first commit

ppocr/data/imaug/rec_img_aug.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import cv2
+import numpy as np
+import random
+import copy
+from PIL import Image
+from .text_image_aug import tia_perspective, tia_stretch, tia_distort
+
+
+class RecAug(object):
+    def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
+        self.use_tia = use_tia
+        self.aug_prob = aug_prob
+
+    def __call__(self, data):
+        img = data['image']
+        img = warp(img, 10, self.use_tia, self.aug_prob)
+        data['image'] = img
+        return data
+
+
+class RecConAug(object):
+    def __init__(self,
+                 prob=0.5,
+                 image_shape=(32, 320, 3),
+                 max_text_length=25,
+                 ext_data_num=1,
+                 **kwargs):
+        self.ext_data_num = ext_data_num
+        self.prob = prob
+        self.max_text_length = max_text_length
+        self.image_shape = image_shape
+        self.max_wh_ratio = self.image_shape[1] / self.image_shape[0]
+
+    def merge_ext_data(self, data, ext_data):
+        ori_w = round(data['image'].shape[1] / data['image'].shape[0] *
+                      self.image_shape[0])
+        ext_w = round(ext_data['image'].shape[1] / ext_data['image'].shape[0] *
+                      self.image_shape[0])
+        data['image'] = cv2.resize(data['image'], (ori_w, self.image_shape[0]))
+        ext_data['image'] = cv2.resize(ext_data['image'],
+                                       (ext_w, self.image_shape[0]))
+        data['image'] = np.concatenate(
+            [data['image'], ext_data['image']], axis=1)
+        data["label"] += ext_data["label"]
+        return data
+
+    def __call__(self, data):
+        rnd_num = random.random()
+        if rnd_num > self.prob:
+            return data
+        for idx, ext_data in enumerate(data["ext_data"]):
+            if len(data["label"]) + len(ext_data[
+                    "label"]) > self.max_text_length:
+                break
+            concat_ratio = data['image'].shape[1] / data['image'].shape[
+                0] + ext_data['image'].shape[1] / ext_data['image'].shape[0]
+            if concat_ratio > self.max_wh_ratio:
+                break
+            data = self.merge_ext_data(data, ext_data)
+        data.pop("ext_data")
+        return data
+
+
+class ClsResizeImg(object):
+    def __init__(self, image_shape, **kwargs):
+        self.image_shape = image_shape
+
+    def __call__(self, data):
+        img = data['image']
+        norm_img, _ = resize_norm_img(img, self.image_shape)
+        data['image'] = norm_img
+        return data
+
+
+class NRTRRecResizeImg(object):
+    def __init__(self, image_shape, resize_type, padding=False, **kwargs):
+        self.image_shape = image_shape
+        self.resize_type = resize_type
+        self.padding = padding
+
+    def __call__(self, data):
+        img = data['image']
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        image_shape = self.image_shape
+        if self.padding:
+            imgC, imgH, imgW = image_shape
+            # todo: change to 0 and modified 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))
+            norm_img = np.expand_dims(resized_image, -1)
+            norm_img = norm_img.transpose((2, 0, 1))
+            resized_image = norm_img.astype(np.float32) / 128. - 1.
+            padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+            padding_im[:, :, 0:resized_w] = resized_image
+            data['image'] = padding_im
+            return data
+        if self.resize_type == 'PIL':
+            image_pil = Image.fromarray(np.uint8(img))
+            img = image_pil.resize(self.image_shape, Image.ANTIALIAS)
+            img = np.array(img)
+        if self.resize_type == 'OpenCV':
+            img = cv2.resize(img, self.image_shape)
+        norm_img = np.expand_dims(img, -1)
+        norm_img = norm_img.transpose((2, 0, 1))
+        data['image'] = norm_img.astype(np.float32) / 128. - 1.
+        return data
+
+
+class RecResizeImg(object):
+    def __init__(self,
+                 image_shape,
+                 infer_mode=False,
+                 character_dict_path='./ppocr/utils/ppocr_keys_v1.txt',
+                 padding=True,
+                 **kwargs):
+        self.image_shape = image_shape
+        self.infer_mode = infer_mode
+        self.character_dict_path = character_dict_path
+        self.padding = padding
+
+    def __call__(self, data):
+        img = data['image']
+        if self.infer_mode and self.character_dict_path is not None:
+            norm_img, valid_ratio = resize_norm_img_chinese(img,
+                                                            self.image_shape)
+        else:
+            norm_img, valid_ratio = resize_norm_img(img, self.image_shape,
+                                                    self.padding)
+        data['image'] = norm_img
+        data['valid_ratio'] = valid_ratio
+        return data
+
+
+class SRNRecResizeImg(object):
+    def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
+        self.image_shape = image_shape
+        self.num_heads = num_heads
+        self.max_text_length = max_text_length
+
+    def __call__(self, data):
+        img = data['image']
+        norm_img = resize_norm_img_srn(img, self.image_shape)
+        data['image'] = norm_img
+        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
+            srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length)
+
+        data['encoder_word_pos'] = encoder_word_pos
+        data['gsrm_word_pos'] = gsrm_word_pos
+        data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1
+        data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2
+        return data
+
+
+class SARRecResizeImg(object):
+    def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs):
+        self.image_shape = image_shape
+        self.width_downsample_ratio = width_downsample_ratio
+
+    def __call__(self, data):
+        img = data['image']
+        norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(
+            img, self.image_shape, self.width_downsample_ratio)
+        data['image'] = norm_img
+        data['resized_shape'] = resize_shape
+        data['pad_shape'] = pad_shape
+        data['valid_ratio'] = valid_ratio
+        return data
+
+
+class PRENResizeImg(object):
+    def __init__(self, image_shape, **kwargs):
+        """
+        Accroding to original paper's realization, it's a hard resize method here. 
