Merge branch 'dygraph' into update_requirements
This commit is contained in:
MissPenguin
GitHub
commit
0e6e30bad1
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@ -62,20 +62,21 @@ PostProcess:
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mode: fast # fast or slow two ways
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Metric:
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name: E2EMetric
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gt_mat_dir: # the dir of gt_mat
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gt_mat_dir: ./train_data/total_text/gt # the dir of gt_mat
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character_dict_path: ppocr/utils/ic15_dict.txt
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main_indicator: f_score_e2e
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Train:
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dataset:
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name: PGDataSet
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label_file_list: [.././train_data/total_text/train/]
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data_dir: ./train_data/total_text/train
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label_file_list: [./train_data/total_text/train/]
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ratio_list: [1.0]
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data_format: icdar #two data format: icdar/textnet
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- E2ELabelEncode:
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- PGProcessTrain:
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batch_size: 14 # same as loader: batch_size_per_card
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min_crop_size: 24
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@ -92,13 +93,12 @@ Train:
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Eval:
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dataset:
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name: PGDataSet
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data_dir: ./train_data/
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data_dir: ./train_data/total_text/test
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label_file_list: [./train_data/total_text/test/]
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transforms:
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- DecodeImage: # load image
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img_mode: RGB
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channel_first: False
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- E2ELabelEncode:
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- E2EResizeForTest:
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max_side_len: 768
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- NormalizeImage:
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@ -108,7 +108,7 @@ Eval:
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order: 'hwc'
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- ToCHWImage:
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- KeepKeys:
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keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags', 'img_id']
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keep_keys: [ 'image', 'shape', 'img_id']
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loader:
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shuffle: False
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drop_last: False
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@ -187,29 +187,31 @@ class CTCLabelEncode(BaseRecLabelEncode):
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return dict_character
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class E2ELabelEncode(BaseRecLabelEncode):
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def __init__(self,
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max_text_length,
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character_dict_path=None,
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character_type='EN',
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use_space_char=False,
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**kwargs):
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super(E2ELabelEncode,
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self).__init__(max_text_length, character_dict_path,
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character_type, use_space_char)
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self.pad_num = len(self.dict) # the length to pad
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class E2ELabelEncode(object):
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def __init__(self, **kwargs):
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pass
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def __call__(self, data):
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texts = data['strs']
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temp_texts = []
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for text in texts:
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text = text.lower()
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text = self.encode(text)
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if text is None:
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return None
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text = text + [self.pad_num] * (self.max_text_len - len(text))
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temp_texts.append(text)
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data['strs'] = np.array(temp_texts)
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import json
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label = data['label']
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label = json.loads(label)
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nBox = len(label)
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boxes, txts, txt_tags = [], [], []
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for bno in range(0, nBox):
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box = label[bno]['points']
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txt = label[bno]['transcription']
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boxes.append(box)
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txts.append(txt)
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if txt in ['*', '###']:
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txt_tags.append(True)
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else:
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txt_tags.append(False)
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boxes = np.array(boxes, dtype=np.float32)
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txt_tags = np.array(txt_tags, dtype=np.bool)
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data['polys'] = boxes
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data['texts'] = txts
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data['ignore_tags'] = txt_tags
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return data
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@ -88,7 +88,7 @@ class PGProcessTrain(object):
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return min_area_quad
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def check_and_validate_polys(self, polys, tags, xxx_todo_changeme):
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def check_and_validate_polys(self, polys, tags, im_size):
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"""
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check so that the text poly is in the same direction,
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and also filter some invalid polygons
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@ -96,7 +96,7 @@ class PGProcessTrain(object):
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:param tags:
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:return:
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"""
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(h, w) = xxx_todo_changeme
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(h, w) = im_size
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if polys.shape[0] == 0:
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return polys, np.array([]), np.array([])
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polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1)
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@ -750,8 +750,8 @@ class PGProcessTrain(object):
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input_size = 512
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im = data['image']
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text_polys = data['polys']
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text_tags = data['tags']
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text_strs = data['strs']
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text_tags = data['ignore_tags']
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text_strs = data['texts']
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h, w, _ = im.shape
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text_polys, text_tags, hv_tags = self.check_and_validate_polys(
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text_polys, text_tags, (h, w))
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@ -29,20 +29,20 @@ class PGDataSet(Dataset):
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dataset_config = config[mode]['dataset']
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loader_config = config[mode]['loader']
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self.delimiter = dataset_config.get('delimiter', '\t')
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label_file_list = dataset_config.pop('label_file_list')
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data_source_num = len(label_file_list)
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ratio_list = dataset_config.get("ratio_list", [1.0])
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if isinstance(ratio_list, (float, int)):
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ratio_list = [float(ratio_list)] * int(data_source_num)
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self.data_format = dataset_config.get('data_format', 'icdar')
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assert len(
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ratio_list
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) == data_source_num, "The length of ratio_list should be the same as the file_list."
