From 88786bc8857f0ccbe399d95ea80afeb8f0178520 Mon Sep 17 00:00:00 2001 From: chenjun2hao <778961303@qq.com> Date: Sun, 24 Mar 2019 15:20:17 +0800 Subject: [PATCH] updata the decoder model for indefinite width image recognition Signed-off-by: chenjun2hao --- src/__init__.py | 0 src/class_attention.py | 107 ++++++++++ src/dataset.py | 120 +++++++++++ src/utils.py | 368 ++++++++++++++++++++++++++++++++ test_img/md_img/attentionV2.png | Bin 0 -> 95950 bytes 5 files changed, 595 insertions(+) create mode 100644 src/__init__.py create mode 100644 src/class_attention.py create mode 100644 src/dataset.py create mode 100644 src/utils.py create mode 100644 test_img/md_img/attentionV2.png diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/class_attention.py b/src/class_attention.py new file mode 100644 index 0000000..26b1537 --- /dev/null +++ b/src/class_attention.py @@ -0,0 +1,107 @@ +# coding:utf-8 +import utils +import torch +import cv2 +import numpy as np +from PIL import Image +import torchvision.transforms as transforms +import models.crnn_lang as crnn + +class attention_ocr(): + '''使用attention_ocr进行字符识别 + 返回: + ocr读数,置信度 + ''' + def __init__(self): + encoder_path = './expr/attentioncnn/encoder_600.pth' + decoder_path = './expr/attentioncnn/decoder_600.pth' + self.alphabet = '0123456789' + self.max_length = 7 # 最长字符串的长度 + self.EOS_TOKEN = 1 + self.use_gpu = True + self.max_width = 220 + self.converter = utils.strLabelConverterForAttention(self.alphabet) + self.transform = transforms.ToTensor() + + nclass = len(self.alphabet) + 3 + encoder = crnn.CNN(32, 1, 256) # 编码器 + decoder = crnn.decoder(256, nclass) # seq to seq的解码器, nclass在decoder中还加了2 + + if encoder_path and decoder_path: + print('loading pretrained models ......') + encoder.load_state_dict(torch.load(encoder_path)) + decoder.load_state_dict(torch.load(decoder_path)) + if torch.cuda.is_available() and self.use_gpu: + encoder = encoder.cuda() + decoder = decoder.cuda() + self.encoder = encoder.eval() + self.decoder = decoder.eval() + + def constant_pad(self, img_crop): + '''把图片等比例缩放到高度:32,再或resize填充到220宽度 + img_crop: + cv2图片,rgb顺序 + 返回: + img tensor + ''' + h, w, c = img_crop.shape + ratio = h / 32 + new_w = int(w / ratio) + new_img = cv2.resize(img_crop,(new_w, 32)) + container = np.ones((32, self.max_width, 3), dtype=np.uint8) * new_img[-3,-3,:] + if new_w <= self.max_width: + container[:,:new_w,:] = new_img + elif new_w > self.max_width: + container = cv2.resize(new_img, (self.max_width, 32)) + + img = Image.fromarray(container.astype('uint8')).convert('L') + img = self.transform(img) + img.sub_(0.5).div_(0.5) + if self.use_gpu: + img = img.cuda() + return img.unsqueeze(0) + + def predict(self, img_crop): + '''attention ocr 做文字识别 + img_crop: + cv2图片,rgb顺序 + 返回: + ocr读数,prob置信度 + ''' + img_tensor = self.constant_pad(img_crop) + encoder_out = self.encoder(img_tensor) + + decoded_words = [] + prob = 1.0 + decoder_input = torch.zeros(1).long() # 初始化decoder的开始,从0开始输出 + decoder_hidden = self.decoder.initHidden(1) + if torch.cuda.is_available() and self.use_gpu: + decoder_input = decoder_input.cuda() + decoder_hidden = decoder_hidden.cuda() + # 预测的时候采用非强制策略,将前一次的输出,作为下一次的输入,直到标签为EOS_TOKEN时停止 + for di in range(self.max_length): # 最大字符串的长度 + decoder_output, decoder_hidden, decoder_attention = self.decoder( + decoder_input, decoder_hidden, encoder_out) + probs = torch.exp(decoder_output) + # decoder_attentions[di] = decoder_attention.data + topv, topi = decoder_output.data.topk(1) + ni = topi.squeeze(1) + decoder_input = ni + prob *= probs[:, ni] + if ni == self.EOS_TOKEN: + # decoded_words.append('') + break + else: + decoded_words.append(self.converter.decode(ni)) + + words = ''.join(decoded_words) + prob = prob.item() + + return words, prob + +if __name__ == '__main__': + path = './test_img/00027_299021_27.jpg' + img = cv2.imread(path) + attention = attention_ocr() + res = attention.predict(img) + print(res) \ No newline at end of file diff --git a/src/dataset.py b/src/dataset.py new file mode 100644 index 0000000..f994a13 --- /dev/null +++ b/src/dataset.py @@ -0,0 +1,120 @@ +#!