add data convert

paddlex/__init__.py

@@ -19,6 +19,7 @@ from . import det
 from . import seg
 from . import cls
 from . import slim
+from . import tools
 
 try:
     import pycocotools

paddlex/tools/__init__.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+# coding: utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 .x2imagenet import EasyData2ImageNet
+from .x2coco import LabelMe2COCO
+from .x2coco import EasyData2COCO
+from .x2voc import LabelMe2VOC
+from .x2voc import EasyData2VOC
+from .x2seg import JingLing2Seg
+from .x2seg import LabelMe2Seg
+from .x2seg import EasyData2Seg

paddlex/tools/base.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+# coding: utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 json
+import chardet
+import numpy as np
+
+class MyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            return super(MyEncoder, self).default(obj)
+        
+def is_pic(img_name):
+    valid_suffix = ["JPEG", "jpeg", "JPG", "jpg", "BMP", "bmp", "PNG", "png"]
+    suffix = img_name.split(".")[-1]
+    if suffix not in valid_suffix:
+        return False
+    return True
+
+def get_encoding(path):
+    f = open(path, 'rb')
+    data = f.read()
+    file_encoding = chardet.detect(data).get('encoding')
+    return file_encoding

paddlex/tools/x2coco.py

@@ -0,0 +1,247 @@
+#!/usr/bin/env python
+# coding: utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 cv2
+import json
+import os
+import os.path as osp
+import shutil
+import numpy as np
+import PIL.ImageDraw
+from .base import MyEncoder, is_pic, get_encoding
+        
+        
+class X2COCO(object):
+    def __init__(self):
+        self.images_list = []
+        self.categories_list = []
+        self.annotations_list = []
+    
+    def generate_categories_field(self, label, labels_list):
+        category = {}
+        category["supercategory"] = "component"
+        category["id"] = len(labels_list) + 1
+        category["name"] = label
+        return category
+    
+    def generate_rectangle_anns_field(self, points, label, image_id, object_id, label_to_num):
+        annotation = {}
+        seg_points = np.asarray(points).copy()
+        seg_points[1, :] = np.asarray(points)[2, :]
+        seg_points[2, :] = np.asarray(points)[1, :]
+        annotation["segmentation"] = [list(seg_points.flatten())]
+        annotation["iscrowd"] = 0
+        annotation["image_id"] = image_id + 1
+        annotation["bbox"] = list(
+            map(float, [
+                points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
+                    1] - points[0][1]
+            ]))
+        annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
+        annotation["category_id"] = label_to_num[label]
+        annotation["id"] = object_id + 1
+        return annotation
+    
+    def convert(self, image_input_dir, json_input_dir, dataset_save_dir):
+        assert osp.exists(image_input_dir), "he image folder does not exist!"
+        assert osp.exists(json_input_dir), "The json folder does not exist!"
+        assert osp.exists(dataset_save_dir), "The save folder does not exist!"
+        # Convert the image files.
+        new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
+        if osp.exists(new_image_dir):
+            shutil.rmtree(new_image_dir)
+        os.makedirs(new_image_dir)
+        for img_name in os.listdir(image_input_dir):
+            if is_pic(img_name):
+                shutil.copyfile(
+                            osp.join(image_input_dir, img_name),
+                            osp.join(new_image_dir, img_name))
+        # Convert the json files.
+        self.analyse_json(new_image_dir, json_input_dir)
+        coco_data = {}
+        coco_data["images"] = self.images_list
+        coco_data["categories"] = self.categories_list
+        coco_data["annotations"] = self.annotations_list
+        json_path = osp.join(dataset_save_dir, "annotations.json")
+        json.dump(
+            coco_data,
+            open(json_path, "w"),
+            indent=4,
+            cls=MyEncoder)
+    
+    
+class LabelMe2COCO(X2COCO):
+    def __init__(self):
+        super(LabelMe2COCO, self).__init__()
+        
+    def generate_images_field(self, json_info, image_id):
+        image = {}
+        image["height"] = json_info["imageHeight"]
+        image["width"] = json_info["imageWidth"]
+        image["id"] = image_id + 1