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self.data_dir = dataset_config['data_dir']
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self.do_shuffle = loader_config['shuffle']
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logger.info("Initialize indexs of datasets:%s" % label_file_list)
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self.data_lines = self.get_image_info_list(label_file_list, ratio_list,
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self.data_format)
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self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
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self.data_idx_order_list = list(range(len(self.data_lines)))
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if mode.lower() == "train":
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self.shuffle_data_random()
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@ -55,108 +55,40 @@ class PGDataSet(Dataset):
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random.shuffle(self.data_lines)
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return
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def extract_polys(self, poly_txt_path):
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"""
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Read text_polys, txt_tags, txts from give txt file.
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"""
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text_polys, txt_tags, txts = [], [], []
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with open(poly_txt_path) as f:
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for line in f.readlines():
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poly_str, txt = line.strip().split('\t')
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poly = list(map(float, poly_str.split(',')))
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text_polys.append(
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np.array(
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poly, dtype=np.float32).reshape(-1, 2))
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txts.append(txt)
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txt_tags.append(txt == '###')
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return np.array(list(map(np.array, text_polys))), \
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np.array(txt_tags, dtype=np.bool), txts
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def extract_info_textnet(self, im_fn, img_dir=''):
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"""
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Extract information from line in textnet format.
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"""
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info_list = im_fn.split('\t')
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img_path = ''
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for ext in [
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'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'JPG'
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]:
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if os.path.exists(os.path.join(img_dir, info_list[0] + "." + ext)):
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img_path = os.path.join(img_dir, info_list[0] + "." + ext)
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break
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if img_path == '':
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print('Image {0} NOT found in {1}, and it will be ignored.'.format(
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info_list[0], img_dir))
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nBox = (len(info_list) - 1) // 9
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wordBBs, txts, txt_tags = [], [], []
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for n in range(0, nBox):
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wordBB = list(map(float, info_list[n * 9 + 1:(n + 1) * 9]))
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txt = info_list[(n + 1) * 9]
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wordBBs.append([[wordBB[0], wordBB[1]], [wordBB[2], wordBB[3]],
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[wordBB[4], wordBB[5]], [wordBB[6], wordBB[7]]])
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txts.append(txt)
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if txt == '###':
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txt_tags.append(True)
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else:
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txt_tags.append(False)
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return img_path, np.array(wordBBs, dtype=np.float32), txt_tags, txts
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def get_image_info_list(self, file_list, ratio_list, data_format='textnet'):
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def get_image_info_list(self, file_list, ratio_list):
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if isinstance(file_list, str):
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file_list = [file_list]
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data_lines = []
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for idx, data_source in enumerate(file_list):
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image_files = []
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if data_format == 'icdar':
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image_files = [(data_source, x) for x in
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os.listdir(os.path.join(data_source, 'rgb'))
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if x.split('.')[-1] in [
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'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif',
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'tiff', 'gif', 'JPG'
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]]
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elif data_format == 'textnet':
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with open(data_source) as f:
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image_files = [(data_source, x.strip())
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for x in f.readlines()]
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else:
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print("Unrecognized data format...")
