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dgstreams examples refactoring (#2)
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‎examples/dgstreams/multi_camera_multi_model_detection.ipynb

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@@ -28,26 +28,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Name: degirum_tools\n",
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"Version: 0.10.1\n",
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"Summary: Tools for PySDK\n",
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"Home-page: \n",
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"Author: DeGirum\n",
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"Author-email: \n",
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"License: \n",
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"Location: c:\\users\\shashichilappagari\\anaconda3\\envs\\supervision\\lib\\site-packages\n",
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"Requires: degirum, ipython, numpy, opencv-python, pafy, pillow, psutil, pycocotools, python-dotenv, pyyaml, requests, scipy, youtube-dl\n",
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"Required-by: \n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# make sure degirum-tools package is installed\n",
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"!pip show degirum-tools || pip install degirum-tools"
@@ -63,7 +46,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -81,167 +64,115 @@
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"# '': ai server serving models from local folder\n",
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"# path to json file: single model zoo in case of @local inference\n",
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"# model_names: list of AI models to use for inferences (NOTE: they should have the same input size)\n",
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"# allow_frame_drop:\n",
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"# when True, we drop video frames in case when AI performance is not enough to work in real time\n",
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"# when False, we buffer video frames to keep up with AI performance\n",
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"hw_location = \"@cloud\"\n",
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"video_sources = [\n",
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" \"https://raw.githubusercontent.com/DeGirum/PySDKExamples/main/images/WalkingPeople.mp4\",\n",
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" \"https://raw.githubusercontent.com/DeGirum/PySDKExamples/main/images/Traffic.mp4\",\n",
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" \"https://raw.githubusercontent.com/DeGirum/PySDKExamples/main/images/TrafficHD.mp4\",\n",
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"]\n",
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"model_zoo_url = \"degirum/public\"\n",
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"model_names = [\n",
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" \"yolo_v5s_hand_det--512x512_quant_n2x_orca1_1\",\n",
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" \"yolo_v5s_face_det--512x512_quant_n2x_orca1_1\",\n",
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" \"yolo_v5n_car_det--512x512_quant_n2x_orca1_1\",\n",
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" \"yolo_v5s_person_det--512x512_quant_n2x_orca1_1\",\n",
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"]\n",
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"allow_frame_drop = False"
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Specify where do you want to run your inferences"
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"#### The rest of the cells below should run without any modifications"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
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"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
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"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
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"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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]
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}
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],
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"source": [
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"import degirum as dg, degirum_tools"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Successfully opened video stream 'https://raw.githubusercontent.com/DeGirum/PySDKExamples/main/images/Traffic.mp4'Successfully opened video stream 'https://raw.githubusercontent.com/DeGirum/PySDKExamples/main/images/TrafficHD.mp4'\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"packet queue is empty, aborting\n",
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"packet queue is empty, aborting\n",
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"packet queue is empty, aborting\n",
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"packet queue is empty, aborting\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import degirum as dg, degirum_tools\n",
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"from degirum_tools import streams as dgstreams\n",
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"\n",
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"c = dgstreams.Composition()\n",
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"\n",
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"batch_size = len(\n",
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" video_sources\n",
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") # set AI server batch size equal to the # of video sources for lowest latency\n",
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"\n",
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"# create PySDK AI model objects\n",
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"models = []\n",
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"for mi, model_name in enumerate(model_names):\n",
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" model = dg.load_model(\n",
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"models = [\n",
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" dg.load_model(\n",
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" model_name=model_name,\n",
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" inference_host_address=hw_location,\n",
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" zoo_url=model_zoo_url,\n",
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" token=degirum_tools.get_token(),\n",
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" overlay_line_width=2,\n",
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" )\n",
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" model.measure_time = True\n",
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" model.eager_batch_size = batch_size\n",
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" model.frame_queue_depth = batch_size\n",
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" models.append(model)\n",
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" for model_name in model_names\n",
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"]\n",
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"\n",
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"# check that all models have the same input configuration\n",
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"models_have_same_input = True\n",
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"for model in models[1:]:\n",
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" if (\n",
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" type(model._preprocessor) != type(models[0]._preprocessor)\n",
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" or model.model_info.InputH != models[0].model_info.InputH\n",
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" or model.model_info.InputW != models[0].model_info.InputW\n",
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" ):\n",
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" models_have_same_input = False\n",
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"\n",
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"resizers = []\n",
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"\n",
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"# create video sources and image resizers\n",
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"# (we use separate resizers to do resize only once per source when possible, to improve performance),\n",
