yolov5实现人群计数

资源文件列表(大概)

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yolov5/

yolov5/.dockerignore

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yolov5/.git/

yolov5/.git/branches/

yolov5/.git/config

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yolov5/.git/description

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yolov5/.git/FETCH_HEAD

yolov5/.git/HEAD

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yolov5/.git/hooks/

yolov5/.git/hooks/applypatch-msg.sample

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yolov5/.git/hooks/commit-msg.sample

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yolov5/.git/hooks/fsmonitor-watchman.sample

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yolov5/.git/hooks/post-update.sample

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yolov5/.git/hooks/pre-applypatch.sample

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yolov5/.git/hooks/pre-merge-commit.sample

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yolov5/.git/hooks/pre-push.sample

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yolov5/.git/hooks/pre-rebase.sample

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yolov5/.git/hooks/pre-receive.sample

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yolov5/.git/hooks/prepare-commit-msg.sample

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yolov5/.git/hooks/push-to-checkout.sample

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yolov5/.git/hooks/update.sample

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yolov5/.git/index

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yolov5/.git/info/

yolov5/.git/info/exclude

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yolov5/.git/logs/

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yolov5/.git/logs/refs/

yolov5/.git/logs/refs/heads/

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yolov5/.git/logs/refs/remotes/

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yolov5/.git/objects/

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yolov5/.git/objects/93/

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yolov5/.git/objects/9f/

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yolov5/.git/objects/c5/

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yolov5/.git/objects/d3/

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yolov5/.git/objects/e2/

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yolov5/.git/objects/info/

yolov5/.git/objects/pack/

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yolov5/.git/objects/pack/pack-3362ae32d143657d388225798105f5f0a60d9ada.pack

15.73MB

yolov5/.git/packed-refs

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yolov5/.git/refs/

yolov5/.git/refs/heads/

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yolov5/.git/refs/remotes/

yolov5/.git/refs/remotes/origin/

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yolov5/.git/refs/remotes/origin/master

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yolov5/.git/refs/tags/

yolov5/.gitattributes

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yolov5/.github/

yolov5/.github/dependabot.yml

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yolov5/.github/ISSUE_TEMPLATE/

yolov5/.github/ISSUE_TEMPLATE/bug-report.yml

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yolov5/.github/ISSUE_TEMPLATE/config.yml

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yolov5/.github/ISSUE_TEMPLATE/feature-request.yml

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yolov5/.github/ISSUE_TEMPLATE/question.yml

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yolov5/.github/workflows/

yolov5/.github/workflows/ci-testing.yml

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yolov5/.github/workflows/cla.yml

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yolov5/.github/workflows/codeql-analysis.yml

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yolov5/.github/workflows/docker.yml

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yolov5/.github/workflows/format.yml

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yolov5/.github/workflows/links.yml

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yolov5/.github/workflows/merge-main-into-prs.yml

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yolov5/.github/workflows/stale.yml

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yolov5/.gitignore

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yolov5/benchmarks.py

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yolov5/CITATION.cff

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yolov5/classify/

yolov5/classify/predict.py

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yolov5/classify/train.py

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yolov5/classify/tutorial.ipynb

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yolov5/classify/val.py

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yolov5/CONTRIBUTING.md

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yolov5/data/

yolov5/data/Argoverse.yaml

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yolov5/data/coco.yaml

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yolov5/data/coco128-seg.yaml

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yolov5/data/coco128.yaml

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yolov5/data/GlobalWheat2020.yaml

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yolov5/data/hyps/

yolov5/data/hyps/hyp.no-augmentation.yaml

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yolov5/data/hyps/hyp.Objects365.yaml

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yolov5/data/hyps/hyp.scratch-high.yaml

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yolov5/data/hyps/hyp.scratch-low.yaml

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yolov5/data/hyps/hyp.scratch-med.yaml

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yolov5/data/hyps/hyp.VOC.yaml

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yolov5/data/ImageNet.yaml

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yolov5/data/ImageNet10.yaml

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yolov5/data/ImageNet100.yaml

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yolov5/data/ImageNet1000.yaml

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yolov5/data/images/

yolov5/data/images/bus.jpg

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yolov5/data/images/zidane.jpg

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yolov5/data/Objects365.yaml

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yolov5/data/scripts/

yolov5/data/scripts/download_weights.sh

641B

yolov5/data/scripts/get_coco.sh

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yolov5/data/scripts/get_coco128.sh

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yolov5/data/scripts/get_imagenet.sh

