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2026-07-17 18:55:31 +08:00
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commit 91cbd01126
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+'''
+把coco数据集合的所有标注转换到voc格式，不改变图片命名方式，
+注意，原来有一些图片是黑白照片，检测出不是 RGB 图像，这样的图像不会被放到新的文件夹中
+'''
+from pycocotools.coco import COCO
+import os, cv2, shutil
+from lxml import etree, objectify
+from tqdm import tqdm
+from PIL import Image
+
+# 1、生成图片保存的路径
+CKimg_dir = '/data4/Hatcher/smallobject/voc/val_images/'
+# 2、生成标注文件保存的路径
+CKanno_dir = '/data4/Hatcher/smallobject/voc/val_annos/'
+
+
+# 若模型保存文件夹不存在，创建模型保存文件夹，若存在，删除重建
+def mkr(path):
+    if os.path.exists(path):
+        shutil.rmtree(path)
+        os.mkdir(path)
+    else:
+        os.mkdir(path)
+
+
+def save_annotations(filename, objs, filepath):
+    annopath = CKanno_dir + "/" + filename[:-3] + "xml"  # 生成的xml文件保存路径
+    dst_path = CKimg_dir + "/" + filename
+    img_path = filepath
+    img = cv2.imread(img_path)
+    im = Image.open(img_path)
+    # if im.mode != "RGB":
+    #     print(filename + " not a RGB image")
+    #     im.close()
+    #     return
+    im.close()
+    shutil.copy(img_path, dst_path)  # 把原始图像复制到目标文件夹
+    E = objectify.ElementMaker(annotate=False)
+    anno_tree = E.annotation(
+        E.folder('1'),
+        E.filename(filename),
+        E.source(
+            E.database('CKdemo'),
+            E.annotation('VOC'),
+            E.image('CK')
+        ),
+        E.size(
+            E.width(img.shape[1]),
+            E.height(img.shape[0]),
+            E.depth(img.shape[2])
+        ),
+        E.segmented(0)
+    )
+    for obj in objs:
+        E2 = objectify.ElementMaker(annotate=False)
+        anno_tree2 = E2.object(
+            E.name(obj[0]),
+            E.pose(),
+            E.truncated("0"),
+            E.difficult(0),
+            E.bndbox(
+                E.xmin(obj[2]),
+                E.ymin(obj[3]),
+                E.xmax(obj[4]),
+                E.ymax(obj[5])
+            )
+        )
+        anno_tree.append(anno_tree2)
+    etree.ElementTree(anno_tree).write(annopath, pretty_print=True)
+
+
+def showbycv(coco,  img, classes, origin_image_dir, verbose=False):
+    filename = img['file_name']
+    filepath = os.path.join(origin_image_dir,  filename)
+    I = cv2.imread(filepath)
+    annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)
+    anns = coco.loadAnns(annIds)
+    objs = []
+    for ann in anns:
+        name = classes[ann['category_id']]
+        if 'bbox' in ann:
+            bbox = ann['bbox']
+            xmin = (int)(bbox[0])
+            ymin = (int)(bbox[1])
+            xmax = (int)(bbox[2] + bbox[0])
+            ymax = (int)(bbox[3] + bbox[1])
+            obj = [name, 1.0, xmin, ymin, xmax, ymax]
+            objs.append(obj)
+            if verbose:
+                cv2.rectangle(I, (xmin, ymin), (xmax, ymax), (255, 0, 0))
+                cv2.putText(I, name, (xmin, ymin), 3, 1, (0, 0, 255))
+    save_annotations(filename, objs, filepath)
+    if verbose:
+        cv2.imshow("img", I)
+        cv2.waitKey(0)
+
+def catid2name(coco):  # 将名字和id号建立一个字典
+    classes = dict()
+    for cat in coco.dataset['categories']:
+        classes[cat['id']] = cat['name']
+        # print(str(cat['id'])+":"+cat['name'])
+    return classes
+
+
+def get_CK5(origin_anno_dir, origin_image_dir, verbose=False):
+    dataTypes = ['val']       # 3、根据你要转化的是训练集还是验证集（annotations名称）
+    for dataType in dataTypes:
+        annFile = 'aitod_{}.json'.format(dataType)
+        annpath = os.path.join(origin_anno_dir, annFile)
+        coco = COCO(annpath)
+        classes = catid2name(coco)
+        imgIds = coco.getImgIds()
+        # imgIds=imgIds[0:1000]#测试用，抽取10张图片，看下存储效果
+        for imgId in tqdm(imgIds):
+            img = coco.loadImgs(imgId)[0]
+            #showbycv(coco, dataType, img, classes, origin_image_dir, verbose=False)
+            showbycv(coco,  img, classes, origin_image_dir, verbose=False)
+
+
+def main():
+    # base_dir = 'C:\\Users\\admin\\Desktop\\few shot competition\\train\\voc'  # step1 这里是一个新的文件夹，存放转换后的图片和标注
+    # image_dir = os.path.join(base_dir, 'images')  # 在上述文件夹中生成images，annotations两个子文件夹
+    # anno_dir = os.path.join(base_dir, 'annotations')
+    # mkr(image_dir)
+    # mkr(anno_dir)
+    # 4、
+    origin_image_dir = '/data4/Hatcher/smallobject/val/images/'  # step 2原始的coco的图像存放位置
+    # 5、
+    origin_anno_dir = '/data4/Hatcher/smallobject/annotations/'  # step 3 原始的coco的标注存放位置
+    print(origin_anno_dir)
+    verbose = True  # 是否需要看下标记是否正确的开关标记，若是true,就会把标记展示到图片上
+    get_CK5(origin_anno_dir, origin_image_dir, verbose)
+
+
+if __name__ == "__main__":
+    main()