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Creating Object Detection Datasets
How to convert a YOLO object detection dataset to Hub format.
Object detection using bounding boxes is one of the most common annotation types for Computer Vision datasets. This tutorial demonstrates how to convert an object detection dataset from YOLO format to Hub, and a similar method can be used to convert object detection datasets from other formats such as COCO and PASCAL VOC.
Create the Hub Dataset
The first step is to download the small dataset below called animals object detection.
animals_od.zip
278KB
Binary
animals object detection dataset
The dataset has the following folder structure:
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data_dir
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|_images
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|_image_1.jpg
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|_image_2.jpg
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|_image_3.jpg
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|_image_4.jpg
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|_boxes
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|_image_1.txt
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|_image_2.txt
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|_image_3.txt
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|_image_4.txt
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|_classes.txt
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Now that you have the data, let's create a Hub Dataset in the
./animals_od_hubfolder by running:1
import hub
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from PIL import Image, ImageDraw
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import numpy as np
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import os
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ds = hub.empty('./animals_od_hub') # Create the dataset locally
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Next, let's specify the folder paths containing the images and annotations in the dataset. In YOLO format, images and annotations are typically matched using a common filename such as
image -> filename.jpeg and annotation -> filename.txt . It's also helpful to create a list of all of the image files and the class names contained in the dataset.1
img_folder = './animals_od/images'
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lbl_folder = './animals_od/boxes'
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# List of all images
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fn_imgs = os.listdir(img_folder)
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# List of all class names
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with open(os.path.join(lbl_folder, 'classes.txt'), 'r') as f:
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class_names = f.read().splitlines()
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Since annotations in YOLO are typically stored in text files, it's useful to write a helper function that parses the annotation file and returns numpy arrays with the bounding box coordinates and bounding box classes.
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def read_yolo_boxes(fn:str):
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"""
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Function reads a label.txt YOLO file and returns a numpy array of yolo_boxes
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for the box geometry and yolo_labels for the corresponding box labels.
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"""
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box_f = open(fn)
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lines = box_f.read()
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box_f.close()
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# Split each box into a separate lines
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lines_split = lines.splitlines()
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yolo_boxes = np.zeros((len(lines_split),4))
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yolo_labels = np.zeros(len(lines_split))
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# Go through each line and parse data
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for l, line in enumerate(lines_split):
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line_split = line.split()
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yolo_boxes[l,:]=np.array((float(line_split[1]), float(line_split[2]), float(line_split[3]), float(line_split[4])))
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yolo_labels[l]=int(line_split[0])
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return yolo_boxes, yolo_labels
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Finally, let's create the tensors and iterate through all the images in the dataset in order to populate the data in Hub. Boxes and their labels will be stored in separate tensors, and for a given sample, the first axis of the boxes array corresponds to the first-and-only axis of the labels array (i.e. if there are 3 boxes in an image, the labels array is 3x1 and the boxes array is 3x4).
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with ds:
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ds.create_tensor('images', htype='image', sample_compression = 'jpeg')
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ds.create_tensor('labels', htype='class_label', class_names = class_names)
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ds.create_tensor('boxes', htype='bbox')
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for fn_img in fn_imgs:
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img_name = os.path.splitext(fn_img)[0]
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fn_box = img_name+'.txt'
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# Get the arrays for the bounding boxes and their classes
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yolo_boxes, yolo_labels = read_yolo_boxes(os.path.join(lbl_folder,fn_box))
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# Append data to tensors
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ds.images.append(hub.read(os.path.join(img_folder, fn_img)))
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ds.labels.append(yolo_labels.astype(np.uint32))
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ds.boxes.append(yolo_boxes.astype(np.float32))
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In order for Activeloop Platform to correctly visualize the labels,
class_names must be a list of strings, where the numerical labels correspond to the index of the label in the list.Inspect the Hub Dataset
Let's check out the third sample from this dataset, which contains two bounding boxes.
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# Draw bounding boxes for the third image
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ind = 2
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img = Image.fromarray(ds.images[ind ].numpy())
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draw = ImageDraw.Draw(img)
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(w,h) = img.size
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boxes = ds.boxes[ind].numpy()
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for b in range(boxes.shape[0]):
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(xc,yc) = (int(boxes[b][0]*w), int(boxes[b][1]*h))
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(x1,y1) = (int(xc-boxes[b][2]*w/2), int(yc-boxes[b][3]*h/2))
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(x2,y2) = (int(xc+boxes[b][2]*w/2), int(yc+boxes[b][3]*h/2))
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draw.rectangle([x1,y1,x2,y2], width=2)
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draw.text((x1,y1), ds.labels.info.class_names[ds.labels[ind].numpy()[b]])
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# Display the image and its bounding boxes
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img
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Congrats! You just created a beautiful object detection dataset! 🎉
Note: For optimal object detection model performance, it is often important for datasets to contain images with no annotations (See the 4th sample in the dataset above). For that use case, in order to maintain equal length between the images, boxes, and labels tensors, users can upload empty numpy arrays as long as
len(sample.shape) for an empty and non-empty sample is equal. Therefore, an empty bounding box can be added using
ds.boxes.append(np.zeros(0,4)) because len(sample.shape) == 2, just like for a bounding box with data.Last modified 2d ago