From bb2d3fce7ec3a4d492b2aa75578871a66884a53e Mon Sep 17 00:00:00 2001 From: shiyu22 Date: Mon, 11 Apr 2022 19:18:55 +0800 Subject: [PATCH] Update yolov5 README Signed-off-by: shiyu22 --- README.md | 87 ++++++++++++++++++++++++++++++++++++++++++++++++++- __init__.py | 3 +- results1.png | Bin 0 -> 18504 bytes results2.png | Bin 0 -> 54866 bytes test.png | Bin 0 -> 307242 bytes yolov5.py | 11 ++++--- 6 files changed, 94 insertions(+), 7 deletions(-) create mode 100644 results1.png create mode 100644 results2.png create mode 100755 test.png diff --git a/README.md b/README.md index 4fb3e03..a756e75 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,87 @@ -# yolo +# 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](https://pytorch.org/hub/ultralytics_yolov5/) to detect the object. + + + +
+ + + +### Code Example + + + +Writing the pipeline in the simplified way + +```Python +import towhee + +towhee.glob('./test.jpg') \ + .image_decode() \ + .object_detection.yolov5() \ + .show() +``` + +results1 + +Writing the same pipeline with explicitly specified inputs and outputs + +```Python +import towhee + +towhee.glob['path']('./test.png') \ + .image_decode['path','img']() \ + .object_detection.yolov5['img', ('box', 'class', 'score')]() \ + .select('box', 'class', 'score') \ + .show() +``` + +results1 + + + +
+ + + +## 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[(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. 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