This code is based on the YOLOv5 from Ultralytics and it has all the functionalities that the original code has:
- Different source: images, videos, webcam, RTSP cameras.
- All the weights are supported: TensorRT, Onnx, DNN, openvino.
The API can be called in an interactive way, and also as a single API called from terminal.
Clone repository with all the submodules:
git clone --recursive https://github.com/hdnh2006/YOLOv5API.gitOr if you have already cloned the repository without the submodule content, you can fetch it with:
git submodule update --initPython 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:
$ pip3 install -r requirements.txtdetect_api.py can deal with several sources and can run into the cpu, but it is highly recommendable to run in gpu.
Usage - sources:
$ python detect_api.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python detect_api.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS-only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s_paddle_model # PaddlePaddlesegment_api.py can deal with several sources and can run into the cpu, but it is highly recommendable to run in gpu.
Usage - sources:
$ python segment_api.py --weights yolov5s-seg.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python segment_api.py --weights yolov5s-seg.pt # PyTorch
yolov5s-seg.torchscript # TorchScript
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s-seg_openvino_model # OpenVINO
yolov5s-seg.engine # TensorRT
yolov5s-seg.mlmodel # CoreML (macOS-only)
yolov5s-seg_saved_model # TensorFlow SavedModel
yolov5s-seg.pb # TensorFlow GraphDef
yolov5s-seg.tflite # TensorFlow Lite
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
yolov5s-seg_paddle_model # PaddlePaddleclassify_api.py can deal with several sources and can run into the cpu, but it is highly recommendable to run in gpu.
Usage - sources:
$ python classify_api.py --weights yolov5s-seg.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python classify_api.py --weights yolov5s-seg.pt # PyTorch
yolov5s-seg.torchscript # TorchScript
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s-seg_openvino_model # OpenVINO
yolov5s-seg.engine # TensorRT
yolov5s-seg.mlmodel # CoreML (macOS-only)
yolov5s-seg_saved_model # TensorFlow SavedModel
yolov5s-seg.pb # TensorFlow GraphDef
yolov5s-seg.tflite # TensorFlow Lite
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
yolov5s-seg_paddle_model # PaddlePaddleYou can deploy the API able to label an interactive way.
Run:
$ python detect_api.py --device cpu # to run into cpu (by default is gpu)Open the application in any browser 0.0.0.0:5000 and upload your image or video as is shown in video above.
Just open your favorite browser and go to 0.0.0.0:5000 and intuitevely load the image you want to label and press the buttom "Upload image".
The API will return the image or video labeled.
The client.py code provides several example about how the API can be called. A very common way to do it is to call a public image from url and to get the coordinates of the bounding boxes:
import requests
resp = requests.get("http://0.0.0.0:5000/detect?url=https://atlassafetysolutions.com/wp/wp-content/uploads/2019/06/ppe.jpeg&save_txt=T",
verify=False)
print(resp.content)And you will get a json with the following data:
b'{"results": [{"class": 72, "x": 0.647187, "y": 0.495779, "w": 0.421875, "h": 0.991557, "conf": null}, {"class": 0, "x": 0.371563, "y": 0.497655, "w": 0.525625, "h": 0.982176, "conf": null}]}'
This code is based on the YOLOv5 from Ultralytics and it has been modified by Henry Navarro
If you want to know more about me, please visit my blog: henrynavarro.org.