diff --git a/docs/data/annotation.md b/docs/data/annotation.md index 7b65f82c99643c6f1cfd964daeb781623f101f2d..b5882ef619ef745068aea4f7b9210d9eaac597ce 100755 --- a/docs/data/annotation.md +++ b/docs/data/annotation.md @@ -10,7 +10,7 @@ PaddleX支持图像分类、目标检测、实例分割和语义分割四大视 | 标注工具 | 图像分类 | 目标检测 | 实例分割 | 语义分割 | 安装 | | :--------- | :------- | :------ | :------ | :------- | :----------------------------------------------- | | Labelme | - | √ | √ | √ | pip install labelme （本地数据标注） | -| 精灵标注 | √ | √ | √ | √ | [官网下载](http://www.jinglingbiaozhu.com/) （本地数据标注） | +| 精灵标注 | √ | - | √ | √ | [官网下载](http://www.jinglingbiaozhu.com/) （本地数据标注） | | EasyData | √ | √ | √ | √ | [Web页面标注](https://ai.baidu.com/easydata/) （需上传数据进行标注) | 数据标注完成后，参照如下流程，将标注数据转为可用PaddleX模型训练的数据组织格式。 @@ -23,9 +23,10 @@ PaddleX支持图像分类、目标检测、实例分割和语义分割四大视 > 2. 将所有的标注json文件放在同一个目录下，如`annotations`目录 > 3. 使用如下命令进行转换 ``` -paddlex --data_conversion --from labelme --to PascalVOC --pics ./pics --annotations ./annotations --save_dir ./converted_dataset_dir +paddlex --data_conversion --source labelme --to PascalVOC --pics ./pics --annotations ./annotations --save_dir ./converted_dataset_dir ``` -> `--from`表示数据标注来源，支持`labelme`、`jingling`和`easydata`（分别表示数据来源于LabelMe，精灵标注助手和EasyData） -> `--to`表示数据需要转换成为的格式，支持`PascalVOC`（目标检测），`MSCOCO`（实例分割，也可用于目标检测）和`SEG`(语义分割) +> `--source`表示数据标注来源，支持`labelme`、`jingling`和`easydata`（分别表示数据来源于LabelMe，精灵标注助手和EasyData） +> `--to`表示数据需要转换成为的格式，支持`ImageNet`（图像分类）、`PascalVOC`（目标检测），`MSCOCO`（实例分割，也可用于目标检测）和`SEG`(语义分割) > `--pics`指定原图所在的目录路径 > `--annotations`指定标注文件所在的目录路径 +> 【备注】由于标注精灵可以标注PascalVOC格式的数据集，所以此处不再支持标注精灵到PascalVOC格式数据集的转换 diff --git a/paddlex/command.py b/paddlex/command.py index 612bc5f3f2b2c3bbec23f56c2983a722d76e21fc..fb79cef4df0c00408e95ca950827bdf7c5528f13 100644 --- a/paddlex/command.py +++ b/paddlex/command.py @@ -50,6 +50,36 @@ def arg_parser(): action="store_true", default=False, help="export onnx model for deployment") + parser.add_argument( + "--data_conversion", + "-dc", + action="store_true", + default=False, + help="convert the dataset to the standard format") + parser.add_argument( + "--source", + "-se", + type=_text_type, + default=None, + help="define dataset format before the conversion") + parser.add_argument( + "--to", + "-to", + type=_text_type, + default=None, + help="define dataset format after the conversion") + parser.add_argument( + "--pics", + "-p", + type=_text_type, + default=None, + help="define pictures directory path") + parser.add_argument( + "--annotations", + "-a", + type=_text_type, + default=None, + help="define annotations directory path") parser.add_argument( "--fixed_input_shape", "-fs", @@ -105,6 +135,24 @@ def main(): "paddlex --export_inference --model_dir model_path --save_dir infer_model" ) pdx.convertor.export_onnx_model(model, args.save_dir) + + if args.data_conversion: + assert args.source is not None, "--source should be defined while converting dataset" + assert args.to is not None, "--to should be defined to confirm the taregt dataset format" + assert args.pics is not None, "--pics should be defined to confirm the pictures path" + assert args.annotations is not None, "--annotations should be defined to confirm the annotations path" + assert args.save_dir is not None, "--save_dir should be defined to store taregt dataset" + if args.source == 'labelme' and args.to == 'ImageNet': + logging.error( + "The labelme dataset can not convert to the ImageNet dataset.", + exit=False) + if args.source == 'jingling' and args.to == 'PascalVOC': + logging.error( + "The jingling dataset can not convert to the PascalVOC dataset.", + exit=False) + pdx.tools.convert.dataset_conversion(args.source, args.to, + args.pics, args.annotations, args.save_dir ) + if __name__ == "__main__": diff --git a/paddlex/tools/base.py b/paddlex/tools/base.py index 94f9fa672f93154c963e75c47867368c42b535ca..fc5d04cb01dac37ac15976299dc50cac0cb7f5b6 100644 --- a/paddlex/tools/base.py +++ b/paddlex/tools/base.py @@ -40,4 +40,5 @@ def get_encoding(path): f = open(path, 'rb') data = f.read() file_encoding = chardet.detect(data).get('encoding') + f.close() return file_encoding \ No newline at end of file diff --git