+        So maybe you should optimize it to fit for your task better.
+        """
+        self.dst_h, self.dst_w = image_shape
+
+    def __call__(self, data):
+        img = data['image']
+        resized_img = cv2.resize(
+            img, (self.dst_w, self.dst_h), interpolation=cv2.INTER_LINEAR)
+        resized_img = resized_img.transpose((2, 0, 1)) / 255
+        resized_img -= 0.5
+        resized_img /= 0.5
+        data['image'] = resized_img.astype(np.float32)
+        return data
+
+
+class SVTRRecResizeImg(object):
+    def __init__(self, image_shape, padding=True, **kwargs):
+        self.image_shape = image_shape
+        self.padding = padding
+
+    def __call__(self, data):
+        img = data['image']
+
+        norm_img, valid_ratio = resize_norm_img(img, self.image_shape,
+                                                self.padding)
+        data['image'] = norm_img
+        data['valid_ratio'] = valid_ratio
+        return data
+
+
+def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
+    imgC, imgH, imgW_min, imgW_max = image_shape
+    h = img.shape[0]
+    w = img.shape[1]
+    valid_ratio = 1.0
+    # make sure new_width is an integral multiple of width_divisor.
+    width_divisor = int(1 / width_downsample_ratio)
+    # resize
+    ratio = w / float(h)
+    resize_w = math.ceil(imgH * ratio)
+    if resize_w % width_divisor != 0:
+        resize_w = round(resize_w / width_divisor) * width_divisor
+    if imgW_min is not None:
+        resize_w = max(imgW_min, resize_w)
+    if imgW_max is not None:
+        valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
+        resize_w = min(imgW_max, resize_w)
+    resized_image = cv2.resize(img, (resize_w, imgH))
+    resized_image = resized_image.astype('float32')
+    # norm 
+    if 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
+    resize_shape = resized_image.shape
+    padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
+    padding_im[:, :, 0:resize_w] = resized_image
+    pad_shape = padding_im.shape
+
+    return padding_im, resize_shape, pad_shape, valid_ratio
+
+
+def resize_norm_img(img, image_shape, padding=True):
+    imgC, imgH, imgW = image_shape
+    h = img.shape[0]
+    w = img.shape[1]
+    if not padding:
+        resized_image = cv2.resize(
+            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_w = imgW
+    else:
+        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))
+    resized_image = resized_image.astype('float32')
+    if 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
+    valid_ratio = min(1.0, float(resized_w / imgW))
+    return padding_im, valid_ratio
+
+
+def resize_norm_img_chinese(img, image_shape):
+    imgC, imgH, imgW = image_shape
+    # todo: change to 0 and modified image shape
+    max_wh_ratio = imgW * 1.0 / imgH
+    h, w = img.shape[0], img.shape[1]
+    ratio = w * 1.0 / h
+    max_wh_ratio = max(max_wh_ratio, ratio)
+    imgW = int(imgH * max_wh_ratio)
+    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))
+    resized_image = resized_image.astype('float32')
+    if 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
+    valid_ratio = min(1.0, float(resized_w / imgW))
+    return padding_im, valid_ratio
+
+
+def resize_norm_img_srn(img, image_shape):
+    imgC, imgH, imgW = image_shape
+
+    img_black = np.zeros((imgH, imgW))
+    im_hei = img.shape[0]
+    im_wid = img.shape[1]
+
+    if im_wid <= im_hei * 1:
+        img_new = cv2.resize(img, (imgH * 1, imgH))
+    elif im_wid <= im_hei * 2:
+        img_new = cv2.resize(img, (imgH * 2, imgH))
+    elif im_wid <= im_hei * 3:
+        img_new = cv2.resize(img, (imgH * 3, imgH))
+    else:
+        img_new = cv2.resize(img, (imgW, imgH))
+
+    img_np = np.asarray(img_new)
+    img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+    img_black[:, 0:img_np.shape[1]] = img_np
+    img_black = img_black[:, :, np.newaxis]
+
+    row, col, c = img_black.shape
+    c = 1
+
+    return np.reshape(img_black, (c, row, col)).astype(np.float32)
+
+
+def srn_other_inputs(image_shape, num_heads, max_text_length):
+
+    imgC, imgH, imgW = image_shape
+    feature_dim = int((imgH / 8) * (imgW / 8))
+
+    encoder_word_pos = np.array(range(0, feature_dim)).reshape(
+        (feature_dim, 1)).astype('int64')
+    gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
+        (max_text_length, 1)).astype('int64')
+
+    gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
+    gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
+        [1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
+                                  [num_heads, 1, 1]) * [-1e9]
+
+    gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
+        [1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
+                                  [num_heads, 1, 1]) * [-1e9]
+
+    return [
+        encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+        gsrm_slf_attn_bias2
+    ]
+
+
+def flag():
+    """
+    flag
+    """
+    return 1 if random.random() > 0.5000001 else -1
+
+
+def cvtColor(img):
+    """
+    cvtColor
+    """
