init

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+.DS_Store
+yolov5s_infer/
+framers/
+poetry.lock
+yolov5s.trt

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/image-identification.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Poetry (image-identification)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/image-identification.iml" filepath="$PROJECT_DIR$/.idea/image-identification.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+  "[python]": {
+    "editor.defaultFormatter": "ms-python.black-formatter"
+  },
+  "python.formatting.provider": "none"
+}

image_identification/__main__.py

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+import asyncio
+from .main import run_app, analyze_video
+
+# run_app()
+asyncio.run(analyze_video())

image_identification/controllers/image.py

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+import asyncio
+import io
+import json
+import os
+import pathlib
+import time
+
+import aiohttp
+import cv2
+import fastdeploy as fd
+import numpy as np
+from fastapi import APIRouter, HTTPException, UploadFile, WebSocket, WebSocketDisconnect
+from fastapi.responses import StreamingResponse
+from fastdeploy.serving.utils import cv2_to_base64
+
+from image_identification.model import model_instance
+
+router = APIRouter(prefix="/image")
+
+
+async def request(url: str, data, headers):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url=url, data=data, headers=headers) as response:
+            return await response.json()
+
+
+# @router.post("/get_visuallized_image", tags=["analyze"])
+# async def getVisualized_image(image: UploadFile):
+#     contents = await image.read()
+#     nparr = np.fromstring(contents, np.uint8)
+#     im = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+#     url = "http://127.0.0.1:8000/fd/yolov5s"
+#     headers = {"Content-Type": "application/json"}
+#     data = {"data": {"image": cv2_to_base64(im)}, "parameters": {}}
+#     try:
+#         resp = await asyncio.gather(
+#             *[request(url=url, data=json.dumps(data), headers=headers)]
+#         )
+#         r_json = json.loads(resp[0]["result"])
+#         det_result = fd.vision.utils.json_to_detection(r_json)
+#         vis_im = fd.vision.vis_detection(im, det_result, score_threshold=0.5)
+#         _res, im_jpg = cv2.imencode(".jpg", vis_im)
+#         return StreamingResponse(io.BytesIO(im_jpg.tobytes()), media_type="image/jpeg")
+#     except:
+#         raise HTTPException(status_code=500, detail="获取失败")
+
+
+@router.post("/analyze", tags=["analyze"], summary="summary", description="description")
+async def analyze(image: UploadFile):
+    image_content = await image.read()
+    nparr = np.fromstring(image_content, np.uint8)
+    im = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    result = model_instance.predict(im)
+    vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
+    im_jpg = cv2.imencode(".jpg", vis_im)[1]
+    return StreamingResponse(io.BytesIO(im_jpg.tobytes()), media_type="image/jpeg")
+
+
+@router.websocket("/analyze_video")
+async def analyze_video(websocket: WebSocket):
+    rtmp_url = "rtmp://localhost/live/livestream"
+    cap = cv2.VideoCapture(rtmp_url)
+    frame_number = 0
+    frame_rate = 30
+
+    await websocket.accept()
+    fourcc = cv2.VideoWriter.fourcc("M", "P", "4", "V")
+    # os.mkdir("framers")
+    print(cap.get(3))
+    print(cap.get(4))
+    os.chdir("framers")
+    video = None
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                print("无法读取视频帧")
+                break
+            # 是否保存帧
+            if True:
+                # if frame_number % 4 == 0:
+                # 处理每一帧的操作，这里只是保存为图像文件
+
+                # cv2.imwrite(frame_filename, frame)
+                result = model_instance.predict(frame)
+                vis_im = fd.vision.vis_detection(frame, result, score_threshold=0.5)
+                # cv2.imwrite(frame_filename, vis_im)
+                if video is None:
+                    video = cv2.VideoWriter(
+                        "__output__.mp4",
+                        fourcc,
+                        30.0,
+                        (vis_im.shape[1], vis_im.shape[0]),
+                    )
+                video.write(vis_im)
+                print(vis_im.shape)
+                await websocket.send_text(fd.vision.fd_result_to_json(result=result))
+                # await websocket.send_text(frame_filename)
+            else:
+                print(frame.shape)
+                video.write(frame)
+            frame_number += 1
+    except WebSocketDisconnect:
+        print("close")
+        await websocket.send_text("error")
+        await websocket.close()