a/paddlex/tools/convert.py b/paddlex/tools/convert.py index b2755f2fbb3ad2876df936612af9a12019523878..02f94ce4835260d0c2d7d86e9b4ec7b205f0943f 100644 --- a/paddlex/tools/convert.py +++ b/paddlex/tools/convert.py @@ -15,8 +15,10 @@ # limitations under the License. from .x2imagenet import EasyData2ImageNet +from .x2imagenet import JingLing2ImageNet from .x2coco import LabelMe2COCO from .x2coco import EasyData2COCO +from .x2coco import JingLing2COCO from .x2voc import LabelMe2VOC from .x2voc import EasyData2VOC from .x2seg import JingLing2Seg @@ -24,10 +26,34 @@ from .x2seg import LabelMe2Seg from .x2seg import EasyData2Seg easydata2imagenet = EasyData2ImageNet().convert +jingling2imagenet = JingLing2ImageNet().convert labelme2coco = LabelMe2COCO().convert easydata2coco = EasyData2COCO().convert +jingling2coco = JingLing2COCO().convert labelme2voc = LabelMe2VOC().convert easydata2voc = EasyData2VOC().convert jingling2seg = JingLing2Seg().convert labelme2seg = LabelMe2Seg().convert easydata2seg = EasyData2Seg().convert + +def dataset_conversion(source, to, pics, anns, save_dir): + if source == 'labelme' and to == 'PascalVOC': + labelme2voc(pics, anns, save_dir) + elif source == 'labelme' and to == 'MSCOCO': + labelme2coco(pics, anns, save_dir) + elif source == 'labelme' and to == 'SEG': + labelme2seg(pics, anns, save_dir) + elif source == 'jingling' and to == 'ImageNet': + jingling2imagenet(pics, anns, save_dir) + elif source == 'jingling' and to == 'MSCOCO': + jingling2coco(pics, anns, save_dir) + elif source == 'jingling' and to == 'SEG': + jingling2seg(pics, anns, save_dir) + elif source == 'easydata' and to == 'ImageNet': + easydata2imagenet(pics, anns, save_dir) + elif source == 'easydata' and to == 'PascalVOC': + easydata2voc(pics, anns, save_dir) + elif source == 'easydata' and to == 'MSCOCO': + easydata2coco(pics, anns, save_dir) + elif source == 'easydata' and to == 'SEG': + easydata2seg(pics, anns, save_dir) \ No newline at end of file diff --git a/paddlex/tools/x2coco.py b/paddlex/tools/x2coco.py index 4c893dcc9319ffc4353d4e376a802301d047120a..1e3ad8e3f00d3bde662ed205855182a500fa19a0 100644 --- a/paddlex/tools/x2coco.py +++ b/paddlex/tools/x2coco.py @@ -100,7 +100,7 @@ class LabelMe2COCO(X2COCO): image["height"] = json_info["imageHeight"] image["width"] = json_info["imageWidth"] image["id"] = image_id + 1 - image["file_name"] = json_info["imagePath"].split("/")[-1] + image["file_name"] = osp.split(json_info["imagePath"])[-1] return image def generate_polygon_anns_field(self, height, width, @@ -144,7 +144,7 @@ class LabelMe2COCO(X2COCO): img_name_part = osp.splitext(img_file)[0] json_file = osp.join(json_dir, img_name_part + ".json") if not osp.exists(json_file): - os.remove(os.remove(osp.join(image_dir, img_file))) + os.remove(osp.join(image_dir, img_file)) continue image_id = image_id + 1 with open(json_file, mode='r', \ @@ -216,7 +216,7 @@ class EasyData2COCO(X2COCO): img_name_part = osp.splitext(img_file)[0] json_file = osp.join(json_dir, img_name_part + ".json") if not osp.exists(json_file): - os.remove(os.remove(osp.join(image_dir, img_file))) + os.remove(osp.join(image_dir, img_file)) continue image_id = image_id + 1 with open(json_file, mode='r', \ @@ -255,3 +255,107 @@ class EasyData2COCO(X2COCO): self.annotations_list.append( self.generate_polygon_anns_field(points, segmentation, label, image_id, object_id, label_to_num)) + + +class JingLing2COCO(X2COCO): + """将使用EasyData标注的检测或分割数据集转换为COCO数据集。 + """ + def __init__(self): + super(JingLing2COCO, self).__init__() + + def generate_images_field(self, json_info, image_id): + image = {} + image["height"] = json_info["size"]["height"] + image["width"] = json_info["size"]["width"] + image["id"] = image_id + 1 + image["file_name"] = osp.split(json_info["path"])[-1] + return image + + def generate_polygon_anns_field(self, height, width, + points, label, image_id, + object_id, label_to_num): + annotation = {} + annotation["segmentation"] = [list(np.asarray(points).flatten())] + annotation["iscrowd"] = 0 + annotation["image_id"] = image_id + 1 + annotation["bbox"] = list(map(float, self.get_bbox(height, width, points))) + annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3] + annotation["category_id"] = label_to_num[label] + annotation["id"] = object_id + 1 + return annotation + + def get_bbox(self, height, width, points): + polygons = points + mask = np.zeros([height, width], dtype=np.uint8) + mask = PIL.Image.fromarray(mask) + xy = list(map(tuple, polygons)) + PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1) + mask = np.array(mask, dtype=bool) + index = np.argwhere(mask == 1) + rows = index[:, 0] + clos = index[:, 1] + left_top_r = np.min(rows) + left_top_c = np.min(clos) + right_bottom_r = np.max(rows) + right_bottom_c = np.max(clos) + return [ + left_top_c, left_top_r, right_bottom_c - left_top_c, + right_bottom_r - left_top_r + ] + + def parse_json(self, img_dir, json_dir): + image_id = -1 + object_id = -1 + labels_list = [] + label_to_num = {} + for img_file in os.listdir(img_dir): + img_name_part = osp.splitext(img_file)[0] + json_file = osp.join(json_dir, img_name_part + ".json") + if not osp.exists(json_file): + os.remove(osp.join(image_dir, img_file)) + continue + image_id = image_id + 1 + with open(json_file, mode='r', \ + encoding=get_encoding(json_file)) as j: + json_info = json.load(j) + img_info = self.generate_images_field(json_info, image_id) + self.images_list.append(img_info) + anns_type = "bndbox" + for i, obj in enumerate(json_info["outputs"]["object"]): + if i == 0: + if "polygon" in obj: + anns_type = "polygon" + else: + if anns_type not in obj: + continue + object_id = object_id + 1 + label = obj["name"] + if label not in labels_list: + self.categories_list.append(\ + self.generate_categories_field(label, labels_list)) + labels_list.append(label) + label_to_num[label] = len(labels_list) + if anns_type == "polygon": + points = [] + for j in range(int(len(obj["polygon"]) / 2.0)): + points.append([obj["polygon"]["x" + str(j + 1)], + obj["polygon"]["y" + str(j + 1)]]) + self.annotations_list.append( + self.generate_polygon_anns_field(json_info["size"]["height"], + json_info["size"]["width"], + points, + label, + image_id, + object_id, + label_to_num)) + if anns_type == "bndbox": + points = [] + points.append([obj["bndbox"]["xmin"], obj["bndbox"]["ymin"]]) + points.append([obj["bndbox"]["xmax"], obj["bndbox"]["ymax"]]) + points.append([obj["bndbox"]["xmin"], obj["bndbox"]["ymax"]]) + points.append([obj["bndbox"]["xmax"], obj["bndbox"]["ymin"]]) + self.annotations_list.append( + self.generate_rectangle_anns_field(points, label, image_id, + object_id, label_to_num)) + + \ No newline at end of file diff --git a/paddlex/tools/x2imagenet.py b/paddlex/tools/x2imagenet.py index 676eaec8d1193c230b01695c968e76536e8632e0..0b9de7c15e2effc2a90726f4c1a0ae964df36e86 100644 --- a/paddlex/tools/x2imagenet.py +++ b/paddlex/tools/x2imagenet.py @@ -22,9 +22,8 @@ import shutil import numpy as np from .base import MyEncoder, is_pic, get_encoding -class EasyData2ImageNet(object): - """将使用EasyData标注的分类数据集转换为COCO数据集。 - """ + +class X2ImageNet(object): def __init__(self): pass @@ -46,8 +45,8 @@ class EasyData2ImageNet(object): continue with open(json_file, mode="r", \ encoding=get_encoding(json_file)) as j: - json_info = json.load(j) - for output in json_info['labels']: + json_info = self.get_json_info(j) + for output in json_info: cls_name = output['name'] new_image_dir = osp.join(dataset_save_dir, cls_name) if not osp.exists(new_image_dir): @@ -55,4 +54,28 @@ class EasyData2ImageNet(object): if is_pic(img_name): shutil.copyfile( osp.join(image_dir, img_name), - osp.join(new_image_dir, img_name)) \ No newline at end of file + osp.join(new_image_dir, img_name)) + + +class EasyData2ImageNet(X2ImageNet): + """将使用EasyData标注的分类数据集转换为ImageNet数据集。 + """ + def __init__(self): + super(EasyData2ImageNet, self).__init__() + + def get_json_info(self, json_file): + json_info = json.load(json_file) + json_info = json_info['labels'] + return json_info + +class JingLing2ImageNet(X2ImageNet): + """将使用标注精灵标注的分类数据集转换为ImageNet数据集。 + """ + def __init__(self): + super(X2ImageNet, self).__init__() + + def get_json_info(self, json_file): + json_info = json.load(json_file) + json_info = json_info['outputs']['object'] + return json_info + \ No newline at end of file