PaddleOCR：基于 MNIST 数据集的手写多数字识别

%cd ~
!mkdir dataset 
!mkdir dataset/train
!mkdir dataset/testimport cv2import randomimport numpy as npfrom tqdm import tqdmfrom paddle.vision.datasets import MNIST# 加载数据集mnist_train = MNIST(mode='train', backend='cv2')
mnist_test = MNIST(mode='test', backend='cv2')# 数据集预处理datas_train = {}for i in range(len(mnist_train)):
    sample = mnist_train[i]
    x, y = sample[0], sample[1]

    _sum = np.sum(x, axis=0)
    _where = np.where(_sum > 0)
    x = 255 - x[:, _where[0][0]: _where[0][-1]+1]    if str(y[0]) in datas_train:
        datas_train[str(y[0])].append(x)    else:
        datas_train[str(y[0])] = [x]

datas_test = {}for i in range(len(mnist_test)):
    sample = mnist_test[i]
    x, y = sample[0], sample[1]

    _sum = np.sum(x, axis=0)
    _where = np.where(_sum > 0)
    x = 255 - x[:, _where[0][0]: _where[0][-1]+1]    if str(y[0]) in datas_test:
        datas_test[str(y[0])].append(x)    else:
        datas_test[str(y[0])] = [x]# 图片拼接采样datas_train_list = []for num in tqdm(range(0, 999)):    for _ in range(1000):
        imgs = [255 - np.zeros((28, np.random.randint(10)))]        for word in str(num):
            index = np.random.randint(0, len(datas_train[word]))
            imgs.append(datas_train[word][index])
            imgs.append(255 - np.zeros((28, np.random.randint(10))))
        img = np.concatenate(imgs, 1)
        cv2.imwrite('dataset/train/%03d_%04d.jpg' % (num, _), img)
        datas_train_list.append('train/%03d_%04d.jpg\t%d\n' % (num, _, num))

datas_test_list = []for num in tqdm(range(0, 999)):    for _ in range(50):
        imgs = [255 - np.zeros((28, np.random.randint(10)))]        for word in str(num):
            index = np.random.randint(0, len(datas_test[word]))
            imgs.append(datas_test[word][index])
            imgs.append(255 - np.zeros((28, np.random.randint(10))))
        img = np.concatenate(imgs, 1)
        cv2.imwrite('dataset/test/%03d_%04d.jpg' % (num, _), img)
        datas_test_list.append('test/%03d_%04d.jpg\t%d\n' % (num, _, num))# 数据列表生成with open('dataset/train.txt', 'w') as f:    for line in datas_train_list:
        f.write(line)with open('dataset/test.txt', 'w') as f:    for line in datas_test_list:
        f.write(line)

!git clone https://gitee.com/PaddlePaddle/PaddleOCR -b release/2.1 --depth 1

!pip install imgaug pyclipper lmdb Levenshtein

%cd ~/PaddleOCR

!wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar
!cd pretrain_models && tar -xf ch_ppocr_mobile_v2.0_rec_pre.tar && rm -rf ch_ppocr_mobile_v2.0_rec_pre.tar

%cd ~/PaddleOCR

!python tools/train.py -c ../multi_mnist.yml

%cd ~/PaddleOCR

!python3 tools/export_model.py \
    -c ../multi_mnist.yml -o Global.pretrained_model=../output/multi_mnist/best_accuracy \
    Global.load_static_weights=False \
    Global.save_inference_dir=../inference/multi_mnist

%cd ~/PaddleOCR
!mkdir ~/test_imgsimport cv2import randomimport numpy as npfrom tqdm import tqdmfrom paddle.vision.datasets import MNIST# 加载数据集mnist_test = MNIST(mode='test', backend='cv2')# 数据集预处理datas_test = {}for i in range(len(mnist_test)):
    sample = mnist_test[i]
    x, y = sample[0], sample[1]

    _sum = np.sum(x, axis=0)
    _where = np.where(_sum > 0)
    x = 255 - x[:, _where[0][0]: _where[0][-1]+1]    if str(y[0]) in datas_test:
        datas_test[str(y[0])].append(x)    else:
        datas_test[str(y[0])] = [x]# 图片拼接采样for num in range(0, 1000):
    imgs = [255 - np.zeros((28, np.random.randint(10)))]    for word in str(num):
        index = np.random.randint(0, len(datas_test[word]))
        imgs.append(datas_test[word][index])
        imgs.append(255 - np.zeros((28, np.random.randint(10))))
    img = np.concatenate(imgs, 1)
    cv2.imwrite('../test_imgs/%03d.jpg' % num , img)

%cd ~/PaddleOCR

!python tools/infer/predict_rec.py \
    --image_dir="../test_imgs" \
    --rec_model_dir="../inference/multi_mnist/" \
    --rec_image_shape="3, 28, 64" \
    --rec_char_type="ch" \
    --rec_char_dict_path="../label_list.txt"