+        image["file_name"] = json_info["imagePath"].split("/")[-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, 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 analyse_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(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)
+                for shapes in json_info["shapes"]:
+                    object_id = object_id + 1
+                    label = shapes["label"]
+                    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)
+                    points = shapes["points"]
+                    p_type = shapes["shape_type"]
+                    if p_type == "polygon":
+                        self.annotations_list.append(
+                            self.generate_polygon_anns_field(json_info["imageHeight"], json_info[
+                                "imageWidth"], points, label, image_id,
+                                                object_id, label_to_num))
+                    if p_type == "rectangle":
+                        points.append([points[0][0], points[1][1]])
+                        points.append([points[1][0], points[0][1]])
+                        self.annotations_list.append(
+                            self.generate_rectangle_anns_field(points, label, image_id,
+                                                  object_id, label_to_num))
+                        
+    
+class EasyData2COCO(X2COCO):
+    def __init__(self):
+        super(EasyData2COCO, self).__init__()        
+    
+    def generate_images_field(self, img_path, image_id):
+        image = {}
+        img = cv2.imread(img_path)
+        image["height"] = img.shape[0]
+        image["width"] = img.shape[1]
+        image["id"] = image_id + 1
+        image["file_name"] = osp.split(img_path)[-1]
+        return image
+    
+    def generate_polygon_anns_field(self, points, segmentation, 
+                                    label, image_id, object_id,
+                                    label_to_num):
+        annotation = {}
+        annotation["segmentation"] = segmentation
+        annotation["iscrowd"] = 1 if len(segmentation) > 1 else 0
+        annotation["image_id"] = image_id + 1
+        annotation["bbox"] = list(map(float, [
+                points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
+                    1] - points[0][1]
+            ]))
+        annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
+        annotation["category_id"] = label_to_num[label]
+        annotation["id"] = object_id + 1
+        return annotation
+        
+    def analyse_json(self, img_dir, json_dir):
+        from pycocotools.mask import decode
+        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(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(osp.join(img_dir, img_file), image_id)
+                self.images_list.append(img_info)
+                for shapes in json_info["labels"]:
+                    object_id = object_id + 1
+                    label = shapes["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)
+                    points = [[shapes["x1"], shapes["y1"]],
+                              [shapes["x2"], shapes["y2"]]]
+                    if "mask" not in shapes:
+                        points.append([points[0][0], points[1][1]])
+                        points.append([points[1][0], points[0][1]])
+                        self.annotations_list.append(
+                            self.generate_rectangle_anns_field(points, label, image_id,
+                                                  object_id, label_to_num))
+                    else:
+                        mask_dict = {}
+                        mask_dict['size'] = [img_info["height"], img_info["width"]]
+                        mask_dict['counts'] = shapes['mask'].encode()
+                        mask = decode(mask_dict)
+                        contours, hierarchy = cv2.findContours(
+                                (mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+                        segmentation = []
+                        for contour in contours:
+                            contour_list = contour.flatten().tolist()
+                            if len(contour_list) > 4:
+                                segmentation.append(contour_list)
+                        self.annotations_list.append(
+                            self.generate_polygon_anns_field(points, segmentation, label, image_id, object_id,
+                                                label_to_num))

