From 7ace58b98c2ef549578632918baf71110f1053ac Mon Sep 17 00:00:00 2001 From: shiyu22 Date: Tue, 8 Nov 2022 17:25:17 +0800 Subject: [PATCH] Add yolo Signed-off-by: shiyu22 --- README.md | 73 ++++++++++++++++++++++++++++++++++++++++++++++- __init__.py | 4 +++ requirements.txt | 5 ++++ test.png | Bin 0 -> 262841 bytes yolov5.py | 21 ++++++++++++++ 5 files changed, 102 insertions(+), 1 deletion(-) create mode 100644 __init__.py create mode 100644 requirements.txt create mode 100755 test.png create mode 100644 yolov5.py diff --git a/README.md b/README.md index 4fb3e03..0b6f175 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,73 @@ -# yolo +# Object Detection with Yolo + +*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.png') \ + .image_decode() \ + .object_detection.yolov5() \ + .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[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. diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..95c54e3 --- /dev/null +++ b/__init__.py @@ -0,0 +1,4 @@ +from .yolov5 import Yolov5 + +def yolo(): + return Yolov5() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..e27b04f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +matplotlib>=3.2.2 +opencv-python>=4.1.2 +torch>=1.7.0 +torchvision>=0.8.1 +seaborn diff --git a/test.png b/test.png new file mode 100755 index 0000000000000000000000000000000000000000..3124577756ff4a8b0eb3087130e91c1ac3676409 GIT binary patch literal 262841 zcmb@tc{tS3+dr;NMNtXaOHo;i>}C|9kbP$;+hFXw8AByWLJ?ydvJPg(U~FTpgcyu{ zUqTFHifa3uk^FRIvgZ~>B{+G;uBsz!|HE_2U}33?VPUy8`9Hi{UszZ;->|UMlAMDBLjJoC7XIUsc%ti+ z^O@vx5cb_m8r&uV4{mJV{U~=$Em`M`NhoLo;s-qwSD6`s@o0q)wXNR!_wU~n%Nv$6 ztgL6xpJiibXS>L8oVc!Bx^#t0h@0m)2}ufE7dU<-#bv~A9_Po3_wFe^HqzA4G;*}C za109w%g!D=@&C1ff8SXypF1;suH)1RQI?aJPn^1Z;@=Mzg=6KMI$p>BS+W03$FHZ) 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