/usr/bin/python +# encoding: utf-8 + +import random +import torch +from torch.utils.data import Dataset +from torch.utils.data import sampler +import torchvision.transforms as transforms +# import lmdb +import six +import sys +from PIL import Image +import numpy as np + + +class listDataset(Dataset): + def __init__(self, list_file=None, transform=None, target_transform=None): + self.list_file = list_file + with open(list_file) as fp: + self.lines = fp.readlines() + self.nSamples = len(self.lines) + + self.transform = transform + self.target_transform = target_transform + + def __len__(self): + return self.nSamples + + def __getitem__(self, index): + assert index <= len(self), 'index range error' + + line_splits = self.lines[index].strip().split(' ') + imgpath = line_splits[0] + try: + if 'train' in self.list_file: + img = Image.open(imgpath).convert('L') + else: + img = Image.open(imgpath).convert('L') + except IOError: + print('Corrupted image for %d' % index) + return self[index + 1] + + if self.transform is not None: + img = self.transform(img) + + label = line_splits[1].decode('utf-8') + + if self.target_transform is not None: + label = self.target_transform(label) + + return (img, label) + +class resizeNormalize(object): + + def __init__(self, size, interpolation=Image.BILINEAR): + self.size = size + self.interpolation = interpolation + self.toTensor = transforms.ToTensor() + + def __call__(self, img): + img = img.resize(self.size, self.interpolation) + img = self.toTensor(img) + img.sub_(0.5).div_(0.5) + return img + + +class randomSequentialSampler(sampler.Sampler): + + def __init__(self, data_source, batch_size): + self.num_samples = len(data_source) + self.batch_size = batch_size + + def __iter__(self): + n_batch = len(self) // self.batch_size + tail = len(self) % self.batch_size + index = torch.LongTensor(len(self)).fill_(0) + for i in range(n_batch): + random_start = random.randint(0, len(self) - self.batch_size) + batch_index = random_start + torch.arange(0, self.batch_size) + index[i * self.batch_size:(i + 1) * self.batch_size] = batch_index + # deal with tail + if tail: + random_start = random.randint(0, len(self) - self.batch_size) + tail_index = random_start + torch.arange(0, tail) + index[(i + 1) * self.batch_size:] = tail_index + + return iter(index) + + def __len__(self): + return self.num_samples + + +class alignCollate(object): + + def __init__(self, imgH=32, imgW=100, keep_ratio=False, min_ratio=1): + self.imgH = imgH + self.imgW = imgW + self.keep_ratio = keep_ratio + self.min_ratio = min_ratio + + def __call__(self, batch): + images, labels = zip(*batch) + + imgH = self.imgH + imgW = self.imgW + if self.keep_ratio: + ratios = [] + for image in images: + w, h = image.size + ratios.append(w / float(h)) + ratios.sort() + max_ratio = ratios[-1] + imgW = int(np.floor(max_ratio * imgH)) + imgW = max(imgH * self.min_ratio, imgW) # assure imgH >= imgW + + transform = resizeNormalize((imgW, imgH)) + images = [transform(image) for image in images] + images = torch.cat([t.unsqueeze(0) for t in images], 0) + + return images, labels diff --git a/src/utils.py b/src/utils.py new file mode 100644 index 0000000..ad2b1e3 --- /dev/null +++ b/src/utils.py @@ -0,0 +1,368 @@ +#!/usr/bin/python +# encoding: utf-8 +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +from torch.autograd import Variable +import collections +from PIL import Image, ImageFilter +import math +import random +import numpy as np +import cv2 + +with open('./data/char_std_5990.txt') as f: + data = f.readlines() + alphabet = [x.rstrip() for x in data] + alphabet = ''.join(alphabet).decode('utf-8') # python2不加decode的时候会乱码 + + +class strLabelConverterForAttention(object): + """Convert between str and label. + + NOTE: + Insert `EOS` to the alphabet for attention. + + Args: + alphabet (str): set of the possible characters. + ignore_case (bool, default=True): whether or not to ignore all of the case. + """ + + def __init__(self, alphabet): + self.alphabet = alphabet + + self.dict = {} + self.dict['SOS'] = 0 # 开始 + self.dict['EOS'] = 1 # 结束 + self.dict['$'] = 2 # blank标识符 + for i, item in enumerate(self.alphabet): + # NOTE: 0 is reserved for 'blank' required by wrap_ctc + self.dict[item] = i + 3 # 从3开始编码 + + def encode(self, text): + """对target_label做编码和对齐 + 对target txt每个字符串的开始加上GO,最后加上EOS,并用最长的字符串做对齐 + Args: + text (str or list of str): texts to convert. + + Returns: + torch.IntTensor targets:max_length × batch_size + """ + if isinstance(text, unicode): + text = [self.dict[item] for item in text] + elif isinstance(text, collections.Iterable): + text = [self.encode(s) for s in text] # 编码 + + max_length = max([len(x) for x in text]) # 对齐 + nb = len(text) + targets = torch.ones(nb, max_length + 2) * 2 # use ‘blank’ for pading + for i in range(nb): + targets[i][0] = 0 # 开始 + targets[i][1:len(text[i]) + 1] = text[i] + targets[i][len(text[i]) + 1] = 1 + text = targets.transpose(0, 1).contiguous() + text = text.long() + return torch.LongTensor(text) + + def decode(self, t): + """Decode encoded texts back into strs. + + Args: + torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts. + torch.IntTensor [n]: length of each text. + + Raises: + AssertionError: when the texts and its length does not match. + + Returns: + text (str or list of str): texts to convert. + """ + + texts = list(self.dict.keys())[list(self.dict.values()).index(t)] + return texts + +class strLabelConverterForCTC(object): + """Convert between str and label. + + NOTE: + Insert `blank` to the alphabet for CTC. + + Args: + alphabet (str): set of the possible characters. + ignore_case (bool, default=True): whether or not to ignore all of the case. + """ + + def __init__(self, alphabet, sep): + self.sep = sep + self.alphabet = alphabet.split(sep) + self.alphabet.append('-') # for `-1` index + + self.dict = {} + for i, item in enumerate(self.alphabet): + # NOTE: 0 is reserved for 'blank' required by wrap_ctc + self.dict[item] = i + 1 + + def encode(self, text): + """Support batch or single str. + + Args: + text (str or list of str): texts to convert. + + Returns: + torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts. + torch.IntTensor [n]: length of each text. + """ + if isinstance(text, str): + text = text.split(self.sep) + text = [self.dict[item] for item in text] + length = [len(text)] + elif isinstance(text, collections.Iterable): + length = [len(s.split(self.sep)) for s in text] + text = self.sep.join(text) + text, _ = self.encode(text) + return (torch.IntTensor(text), torch.IntTensor(length)) + + def decode(self, t, length, raw=False): + """Decode encoded texts back into strs. + + Args: + torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts. + torch.IntTensor [n]: length of each text. + + Raises: + AssertionError: when the texts and its length does not match. + + Returns: + text (str or list of str): texts to convert. + """ + if length.numel() == 1: + length = length[0] + assert t.numel() == length, "text with length: {} does not match declared length: {}".format(t.numel(), length) + if raw: + return ''.join([self.alphabet[i - 1] for i in t]) + else: + char_list = [] + for i in range(length): + if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])): + char_list.append(self.alphabet[t[i] - 1]) + return ''.join(char_list) + else: + # batch mode + assert t.numel() == length.sum(), "texts with length: {} does not match declared length: {}".format(t.numel(), length.sum()) + texts = [] + index = 0 + for i in range(length.numel()): + l = length[i] + texts.append( + self.decode( + t[index:index + l], torch.IntTensor([l]), raw=raw)) + index += l + return texts + + +class averager(object): + """Compute average for `torch.Variable` and `torch.Tensor`. """ + + def __init__(self): + self.reset() + + def add(self, v): + if isinstance(v, Variable): + count = v.data.numel() + v = v.data.sum() + elif isinstance(v, torch.Tensor): + count = v.numel() + v = v.sum() + + self.n_count += count + self.sum += v + + def reset(self): + self.n_count = 0 + self.sum = 0 + + def val(self): + res = 0 + if self.n_count != 0: + res = self.sum / float(self.n_count) + return res + + +def oneHot(v, v_length, nc): + batchSize = v_length.size(0) + maxLength = v_length.max() + v_onehot = torch.FloatTensor(batchSize, maxLength, nc).fill_(0) + acc = 0 + for i in range(batchSize): + length = v_length[i] + label = v[acc:acc + length].view(-1, 1).long() + v_onehot[i, :length].scatter_(1, label, 1.0) + acc += length + return