301 lines
11 KiB
Python
Executable File
301 lines
11 KiB
Python
Executable File
"""
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Copyright (c) Alibaba, Inc. and its affiliates.
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"""
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import os
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import cv2
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import numpy as np
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import math
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import traceback
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from easydict import EasyDict as edict
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import time
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from iopaint.model.anytext.ocr_recog.RecModel import RecModel
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import torch
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import torch.nn.functional as F
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def min_bounding_rect(img):
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ret, thresh = cv2.threshold(img, 127, 255, 0)
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contours, hierarchy = cv2.findContours(
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thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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if len(contours) == 0:
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print("Bad contours, using fake bbox...")
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return np.array([[0, 0], [100, 0], [100, 100], [0, 100]])
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max_contour = max(contours, key=cv2.contourArea)
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rect = cv2.minAreaRect(max_contour)
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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# sort
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x_sorted = sorted(box, key=lambda x: x[0])
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left = x_sorted[:2]
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right = x_sorted[2:]
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left = sorted(left, key=lambda x: x[1])
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(tl, bl) = left
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right = sorted(right, key=lambda x: x[1])
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(tr, br) = right
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if tl[1] > bl[1]:
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(tl, bl) = (bl, tl)
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if tr[1] > br[1]:
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(tr, br) = (br, tr)
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return np.array([tl, tr, br, bl])
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def create_predictor(model_dir=None, model_lang="ch", is_onnx=False):
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model_file_path = model_dir
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if model_file_path is not None and not os.path.exists(model_file_path):
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raise ValueError("not find model file path {}".format(model_file_path))
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if is_onnx:
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import onnxruntime as ort
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sess = ort.InferenceSession(
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model_file_path, providers=["CPUExecutionProvider"]
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) # 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'
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return sess
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else:
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if model_lang == "ch":
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n_class = 6625
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elif model_lang == "en":
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n_class = 97
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else:
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raise ValueError(f"Unsupported OCR recog model_lang: {model_lang}")
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rec_config = edict(
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in_channels=3,
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backbone=edict(
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type="MobileNetV1Enhance",
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scale=0.5,
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last_conv_stride=[1, 2],
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last_pool_type="avg",
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),
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neck=edict(
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type="SequenceEncoder",
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encoder_type="svtr",
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dims=64,
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depth=2,
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hidden_dims=120,
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use_guide=True,
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),
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head=edict(
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type="CTCHead",
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fc_decay=0.00001,
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out_channels=n_class,
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return_feats=True,
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),
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)
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rec_model = RecModel(rec_config)
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if model_file_path is not None:
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rec_model.load_state_dict(torch.load(model_file_path, map_location="cpu"))
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rec_model.eval()
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return rec_model.eval()
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def _check_image_file(path):
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img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff"}
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return any([path.lower().endswith(e) for e in img_end])
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def get_image_file_list(img_file):
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imgs_lists = []
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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if os.path.isfile(img_file) and _check_image_file(img_file):
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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file_path = os.path.join(img_file, single_file)
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if os.path.isfile(file_path) and _check_image_file(file_path):
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imgs_lists.append(file_path)
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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imgs_lists = sorted(imgs_lists)
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return imgs_lists
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class TextRecognizer(object):
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def __init__(self, args, predictor):
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self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.rec_batch_num = args.rec_batch_num
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self.predictor = predictor
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self.chars = self.get_char_dict(args.rec_char_dict_path)
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self.char2id = {x: i for i, x in enumerate(self.chars)}
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self.is_onnx = not isinstance(self.predictor, torch.nn.Module)
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self.use_fp16 = args.use_fp16
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# img: CHW
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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assert imgC == img.shape[0]
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imgW = int((imgH * max_wh_ratio))
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h, w = img.shape[1:]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = torch.nn.functional.interpolate(
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img.unsqueeze(0),
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size=(imgH, resized_w),
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mode="bilinear",
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align_corners=True,
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)
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resized_image /= 255.0
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = torch.zeros((imgC, imgH, imgW), dtype=torch.float32).to(img.device)
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padding_im[:, :, 0:resized_w] = resized_image[0]
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return padding_im
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# img_list: list of tensors with shape chw 0-255
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def pred_imglist(self, img_list, show_debug=False, is_ori=False):
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img_num = len(img_list)
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assert img_num > 0
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[2] / float(img.shape[1]))
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# Sorting can speed up the recognition process
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indices = torch.from_numpy(np.argsort(np.array(width_list)))
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batch_num = self.rec_batch_num
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preds_all = [None] * img_num
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preds_neck_all = [None] * img_num
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
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imgC, imgH, imgW = self.rec_image_shape[:3]
