1. 数据整体官方描述

SynthText in the Wild Dataset
-----------------------------
Ankush Gupta, Andrea Vedaldi, and Andrew Zisserman
Visual Geometry Group, University of Oxford, 2016Data format:
------------SynthText.zip (size = 42074172 bytes (41GB)) contains 858,750 synthetic
scene-image files (.jpg) split into 200 directories, with
7,266,866 word-instances, and 28,971,487 characters.Ground-truth annotations are contained in the file "gt.mat" (Matlab format).
The file "gt.mat" contains the following cell-arrays, each of size 1x858750:1. imnames :  names of the image files2. wordBB  :  word-level bounding-boxes for each image, represented bytensors of size 2x4xNWORDS_i, where:- the first dimension is 2 for x and y respectively,- the second dimension corresponds to the 4 points(clockwise, starting from top-left), and-  the third dimension of size NWORDS_i, corresponds tothe number of words in the i_th image.3. charBB  : character-level bounding-boxes,each represented by a tensor of size 2x4xNCHARS_i(format is same as wordBB's above)4. txt     : text-strings contained in each image (char array).Words which belong to the same "instance", i.e.,those rendered in the same region with the same font, color,distortion etc., are grouped together; the instanceboundaries are demarcated by the line-feed character (ASCII: 10)A "word" is any contiguous substring of non-whitespacecharacters.A "character" is defined as any non-whitespace character.For any questions or comments, contact Ankush Gupta at:
removethisifyouarehuman-ankush@robots.ox.ac.uk

2. 数据特点

数据集下文件如下。

（1）数据集总共有41g，858750张合成图片，jpg格式，这么图片分成200个场景图片（即图片背景不同，其实有202个场景），单词有7,266,866个，字符有28,971,487个；

（2）标注文件时mat格式，读取后保存内容如下。

2.1 imnames

保存图片文件相对路径

2.2 wordBB：单词级别

每张图片对应其中一个标注tensor，该tensor的size是(2, 4, n_word_i)：2是xy坐标；4是表示4个点，左上角开始，顺时针方向；n_word_i是第i张图片中的word个数。

“单词”是指任何非空白的连续字符串。

2.3 charBB：字符级别的bbox

size也是(2, 4, n_char_i). 意义同wordBB.

字符是指任何非空白字符。

char_bbox 转labelme格式的json标注文件：

def syntext2json_char_level():data_dir = r"F:\BaiduNetdiskDownload\SynthText800k\detection"gt_path = os.path.join(data_dir, "gt.mat")img_paths = os.path.join(data_dir, "imgs")gt_mat = loadmat(gt_path)# word_bboxes = gt_mat['wordBB'][0]img_names = gt_mat['imnames'][0]char_bboxes = gt_mat['charBB'][0]for i in tqdm(range(img_names.size)):coco_output = {"version": "3.16.7","flags": {},# "fillColor": [255, 0, 0, 128],# "lineColor": [0, 255, 0, 128],"imagePath": {},"shapes": [],"imageData": {}}img_name = img_names[i][0]img_full_path = os.path.join(img_paths, img_name)coco_output["imagePath"] = os.path.basename(img_full_path)coco_output["imageData"] = Nonejson_full_path = img_full_path.replace(".jpg", ".json")# print(json_full_path)cur_img = cv2.imread(img_full_path)if cur_img is None:continuecur_bboxes = char_bboxes[i]  # (2,4,n)if len(cur_bboxes.shape) != 3:cur_bboxes = np.expand_dims(cur_bboxes, 2)# rectify_bboxes = np.zeros((cur_bboxes.shape[2], 4, 2))for j in range(cur_bboxes.shape[2]):  # (2,4,15)  多个cnt，多个字符bbox = cur_bboxes[:, :, j]  # (2,4)pt_list = [[int(bbox[0][m]), int(bbox[1][m])] for m in range(4)]  # 记录当前字符x, y, w, h = cv2.boundingRect(np.array(pt_list))rect = [[x, y], [x + w, y + h]]# cv2.rectangle(cur_img, pt_list[0], pt_list[2], (0, 0, 255), 3)# cv2.namedWindow("img", cv2.WINDOW_NORMAL), cv2.imshow("img", cur_img), cv2.waitKey()shape_info = {'points': rect,'group_id': None,# "fill_color": None,# "line_color": None,"label": "loc","shape_type": "rectangle","flags": {}}coco_output["shapes"].append(shape_info)coco_output["imageHeight"] = cur_img.shape[0]coco_output["imageWidth"] = cur_img.shape[1]with open(json_full_path, 'w') as output_json_file:json.dump(coco_output, output_json_file, indent=4)output_json_file.close()

2.4 txt：文本级别

每个图像中包含的文本字符串（字符数组）。

以图片ballet_106_0.jpg为例. 其标注有8个文本，同一个区域、且字体、颜色、扭曲等特征相同的单词被视为一个文本。

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