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    delta = 0.001 * random.random() * flag()
+    hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta)
+    new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+    return new_img
+
+
+def blur(img):
+    """
+    blur
+    """
+    h, w, _ = img.shape
+    if h > 10 and w > 10:
+        return cv2.GaussianBlur(img, (5, 5), 1)
+    else:
+        return img
+
+
+def jitter(img):
+    """
+    jitter
+    """
+    w, h, _ = img.shape
+    if h > 10 and w > 10:
+        thres = min(w, h)
+        s = int(random.random() * thres * 0.01)
+        src_img = img.copy()
+        for i in range(s):
+            img[i:, i:, :] = src_img[:w - i, :h - i, :]
+        return img
+    else:
+        return img
+
+
+def add_gasuss_noise(image, mean=0, var=0.1):
+    """
+    Gasuss noise
+    """
+
+    noise = np.random.normal(mean, var**0.5, image.shape)
+    out = image + 0.5 * noise
+    out = np.clip(out, 0, 255)
+    out = np.uint8(out)
+    return out
+
+
+def get_crop(image):
+    """
+    random crop
+    """
+    h, w, _ = image.shape
+    top_min = 1
+    top_max = 8
+    top_crop = int(random.randint(top_min, top_max))
+    top_crop = min(top_crop, h - 1)
+    crop_img = image.copy()
+    ratio = random.randint(0, 1)
+    if ratio:
+        crop_img = crop_img[top_crop:h, :, :]
+    else:
+        crop_img = crop_img[0:h - top_crop, :, :]
+    return crop_img
+
+
+class Config:
+    """
+    Config
+    """
+
+    def __init__(self, use_tia):
+        self.anglex = random.random() * 30
+        self.angley = random.random() * 15
+        self.anglez = random.random() * 10
+        self.fov = 42
+        self.r = 0
+        self.shearx = random.random() * 0.3
+        self.sheary = random.random() * 0.05
+        self.borderMode = cv2.BORDER_REPLICATE
+        self.use_tia = use_tia
+
+    def make(self, w, h, ang):
+        """
+        make
+        """
+        self.anglex = random.random() * 5 * flag()
+        self.angley = random.random() * 5 * flag()
+        self.anglez = -1 * random.random() * int(ang) * flag()
+        self.fov = 42
+        self.r = 0
+        self.shearx = 0
+        self.sheary = 0
+        self.borderMode = cv2.BORDER_REPLICATE
+        self.w = w
+        self.h = h
+
+        self.perspective = self.use_tia
+        self.stretch = self.use_tia
+        self.distort = self.use_tia
+
+        self.crop = True
+        self.affine = False
+        self.reverse = True
+        self.noise = True
+        self.jitter = True
+        self.blur = True
+        self.color = True
+
+
+def rad(x):
+    """
+    rad
+    """
+    return x * np.pi / 180
+
+
+def get_warpR(config):
+    """
+    get_warpR
+    """
+    anglex, angley, anglez, fov, w, h, r = \
+        config.anglex, config.angley, config.anglez, config.fov, config.w, config.h, config.r
+    if w > 69 and w < 112:
+        anglex = anglex * 1.5
+
+    z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2))
+    # Homogeneous coordinate transformation matrix
+    rx = np.array([[1, 0, 0, 0],
+                   [0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0], [
+                       0,
+                       -np.sin(rad(anglex)),
+                       np.cos(rad(anglex)),
+                       0,
+                   ], [0, 0, 0, 1]], np.float32)
+    ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
+                   [0, 1, 0, 0], [
+                       -np.sin(rad(angley)),
+                       0,
+                       np.cos(rad(angley)),
+                       0,
+                   ], [0, 0, 0, 1]], np.float32)
+    rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
+                   [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
+                   [0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
+    r = rx.dot(ry).dot(rz)
+    # generate 4 points
+    pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)
+    p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
+    p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
+    p3 = np.array([0, h, 0, 0], np.float32) - pcenter
+    p4 = np.array([w, h, 0, 0], np.float32) - pcenter
+    dst1 = r.dot(p1)
+    dst2 = r.dot(p2)
+    dst3 = r.dot(p3)
+    dst4 = r.dot(p4)
+    list_dst = np.array([dst1, dst2, dst3, dst4])
+    org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
+    dst = np.zeros((4, 2), np.float32)
+    # Project onto the image plane
+    dst[:, 0] = list_dst[:, 0] * z / (z - list_dst[:, 2]) + pcenter[0]
+    dst[:, 1] = list_dst[:, 1] * z / (z - list_dst[:, 2]) + pcenter[1]
+
+    warpR = cv2.getPerspectiveTransform(org, dst)
+
+    dst1, dst2, dst3, dst4 = dst
+    r1 = int(min(dst1[1], dst2[1]))
+    r2 = int(max(dst3[1], dst4[1]))
+    c1 = int(min(dst1[0], dst3[0]))
+    c2 = int(max(dst2[0], dst4[0]))
+
+    try:
+        ratio = min(1.0 * h / (r2 - r1), 1.0 * w / (c2 - c1))
+
+        dx = -c1
+        dy = -r1
+        T1 = np.float32([[1., 0, dx], [0, 1., dy], [0, 0, 1.0 / ratio]])
+        ret = T1.dot(warpR)
+    except:
+        ratio = 1.0
+        T1 = np.float32([[1., 0, 0], [0, 1., 0], [0, 0, 1.]])
