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@@ -19,26 +19,30 @@ import json
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import os
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import os.path as osp
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import shutil
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+import re
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import numpy as np
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import PIL.ImageDraw
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+import xml.etree.ElementTree as ET
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from .base import MyEncoder, is_pic, get_encoding
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from paddlex.utils import path_normalization
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-
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-
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+import paddlex.utils.logging as logging
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+
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+
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class X2COCO(object):
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def __init__(self):
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self.images_list = []
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self.categories_list = []
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self.annotations_list = []
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-
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+
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def generate_categories_field(self, label, labels_list):
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category = {}
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category["supercategory"] = "component"
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category["id"] = len(labels_list) + 1
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category["name"] = label
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return category
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-
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- def generate_rectangle_anns_field(self, points, label, image_id, object_id, label_to_num):
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+
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+ def generate_rectangle_anns_field(self, points, label, image_id, object_id,
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+ label_to_num):
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annotation = {}
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seg_points = np.asarray(points).copy()
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seg_points[1, :] = np.asarray(points)[2, :]
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@@ -48,14 +52,14 @@ class X2COCO(object):
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annotation["image_id"] = image_id + 1
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annotation["bbox"] = list(
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map(float, [
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- points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
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- 1] - points[0][1]
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+ points[0][0], points[0][1], points[1][0] - points[0][0],
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+ points[1][1] - points[0][1]
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]))
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annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
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annotation["category_id"] = label_to_num[label]
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annotation["id"] = object_id + 1
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return annotation
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-
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+
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def convert(self, image_dir, json_dir, dataset_save_dir):
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"""转换。
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Args:
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@@ -74,8 +78,8 @@ class X2COCO(object):
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for img_name in os.listdir(image_dir):
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if is_pic(img_name):
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shutil.copyfile(
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- osp.join(image_dir, img_name),
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- osp.join(new_image_dir, img_name))
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+ osp.join(image_dir, img_name),
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+ osp.join(new_image_dir, img_name))
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# Convert the json files.
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self.parse_json(new_image_dir, json_dir)
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coco_data = {}
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@@ -83,42 +87,40 @@ class X2COCO(object):
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coco_data["categories"] = self.categories_list
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coco_data["annotations"] = self.annotations_list
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json_path = osp.join(dataset_save_dir, "annotations.json")
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- json.dump(
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- coco_data,
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- open(json_path, "w"),
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- indent=4,
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- cls=MyEncoder)
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-
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-
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+ json.dump(coco_data, open(json_path, "w"), indent=4, cls=MyEncoder)
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+
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+
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class LabelMe2COCO(X2COCO):
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"""将使用LabelMe标注的数据集转换为COCO数据集。
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"""
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+
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def __init__(self):
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super(LabelMe2COCO, self).__init__()
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-
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+
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def generate_images_field(self, json_info, image_file, image_id):
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image = {}
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image["height"] = json_info["imageHeight"]
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image["width"] = json_info["imageWidth"]
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image["id"] = image_id + 1
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json_img_path = path_normalization(json_info["imagePath"])
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- json_info["imagePath"] = osp.join(osp.split(json_img_path)[0], image_file)
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+ json_info["imagePath"] = osp.join(
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+ osp.split(json_img_path)[0], image_file)
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image["file_name"] = osp.split(json_info["imagePath"])[-1]
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return image