image_identification/main.py

@@ -0,0 +1,73 @@
+import logging
+import os
+import time
+import cv2
+import uvicorn
+from fastdeploy.serving.server import SimpleServer
+import fastdeploy as fd
+from .controllers import image
+from .model import model_instance
+
+logging.getLogger().setLevel(logging.INFO)
+
+# Create server, setup REST API
+app = SimpleServer()
+app.register(
+    task_name="fd/yolov5s",
+    model_handler=fd.serving.handler.VisionModelHandler,
+    predictor=model_instance,
+)
+
+app.include_router(image.router)
+
+
+def run_app():
+    uvicorn.run("image_identification.main:app", host="0.0.0.0", port=8000, reload=True)
+
+
+async def analyze_video():
+    rtmp_url = "rtmp://localhost/live/livestream"
+    cap = cv2.VideoCapture(rtmp_url)
+    frame_number = 0
+    frame_rate = 30
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    print(fps)
+    fourcc = cv2.VideoWriter.fourcc("M", "P", "4", "V")
+    # os.mkdir("framers")
+
+    os.chdir("framers")
+    video = None
+    while True:
+        start = time.perf_counter()
+        ret, frame = cap.read()
+        if not ret:
+            print("无法读取视频帧")
+            break
+        # 是否保存帧
+        if True:
+            # if frame_number % 4 == 0:
+            # 处理每一帧的操作，这里只是保存为图像文件
+
+            result = model_instance.predict(frame)
+            print(fd.vision.fd_result_to_json(result=result))
+            print(f"第{frame_number}帧")
+            vis_im = fd.vision.vis_detection(frame, result, score_threshold=0.5)
+
+            if video is None:
+                video = cv2.VideoWriter(
+                    "__output__.mp4",
+                    fourcc,
+                    fps,
+                    (vis_im.shape[1], vis_im.shape[0]),
+                )
+            video.write(vis_im)
+        else:
+            video.write(frame)
+        end = time.perf_counter()
+        cost = end - start
+        print(f"spend time: {cost}\n\n")
+        frame_number += 1
+
+
+if __name__ == "__main__":
+    run_app()

image_identification/model/__init__.py

@@ -0,0 +1 @@
+from .model import model_instance

image_identification/model/model.py

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+import os
+
+import fastdeploy as fd
+
+# Configurations
+model_dir = "yolov5s_infer"
+device = "gpu"
+use_trt = True
+
+# Prepare model
+model_file = os.path.join(model_dir, "model.pdmodel")
+params_file = os.path.join(model_dir, "model.pdiparams")
+
+# Setup runtime option to select hardware, backend, etc.
+option = fd.RuntimeOption()
+if device.lower() == "gpu":
+    option.use_gpu()
+if use_trt:
+    option.use_trt_backend()
+    option.set_trt_input_shape("images", [1, 3, 640, 640])
+    option.set_trt_cache_file("yolov5s.trt")
+
+# Create model instance
+model_instance = fd.vision.detection.YOLOv5(
+    model_file, params_file, runtime_option=option, model_format=fd.ModelFormat.PADDLE
+)

image_identification/predict.py

@@ -0,0 +1,26 @@
+import os
+
+import fastdeploy as fd
+
+# Configurations
+model_dir = "yolov5s_infer"
+device = "gpu"
+use_trt = True
+
+# Prepare model
+model_file = os.path.join(model_dir, "model.pdmodel")
+params_file = os.path.join(model_dir, "model.pdiparams")
+
+# Setup runtime option to select hardware, backend, etc.
+option = fd.RuntimeOption()
+if device.lower() == "gpu":
+    option.use_gpu()
+if use_trt:
+    option.use_trt_backend()
+    option.set_trt_input_shape("images", [1, 3, 640, 640])
+    option.set_trt_cache_file("yolov5s.trt")
+
+# Create model instance
+model_instance = fd.vision.detection.YOLOv5(
+    model_file, params_file, runtime_option=option, model_format=fd.ModelFormat.PADDLE
+)

pyproject.toml

@@ -0,0 +1,29 @@
+[tool.poetry]
+name = "image-identification"
+version = "0.1.0"
+description = ""
+authors = ["quantulr <35954003+quantulr@users.noreply.github.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.13"
+fastdeploy-gpu-python = { url = "https://bj.bcebos.com/fastdeploy/release/wheels/fastdeploy_gpu_python-1.0.7-cp310-cp310-manylinux1_x86_64.whl" }
+numpy = "^1.26.0"
+opencv-python = "^4.8.0.76"
+python-multipart = "^0.0.6"
+aiohttp = "^3.8.5"
+websockets = "^11.0.3"
+setuptools = "^68.2.2"
+
+
+[[tool.poetry.source]]
+name = "mirrors"
+url = "https://pypi.tuna.tsinghua.edu.cn/simple/"
+priority = "default"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.scripts]
+serve = "image_identification.main:run_app"