CustomVision upload own labelled images

Question

Hi,

On the Custom Vision homepage it says you can bring your "own labelled images" however there doesn't appear to be a way to upload the annotations?

There also does not appear to be any instructions anywhere on how to do this.

How is this done?

Thanks Chris

Answer 1

Thanks for the answer.

I already have over 10,000 images and 25,000 tags so CustomVision may not be the right system for me sadly.

Real shame there isn't a simple way to import tagged images and annotation files because the rest of the system looks good

If there are any other methods please advise

thanks

Answer 2

Thanks for reaching out. The portal doesn’t allow uploading tagged images, you have to use our APIs/SDKs. Also, there's a limit on number of images you can upload at a time ( 64 images and 20 tags). You can upload tagged images using the following APIs (CreateImagesFromFiles or CreateImagesFromData or CreateImagesFromUrls). Here's an example using SDK. Hope this helps.

Answer 3

@mrcday , I have built a Python package called PyLabel that can be used to import pre-labelled images to Azure Custom Vision. You can see a proof of concept in this notebook https://github.com/pylabel-project/samples/blob/main/pylabel2azure_custom_vision.ipynb.

PyLabel can read annotations from COCO, YOLO, or VOC format into a dataframe. Once they are in the data frame you can loop through the dataframe of annotations and use the Custom Vision APIs to upload the images and annotations.

Here is a snippet of the code from the notebook mentioned above:

#Iterate the rows for each image in the dataframe  
for img_filename, img_df in dataset.df.groupby('img_filename'):  
    img_path = str(PurePath(dataset.path_to_annotations, str(img_df.iloc[0].img_folder), img_filename))  
    assert exists(img_path), f"File does not exist: {img_path}"  
  
    #Create a region object for each bounding box in the dataset   
    regions = []  
    for index, row in img_df.iterrows():  
  
        #Normalize the boundings box coordinates between 0 and 1  
        x = Decimal(row.ann_bbox_xmin / row.img_width).min(1)  
        y = Decimal(row.ann_bbox_ymin / row.img_height).min(1)  
        w = Decimal(row.ann_bbox_width / row.img_width).min(1-x)  
        h = Decimal(row.ann_bbox_height / row.img_height).min(1-y)  
  
        regions.append(Region(  
                tag_id=tags[row.cat_name].id,   
                left=x,  
                top=y,  
                width=w,  
                height=h  
            )  
        )  
  
    #Create an object with the image and all of the annotations for that image  
    with open(img_path, mode="rb") as image_contents:  
        image_and_annotations = [ImageFileCreateEntry(name=img_filename, contents=image_contents.read(), regions=regions)]  
  
    #Upload the image and all annnotations for that image  
    upload_result = trainer.create_images_from_files(  
            project.id,   
            ImageFileCreateBatch(images=image_and_annotations)  
        )  
  
    #If upload is not successful, print details about that image for debugging   
    if not upload_result.is_batch_successful:  
        print("Image upload failed.")  
        for image in upload_result.images:  
            print(img_path)  
            print("Image status: ", image.status)  
            print(regions)  
  
#This will take a few minutes   
print("Upload complete")  

Note, I am a Microsoft employee but I am not a member of the Azure Custom Vision team. But I think PyLabel can help with this task so I wanted to share this with you.