+        ret = T1
+    return ret, (-r1, -c1), ratio, dst
+
+
+def get_warpAffine(config):
+    """
+    get_warpAffine
+    """
+    anglez = config.anglez
+    rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
+                   [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32)
+    return rz
+
+
+def warp(img, ang, use_tia=True, prob=0.4):
+    """
+    warp
+    """
+    h, w, _ = img.shape
+    config = Config(use_tia=use_tia)
+    config.make(w, h, ang)
+    new_img = img
+
+    if config.distort:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = tia_distort(new_img, random.randint(3, 6))
+
+    if config.stretch:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = tia_stretch(new_img, random.randint(3, 6))
+
+    if config.perspective:
+        if random.random() <= prob:
+            new_img = tia_perspective(new_img)
+
+    if config.crop:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = get_crop(new_img)
+
+    if config.blur:
+        if random.random() <= prob:
+            new_img = blur(new_img)
+    if config.color:
+        if random.random() <= prob:
+            new_img = cvtColor(new_img)
+    if config.jitter:
+        new_img = jitter(new_img)
+    if config.noise:
+        if random.random() <= prob:
+            new_img = add_gasuss_noise(new_img)
+    if config.reverse:
+        if random.random() <= prob:
+            new_img = 255 - new_img
+    return new_img

ppocr/data/imaug/ssl_img_aug.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import cv2
+import numpy as np
+import random
+from PIL import Image
+
+from .rec_img_aug import resize_norm_img
+
+
+class SSLRotateResize(object):
+    def __init__(self,
+                 image_shape,
+                 padding=False,
+                 select_all=True,
+                 mode="train",
+                 **kwargs):
+        self.image_shape = image_shape
+        self.padding = padding
+        self.select_all = select_all
+        self.mode = mode
+
+    def __call__(self, data):
+        img = data["image"]
+
+        data["image_r90"] = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
+        data["image_r180"] = cv2.rotate(data["image_r90"],
+                                        cv2.ROTATE_90_CLOCKWISE)
+        data["image_r270"] = cv2.rotate(data["image_r180"],
+                                        cv2.ROTATE_90_CLOCKWISE)
+
+        images = []
+        for key in ["image", "image_r90", "image_r180", "image_r270"]:
+            images.append(
+                resize_norm_img(
+                    data.pop(key),
+                    image_shape=self.image_shape,
+                    padding=self.padding)[0])
+        data["image"] = np.stack(images, axis=0)
+        data["label"] = np.array(list(range(4)))
+        if not self.select_all:
+            data["image"] = data["image"][0::2]  # just choose 0 and 180
+            data["label"] = data["label"][0:2]  # label needs to be continuous
+        if self.mode == "test":
+            data["image"] = data["image"][0]
+            data["label"] = data["label"][0]
+        return data

ppocr/data/imaug/text_image_aug/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .augment import tia_perspective, tia_distort, tia_stretch
+
+__all__ = ['tia_distort', 'tia_stretch', 'tia_perspective']

ppocr/data/imaug/text_image_aug/augment.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/RubanSeven/Text-Image-Augmentation-python/blob/master/augment.py
+"""
+
+import numpy as np
+from .warp_mls import WarpMLS
+
+
+def tia_distort(src, segment=4):
+    img_h, img_w = src.shape[:2]
+
+    cut = img_w // segment
+    thresh = cut // 3
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([np.random.randint(thresh), np.random.randint(thresh)])
+    dst_pts.append(
+        [img_w - np.random.randint(thresh), np.random.randint(thresh)])
+    dst_pts.append(
+        [img_w - np.random.randint(thresh), img_h - np.random.randint(thresh)])
+    dst_pts.append(
+        [np.random.randint(thresh), img_h - np.random.randint(thresh)])
+
+    half_thresh = thresh * 0.5
+
+    for cut_idx in np.arange(1, segment, 1):
+        src_pts.append([cut * cut_idx, 0])
+        src_pts.append([cut * cut_idx, img_h])
+        dst_pts.append([
+            cut * cut_idx + np.random.randint(thresh) - half_thresh,
+            np.random.randint(thresh) - half_thresh
+        ])
+        dst_pts.append([
+            cut * cut_idx + np.random.randint(thresh) - half_thresh,
+            img_h + np.random.randint(thresh) - half_thresh
+        ])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
+
+
+def tia_stretch(src, segment=4):
+    img_h, img_w = src.shape[:2]
+
+    cut = img_w // segment
+    thresh = cut * 4 // 5
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([0, 0])
+    dst_pts.append([img_w, 0])
+    dst_pts.append([img_w, img_h])
+    dst_pts.append([0, img_h])
+
+    half_thresh = thresh * 0.5
+
+    for cut_idx in np.arange(1, segment, 1):
+        move = np.random.randint(thresh) - half_thresh
+        src_pts.append([cut * cut_idx, 0])
+        src_pts.append([cut * cut_idx, img_h])
+        dst_pts.append([cut * cut_idx + move, 0])
+        dst_pts.append([cut * cut_idx + move, img_h])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
+
+
+def tia_perspective(src):
+    img_h, img_w = src.shape[:2]
+
+    thresh = img_h // 2
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([0, np.random.randint(thresh)])
+    dst_pts.append([img_w, np.random.randint(thresh)])
+    dst_pts.append([img_w, img_h - np.random.randint(thresh)])
+    dst_pts.append([0, img_h - np.random.randint(thresh)])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
\ No newline at end of file

ppocr/data/imaug/text_image_aug/warp_mls.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/RubanSeven/Text-Image-Augmentation-python/blob/master/warp_mls.py
