All/Python/PaddleOCR/hmOCR/parts/classifier.py

import cv2
import math
import numpy as np
from .utils import *
from copy import deepcopy
from hmOCR.argument import Args


class Classifier:
    def __init__(self, args: "Args"):
        self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
        self.cls_batch_num = args.cls_batch_num
        self.cls_thresh = args.cls_thresh
        postprocess_params = {
            "name": "ClsPostProcess",
            "label_list": args.cls_label_list
        }
        self.postprocess_op = build_post_process(postprocess_params)
        self.predictor, self.input_tensor, self.output_tensors = create_predictor(args, "cls")

    def resize_norm_img(self, img):
        imgC, imgH, imgW = self.cls_image_shape
        h = img.shape[0]
        w = img.shape[1]
        ratio = w / float(h)
        if math.ceil(imgH * ratio) > imgW:
            resized_w = imgW
        else:
            resized_w = int(math.ceil(imgH * ratio))
        resized_image = cv2.resize(img, (resized_w, imgH))  # noqa
        resized_image = resized_image.astype("float32")
        if self.cls_image_shape[0] == 1:
            resized_image = resized_image / 255
            resized_image = resized_image[np.newaxis, :]
        else:
            resized_image = resized_image.transpose((2, 0, 1)) / 255
        resized_image -= 0.5
        resized_image /= 0.5
        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
        padding_im[:, :, 0:resized_w] = resized_image
        return padding_im

    def __call__(self, img_list):
        img_list = deepcopy(img_list)
        img_num = len(img_list)
        # Calculate the aspect ratio of all text bars
        width_list = []
        for img in img_list:
            width_list.append(img.shape[1] / float(img.shape[0]))
        # Sorting can speed up the cls process
        indices = np.argsort(np.array(width_list))

        batch_num = self.cls_batch_num

        for beg_img_no in range(0, img_num, batch_num):
            end_img_no = min(img_num, beg_img_no + batch_num)
            norm_img_batch = []
            max_wh_ratio = 0
            for ino in range(beg_img_no, end_img_no):
                h, w = img_list[indices[ino]].shape[0:2]
                wh_ratio = w * 1.0 / h
                max_wh_ratio = max(max_wh_ratio, wh_ratio)
            for ino in range(beg_img_no, end_img_no):
                norm_img = self.resize_norm_img(img_list[indices[ino]])
                norm_img = norm_img[np.newaxis, :]
                norm_img_batch.append(norm_img)
            norm_img_batch = np.concatenate(norm_img_batch)
            norm_img_batch = norm_img_batch.copy()

            self.input_tensor.copy_from_cpu(norm_img_batch)
            self.predictor.run()
            prob_out = self.output_tensors[0].copy_to_cpu()
            self.predictor.try_shrink_memory()

            cls_result = self.postprocess_op(prob_out)
            for rno in range(len(cls_result)):
                label, score = cls_result[rno]
                if "180" in label and score > self.cls_thresh:
                    img_list[indices[beg_img_no + rno]] = cv2.rotate(img_list[indices[beg_img_no + rno]], 1)  # noqa
        return img_list