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-
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- def generate_polygon_anns_field(self, height, width,
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- points, label, image_id,
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- object_id, label_to_num):
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+
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+ def generate_polygon_anns_field(self, height, width, points, label,
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+ image_id, object_id, label_to_num):
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annotation = {}
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annotation["segmentation"] = [list(np.asarray(points).flatten())]
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annotation["iscrowd"] = 0
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annotation["image_id"] = image_id + 1
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- annotation["bbox"] = list(map(float, self.get_bbox(height, width, points)))
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+ annotation["bbox"] = list(
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+ map(float, self.get_bbox(height, width, points)))
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annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
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annotation["category_id"] = label_to_num[label]
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annotation["id"] = object_id + 1
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return annotation
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-
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+
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def get_bbox(self, height, width, points):
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polygons = points
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mask = np.zeros([height, width], dtype=np.uint8)
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@@ -137,7 +139,7 @@ class LabelMe2COCO(X2COCO):
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left_top_c, left_top_r, right_bottom_c - left_top_c,
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right_bottom_r - left_top_r
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]
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-
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+
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def parse_json(self, img_dir, json_dir):
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image_id = -1
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object_id = -1
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@@ -153,7 +155,8 @@ class LabelMe2COCO(X2COCO):
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with open(json_file, mode='r', \
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encoding=get_encoding(json_file)) as j:
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json_info = json.load(j)
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- img_info = self.generate_images_field(json_info, img_file, image_id)
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+ img_info = self.generate_images_field(json_info, img_file,
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+ image_id)
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self.images_list.append(img_info)
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for shapes in json_info["shapes"]:
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object_id = object_id + 1
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@@ -167,23 +170,26 @@ class LabelMe2COCO(X2COCO):
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p_type = shapes["shape_type"]
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if p_type == "polygon":
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self.annotations_list.append(
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- self.generate_polygon_anns_field(json_info["imageHeight"], json_info[
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- "imageWidth"], points, label, image_id,
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- object_id, label_to_num))
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+ self.generate_polygon_anns_field(
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+ json_info["imageHeight"], json_info[
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+ "imageWidth"], points, label, image_id,
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+ object_id, label_to_num))
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if p_type == "rectangle":
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points.append([points[0][0], points[1][1]])
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points.append([points[1][0], points[0][1]])
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self.annotations_list.append(
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- self.generate_rectangle_anns_field(points, label, image_id,
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- object_id, label_to_num))
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-
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-
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+ self.generate_rectangle_anns_field(
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+ points, label, image_id, object_id,
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+ label_to_num))
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+
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+
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class EasyData2COCO(X2COCO):
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"""将使用EasyData标注的检测或分割数据集转换为COCO数据集。
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"""
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+
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def __init__(self):
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- super(EasyData2COCO, self).__init__()
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-
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+ super(EasyData2COCO, self).__init__()
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+
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def generate_images_field(self, img_path, image_id):
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image = {}
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img = cv2.imread(img_path)
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@@ -193,23 +199,23 @@ class EasyData2COCO(X2COCO):
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img_path = path_normalization(img_path)
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image["file_name"] = osp.split(img_path)[-1]
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return image
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-
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- def generate_polygon_anns_field(self, points, segmentation,
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- label, image_id, object_id,
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- label_to_num):
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+
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+ def generate_polygon_anns_field(self, points, segmentation, label,
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+ image_id, object_id, label_to_num):
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annotation = {}
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annotation["segmentation"] = segmentation
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annotation["iscrowd"] = 1 if len(segmentation) > 1 else 0
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annotation["image_id"] = image_id + 1
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- annotation["bbox"] = list(map(float, [
