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object-detection
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
Writing the pipeline in the simplified way
import towhee
towhee.glob('./test.png') \
.image_decode() \
.object_detection.yolov5() \
.show()
Writing the same pipeline with explicitly specified inputs and outputs
import towhee
towhee.glob['path']('./test.png') \
.image_decode['path','img']() \
.object_detection.yolov5['img', ('box', 'class', 'score')]() \
.image_crop[('img', 'box'), 'object'](clamp = True) \
.select['img','object']() \
.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 gives the bounding box of 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 the confidence scores.
shiyu22
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.gitattributes |
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README.md |
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__init__.py |
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requirements.txt |
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results1.png |
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results2.png |
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test.png |
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yolov5.py |
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