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max_wh_ratio = imgW / imgH
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[indices[ino]].shape[1:]
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if h > w * 1.2:
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img = img_list[indices[ino]]
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img = torch.transpose(img, 1, 2).flip(dims=[1])
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img_list[indices[ino]] = img
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h, w = img.shape[1:]
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# wh_ratio = w * 1.0 / h
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# max_wh_ratio = max(max_wh_ratio, wh_ratio) # comment to not use different ratio
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
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if self.use_fp16:
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norm_img = norm_img.half()
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norm_img = norm_img.unsqueeze(0)
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norm_img_batch.append(norm_img)
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norm_img_batch = torch.cat(norm_img_batch, dim=0)
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if show_debug:
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for i in range(len(norm_img_batch)):
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_img = norm_img_batch[i].permute(1, 2, 0).detach().cpu().numpy()
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_img = (_img + 0.5) * 255
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_img = _img[:, :, ::-1]
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file_name = f"{indices[beg_img_no + i]}"
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file_name = file_name + "_ori" if is_ori else file_name
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cv2.imwrite(file_name + ".jpg", _img)
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if self.is_onnx:
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input_dict = {}
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input_dict[self.predictor.get_inputs()[0].name] = (
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norm_img_batch.detach().cpu().numpy()
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)
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outputs = self.predictor.run(None, input_dict)
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preds = {}
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preds["ctc"] = torch.from_numpy(outputs[0])
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preds["ctc_neck"] = [torch.zeros(1)] * img_num
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else:
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preds = self.predictor(norm_img_batch)
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for rno in range(preds["ctc"].shape[0]):
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preds_all[indices[beg_img_no + rno]] = preds["ctc"][rno]
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preds_neck_all[indices[beg_img_no + rno]] = preds["ctc_neck"][rno]
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return torch.stack(preds_all, dim=0), torch.stack(preds_neck_all, dim=0)
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def get_char_dict(self, character_dict_path):
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character_str = []
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with open(character_dict_path, "rb") as fin:
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lines = fin.readlines()
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for line in lines:
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line = line.decode("utf-8").strip("\n").strip("\r\n")
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character_str.append(line)
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dict_character = list(character_str)
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dict_character = ["sos"] + dict_character + [" "] # eos is space
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return dict_character
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def get_text(self, order):
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char_list = [self.chars[text_id] for text_id in order]
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return "".join(char_list)
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def decode(self, mat):
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text_index = mat.detach().cpu().numpy().argmax(axis=1)
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ignored_tokens = [0]
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selection = np.ones(len(text_index), dtype=bool)
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selection[1:] = text_index[1:] != text_index[:-1]
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for ignored_token in ignored_tokens:
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selection &= text_index != ignored_token
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return text_index[selection], np.where(selection)[0]
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def get_ctcloss(self, preds, gt_text, weight):
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if not isinstance(weight, torch.Tensor):
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weight = torch.tensor(weight).to(preds.device)
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ctc_loss = torch.nn.CTCLoss(reduction="none")
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log_probs = preds.log_softmax(dim=2).permute(1, 0, 2) # NTC-->TNC
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targets = []
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target_lengths = []
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for t in gt_text:
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targets += [self.char2id.get(i, len(self.chars) - 1) for i in t]
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target_lengths += [len(t)]
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targets = torch.tensor(targets).to(preds.device)
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target_lengths = torch.tensor(target_lengths).to(preds.device)
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input_lengths = torch.tensor([log_probs.shape[0]] * (log_probs.shape[1])).to(
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preds.device
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)
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loss = ctc_loss(log_probs, targets, input_lengths, target_lengths)
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loss = loss / input_lengths * weight
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return loss
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def main():
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rec_model_dir = "./ocr_weights/ppv3_rec.pth"
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predictor = create_predictor(rec_model_dir)
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args = edict()
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args.rec_image_shape = "3, 48, 320"
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args.rec_char_dict_path = "./ocr_weights/ppocr_keys_v1.txt"
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args.rec_batch_num = 6
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text_recognizer = TextRecognizer(args, predictor)
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image_dir = "./test_imgs_cn"
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gt_text = ["韩国小馆"] * 14
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image_file_list = get_image_file_list(image_dir)
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valid_image_file_list = []
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img_list = []
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for image_file in image_file_list:
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img = cv2.imread(image_file)
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if img is None:
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print("error in loading image:{}".format(image_file))
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continue
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valid_image_file_list.append(image_file)
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img_list.append(torch.from_numpy(img).permute(2, 0, 1).float())
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try:
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tic = time.time()
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times = []
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for i in range(10):
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preds, _ = text_recognizer.pred_imglist(img_list) # get text
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preds_all = preds.softmax(dim=2)
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times += [(time.time() - tic) * 1000.0]
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tic = time.time()
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print(times)
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print(np.mean(times[1:]) / len(preds_all))
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weight = np.ones(len(gt_text))
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loss = text_recognizer.get_ctcloss(preds, gt_text, weight)
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for i in range(len(valid_image_file_list)):
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pred = preds_all[i]
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order, idx = text_recognizer.decode(pred)
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text = text_recognizer.get_text(order)
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print(
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f'{valid_image_file_list[i]}: pred/gt="{text}"/"{gt_text[i]}", loss={loss[i]:.2f}'
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)
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except Exception as E:
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print(traceback.format_exc(), E)
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if __name__ == "__main__":
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main()
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