parser.add_argument("--cls_model_dir", type=str)
    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
    parser.add_argument("--label_list", type=list, default=['0', '180'])
    parser.add_argument("--cls_batch_num", type=int, default=6)
    parser.add_argument("--cls_thresh", type=float, default=0.9)

    parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
    parser.add_argument("--cpu_threads", type=int, default=10)
    parser.add_argument("--use_pdserving", type=str2bool, default=False)
    parser.add_argument("--warmup", type=str2bool, default=False)

    #
    parser.add_argument(
        "--draw_img_save_dir", type=str, default="./inference_results")
    parser.add_argument("--save_crop_res", type=str2bool, default=False)
    parser.add_argument("--crop_res_save_dir", type=str, default="./output")

    # multi-process
    parser.add_argument("--use_mp", type=str2bool, default=False)
    parser.add_argument("--total_process_num", type=int, default=1)
    parser.add_argument("--process_id", type=int, default=0)

    parser.add_argument("--benchmark", type=str2bool, default=False)
    parser.add_argument("--save_log_path", type=str, default="./log_output/")

    parser.add_argument("--show_log", type=str2bool, default=True)
    parser.add_argument("--use_onnx", type=str2bool, default=False)
    return parser


def parse_args():
    parser = init_args()
    return parser.parse_args()


def create_predictor(args, mode, logger):
    if mode == "det":
        model_dir = args.det_model_dir
    elif mode == 'cls':
        model_dir = args.cls_model_dir
    else:
        model_dir = args.rec_model_dir

    if model_dir is None:
        logger.info("not find {} model file path {}".format(mode, model_dir))
        sys.exit(0)

    if args.use_onnx:
        import onnxruntime as ort
        model_file_path = model_dir
        if not os.path.exists(model_file_path):
            raise ValueError("not find model file path {}".format(
                model_file_path))
        sess = ort.InferenceSession(model_file_path, providers=[('ROCMExecutionProvider', {'device_id': '4'}),'CPUExecutionProvider'])
        return sess, sess.get_inputs()[0], None, None
    
    else:
        model_file_path = model_dir + "/inference.pdmodel"
        params_file_path = model_dir + "/inference.pdiparams"
        if not os.path.exists(model_file_path):
            raise ValueError("not find model file path {}".format(
                model_file_path))
        if not os.path.exists(params_file_path):
            raise ValueError("not find params file path {}".format(
                params_file_path))

        config = inference.Config(model_file_path, params_file_path)

        if hasattr(args, 'precision'):
            if args.precision == "fp16" and args.use_tensorrt:
                precision = inference.PrecisionType.Half
                print("fp16 set success!")
            elif args.precision == "int8":
                precision = inference.PrecisionType.Int8
            else:
                precision = inference.PrecisionType.Float32
        else:
            precision = inference.PrecisionType.Float32

        if args.use_gpu:
            gpu_id = get_infer_gpuid()
            if gpu_id is None:
                logger.warning(
                    "GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jetson."
                )
            config.enable_use_gpu(args.gpu_mem, 0)
            use_dynamic_shape = True
            if mode == "det":
                min_input_shape = {
                    "x": [1, 3, 50, 50],
                    "conv2d_92.tmp_0": [1, 120, 20, 20],
                    "conv2d_91.tmp_0": [1, 24, 10, 10],
                    "conv2d_59.tmp_0": [1, 96, 20, 20],
                    "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
                    "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
                    "conv2d_124.tmp_0": [1, 256, 20, 20],
                    "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
                    "elementwise_add_7": [1, 56, 2, 2],
                    "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
                }
                max_input_shape = {
                    "x": [1, 3, 1536, 1536],
                    "conv2d_92.tmp_0": [1, 120, 400, 400],
                    "conv2d_91.tmp_0": [1, 24, 200, 200],
                    "conv2d_59.tmp_0": [1, 96, 400, 400],
                    "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
                    "conv2d_124.tmp_0": [1, 256, 400, 400],
                    "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
                    "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
                    "elementwise_add_7": [1, 56, 400, 400],
                    "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
                }
                opt_input_shape = {
                    "x": [1, 3, 640, 640],
                    "conv2d_92.tmp_0": [1, 120, 160, 160],
                    "conv2d_91.tmp_0": [1, 24, 80, 80],
                    "conv2d_59.tmp_0": [1, 96, 160, 160],
                    "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
                    "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
                    "conv2d_124.tmp_0": [1, 256, 160, 160],
                    "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
                    "elementwise_add_7": [1, 56, 40, 40],
                    "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
                }
                min_pact_shape = {
                    "nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
                    "nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
                }
                max_pact_shape = {
                    "nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
                    "nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
                }
                opt_pact_shape = {
                    "nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
                    "nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
                }
                min_input_shape.update(min_pact_shape)
                max_input_shape.update(max_pact_shape)
                opt_input_shape.update(opt_pact_shape)
            elif mode == "rec":
                if args.rec_algorithm not in ["CRNN", "SVTR_LCNet"]:
                    use_dynamic_shape = False
                imgH = int(args.rec_image_shape.split(',')[-2])
                min_input_shape = {"x": [1, 3, imgH, 10]}
                max_input_shape = {"x": [args.rec_batch_num, 3, imgH, 2304]}
                opt_input_shape = {"x": [args.rec_batch_num, 3, imgH, 320]}
                config.exp_disable_tensorrt_ops(["transpose2"])
            elif mode == "cls":
                min_input_shape = {"x": [1, 3, 48, 10]}
                max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
                opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
            else:
                use_dynamic_shape = False
            if use_dynamic_shape:
                config.set_trt_dynamic_shape_info(
                    min_input_shape, max_input_shape, opt_input_shape)