+"""
+
+import numpy as np
+
+
+class WarpMLS:
+    def __init__(self, src, src_pts, dst_pts, dst_w, dst_h, trans_ratio=1.):
+        self.src = src
+        self.src_pts = src_pts
+        self.dst_pts = dst_pts
+        self.pt_count = len(self.dst_pts)
+        self.dst_w = dst_w
+        self.dst_h = dst_h
+        self.trans_ratio = trans_ratio
+        self.grid_size = 100
+        self.rdx = np.zeros((self.dst_h, self.dst_w))
+        self.rdy = np.zeros((self.dst_h, self.dst_w))
+
+    @staticmethod
+    def __bilinear_interp(x, y, v11, v12, v21, v22):
+        return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 *
+                                                      (1 - y) + v22 * y) * x
+
+    def generate(self):
+        self.calc_delta()
+        return self.gen_img()
+
+    def calc_delta(self):
+        w = np.zeros(self.pt_count, dtype=np.float32)
+
+        if self.pt_count < 2:
+            return
+
+        i = 0
+        while 1:
+            if self.dst_w <= i < self.dst_w + self.grid_size - 1:
+                i = self.dst_w - 1
+            elif i >= self.dst_w:
+                break
+
+            j = 0
+            while 1:
+                if self.dst_h <= j < self.dst_h + self.grid_size - 1:
+                    j = self.dst_h - 1
+                elif j >= self.dst_h:
+                    break
+
+                sw = 0
+                swp = np.zeros(2, dtype=np.float32)
+                swq = np.zeros(2, dtype=np.float32)
+                new_pt = np.zeros(2, dtype=np.float32)
+                cur_pt = np.array([i, j], dtype=np.float32)
+
+                k = 0
+                for k in range(self.pt_count):
+                    if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                        break
+
+                    w[k] = 1. / (
+                        (i - self.dst_pts[k][0]) * (i - self.dst_pts[k][0]) +
+                        (j - self.dst_pts[k][1]) * (j - self.dst_pts[k][1]))
+
+                    sw += w[k]
+                    swp = swp + w[k] * np.array(self.dst_pts[k])
+                    swq = swq + w[k] * np.array(self.src_pts[k])
+
+                if k == self.pt_count - 1:
+                    pstar = 1 / sw * swp
+                    qstar = 1 / sw * swq
+
+                    miu_s = 0
+                    for k in range(self.pt_count):
+                        if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                            continue
+                        pt_i = self.dst_pts[k] - pstar
+                        miu_s += w[k] * np.sum(pt_i * pt_i)
+
+                    cur_pt -= pstar
+                    cur_pt_j = np.array([-cur_pt[1], cur_pt[0]])
+
+                    for k in range(self.pt_count):
+                        if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                            continue
+
+                        pt_i = self.dst_pts[k] - pstar
+                        pt_j = np.array([-pt_i[1], pt_i[0]])
+
+                        tmp_pt = np.zeros(2, dtype=np.float32)
+                        tmp_pt[0] = np.sum(pt_i * cur_pt) * self.src_pts[k][0] - \
+                                    np.sum(pt_j * cur_pt) * self.src_pts[k][1]
+                        tmp_pt[1] = -np.sum(pt_i * cur_pt_j) * self.src_pts[k][0] + \
+                                    np.sum(pt_j * cur_pt_j) * self.src_pts[k][1]
+                        tmp_pt *= (w[k] / miu_s)
+                        new_pt += tmp_pt
+
+                    new_pt += qstar
+                else:
+                    new_pt = self.src_pts[k]
+
+                self.rdx[j, i] = new_pt[0] - i
+                self.rdy[j, i] = new_pt[1] - j
+
+                j += self.grid_size
+            i += self.grid_size
+
+    def gen_img(self):
+        src_h, src_w = self.src.shape[:2]
+        dst = np.zeros_like(self.src, dtype=np.float32)
+
+        for i in np.arange(0, self.dst_h, self.grid_size):
+            for j in np.arange(0, self.dst_w, self.grid_size):
+                ni = i + self.grid_size
+                nj = j + self.grid_size
+                w = h = self.grid_size
+                if ni >= self.dst_h:
+                    ni = self.dst_h - 1
+                    h = ni - i + 1
+                if nj >= self.dst_w:
+                    nj = self.dst_w - 1
+                    w = nj - j + 1
+
+                di = np.reshape(np.arange(h), (-1, 1))
+                dj = np.reshape(np.arange(w), (1, -1))
+                delta_x = self.__bilinear_interp(
+                    di / h, dj / w, self.rdx[i, j], self.rdx[i, nj],
+                    self.rdx[ni, j], self.rdx[ni, nj])
+                delta_y = self.__bilinear_interp(
+                    di / h, dj / w, self.rdy[i, j], self.rdy[i, nj],
+                    self.rdy[ni, j], self.rdy[ni, nj])
+                nx = j + dj + delta_x * self.trans_ratio
+                ny = i + di + delta_y * self.trans_ratio
+                nx = np.clip(nx, 0, src_w - 1)
+                ny = np.clip(ny, 0, src_h - 1)
+                nxi = np.array(np.floor(nx), dtype=np.int32)
+                nyi = np.array(np.floor(ny), dtype=np.int32)
+                nxi1 = np.array(np.ceil(nx), dtype=np.int32)
+                nyi1 = np.array(np.ceil(ny), dtype=np.int32)
+
+                if len(self.src.shape) == 3:
+                    x = np.tile(np.expand_dims(ny - nyi, axis=-1), (1, 1, 3))
+                    y = np.tile(np.expand_dims(nx - nxi, axis=-1), (1, 1, 3))
+                else:
+                    x = ny - nyi
+                    y = nx - nxi
+                dst[i:i + h, j:j + w] = self.__bilinear_interp(
+                    x, y, self.src[nyi, nxi], self.src[nyi, nxi1],