1.63KB

yolov5/data/scripts/get_imagenet10.sh

734B

yolov5/data/scripts/get_imagenet100.sh

738B

yolov5/data/scripts/get_imagenet1000.sh

742B

yolov5/data/SKU-110K.yaml

2.28KB

yolov5/data/VisDrone.yaml

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yolov5/data/VOC.yaml

3.41KB

yolov5/data/xView.yaml

5.04KB

yolov5/export.py

66.14KB

yolov5/hubconf.py

23.41KB

yolov5/LICENSE

33.71KB

yolov5/models/

yolov5/models/common.py

50.89KB

yolov5/models/experimental.py

5.03KB

yolov5/models/hub/

yolov5/models/hub/anchors.yaml

3.26KB

yolov5/models/hub/yolov3-spp.yaml

1.55KB

yolov5/models/hub/yolov3-tiny.yaml

1.22KB

yolov5/models/hub/yolov3.yaml

1.54KB

yolov5/models/hub/yolov5-bifpn.yaml

1.41KB

yolov5/models/hub/yolov5-fpn.yaml

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yolov5/models/hub/yolov5-p2.yaml

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yolov5/models/hub/yolov5-p34.yaml

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yolov5/models/hub/yolov5-p6.yaml

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yolov5/models/hub/yolov5-p7.yaml

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yolov5/models/hub/yolov5-panet.yaml

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yolov5/models/hub/yolov5l6.yaml

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yolov5/models/hub/yolov5m6.yaml

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yolov5/models/hub/yolov5n6.yaml

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yolov5/models/hub/yolov5s-ghost.yaml

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yolov5/models/hub/yolov5s-LeakyReLU.yaml

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yolov5/models/hub/yolov5s-transformer.yaml

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yolov5/models/hub/yolov5s6.yaml

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yolov5/models/hub/yolov5x6.yaml

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yolov5/models/segment/

yolov5/models/segment/yolov5l-seg.yaml

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yolov5/models/segment/yolov5m-seg.yaml

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yolov5/models/segment/yolov5n-seg.yaml

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yolov5/models/segment/yolov5s-seg.yaml

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yolov5/models/segment/yolov5x-seg.yaml

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yolov5/models/tf.py

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yolov5/models/yolo.py

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yolov5/models/yolov5l.yaml

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yolov5/models/yolov5m.yaml

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yolov5/models/yolov5n.yaml

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yolov5/models/yolov5s.yaml

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yolov5/models/yolov5x.yaml

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yolov5/models/__init__.py

yolov5/models/__pycache__/

yolov5/models/__pycache__/common.cpython-38.pyc

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yolov5/models/__pycache__/experimental.cpython-38.pyc

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yolov5/models/__pycache__/yolo.cpython-38.pyc

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yolov5/models/__pycache__/__init__.cpython-38.pyc

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yolov5/person_count.py

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yolov5/pyproject.toml

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yolov5/README.md

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yolov5/README.zh-CN.md

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yolov5/requirements.txt

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yolov5/runs/

yolov5/segment/

yolov5/segment/predict.py

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yolov5/segment/train.py

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yolov5/segment/tutorial.ipynb

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yolov5/segment/val.py

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yolov5/test.jpg

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yolov5/train.py

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yolov5/tutorial.ipynb

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yolov5/utils/

yolov5/utils/activations.py

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yolov5/utils/augmentations.py

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yolov5/utils/autoanchor.py

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yolov5/utils/autobatch.py

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yolov5/utils/aws/

yolov5/utils/aws/mime.sh

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yolov5/utils/aws/resume.py

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yolov5/utils/aws/userdata.sh

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yolov5/utils/aws/__init__.py

yolov5/utils/callbacks.py

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yolov5/utils/dataloaders.py

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yolov5/utils/docker/

yolov5/utils/docker/Dockerfile

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yolov5/utils/docker/Dockerfile-arm64

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yolov5/utils/docker/Dockerfile-cpu

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yolov5/utils/downloads.py

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yolov5/utils/flask_rest_api/

yolov5/utils/flask_rest_api/example_request.py

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yolov5/utils/flask_rest_api/README.md

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yolov5/utils/flask_rest_api/restapi.py

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yolov5/utils/general.py

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yolov5/utils/google_app_engine/

yolov5/utils/google_app_engine/additional_requirements.txt

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yolov5/utils/google_app_engine/app.yaml

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yolov5/utils/google_app_engine/Dockerfile

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yolov5/utils/loggers/

yolov5/utils/loggers/clearml/

yolov5/utils/loggers/clearml/clearml_utils.py

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yolov5/utils/loggers/clearml/hpo.py

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yolov5/utils/loggers/clearml/README.md

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yolov5/utils/loggers/clearml/__init__.py

yolov5/utils/loggers/comet/

yolov5/utils/loggers/comet/comet_utils.py

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yolov5/utils/loggers/comet/hpo.py

