Object Detection with Yolov5

author: shiyu22

Description

Object Detection is a computer vision technique that locates and identifies people, items, or other objects in an image. Object detection has applications in many areas of computer vision, including image retrieval, image annotation, vehicle counting, object tracking, etc.

This operator uses PyTorch.yolov5 to detect the object.

Code Example

Load an image from path './test.png' and use yolov5 model to detect objects in the image.

Write a same pipeline with explicit inputs/outputs name specifications:

from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', ('box', 'class', 'score'), ops.object_detection.yolov5())
        .map(('img', 'box'), 'object', ops.image_crop(clamp=True))
        .output('img', 'object', 'class')
)

DataCollection(p('./test.png')).show()

Factory Constructor

Create the operator via the following factory method:

object_detection.yolov5()

Interface

The operator takes an image as input. It first detects the objects appeared in the image, and generates a bounding box around each object.

Parameters:

​ img: numpy.ndarray

​ Image data in ndarray format.

Return: List[List[(int, int, int, int)], ...], List[str], List[float]

The return value is a tuple of (boxes, classes, scores). The boxes is a list of bounding boxes. Each bounding box is represented by the top-left and the bottom right points, i.e. (x1, y1, x2, y2). The classes is a list of prediction labels. The scores is a list of confidence scores.