+                    self.src[nyi1, nxi], self.src[nyi1, nxi1])
+
+        dst = np.clip(dst, 0, 255)
+        dst = np.array(dst, dtype=np.uint8)
+
+        return dst

ppocr/data/lmdb_dataset.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import os
+from paddle.io import Dataset
+import lmdb
+import cv2
+
+from .imaug import transform, create_operators
+
+
+class LMDBDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(LMDBDataSet, self).__init__()
+
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+        batch_size = loader_config['batch_size_per_card']
+        data_dir = dataset_config['data_dir']
+        self.do_shuffle = loader_config['shuffle']
+
+        self.lmdb_sets = self.load_hierarchical_lmdb_dataset(data_dir)
+        logger.info("Initialize indexs of datasets:%s" % data_dir)
+        self.data_idx_order_list = self.dataset_traversal()
+        if self.do_shuffle:
+            np.random.shuffle(self.data_idx_order_list)
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+        self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx",
+                                                       2)
+
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        self.need_reset = True in [x < 1 for x in ratio_list]
+
+    def load_hierarchical_lmdb_dataset(self, data_dir):
+        lmdb_sets = {}
+        dataset_idx = 0
+        for dirpath, dirnames, filenames in os.walk(data_dir + '/'):
+            if not dirnames:
+                env = lmdb.open(
+                    dirpath,
+                    max_readers=32,
+                    readonly=True,
+                    lock=False,
+                    readahead=False,
+                    meminit=False)
+                txn = env.begin(write=False)
+                num_samples = int(txn.get('num-samples'.encode()))
+                lmdb_sets[dataset_idx] = {"dirpath":dirpath, "env":env, \
+                    "txn":txn, "num_samples":num_samples}
+                dataset_idx += 1
+        return lmdb_sets
+
+    def dataset_traversal(self):
+        lmdb_num = len(self.lmdb_sets)
+        total_sample_num = 0
+        for lno in range(lmdb_num):
+            total_sample_num += self.lmdb_sets[lno]['num_samples']
+        data_idx_order_list = np.zeros((total_sample_num, 2))
+        beg_idx = 0
+        for lno in range(lmdb_num):
+            tmp_sample_num = self.lmdb_sets[lno]['num_samples']
+            end_idx = beg_idx + tmp_sample_num
+            data_idx_order_list[beg_idx:end_idx, 0] = lno
+            data_idx_order_list[beg_idx:end_idx, 1] \
+                = list(range(tmp_sample_num))
+            data_idx_order_list[beg_idx:end_idx, 1] += 1
+            beg_idx = beg_idx + tmp_sample_num
+        return data_idx_order_list
+
+    def get_img_data(self, value):
+        """get_img_data"""
+        if not value:
+            return None
+        imgdata = np.frombuffer(value, dtype='uint8')
+        if imgdata is None:
+            return None
+        imgori = cv2.imdecode(imgdata, 1)
+        if imgori is None:
+            return None
+        return imgori
+    
+    def get_ext_data(self):
+        ext_data_num = 0
+        for op in self.ops:
+            if hasattr(op, 'ext_data_num'):
+                ext_data_num = getattr(op, 'ext_data_num')
+                break
+        load_data_ops = self.ops[:self.ext_op_transform_idx]
+        ext_data = []
+        
+        while len(ext_data) < ext_data_num:
+            lmdb_idx, file_idx = self.data_idx_order_list[np.random.randint(self.__len__())]
+            lmdb_idx = int(lmdb_idx)
+            file_idx = int(file_idx)
+            sample_info = self.get_lmdb_sample_info(self.lmdb_sets[lmdb_idx]['txn'],
+                                                file_idx)
+            if sample_info is None:
+                continue
+            img, label = sample_info
+            data = {'image': img, 'label': label}
+            outs = transform(data, load_data_ops)
+            ext_data.append(data)
+        return ext_data
+
+    def get_lmdb_sample_info(self, txn, index):
+        label_key = 'label-%09d'.encode() % index
+        label = txn.get(label_key)
+        if label is None:
+            return None
+        label = label.decode('utf-8')
+        img_key = 'image-%09d'.encode() % index
+        imgbuf = txn.get(img_key)
+        return imgbuf, label
+
+    def __getitem__(self, idx):
+        lmdb_idx, file_idx = self.data_idx_order_list[idx]
+        lmdb_idx = int(lmdb_idx)
+        file_idx = int(file_idx)
+        sample_info = self.get_lmdb_sample_info(self.lmdb_sets[lmdb_idx]['txn'],
+                                                file_idx)
+        if sample_info is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        img, label = sample_info
+        data = {'image': img, 'label': label}
+        data['ext_data'] = self.get_ext_data()
+        outs = transform(data, self.ops)
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return self.data_idx_order_list.shape[0]

ppocr/data/pgnet_dataset.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import os
+from paddle.io import Dataset
+from .imaug import transform, create_operators
+import random
+
+
+class PGDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(PGDataSet, self).__init__()
+
+        self.logger = logger
+        self.seed = seed
+        self.mode = mode
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+
+        self.delimiter = dataset_config.get('delimiter', '\t')
+        label_file_list = dataset_config.pop('label_file_list')
+        data_source_num = len(label_file_list)
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * int(data_source_num)
+        assert len(
+            ratio_list
+        ) == data_source_num, "The length of ratio_list should be the same as the file_list."