v_onehot + + +def loadData(v, data): + v.data.resize_(data.size()).copy_(data) + + +def prettyPrint(v): + print('Size {0}, Type: {1}'.format(str(v.size()), v.data.type())) + print('| Max: %f | Min: %f | Mean: %f' % (v.max().data[0], v.min().data[0], + v.mean().data[0])) + + +def assureRatio(img): + """Ensure imgH <= imgW.""" + b, c, h, w = img.size() + if h > w: + main = nn.UpsamplingBilinear2d(size=(h, h), scale_factor=None) + img = main(img) + return img + + +class halo(): + ''' + u:高斯分布的均值 + sigma:方差 + nums:在一张图片中随机添加几个光点 + prob:使用halo的概率 + ''' + + def __init__(self, nums, u=0, sigma=0.2, prob=0.5): + self.u = u # 均值μ + self.sig = math.sqrt(sigma) # 标准差δ + self.nums = nums + self.prob = prob + + def create_kernel(self, maxh=32, maxw=50): + height_scope = [10, maxh] # 高度范围 随机生成高斯 + weight_scope = [20, maxw] # 宽度范围 + + x = np.linspace(self.u - 3 * self.sig, self.u + 3 * self.sig, random.randint(*height_scope)) + y = np.linspace(self.u - 3 * self.sig, self.u + 3 * self.sig, random.randint(*weight_scope)) + Gauss_map = np.zeros((len(x), len(y))) + for i in range(len(x)): + for j in range(len(y)): + Gauss_map[i, j] = np.exp(-((x[i] - self.u) ** 2 + (y[j] - self.u) ** 2) / (2 * self.sig ** 2)) / ( + math.sqrt(2 * math.pi) * self.sig) + + return Gauss_map + + def __call__(self, img): + if random.random() < self.prob: + Gauss_map = self.create_kernel(32, 60) # 初始化一个高斯核,32为高度方向的最大值,60为w方向 + img1 = np.asarray(img) + img1.flags.writeable = True # 将数组改为读写模式 + nums = random.randint(1, self.nums) # 随机生成nums个光点 + img1 = img1.astype(np.float) + # print(nums) + for i in range(nums): + img_h, img_w = img1.shape + pointx = random.randint(0, img_h - 10) # 在原图中随机找一个点 + pointy = random.randint(0, img_w - 10) + + h, w = Gauss_map.shape # 判断是否超限 + endx = pointx + h + endy = pointy + w + + if pointx + h > img_h: + endx = img_h + Gauss_map = Gauss_map[1:img_h - pointx + 1, :] + if img_w < pointy + w: + endy = img_w + Gauss_map = Gauss_map[:, 1:img_w - pointy + 1] + + # 加上不均匀光照 + img1[pointx:endx, pointy:endy] = img1[pointx:endx, pointy:endy] + Gauss_map * 255.0 + img1[img1 > 255.0] = 255.0 # 进行限幅,不然uint8会从0开始重新计数 + img = img1 + return Image.fromarray(np.uint8(img)) + + +class MyGaussianBlur(ImageFilter.Filter): + name = "GaussianBlur" + + def __init__(self, radius=2, bounds=None): + self.radius = radius + self.bounds = bounds + + def filter(self, image): + if self.bounds: + clips = image.crop(self.bounds).gaussian_blur(self.radius) + image.paste(clips, self.bounds) + return image + else: + return image.gaussian_blur(self.radius) + + +class GBlur(object): + def __init__(self, radius=2, prob=0.5): + radius = random.randint(0, radius) + self.blur = MyGaussianBlur(radius=radius) + self.prob = prob + + def __call__(self, img): + if random.random() < self.prob: + img = img.filter(self.blur) + return img + + +class RandomBrightness(object): + """随机改变亮度 + pil:pil格式的图片 + """ + + def __init__(self, prob=1.5): + self.prob = prob + + def __call__(self, pil): + rgb = np.asarray(pil) + if random.random() < self.prob: + hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV) + h, s, v = cv2.split(hsv) + adjust = random.choice([0.5, 0.7, 0.9, 1.2, 1.5, 1.7]) # 随机选择一个 + # adjust = random.choice([1.2, 1.5, 1.7, 2.0]) # 随机选择一个 + v = v * adjust + v = np.clip(v, 0, 255).astype(hsv.dtype) + hsv = cv2.merge((h, s, v)) + rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB) + return Image.fromarray(np.uint8(rgb)).convert('L') + + +class randapply(object): + """随机决定是否应用光晕、模糊或者二者都用 + + Args: + transforms (list or tuple): list of transformations + """ + + def __init__(self, transforms): + assert isinstance(transforms, (list, tuple)) + self.transforms = transforms + + def __call__(self, img): + for t in self.transforms: + img = t(img) + return img + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + format_string += '\n p={}'.format(self.p) + for t in self.transforms: + format_string += '\n' + format_string += ' {0}'.format(t) + format_string += '\n)' + return format_string + + +def weights_init(model): + # Official init from torch repo. + for m in model.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) 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