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- points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
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- 1] - points[0][1]
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+ annotation["bbox"] = list(
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+ map(float, [
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+ points[0][0], points[0][1], points[1][0] - points[0][0],
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+ points[1][1] - points[0][1]
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]))
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annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
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annotation["category_id"] = label_to_num[label]
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annotation["id"] = object_id + 1
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return annotation
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-
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+
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def parse_json(self, img_dir, json_dir):
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from pycocotools.mask import decode
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image_id = -1
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@@ -226,7 +232,8 @@ class EasyData2COCO(X2COCO):
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with open(json_file, mode='r', \
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encoding=get_encoding(json_file)) as j:
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json_info = json.load(j)
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- img_info = self.generate_images_field(osp.join(img_dir, img_file), image_id)
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+ img_info = self.generate_images_field(
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+ osp.join(img_dir, img_file), image_id)
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self.images_list.append(img_info)
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for shapes in json_info["labels"]:
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object_id = object_id + 1
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@@ -242,31 +249,36 @@ class EasyData2COCO(X2COCO):
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points.append([points[0][0], points[1][1]])
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points.append([points[1][0], points[0][1]])
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self.annotations_list.append(
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- self.generate_rectangle_anns_field(points, label, image_id,
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- object_id, label_to_num))
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+ self.generate_rectangle_anns_field(
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+ points, label, image_id, object_id,
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+ label_to_num))
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else:
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mask_dict = {}
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- mask_dict['size'] = [img_info["height"], img_info["width"]]
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+ mask_dict[
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+ 'size'] = [img_info["height"], img_info["width"]]
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mask_dict['counts'] = shapes['mask'].encode()
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mask = decode(mask_dict)
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contours, hierarchy = cv2.findContours(
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- (mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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+ (mask).astype(np.uint8), cv2.RETR_TREE,
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+ cv2.CHAIN_APPROX_SIMPLE)
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segmentation = []
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for contour in contours:
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contour_list = contour.flatten().tolist()
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if len(contour_list) > 4:
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segmentation.append(contour_list)
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self.annotations_list.append(
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- self.generate_polygon_anns_field(points, segmentation, label, image_id, object_id,
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- label_to_num))
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-
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+ self.generate_polygon_anns_field(
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+ points, segmentation, label, image_id,
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+ object_id, label_to_num))
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+
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class JingLing2COCO(X2COCO):
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"""将使用EasyData标注的检测或分割数据集转换为COCO数据集。
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"""
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+
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def __init__(self):
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super(JingLing2COCO, self).__init__()
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-
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+
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def generate_images_field(self, json_info, image_id):
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image = {}
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image["height"] = json_info["size"]["height"]
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@@ -275,20 +287,20 @@ class JingLing2COCO(X2COCO):
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json_info["path"] = path_normalization(json_info["path"])
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image["file_name"] = osp.split(json_info["path"])[-1]
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return image
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-
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- def generate_polygon_anns_field(self, height, width,
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- points, label, image_id,
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- object_id, label_to_num):
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+
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+ def generate_polygon_anns_field(self, height, width, points, label,
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+ image_id, object_id, label_to_num):
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annotation = {}
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annotation["segmentation"] = [list(np.asarray(points).flatten())]
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annotation["iscrowd"] = 0
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annotation["image_id"] = image_id + 1
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- annotation["bbox"] = list(map(float, self.get_bbox(height, width, points)))
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+ annotation["bbox"] = list(
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+ map(float, self.get_bbox(height, width, points)))
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annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
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annotation["category_id"] = label_to_num[label]
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annotation["id"] = object_id + 1
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return annotation