paddlex/tools/x2imagenet.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+# coding: utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 cv2
+import json
+import os
+import os.path as osp
+import shutil
+import numpy as np
+from .base import MyEncoder, is_pic, get_encoding
+
+class EasyData2ImageNet(object):
+    def __init__(self):
+        pass
+    
+    def convert(self, image_dir, json_dir, dataset_save_dir):
+        assert osp.exists(image_dir), "The image folder does not exist!"
+        assert osp.exists(json_dir), "The json folder does not exist!"
+        assert osp.exists(dataset_save_dir), "The save folder does not exist!"
+        assert len(os.listdir(dataset_save_dir)) == 0, "The save folder must be empty!"
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[0]
+            json_file = osp.join(json_dir, img_name_part + ".json")
+            if not osp.exists(json_file):
+                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']:
+                    cls_name = output['name']
+                    new_image_dir = osp.join(dataset_save_dir, cls_name)
+                    if not osp.exists(new_image_dir):
+                        os.makedirs(new_image_dir)
+                    if is_pic(img_name):
+                        shutil.copyfile(
+                                    osp.join(image_dir, img_name),
+                                    osp.join(new_image_dir, img_name))

paddlex/tools/x2seg.py

@@ -0,0 +1,326 @@
+#!/usr/bin/env python
+# coding: utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 cv2
+import uuid
+import json
+import os
+import os.path as osp
+import shutil
+import numpy as np
+import PIL.Image
+from .base import MyEncoder, is_pic, get_encoding
+
+class X2Seg(object):
+    def __init__(self):
+        self.labels2ids = {'_background_': 0}
+        
+    def shapes_to_label(self, img_shape, shapes, label_name_to_value):
+        def shape_to_mask(img_shape, points, shape_type=None,
+                  line_width=10, point_size=5):
+            mask = np.zeros(img_shape[:2], dtype=np.uint8)
+            mask = PIL.Image.fromarray(mask)
+            draw = PIL.ImageDraw.Draw(mask)
+            xy = [tuple(point) for point in points]
+            if shape_type == 'circle':
+                assert len(xy) == 2, 'Shape of shape_type=circle must have 2 points'
+                (cx, cy), (px, py) = xy
+                d = math.sqrt((cx - px) ** 2 + (cy - py) ** 2)
+                draw.ellipse([cx - d, cy - d, cx + d, cy + d], outline=1, fill=1)
+            elif shape_type == 'rectangle':
+                assert len(xy) == 2, 'Shape of shape_type=rectangle must have 2 points'
+                draw.rectangle(xy, outline=1, fill=1)
+            elif shape_type == 'line':
+                assert len(xy) == 2, 'Shape of shape_type=line must have 2 points'
+                draw.line(xy=xy, fill=1, width=line_width)
+            elif shape_type == 'linestrip':
+                draw.line(xy=xy, fill=1, width=line_width)
+            elif shape_type == 'point':
+                assert len(xy) == 1, 'Shape of shape_type=point must have 1 points'
+                cx, cy = xy[0]
+                r = point_size
+                draw.ellipse([cx - r, cy - r, cx + r, cy + r], outline=1, fill=1)
+            else:
+                assert len(xy) > 2, 'Polygon must have points more than 2'
+                draw.polygon(xy=xy, outline=1, fill=1)
+            mask = np.array(mask, dtype=bool)
+            return mask
+        cls = np.zeros(img_shape[:2], dtype=np.int32)
+        ins = np.zeros_like(cls)
+        instances = []
+        for shape in shapes:
+            points = shape['points']
+            label = shape['label']
+            group_id = shape.get('group_id')
+            if group_id is None:
+                group_id = uuid.uuid1()
+            shape_type = shape.get('shape_type', None)
+
+            cls_name = label
+            instance = (cls_name, group_id)
+
+            if instance not in instances:
+                instances.append(instance)
+            ins_id = instances.index(instance) + 1
+            cls_id = label_name_to_value[cls_name]
+            mask = shape_to_mask(img_shape[:2], points, shape_type)
+            cls[mask] = cls_id
+            ins[mask] = ins_id
+        return cls, ins
+    
+    def get_color_map_list(self, num_classes):
+        """ Returns the color map for visualizing the segmentation mask,
+            which can support arbitrary number of classes.
+        Args:
+            num_classes: Number of classes
+        Returns:
+            The color map
+        """
+        color_map = num_classes * [0, 0, 0]
+        for i in range(0, num_classes):
+            j = 0
+            lab = i
+            while lab:
+                color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+                color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+                color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+                j += 1
+                lab >>= 3
+        return color_map
+    
+    def convert(self, image_dir, json_dir, dataset_save_dir):
+        assert osp.exists(image_dir), "The image folder does not exist!"
+        assert osp.exists(json_dir), "The json folder does not exist!"
+        assert osp.exists(dataset_save_dir), "The save folder does not exist!"
+        # Convert the image files.
+        new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
+        if osp.exists(new_image_dir):
+            shutil.rmtree(new_image_dir)
+        os.makedirs(new_image_dir)
+        for img_name in os.listdir(image_dir):
+            if is_pic(img_name):
+                shutil.copyfile(
+                            osp.join(image_dir, img_name),
+                            osp.join(new_image_dir, img_name))
+        # Convert the json files.
+        png_dir = osp.join(dataset_save_dir, "Annotations")
+        if osp.exists(png_dir):
+            shutil.rmtree(png_dir)
+        os.makedirs(png_dir)
+        self.get_labels2ids(new_image_dir, json_dir)
+        self.json2png(new_image_dir, json_dir, png_dir)
+        # Generate the labels.txt
+        ids2labels = {v : k for k, v in self.labels2ids.items()}
+        with open(osp.join(dataset_save_dir, 'labels.txt'), 'w') as fw:
+            for i in range(len(ids2labels)):
+                fw.write(ids2labels[i] + '\n')
+        
+
+class JingLing2Seg(X2Seg):
+    def __init__(self):
+        super(JingLing2Seg, self).__init__() 
+        
+    def get_labels2ids(self, image_dir, json_dir):
