你好，我在使用你的yolov8实例分割训练自己的数据集这一教程进行json转化yolo时出现了以下报错
0%| | 1/282 [00:00<00:00, 921.62it/s]
Traceback (most recent call last):
File "C:\Github\ultralytics-main\json2txt.py", line 53, in <module>
convert_label_json(json_dir, save_dir, classes)
File "C:\Github\ultralytics-main\json2txt.py", line 16, in convert_label_json
json_dict = json.load(load_f)
File "C:\anaconda\envs\yolo8_truck\lib\json\__init__.py", line 296, in load
parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)
File "C:\anaconda\envs\yolo8_truck\lib\json\__init__.py", line 348, in loads
return _default_decoder.decode(s)
File "C:\anaconda\envs\yolo8_truck\lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "C:\anaconda\envs\yolo8_truck\lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
Process finished with exit code 1
请问以下这是什么问题呢
json文件的格式不对还是编码有问题吧，导致读取json失败了
def save_json(self,image,file_name,image_id,categories):
image_shape = np.array(np.shape(image)[0:2])
if self.letterbox_image:
crop_img = np.array(letterbox_image(image, (self.model_image_size[1],self.model_image_size[0])))
else:
crop_img = image.convert('RGB')
crop_img = crop_img.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
photo = np.array(crop_img,dtype = np.float32) / 255.0
photo = np.transpose(photo, (2, 0, 1))
images = [photo]
with torch.no_grad():
images = torch.from_numpy(np.asarray(images))
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output, len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return []
top_index = batch_detections[:,4] * batch_detections[:,5] > self.confidence
top_conf = batch_detections[top_index,4]*batch_detections[top_index,5]
top_label = np.array(batch_detections[top_index,-1],np.int32)
top_bboxes = np.array(batch_detections[top_index,:4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1)
if self.letterbox_image:
boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
else:
top_xmin = top_xmin / self.model_image_size[1] * image_shape[1]
top_ymin = top_ymin / self.model_image_size[0] * image_shape[0]
top_xmax = top_xmax / self.model_image_size[1] * image_shape[1]
top_ymax = top_ymax / self.model_image_size[0] * image_shape[0]
boxes = np.concatenate([top_ymin,top_xmin,top_ymax,top_xmax], axis=-1)
font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
thickness = max((np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0], 1)
content_json=[]
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
w=right-left
h=bottom-top
y = float(max(0, top))
x = float(max(0, left))
w = float(max(0, w))
h = float(max(0, h))
score = float(score)
bbox=[x,y,w,h]
# print(label, top, left, bottom, right)
cat_id=categories[c]['id']
content_dic = {
"image_id":image_id,
"category_id": cat_id,
"bbox": bbox,
"score": score
}
content_json.append(content_dic)
return content_json