+        self.data_dir = dataset_config['data_dir']
+        self.do_shuffle = loader_config['shuffle']
+
+        logger.info("Initialize indexs of datasets:%s" % label_file_list)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
+            self.shuffle_data_random()
+
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+
+        self.need_reset = True in [x < 1 for x in ratio_list]
+
+    def shuffle_data_random(self):
+        if self.do_shuffle:
+            random.seed(self.seed)
+            random.shuffle(self.data_lines)
+        return
+
+    def get_image_info_list(self, file_list, ratio_list):
+        if isinstance(file_list, str):
+            file_list = [file_list]
+        data_lines = []
+        for idx, file in enumerate(file_list):
+            with open(file, "rb") as f:
+                lines = f.readlines()
+                if self.mode == "train" or ratio_list[idx] < 1.0:
+                    random.seed(self.seed)
+                    lines = random.sample(lines,
+                                          round(len(lines) * ratio_list[idx]))
+                data_lines.extend(lines)
+        return data_lines
+
+    def __getitem__(self, idx):
+        file_idx = self.data_idx_order_list[idx]
+        data_line = self.data_lines[file_idx]
+        img_id = 0
+        try:
+            data_line = data_line.decode('utf-8')
+            substr = data_line.strip("\n").split(self.delimiter)
+            file_name = substr[0]
+            label = substr[1]
+            img_path = os.path.join(self.data_dir, file_name)
+            if self.mode.lower() == 'eval':
+                try:
+                    img_id = int(data_line.split(".")[0][7:])
+                except:
+                    img_id = 0
+            data = {'img_path': img_path, 'label': label, 'img_id': img_id}
+            if not os.path.exists(img_path):
+                raise Exception("{} does not exist!".format(img_path))
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            outs = transform(data, self.ops)
+        except Exception as e:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    self.data_idx_order_list[idx], e))
+            outs = None
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return len(self.data_idx_order_list)

ppocr/data/pubtab_dataset.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import os
+import random
+from paddle.io import Dataset
+import json
+
+from .imaug import transform, create_operators
+
+
+class PubTabDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(PubTabDataSet, self).__init__()
+        self.logger = logger
+
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+
+        label_file_path = dataset_config.pop('label_file_path')
+
+        self.data_dir = dataset_config['data_dir']
+        self.do_shuffle = loader_config['shuffle']
+        self.do_hard_select = False
+        if 'hard_select' in loader_config:
+            self.do_hard_select = loader_config['hard_select']
+            self.hard_prob = loader_config['hard_prob']
+        if self.do_hard_select:
+            self.img_select_prob = self.load_hard_select_prob()
+        self.table_select_type = None
+        if 'table_select_type' in loader_config:
+            self.table_select_type = loader_config['table_select_type']
+            self.table_select_prob = loader_config['table_select_prob']
+
+        self.seed = seed
+        logger.info("Initialize indexs of datasets:%s" % label_file_path)
+        with open(label_file_path, "rb") as f:
+            self.data_lines = f.readlines()
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
+            self.shuffle_data_random()
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        self.need_reset = True in [x < 1 for x in ratio_list]
+
+    def shuffle_data_random(self):
+        if self.do_shuffle:
+            random.seed(self.seed)
+            random.shuffle(self.data_lines)
+        return
+
+    def __getitem__(self, idx):
+        try:
+            data_line = self.data_lines[idx]
+            data_line = data_line.decode('utf-8').strip("\n")
+            info = json.loads(data_line)
+            file_name = info['filename']
+            select_flag = True
+            if self.do_hard_select:
+                prob = self.img_select_prob[file_name]
+                if prob < random.uniform(0, 1):
+                    select_flag = False
+
+            if self.table_select_type:
+                structure = info['html']['structure']['tokens'].copy()
+                structure_str = ''.join(structure)
+                table_type = "simple"
+                if 'colspan' in structure_str or 'rowspan' in structure_str:
+                    table_type = "complex"
+                if table_type == "complex":
+                    if self.table_select_prob < random.uniform(0, 1):
+                        select_flag = False
+
+            if select_flag:
+                cells = info['html']['cells'].copy()
+                structure = info['html']['structure'].copy()
+                img_path = os.path.join(self.data_dir, file_name)
+                data = {
+                    'img_path': img_path,
+                    'cells': cells,
+                    'structure': structure
+                }
+                if not os.path.exists(img_path):
+                    raise Exception("{} does not exist!".format(img_path))
+                with open(data['img_path'], 'rb') as f:
+                    img = f.read()
+                    data['image'] = img
+                outs = transform(data, self.ops)
+            else:
+                outs = None
+        except Exception as e:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    data_line, e))
+            outs = None
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return len(self.data_idx_order_list)

ppocr/data/simple_dataset.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import os
+import json
+import random
+import traceback
+from paddle.io import Dataset
+from .imaug import transform, create_operators
+
+
+class SimpleDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(SimpleDataSet, self).__init__()
+        self.logger = logger
+        self.mode = mode.lower()
+
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+
+        self.delimiter = dataset_config.get('delimiter', '\t')
+        label_file_list = dataset_config.pop('label_file_list')
+        data_source_num = len(label_file_list)
+        ratio_list = dataset_config.get("ratio_list", 1.0)
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * int(data_source_num)
+
+        assert len(
+            ratio_list
+        ) == data_source_num, "The length of ratio_list should be the same as the file_list."