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+                if 'outputs' in json_info:
+                    for output in json_info['outputs']['object']:
+                        cls_name = output['name']
+                        if cls_name not in self.labels2ids:
+                            self.labels2ids[cls_name] =  len(self.labels2ids)
+    
+    def json2png(self, image_dir, json_dir, png_dir):
+        color_map = self.get_color_map_list(256)
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+                data_shapes = []
+                if 'outputs' in json_info:
+                    for output in json_info['outputs']['object']:
+                        if 'polygon' in output.keys():
+                            polygon = output['polygon']
+                            name = output['name']
+                            points = []
+                            for i in range(1, int(len(polygon) / 2) + 1):
+                                points.append(
+                                    [polygon['x' + str(i)], polygon['y' + str(i)]])
+                            shape = {
+                                'label': name,
+                                'points': points,
+                                'shape_type': 'polygon'
+                            }
+                            data_shapes.append(shape)
+                if 'size' not in json_info:
+                    continue
+            img_shape = (json_info['size']['height'], 
+                         json_info['size']['width'],
+                         json_info['size']['depth'])
+            lbl, _ = self.shapes_to_label(
+                img_shape=img_shape,
+                shapes=data_shapes,
+                label_name_to_value=self.labels2ids,
+            )
+            out_png_file = osp.join(png_dir, img_name_part + '.png')
+            if lbl.min() >= 0 and lbl.max() <= 255:
+                lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
+                lbl_pil.putpalette(color_map)
+                lbl_pil.save(out_png_file)
+            else:
+                raise ValueError(
+                    '[%s] Cannot save the pixel-wise class label as PNG. '
+                    'Please consider using the .npy format.' % out_png_file)
+                
+                
+class LabelMe2Seg(X2Seg):
+    def __init__(self):
+        super(LabelMe2Seg, self).__init__()
+    
+    def get_labels2ids(self, image_dir, json_dir):
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+                for shape in json_info['shapes']:
+                    cls_name = shape['label']
+                    if cls_name not in self.labels2ids:
+                        self.labels2ids[cls_name] =  len(self.labels2ids)
+                     
+    def json2png(self, image_dir, json_dir, png_dir):
+        color_map = self.get_color_map_list(256)
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            img_file = osp.join(image_dir, img_name)
+            img = np.asarray(PIL.Image.open(img_file))
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+            lbl, _ = self.shapes_to_label(
+                img_shape=img.shape,
+                shapes=json_info['shapes'],
+                label_name_to_value=self.labels2ids,
+            )
+            out_png_file = osp.join(png_dir, img_name_part + '.png')
+            if lbl.min() >= 0 and lbl.max() <= 255:
+                lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
+                lbl_pil.putpalette(color_map)
+                lbl_pil.save(out_png_file)
+            else:
+                raise ValueError(
+                    '[%s] Cannot save the pixel-wise class label as PNG. '
+                    'Please consider using the .npy format.' % out_png_file)
+                
+                            
+class EasyData2Seg(X2Seg):
+    def __init__(self):
+        super(EasyData2Seg, self).__init__()
+    
+    def get_labels2ids(self, image_dir, json_dir):
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+                for shape in json_info["labels"]:
+                    cls_name = shape['name']
+                    if cls_name not in self.labels2ids:
+                        self.labels2ids[cls_name] =  len(self.labels2ids)
+                        
+    def mask2polygon(self, mask, label):
+        contours, hierarchy = cv2.findContours(
+            (mask).astype(np.uint8), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        segmentation = []
+        for contour in contours:
+            contour_list = contour.flatten().tolist()
+            if len(contour_list) > 4:
+                points = []
+                for i in range(0, len(contour_list), 2):
+                    points.append(
+                                [contour_list[i], contour_list[i + 1]])
+                shape = {
+                    'label': label,
+                    'points': points,
+                    'shape_type': 'polygon'
+                }
+                segmentation.append(shape)
+        return segmentation
+    
+    def json2png(self, image_dir, json_dir, png_dir):
+        from pycocotools.mask import decode
+        color_map = self.get_color_map_list(256)
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            img_file = osp.join(image_dir, img_name)
+            img = np.asarray(PIL.Image.open(img_file))
+            img_h = img.shape[0]
+            img_w = img.shape[1]
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+                data_shapes = []
+                for shape in json_info['labels']:
+                    mask_dict = {}
+                    mask_dict['size'] = [img_h, img_w]
+                    mask_dict['counts'] = shape['mask'].encode()
+                    mask = decode(mask_dict)
+                    polygon = self.mask2polygon(mask, shape["name"])
+                    data_shapes.extend(polygon)
+            lbl, _ = self.shapes_to_label(
+                img_shape=img.shape,
+                shapes=data_shapes,
+                label_name_to_value=self.labels2ids,
+            )
+            out_png_file = osp.join(png_dir, img_name_part + '.png')
+            if lbl.min() >= 0 and lbl.max() <= 255:
+                lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
+                lbl_pil.putpalette(color_map)
+                lbl_pil.save(out_png_file)
+            else:
+                raise ValueError(
+                    '[%s] Cannot save the pixel-wise class label as PNG. '
+                    'Please consider using the .npy format.' % out_png_file)
+            
+
+