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yolov5/utils/loggers/comet/optimizer_config.json

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yolov5/utils/loggers/comet/README.md

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yolov5/utils/loggers/comet/__init__.py

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yolov5/utils/loggers/wandb/

yolov5/utils/loggers/wandb/wandb_utils.py

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yolov5/utils/loggers/wandb/__init__.py

yolov5/utils/loggers/__init__.py

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yolov5/utils/loss.py

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yolov5/utils/metrics.py

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yolov5/utils/plots.py

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yolov5/utils/segment/

yolov5/utils/segment/augmentations.py

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yolov5/utils/segment/dataloaders.py

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yolov5/utils/segment/general.py

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yolov5/utils/segment/loss.py

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yolov5/utils/segment/metrics.py

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yolov5/utils/segment/plots.py

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yolov5/utils/segment/__init__.py

yolov5/utils/torch_utils.py

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yolov5/utils/triton.py

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yolov5/utils/__init__.py

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yolov5/utils/__pycache__/

yolov5/utils/__pycache__/augmentations.cpython-38.pyc

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yolov5/utils/__pycache__/autoanchor.cpython-38.pyc

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yolov5/utils/__pycache__/dataloaders.cpython-38.pyc

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yolov5/utils/__pycache__/downloads.cpython-38.pyc

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yolov5/utils/__pycache__/general.cpython-38.pyc

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yolov5/utils/__pycache__/metrics.cpython-38.pyc

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yolov5/utils/__pycache__/plots.cpython-38.pyc

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yolov5/utils/__pycache__/torch_utils.cpython-38.pyc

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yolov5/utils/__pycache__/__init__.cpython-38.pyc

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yolov5/val.py

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yolov5/yolov5s.pt

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yolov5/__pycache__/

yolov5/__pycache__/export.cpython-38.pyc

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资源内容介绍

本项目是一个使用 YOLOv5 模型实现的人群计数 Python 应用。YOLOv5 是一个流行的目标检测模型，以其速度快和准确性高而闻名。通过这个项目，你可以快速部署一个能够识别图像中人数的系统。功能特点：高精度人群计数：利用 YOLOv5 模型的高效目标检测能力，实现对人群的精确计数。实时图像处理：支持从摄像头或视频文件中实时读取图像，并进行人群计数。易于集成：代码结构清晰，易于与其他系统或应用集成。跨平台支持：兼容主流操作系统，包括 Windows、Linux 和 macOS。技术栈：Python：编程语言。YOLOv5：目标检测模型。OpenCV：用于图像处理和显示。

<div align="center"> <p> <a href="https://www.ultralytics.com/events/yolovision" target="_blank"> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a> </p>[涓枃](https://docs.ultralytics.com/zh) | [頃滉淡鞏碷(https://docs.ultralytics.com/ko) | [鏃ユ湰瑾瀅(https://docs.ultralytics.com/ja) | [袪褍褋褋泻懈泄](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Fran莽ais](https://docs.ultralytics.com/fr) | [Espa帽ol](https://docs.ultralytics.com/es) | [Portugu锚s](https://docs.ultralytics.com/pt) | [T眉rk莽e](https://docs.ultralytics.com/tr) | [Ti岷縩g Vi峄噒](https://docs.ultralytics.com/vi) | [丕賱毓乇亘賷丞](https://docs.ultralytics.com/ar)<div> <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a> <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a> <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> <a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a> <br> <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> </div> <br>YOLOv5 馃殌 is the world's most loved vision AI, representing <a href="https://www.ultralytics.com/">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href="https://docs.ultralytics.com/yolov5/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.com/invite/ultralytics">Discord</a> community for questions and discussions!To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).<div align="center"> <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a></div></div><br>## <div align="center">YOLOv8 馃殌 NEW</div>We are thrilled to announce the launch of Ultralytics YOLOv8 馃殌, our NEW cutting-edge, state-of-the-art (SOTA) model released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.See the [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with:[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)```bashpip install ultralytics```<div align="center"> <a href="https://www.ultralytics.com/yolo" target="_blank"> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a></div>## <div align="center">Documentation</div>See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing and deployment. See below for quickstart examples.<details open><summary>Install</summary>Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).```bashgit clone https://github.com/ultralytics/yolov5 # clonecd yolov5pip install -r requirements.txt # install```</details><details><summary>Inference</summary>YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).```pythonimport torch# Modelmodel = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom# Imagesimg = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list# Inferenceresults = model(img)# Resultsresults.print() # or .show(), .save(), .crop(), .pandas(), etc.```</details><details><summary>Inference with detect.py</summary>`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.```bashpython detect.py --weights yolov5s.pt --source 0 # webcam img.jpg # image vid.mp4 # video scre

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