        elif args.use_xpu:
            config.enable_xpu(10 * 1024 * 1024)
        else:
            config.disable_gpu()
            if hasattr(args, "cpu_threads"):
                config.set_cpu_math_library_num_threads(args.cpu_threads)
            else:
                # default cpu threads as 10
                config.set_cpu_math_library_num_threads(10)
            if args.enable_mkldnn:
                # cache 10 different shapes for mkldnn to avoid memory leak
                config.set_mkldnn_cache_capacity(10)
                config.enable_mkldnn()
                if args.precision == "fp16":
                    config.enable_mkldnn_bfloat16()
        # enable memory optim
        config.enable_memory_optim()
        config.disable_glog_info()
        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
        config.delete_pass("matmul_transpose_reshape_fuse_pass")
        if mode == 'table':
            config.delete_pass("fc_fuse_pass")  # not supported for table
        config.switch_use_feed_fetch_ops(False)
        config.switch_ir_optim(True)

        # create predictor
        predictor = inference.create_predictor(config)
        input_names = predictor.get_input_names()
        for name in input_names:
            input_tensor = predictor.get_input_handle(name)
        output_tensors = get_output_tensors(args, mode, predictor)
        return predictor, input_tensor, output_tensors, config


def get_output_tensors(args, mode, predictor):
    output_names = predictor.get_output_names()
    output_tensors = []
    if mode == "rec" and args.rec_algorithm in ["CRNN", "SVTR_LCNet"]:
        output_name = 'softmax_0.tmp_0'
        if output_name in output_names:
            return [predictor.get_output_handle(output_name)]
        else:
            for output_name in output_names:
                output_tensor = predictor.get_output_handle(output_name)
                output_tensors.append(output_tensor)
    else:
        for output_name in output_names:
            output_tensor = predictor.get_output_handle(output_name)
            output_tensors.append(output_tensor)
    return output_tensors


def get_infer_gpuid():
    sysstr = platform.system()
    if sysstr == "Windows":
        return 0

    if not paddle.core.is_compiled_with_rocm():
        cmd = "env | grep CUDA_VISIBLE_DEVICES"
    else:
        cmd = "env | grep HIP_VISIBLE_DEVICES"
    env_cuda = os.popen(cmd).readlines()
    if len(env_cuda) == 0:
        return 0
    else:
        gpu_id = env_cuda[0].strip().split("=")[1]
        return int(gpu_id[0])


def draw_text_det_res(dt_boxes, img_path):
    src_im = cv2.imread(img_path)
    for box in dt_boxes:
        box = np.array(box).astype(np.int32).reshape(-1, 2)
        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
    return src_im


def draw_ocr_box_txt(image,
                     boxes,
                     txts,
                     scores=None,
                     drop_score=0.5,
                     font_path="./doc/simfang.ttf"):
    h, w = image.height, image.width
    img_left = image.copy()
    img_right = Image.new('RGB', (w, h), (255, 255, 255))

    import random

    random.seed(0)
    draw_left = ImageDraw.Draw(img_left)
    draw_right = ImageDraw.Draw(img_right)
    for idx, (box, txt) in enumerate(zip(boxes, txts)):
        if scores is not None and scores[idx] < drop_score:
            continue
        color = (random.randint(0, 255), random.randint(0, 255),
                 random.randint(0, 255))
        draw_left.polygon(box, fill=color)
        draw_right.polygon(
            [
                box[0][0], box[0][1], box[1][0], box[1][1], box[2][0],
                box[2][1], box[3][0], box[3][1]
            ],
            outline=color)
        box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][
            1])**2)
        box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][
            1])**2)
        if box_height > 2 * box_width:
            font_size = max(int(box_width * 0.9), 10)
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
            cur_y = box[0][1]
            for c in txt:
                char_size = font.getsize(c)
                draw_right.text(
                    (box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
                cur_y += char_size[1]
        else:
            font_size = max(int(box_height * 0.8), 10)
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
            draw_right.text(
                [box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
    img_left = Image.blend(image, img_left, 0.5)
    img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))
    img_show.paste(img_left, (0, 0, w, h))
    img_show.paste(img_right, (w, 0, w * 2, h))
    return np.array(img_show)


def get_rotate_crop_image(img, points):
    '''
    img_height, img_width = img.shape[0:2]
    left = int(np.min(points[:, 0]))
    right = int(np.max(points[:, 0]))
    top = int(np.min(points[:, 1]))
    bottom = int(np.max(points[:, 1]))
    img_crop = img[top:bottom, left:right, :].copy()
    points[:, 0] = points[:, 0] - left
    points[:, 1] = points[:, 1] - top
    '''
    assert len(points) == 4, "shape of points must be 4*2"
    img_crop_width = int(
        max(
            np.linalg.norm(points[0] - points[1]),
            np.linalg.norm(points[2] - points[3])))
    img_crop_height = int(
        max(
            np.linalg.norm(points[0] - points[3]),
            np.linalg.norm(points[1] - points[2])))
    pts_std = np.float32([[0, 0], [img_crop_width, 0],
                          [img_crop_width, img_crop_height],
                          [0, img_crop_height]])
    M = cv2.getPerspectiveTransform(points, pts_std)
    dst_img = cv2.warpPerspective(
        img,
        M, (img_crop_width, img_crop_height),
        borderMode=cv2.BORDER_REPLICATE,
        flags=cv2.INTER_CUBIC)
    dst_img_height, dst_img_width = dst_img.shape[0:2]
    if dst_img_height * 1.0 / dst_img_width >= 1.5:
        dst_img = np.rot90(dst_img)
    return dst_img


if __name__ == '__main__':
    pass