paddlex/tools/x2voc.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+# coding: utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 cv2
+import json
+import os
+import os.path as osp
+import shutil
+import numpy as np
+from .base import MyEncoder, is_pic, get_encoding
+
+class X2VOC(object):
+    def __init__(self):
+        pass
+    
+    def convert(self, image_dir, json_dir, dataset_save_dir):
+        assert osp.exists(image_dir), "The image folder does not exist!"
+        assert osp.exists(json_dir), "The json folder does not exist!"
+        assert osp.exists(dataset_save_dir), "The save folder does not exist!"
+        # Convert the image files.
+        new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
+        if osp.exists(new_image_dir):
+            shutil.rmtree(new_image_dir)
+        os.makedirs(new_image_dir)
+        for img_name in os.listdir(image_dir):
+            if is_pic(img_name):
+                shutil.copyfile(
+                            osp.join(image_dir, img_name),
+                            osp.join(new_image_dir, img_name))
+        # Convert the json files.
+        xml_dir = osp.join(dataset_save_dir, "Annotations")
+        if osp.exists(xml_dir):
+            shutil.rmtree(xml_dir)
+        os.makedirs(xml_dir)
+        self.json2xml(new_image_dir, json_dir, xml_dir)
+        
+        
+class LabelMe2VOC(X2VOC):
+    def __init__(self):
+        pass
+    
+    def json2xml(self, image_dir, json_dir, xml_dir):
+        import xml.dom.minidom as minidom
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            xml_doc = minidom.Document() 
+            root = xml_doc.createElement("annotation") 
+            xml_doc.appendChild(root)
+            node_folder = xml_doc.createElement("folder")
+            node_folder.appendChild(xml_doc.createTextNode("JPEGImages"))
+            root.appendChild(node_folder)
+            node_filename = xml_doc.createElement("filename")
+            node_filename.appendChild(xml_doc.createTextNode(img_name))
+            root.appendChild(node_filename)
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+                h = json_info["imageHeight"]
+                w = json_info["imageWidth"]
+                node_size = xml_doc.createElement("size")
+                node_width = xml_doc.createElement("width")
+                node_width.appendChild(xml_doc.createTextNode(str(w)))
+                node_size.appendChild(node_width)
+                node_height = xml_doc.createElement("height")
+                node_height.appendChild(xml_doc.createTextNode(str(h)))
+                node_size.appendChild(node_height)
+                node_depth = xml_doc.createElement("depth")
+                node_depth.appendChild(xml_doc.createTextNode(str(3)))
+                node_size.appendChild(node_depth)
+                root.appendChild(node_size)
+                for shape in json_info["shapes"]:
+                    if shape["shape_type"] != "rectangle":
+                        continue
+                    label = shape["label"]
+                    (xmin, ymin), (xmax, ymax) = shape["points"]
+                    xmin, xmax = sorted([xmin, xmax])
+                    ymin, ymax = sorted([ymin, ymax])
+                    node_obj = xml_doc.createElement("object")
+                    node_name = xml_doc.createElement("name")
+                    node_name.appendChild(xml_doc.createTextNode(label))
+                    node_obj.appendChild(node_name)
+                    node_diff = xml_doc.createElement("difficult")
+                    node_diff.appendChild(xml_doc.createTextNode(str(0)))
+                    node_obj.appendChild(node_diff)
+                    node_box = xml_doc.createElement("bndbox")
+                    node_xmin = xml_doc.createElement("xmin")
+                    node_xmin.appendChild(xml_doc.createTextNode(str(xmin)))
+                    node_box.appendChild(node_xmin)
+                    node_ymin = xml_doc.createElement("ymin")
+                    node_ymin.appendChild(xml_doc.createTextNode(str(ymin)))
+                    node_box.appendChild(node_ymin)
+                    node_xmax = xml_doc.createElement("xmax")
