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from PIL import Image
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
from models.tools.draw import add_bboxes
class YoloModel:
def __init__(self, repo_name: str, file_name: str):
weight_file = YoloModel.download_weight_file(repo_name, file_name)
self.model = YOLO(weight_file)
@staticmethod
def download_weight_file(repo_name: str, file_name: str):
return hf_hub_download(repo_name, file_name)
def detect(self, im):
return self.model(source=im)
def preview_detect(self, filename, confidence):
image = Image.open(filename)
results = self.model(source=image)
res_img = image
for result in results:
res_img = add_bboxes(res_img, result, confidence)
return res_img
def test():
model = YoloModel("SHOU-ISD/fire-and-smoke", "yolov8n.pt")
im = Image.open("./tests/fire1.jpg")
results = model.model(source=im)
for result in results:
im = add_bboxes(im, result, confidence=0.1)
print(result.boxes)
def argument_parser():
"""
Argument Parser
:return: args
"""
import argparse
parser = argparse.ArgumentParser(description='Help for YoloModel')
parser.add_argument('--test', '-t', action='store_true', help='Run test')
# list of repo_name&file_name
parser.add_argument('--weight_files', '-w', nargs='+', help='List of weight files')
return parser.parse_args()
def pre_cache_weight_files(weight_files: list[str]):
"""
Pre-cache weight files
:return: None
"""
for weight_file in weight_files:
weight_file = weight_file.split(":")
YoloModel.download_weight_file(weight_file[0], weight_file[1])
if __name__ == '__main__':
args = argument_parser()
if args.test:
test()
else:
pre_cache_weight_files(args.weight_files)
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