+        self.data_dir = dataset_config['data_dir']
+        self.do_shuffle = loader_config['shuffle']
+        self.seed = seed
+        logger.info("Initialize indexs of datasets:%s" % label_file_list)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if self.mode == "train" and self.do_shuffle:
+            self.shuffle_data_random()
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+        self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx",
+                                                       2)
+        self.need_reset = True in [x < 1 for x in ratio_list]
+
+    def get_image_info_list(self, file_list, ratio_list):
+        if isinstance(file_list, str):
+            file_list = [file_list]
+        data_lines = []
+        for idx, file in enumerate(file_list):
+            with open(file, "rb") as f:
+                lines = f.readlines()
+                if self.mode == "train" or ratio_list[idx] < 1.0:
+                    random.seed(self.seed)
+                    lines = random.sample(lines,
+                                          round(len(lines) * ratio_list[idx]))
+                data_lines.extend(lines)
+        return data_lines
+
+    def shuffle_data_random(self):
+        random.seed(self.seed)
+        random.shuffle(self.data_lines)
+        return
+
+    def _try_parse_filename_list(self, file_name):
+        # multiple images -> one gt label
+        if len(file_name) > 0 and file_name[0] == "[":
+            try:
+                info = json.loads(file_name)
+                file_name = random.choice(info)
+            except:
+                pass
+        return file_name
+
+    def get_ext_data(self):
+        ext_data_num = 0
+        for op in self.ops:
+            if hasattr(op, 'ext_data_num'):
+                ext_data_num = getattr(op, 'ext_data_num')
+                break
+        load_data_ops = self.ops[:self.ext_op_transform_idx]
+        ext_data = []
+
+        while len(ext_data) < ext_data_num:
+            file_idx = self.data_idx_order_list[np.random.randint(self.__len__(
+            ))]
+            data_line = self.data_lines[file_idx]
+            data_line = data_line.decode('utf-8')
+            substr = data_line.strip("\n").split(self.delimiter)
+            file_name = substr[0]
+            file_name = self._try_parse_filename_list(file_name)
+            label = substr[1]
+            img_path = os.path.join(self.data_dir, file_name)
+            data = {'img_path': img_path, 'label': label}
+            if not os.path.exists(img_path):
+                continue
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            data = transform(data, load_data_ops)
+
+            if data is None:
+                continue
+            if 'polys' in data.keys():
+                if data['polys'].shape[1] != 4:
+                    continue
+            ext_data.append(data)
+        return ext_data
+
+    def __getitem__(self, idx):
+        file_idx = self.data_idx_order_list[idx]
+        data_line = self.data_lines[file_idx]
+        try:
+            data_line = data_line.decode('utf-8')
+            substr = data_line.strip("\n").split(self.delimiter)
+            file_name = substr[0]
+            file_name = self._try_parse_filename_list(file_name)
+            label = substr[1]
+            img_path = os.path.join(self.data_dir, file_name)
+            data = {'img_path': img_path, 'label': label}
+            if not os.path.exists(img_path):
+                raise Exception("{} does not exist!".format(img_path))
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            data['ext_data'] = self.get_ext_data()
+            outs = transform(data, self.ops)
+        except:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    data_line, traceback.format_exc()))
+            outs = None
+        if outs is None:
+            # during evaluation, we should fix the idx to get same results for many times of evaluation.
+            rnd_idx = np.random.randint(self.__len__(
+            )) if self.mode == "train" else (idx + 1) % self.__len__()
+            return self.__getitem__(rnd_idx)
+        return outs
+
+    def __len__(self):
+        return len(self.data_idx_order_list)

ppocr/metrics/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import copy
+
+__all__ = ["build_metric"]
+
+from .det_metric import DetMetric, DetFCEMetric
+from .rec_metric import RecMetric
+from .cls_metric import ClsMetric
+from .e2e_metric import E2EMetric
+from .distillation_metric import DistillationMetric
+from .table_metric import TableMetric
+from .kie_metric import KIEMetric
+from .vqa_token_ser_metric import VQASerTokenMetric
+from .vqa_token_re_metric import VQAReTokenMetric
+
+
+def build_metric(config):
+    support_dict = [
+        "DetMetric", "DetFCEMetric", "RecMetric", "ClsMetric", "E2EMetric",
+        "DistillationMetric", "TableMetric", 'KIEMetric', 'VQASerTokenMetric',
+        'VQAReTokenMetric'
+    ]
+
+    config = copy.deepcopy(config)
+    module_name = config.pop("name")
+    assert module_name in support_dict, Exception(
+        "metric only support {}".format(support_dict))
+    module_class = eval(module_name)(**config)
+    return module_class