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-
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+
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def get_bbox(self, height, width, points):
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polygons = points
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mask = np.zeros([height, width], dtype=np.uint8)
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@@ -307,7 +319,7 @@ class JingLing2COCO(X2COCO):
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left_top_c, left_top_r, right_bottom_c - left_top_c,
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right_bottom_r - left_top_r
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]
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-
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+
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def parse_json(self, img_dir, json_dir):
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image_id = -1
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object_id = -1
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@@ -329,7 +341,7 @@ class JingLing2COCO(X2COCO):
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for i, obj in enumerate(json_info["outputs"]["object"]):
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if i == 0:
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if "polygon" in obj:
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- anns_type = "polygon"
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+ anns_type = "polygon"
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else:
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if anns_type not in obj:
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continue
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@@ -343,22 +355,229 @@ class JingLing2COCO(X2COCO):
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if anns_type == "polygon":
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points = []
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for j in range(int(len(obj["polygon"]) / 2.0)):
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- points.append([obj["polygon"]["x" + str(j + 1)],
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- obj["polygon"]["y" + str(j + 1)]])
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+ points.append([
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+ obj["polygon"]["x" + str(j + 1)],
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+ obj["polygon"]["y" + str(j + 1)]
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+ ])
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self.annotations_list.append(
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- self.generate_polygon_anns_field(json_info["size"]["height"],
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- json_info["size"]["width"],
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- points,
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- label,
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- image_id,
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- object_id,
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- label_to_num))
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+ self.generate_polygon_anns_field(
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+ json_info["size"]["height"], json_info["size"][
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+ "width"], points, label, image_id,
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+ object_id, label_to_num))
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if anns_type == "bndbox":
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points = []
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- points.append([obj["bndbox"]["xmin"], obj["bndbox"]["ymin"]])
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- points.append([obj["bndbox"]["xmax"], obj["bndbox"]["ymax"]])
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- points.append([obj["bndbox"]["xmin"], obj["bndbox"]["ymax"]])
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- points.append([obj["bndbox"]["xmax"], obj["bndbox"]["ymin"]])
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+ points.append(
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+ [obj["bndbox"]["xmin"], obj["bndbox"]["ymin"]])
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+ points.append(
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+ [obj["bndbox"]["xmax"], obj["bndbox"]["ymax"]])
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+ points.append(
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+ [obj["bndbox"]["xmin"], obj["bndbox"]["ymax"]])
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+ points.append(
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+ [obj["bndbox"]["xmax"], obj["bndbox"]["ymin"]])
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self.annotations_list.append(
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- self.generate_rectangle_anns_field(points, label, image_id,
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- object_id, label_to_num))
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+ self.generate_rectangle_anns_field(
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+ points, label, image_id, object_id,
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+ label_to_num))
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+
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+
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+class VOC2COCO(X2COCO):
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+ """将使用VOC标注的数据集转换为COCO数据集。
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+ """
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+
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+ def __init__(self):
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+ super(VOC2COCO, self).__init__()
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+
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+ def generate_categories_field(self, label, labels_list):
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+ category = {}
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+ category["supercategory"] = "component"
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+ category["id"] = len(labels_list) + 1
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+ category["name"] = label
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+ return category
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+
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+ def generate_images_field(self, xml_info, image_file, image_id):
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+ image = {}
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+ image["height"] = xml_info["imageHeight"]
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|
|
+ image["width"] = xml_info["imageWidth"]
|
|
|
+ image["id"] = image_id + 1
|
|
|
+ image["imagePath"] = image_file
|
|
|
+ image["file_name"] = osp.split(image_file)[-1]
|
|
|
+ return image
|
|
|
+
|
|
|
+ def generate_label_list(self, xml_dir):
|
|
|
+ xml_dir_dir = os.path.abspath(
|
|
|
+ os.path.join(os.path.dirname(xml_dir), os.path.pardir))
|
|
|
+ self.labels_list = []
|
|
|
+ self.label_to_num = {}
|
|
|
+ if osp.exists(osp.join(xml_dir_dir, 'labels.txt')):
|
|
|
+ with open(osp.join(xml_dir_dir, 'labels.txt'), 'r') as fr:
|
|
|
+ while True:
|
|
|
+ label = fr.readline().strip()
|
|
|
+ if not label:
|
|
|
+ break
|
|
|
+ if label not in self.labels_list:
|
|
|
+ self.categories_list.append(\
|
|
|
+ self.generate_categories_field(label, self.labels_list))
|
|
|
+ self.labels_list.append(label)
|
|
|
+ self.label_to_num[label] = len(self.labels_list)
|
|
|
+ return
|
|
|
+ logging.info(
|
|
|
+ 'labels.txt is not in the folder {}, so categories are ordered randomly in annotation.json.'.