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"# connect each resizer to corresponding video source\n",
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"for src in video_sources:\n",
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" source = c.add(dgstreams.VideoSourceGizmo(src))\n",
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" if models_have_same_input:\n",
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" resizer = c.add(\n",
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" dgstreams.AiPreprocessGizmo(\n",
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" models[0], stream_depth=2, allow_drop=allow_frame_drop\n",
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" )\n",
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" )\n",
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" else:\n",
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" resizer = c.add(dgstreams.FanoutGizmo(allow_drop=allow_frame_drop))\n",
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"assert all(\n",
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" type(model._preprocessor) == type(models[0]._preprocessor)\n",
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" and model.model_info.InputH == models[0].model_info.InputH\n",
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" and model.model_info.InputW == models[0].model_info.InputW\n",
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" for model in models[1:]\n",
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")\n",
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"\n",
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" resizer.connect_to(source) # connect resizer to video source\n",
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" resizers.append(resizer)\n",
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"# create video source gizmos;\n",
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"# stop_composition_on_end=True to stop whole composition when one (shorter) video source ends\n",
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"sources = [\n",
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" dgstreams.VideoSourceGizmo(src, stop_composition_on_end=True)\n",
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" for src in video_sources\n",
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"]\n",
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"\n",
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"# create result combiner\n",
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"combiner = c.add(dgstreams.AiResultCombiningGizmo(len(models)))\n",
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"# create image resizer gizmos, one per video source\n",
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"# (we use separate resizers to do resize only once per source to improve performance)\n",
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"resizers = [dgstreams.AiPreprocessGizmo(models[0]) for _ in video_sources]\n",
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"\n",
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"# create multi-input detector gizmos,\n",
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"# connect each detector gizmo to every resizer gizmo,\n",
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"# connect result combiner gizmo to each detector gizmo\n",
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"for mi, model in enumerate(models):\n",
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" # create AI gizmo (aka detector) from the model\n",
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" detector = c.add(\n",
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" dgstreams.AiSimpleGizmo(model, stream_depth=2, inp_cnt=len(video_sources))\n",
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" )\n",
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"\n",
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" # connect detector gizmo to each resizer gizmo\n",
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" for fi, resizer in enumerate(resizers):\n",
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" detector.connect_to(resizer, fi)\n",
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"# create multi-input detector gizmos, one per model\n",
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"detectors = [\n",
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" dgstreams.AiSimpleGizmo(model, inp_cnt=len(video_sources)) for model in models\n",
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"]\n",
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"\n",
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" # connect result combiner gizmo to detector gizmo\n",
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" combiner.connect_to(detector, mi)\n",
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"# create result combiner gizmo to combine results from all detectors into single result\n",
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"combiner = dgstreams.AiResultCombiningGizmo(len(models))\n",
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"\n",
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"# create multi-window video multiplexing display gizmo\n",
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"# and connect it to combiner gizmo\n",
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"win_captions = [f\"Stream #{i}: {str(src)}\" for i, src in enumerate(video_sources)]\n",
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"display = c.add(\n",
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" dgstreams.VideoDisplayGizmo(\n",
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" win_captions, show_ai_overlay=True, show_fps=True, multiplex=True\n",
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" )\n",
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"display = dgstreams.VideoDisplayGizmo(\n",
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" win_captions, show_ai_overlay=True, show_fps=True, multiplex=True\n",
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")\n",
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"display.connect_to(combiner)\n",
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"\n",
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"# start composition\n",
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"c.start()"
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"# connect all gizmos in the pipeline\n",
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"# source[i] -> resizer[i] -> detector[j] -> combiner -> display\n",
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"pipeline = (\n",
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" # each source is connected to corresponding resizer\n",
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" (source >> resizer for source, resizer in zip(sources, resizers)),\n",
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" # each resizer is connected to every detector\n",
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" (\n",
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" resizer >> detector[ri]\n",
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" for detector in detectors\n",
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" for ri, resizer in enumerate(resizers)\n",
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" ),\n",
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" # each detector is connected to result combiner\n",
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" (detector >> combiner[di] for di, detector in enumerate(detectors)),\n",
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" # result combiner is connected to display\n",
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" combiner >> display,\n",
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")\n",
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"\n",
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"# create and start composition with given pipeline\n",
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"dgstreams.Composition(*pipeline).start()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python (supervision)",
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"display_name": "base",
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"language": "python",
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"name": "supervision"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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"version": "3.9.16"
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},
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"orig_nbformat": 4
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},

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