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+import numpy as np
+import json
+import pandas as pd
+
+path = '../dataset/test/test1/annotations.csv'
+classpath = '../dataset/e_label2.csv'
+save_json_path = 'testcoco.json'
+
+
+data = pd.read_csv(path, names=['filename', 'width', 'height', 'xmin', 'ymin', 'xmax', 'ymax', 'class'])
+
+images = []
+categories = []
+annotations = []
+
+category = {}
+category["supercategory"] = 'none'
+category["id"] = 0
+category["name"] = 'None'
+categories.append(category)
+
+data['fileid'] = data['filename'].astype('category').cat.codes
+data['categoryid']= pd.Categorical(data['class'],ordered= True).codes
+data['categoryid'] = data['categoryid']+1
+data['annid'] = data.index
+
+def image(row):
+    image = {}
+    image["height"] = row.height
+    image["width"] = row.width
+    image["id"] = row.fileid
+    image["file_name"] = row.filename
+    return image
+
+def category(row):
+    category = {}
+    category["supercategory"] = 'None'
+    category["id"] = row.categoryid
+    category["name"] = row[2]
+    return category
+
+def annotation(row):
+    annotation = {}
+    area = (row.xmax -row.xmin)*(row.ymax - row.ymin)
+    annotation["segmentation"] = []
+    annotation["iscrowd"] = 0
+    annotation["area"] = area
+    annotation["image_id"] = row.fileid
+
+    annotation["bbox"] = [row.xmin, row.ymin, row.xmax -row.xmin,row.ymax-row.ymin ]
+
+    annotation["category_id"] = row.categoryid
+    annotation["id"] = row.annid
+    return annotation
+
+for row in data.itertuples():
+    annotations.append(annotation(row))
+
+imagedf = data.drop_duplicates(subset=['fileid']).sort_values(by='fileid')
+for row in imagedf.itertuples():
+    images.append(image(row))
+
+catdf = data.drop_duplicates(subset=['categoryid']).sort_values(by='categoryid')
+for row in catdf.itertuples():
+    categories.append(category(row))
+
+data_coco = {}
+data_coco["images"] = images
+data_coco["categories"] = categories
+data_coco["annotations"] = annotations
+
+
+json.dump(data_coco, open(save_json_path, "w"), indent=4)
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