|
|
|
+ format(xml_dir_dir))
|
|
|
+ return
|
|
|
+
|
|
|
+ def parse_xml(self, xml_file):
|
|
|
+ xml_info = {'im_info': {}, 'annotations': []}
|
|
|
+ tree = ET.parse(xml_file)
|
|
|
+ pattern = re.compile('<object>', re.IGNORECASE)
|
|
|
+ obj_match = pattern.findall(str(ET.tostringlist(tree.getroot())))
|
|
|
+ obj_tag = obj_match[0][1:-1]
|
|
|
+ objs = tree.findall(obj_tag)
|
|
|
+ pattern = re.compile('<size>', re.IGNORECASE)
|
|
|
+ size_tag = pattern.findall(str(ET.tostringlist(tree.getroot())))[0][1:
|
|
|
+ -1]
|
|
|
+ size_element = tree.find(size_tag)
|
|
|
+ pattern = re.compile('<width>', re.IGNORECASE)
|
|
|
+ width_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][1:
|
|
|
+ -1]
|
|
|
+ im_w = float(size_element.find(width_tag).text)
|
|
|
+ pattern = re.compile('<height>', re.IGNORECASE)
|
|
|
+ height_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][1:
|
|
|
+ -1]
|
|
|
+ im_h = float(size_element.find(height_tag).text)
|
|
|
+ xml_info['im_info']['imageWidth'] = im_w
|
|
|
+ xml_info['im_info']['imageHeight'] = im_h
|
|
|
+ for i, obj in enumerate(objs):
|
|
|
+ pattern = re.compile('<name>', re.IGNORECASE)
|
|
|
+ name_tag = pattern.findall(str(ET.tostringlist(obj)))[0][1:-1]
|
|
|
+ cname = obj.find(name_tag).text.strip()
|
|
|
+ pattern = re.compile('<bndbox>', re.IGNORECASE)
|
|
|
+ box_tag = pattern.findall(str(ET.tostringlist(obj)))[0][1:-1]
|
|
|
+ box_element = obj.find(box_tag)
|
|
|
+ pattern = re.compile('<xmin>', re.IGNORECASE)
|
|
|
+ xmin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][
|
|
|
+ 1:-1]
|
|
|
+ x1 = float(box_element.find(xmin_tag).text)
|
|
|
+ pattern = re.compile('<ymin>', re.IGNORECASE)
|
|
|
+ ymin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][
|
|
|
+ 1:-1]
|
|
|
+ y1 = float(box_element.find(ymin_tag).text)
|
|
|
+ pattern = re.compile('<xmax>', re.IGNORECASE)
|
|
|
+ xmax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][
|
|
|
+ 1:-1]
|
|
|
+ x2 = float(box_element.find(xmax_tag).text)
|
|
|
+ pattern = re.compile('<ymax>', re.IGNORECASE)
|
|
|
+ ymax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][
|
|
|
+ 1:-1]
|
|
|
+ y2 = float(box_element.find(ymax_tag).text)
|
|
|
+ x1 = max(0, x1)
|
|
|
+ y1 = max(0, y1)
|
|
|
+ if im_w > 0.5 and im_h > 0.5:
|
|
|
+ x2 = min(im_w - 1, x2)
|
|
|
+ y2 = min(im_h - 1, y2)
|
|
|
+ xml_info['annotations'].append({
|
|
|
+ 'bbox': [[x1, y1], [x2, y2], [x1, y2], [x2, y1]],
|
|
|
+ 'category': cname,
|
|
|
+ })
|
|
|
+ return xml_info
|
|
|
+
|
|
|
+ def parse_json(self, img_dir, xml_dir, file_list=None):
|
|
|
+ image_id = -1
|
|
|
+ object_id = -1
|
|
|
+ self.generate_label_list(xml_dir)
|
|
|
+ for img_file in os.listdir(img_dir):
|
|
|
+ if file_list is not None and img_file not in file_list:
|
|
|
+ continue
|
|
|
+ img_name_part = osp.splitext(img_file)[0]
|
|
|
+ xml_file = osp.join(xml_dir, img_name_part + ".xml")
|
|
|
+ if not osp.exists(xml_file):