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exit(-1)
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random.seed(self.seed)
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image_files = random.sample(
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image_files, round(len(image_files) * ratio_list[idx]))
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data_lines.extend(image_files)
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for idx, file in enumerate(file_list):
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with open(file, "rb") as f:
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lines = f.readlines()
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if self.mode == "train" or ratio_list[idx] < 1.0:
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random.seed(self.seed)
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lines = random.sample(lines,
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round(len(lines) * ratio_list[idx]))
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data_lines.extend(lines)
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return data_lines
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def __getitem__(self, idx):
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file_idx = self.data_idx_order_list[idx]
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data_path, data_line = self.data_lines[file_idx]
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data_line = self.data_lines[file_idx]
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try:
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if self.data_format == 'icdar':
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im_path = os.path.join(data_path, 'rgb', data_line)
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poly_path = os.path.join(data_path, 'poly',
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data_line.split('.')[0] + '.txt')
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text_polys, text_tags, text_strs = self.extract_polys(poly_path)
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data_line = data_line.decode('utf-8')
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substr = data_line.strip("\n").split(self.delimiter)
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file_name = substr[0]
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label = substr[1]
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img_path = os.path.join(self.data_dir, file_name)
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if self.mode.lower() == 'eval':
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img_id = int(data_line.split(".")[0][7:])
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else:
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image_dir = os.path.join(os.path.dirname(data_path), 'image')
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im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
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data_line, image_dir)
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img_id = int(data_line.split(".")[0][3:])
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data = {
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'img_path': im_path,
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'polys': text_polys,
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'tags': text_tags,
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'strs': text_strs,
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'img_id': img_id
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}
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img_id = 0
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data = {'img_path': img_path, 'label': label, 'img_id': img_id}
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if not os.path.exists(img_path):
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raise Exception("{} does not exist!".format(img_path))
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with open(data['img_path'], 'rb') as f:
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img = f.read()
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data['image'] = img
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outs = transform(data, self.ops)
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except Exception as e:
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self.logger.error(
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"When parsing line {}, error happened with msg: {}".format(
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@ -35,11 +35,11 @@ class E2EMetric(object):
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self.reset()
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def __call__(self, preds, batch, **kwargs):
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img_id = batch[5][0]
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img_id = batch[2][0]
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e2e_info_list = [{
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'points': det_polyon,
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'text': pred_str
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} for det_polyon, pred_str in zip(preds['points'], preds['strs'])]
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'texts': pred_str
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} for det_polyon, pred_str in zip(preds['points'], preds['texts'])]
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result = get_socre(self.gt_mat_dir, img_id, e2e_info_list)
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self.results.append(result)
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@ -26,7 +26,7 @@ def get_socre(gt_dir, img_id, pred_dict):
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n = len(pred_dict)
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for i in range(n):
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points = pred_dict[i]['points']
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text = pred_dict[i]['text']
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text = pred_dict[i]['texts']
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point = ",".join(map(str, points.reshape(-1, )))
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det.append([point, text])
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return det
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@ -21,6 +21,7 @@ import math
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import numpy as np
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from itertools import groupby
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from cv2.ximgproc import thinning as thin
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from skimage.morphology._skeletonize import thin
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@ -64,7 +64,7 @@ class PGNet_PostProcess(object):
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src_w, src_h, self.valid_set)
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data = {
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'points': poly_list,
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'strs': keep_str_list,
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'texts': keep_str_list,
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}
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return data
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@ -176,6 +176,6 @@ class PGNet_PostProcess(object):
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exit(-1)
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data = {
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'points': poly_list,
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'strs': keep_str_list,
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'texts': keep_str_list,
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}
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return data
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@ -122,7 +122,7 @@ class TextE2E(object):
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else:
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raise NotImplementedError
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post_result = self.postprocess_op(preds, shape_list)
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points, strs = post_result['points'], post_result['strs']
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points, strs = post_result['points'], post_result['texts']
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dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
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elapse = time.time() - starttime
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return dt_boxes, strs, elapse
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@ -103,7 +103,7 @@ def main():
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images = paddle.to_tensor(images)
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preds = model(images)
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post_result = post_process_class(preds, shape_list)
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points, strs = post_result['points'], post_result['strs']
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points, strs = post_result['points'], post_result['texts']
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# write resule
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dt_boxes_json = []
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for poly, str in zip(points, strs):
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