+                    node_xmax.appendChild(xml_doc.createTextNode(str(xmax)))
+                    node_box.appendChild(node_xmax)
+                    node_ymax = xml_doc.createElement("ymax")
+                    node_ymax.appendChild(xml_doc.createTextNode(str(ymax)))
+                    node_box.appendChild(node_ymax)
+                    node_obj.appendChild(node_box)
+                    root.appendChild(node_obj)
+            with open(osp.join(xml_dir, img_name_part + ".xml"), 'w') as fxml:
+                xml_doc.writexml(fxml, indent='\t', addindent='\t', newl='\n', encoding="utf-8")
+                    
+                    
+class EasyData2VOC(X2VOC):
+    def __init__(self):
+        pass
+    
+    def json2xml(self, image_dir, json_dir, xml_dir):
+        import xml.dom.minidom as minidom
+        for img_name in os.listdir(image_dir):
+            img_name_part = osp.splitext(img_name)[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_name)))
+                continue
+            xml_doc = minidom.Document() 
+            root = xml_doc.createElement("annotation") 
+            xml_doc.appendChild(root)
+            node_folder = xml_doc.createElement("folder")
+            node_folder.appendChild(xml_doc.createTextNode("JPEGImages"))
+            root.appendChild(node_folder)
+            node_filename = xml_doc.createElement("filename")
+            node_filename.appendChild(xml_doc.createTextNode(img_name))
+            root.appendChild(node_filename)
+            img = cv2.imread(osp.join(image_dir, img_name))
+            h = img.shape[0]
+            w = img.shape[1]
+            node_size = xml_doc.createElement("size")
+            node_width = xml_doc.createElement("width")
+            node_width.appendChild(xml_doc.createTextNode(str(w)))
+            node_size.appendChild(node_width)
+            node_height = xml_doc.createElement("height")
+            node_height.appendChild(xml_doc.createTextNode(str(h)))
+            node_size.appendChild(node_height)
+            node_depth = xml_doc.createElement("depth")
+            node_depth.appendChild(xml_doc.createTextNode(str(3)))
+            node_size.appendChild(node_depth)
+            root.appendChild(node_size)
+            with open(json_file, mode="r", \
+                              encoding=get_encoding(json_file)) as j:
+                json_info = json.load(j)
+                for shape in json_info["labels"]:
+                    label = shape["name"]
+                    xmin = shape["x1"]
+                    ymin = shape["y1"]
+                    xmax = shape["x2"]
+                    ymax = shape["y2"]
+                    node_obj = xml_doc.createElement("object")
+                    node_name = xml_doc.createElement("name")
+                    node_name.appendChild(xml_doc.createTextNode(label))
+                    node_obj.appendChild(node_name)
+                    node_diff = xml_doc.createElement("difficult")
+                    node_diff.appendChild(xml_doc.createTextNode(str(0)))
+                    node_obj.appendChild(node_diff)
+                    node_box = xml_doc.createElement("bndbox")
+                    node_xmin = xml_doc.createElement("xmin")
+                    node_xmin.appendChild(xml_doc.createTextNode(str(xmin)))
+                    node_box.appendChild(node_xmin)
+                    node_ymin = xml_doc.createElement("ymin")
+                    node_ymin.appendChild(xml_doc.createTextNode(str(ymin)))
+                    node_box.appendChild(node_ymin)
+                    node_xmax = xml_doc.createElement("xmax")
+                    node_xmax.appendChild(xml_doc.createTextNode(str(xmax)))
+                    node_box.appendChild(node_xmax)
+                    node_ymax = xml_doc.createElement("ymax")
+                    node_ymax.appendChild(xml_doc.createTextNode(str(ymax)))
+                    node_box.appendChild(node_ymax)
+                    node_obj.appendChild(node_box)
+                    root.appendChild(node_obj)
+            with open(osp.join(xml_dir, img_name_part + ".xml"), 'w') as fxml:
+                xml_doc.writexml(fxml, indent='\t', addindent='\t', newl='\n', encoding="utf-8")                    
+