|
|
|
+ os.remove(osp.join(img_dir, img_file))
|
|
|
+ continue
|
|
|
+ image_id = image_id + 1
|
|
|
+ xml_info = self.parse_xml(xml_file)
|
|
|
+ img_info = self.generate_images_field(xml_info['im_info'],
|
|
|
+ osp.join(img_dir, img_file),
|
|
|
+ image_id)
|
|
|
+ self.images_list.append(img_info)
|
|
|
+ annos = xml_info['annotations']
|
|
|
+ for anno in annos:
|
|
|
+ object_id = object_id + 1
|
|
|
+ label = anno["category"]
|
|
|
+ if label not in self.labels_list:
|
|
|
+ self.categories_list.append(\
|
|
|
+ self.generate_categories_field(label, self.labels_list))
|
|
|
+ self.labels_list.append(label)
|
|
|
+ self.label_to_num[label] = len(self.labels_list)
|
|
|
+ self.annotations_list.append(
|
|
|
+ self.generate_rectangle_anns_field(anno[
|
|
|
+ 'bbox'], label, image_id, object_id,
|
|
|
+ self.label_to_num))
|
|
|
+
|
|
|
+ def convert(self, image_dir, json_dir, dataset_save_dir):
|
|
|
+ """转换。
|
|
|
+ Args:
|
|
|
+ image_dir (str): 图像文件存放的路径。
|
|
|
+ json_dir (str): 与每张图像对应的json文件的存放路径。
|
|
|
+ dataset_save_dir (str): 转换后数据集存放路径。
|
|
|
+ """
|
|
|
+ assert osp.exists(image_dir), "he 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_dir = os.path.abspath(
|
|
|
+ os.path.join(os.path.dirname(json_dir), os.path.pardir))
|
|
|
+ for part in ['train', 'val', 'test']:
|
|
|
+ part_list_file = osp.join(xml_dir_dir, '{}_list.txt'.format(part))
|
|
|
+ if osp.exists(part_list_file):
|
|
|
+ file_list = list()
|
|
|
+ with open(part_list_file, 'r') as f:
|
|
|
+ while True:
|
|
|
+ line = f.readline()
|
|
|
+ if not line:
|
|
|
+ break
|
|
|
+ if len(line.strip().split()) > 2:
|
|
|
+ raise Exception(
|
|
|
+ "A space is defined as the separator, but it exists in image or label name {}."
|
|
|
+ .format(line))
|
|
|
+ img_file = osp.join(
|
|
|
+ image_dir, osp.split(line.strip().split()[0])[-1])
|
|
|
+ xml_file = osp.join(
|
|
|
+ json_dir, osp.split(line.strip().split()[1])[-1])
|
|
|
+ img_file = path_normalization(img_file)
|
|
|
+ xml_file = path_normalization(xml_file)
|
|
|
+ if not is_pic(img_file):
|
|
|
+ continue
|
|
|
+ if not osp.isfile(xml_file):
|
|
|
+ continue
|
|
|
+ if not osp.exists(img_file):
|
|
|
+ raise IOError('The image file {} is not exist!'.
|
|
|
+ format(img_file))
|
|
|
+ file_list.append(osp.split(img_file)[-1])
|
|
|
+ self.parse_json(new_image_dir, json_dir, file_list)
|
|
|
+ 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, "{}.json".format(part))
|
|
|
+ json.dump(
|
|
|
+ coco_data, open(json_path, "w"), indent=4, cls=MyEncoder)
|
|
|
+ logging.info("xml files in {} are converted to the MSCOCO format stored in {}".format(\
|
|
|
+ osp.join(xml_dir_dir, '{}_list.txt'.format(part)), osp.join(dataset_save_dir, "{}.json".format(part))))
|
|
|
+ self.images_list = []
|
|
|
+ self.annotations_list = []
|
|
|
+ self.parse_json(new_image_dir, json_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)
|
FlyingQianMM