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vlad-nnvlad-nn
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dgstream meta refactoring (#58)
* dgstreams examples refactoring (#1) * dgstreams examples refactoring (#2) * Unit tests update
1 parent a4ee223 commit c1288f9

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-256
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‎examples/dgstreams/dgstreams_demo.ipynb

+46-67
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@@ -24,7 +24,7 @@
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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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"execution_count": 13,
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"id": "0b76d324",
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"metadata": {},
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"outputs": [],
@@ -34,9 +34,7 @@
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"# URL of RTSP stream\n",
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"# URL of YouTube Video\n",
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"# path to video file (mp4 etc)\n",
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"video_source = (\n",
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" \"https://raw.githubusercontent.com/DeGirum/PySDKExamples/main/images/example_video.mp4\"\n",
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")"
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"video_source = \"https://raw.githubusercontent.com/DeGirum/PySDKExamples/main/images/example_video.mp4\""
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]
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},
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{
@@ -61,9 +59,7 @@
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"\n",
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"# create gizmos:\n",
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"source = VideoSourceGizmo(video_source) # video source gizmo\n",
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"display = VideoDisplayGizmo(\n",
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" \"press `x` or `q` to stop\", allow_drop=False\n",
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") # video display gizmo\n",
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"display = VideoDisplayGizmo(\"`q` to exit\", allow_drop=False) # video display gizmo\n",
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"\n",
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"# Create pipeline: connect display input to source output\n",
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"display.connect_to(source)\n",
@@ -95,14 +91,10 @@
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"source": [
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"from degirum_tools.streams import *\n",
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"\n",
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"c = Composition()\n",
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"\n",
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"# Create gizmos and pipeline as a single-liner:\n",
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"# we use __call__() operator of Composition class instead of add() method\n",
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"# we construct composition passing gizmo pipeline as a parameter\n",
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"# and we use `>>` operator of gizmo classes instead of connect_to() method\n",
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"c(VideoSourceGizmo(video_source)) >> c(VideoDisplayGizmo())\n",
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"\n",
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"c.start()"
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"Composition(VideoSourceGizmo(video_source) >> VideoDisplayGizmo()).start()"
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]
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},
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{
@@ -124,23 +116,16 @@
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"source": [
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"from degirum_tools.streams import *\n",
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"\n",
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"c = Composition()\n",
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"\n",
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"# create and add to composition all required gizmos\n",
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"source = c.add(VideoSourceGizmo(video_source)) # video source gizmo\n",
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"display = c.add(\n",
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" VideoDisplayGizmo([\"Original\", \"Resized\"])\n",
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") # two-input display gizmo: will show two windows\n",
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"resizer = c.add(ResizingGizmo(300, 200)) # resizing gizmo\n",
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"\n",
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"# Create pipeline: the image source is fed to a display and to the image resizing gizmo,\n",
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"# which is then fed to another display.\n",
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"\n",
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"display.connect_to(source, 0) # display input 0 is \"Original\"\n",
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"resizer.connect_to(source)\n",
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"display.connect_to(resizer, 1) # display input 1 is \"Resized\"\n",
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"\n",
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"c.start()"
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"source = VideoSourceGizmo(video_source) # video source gizmo\n",
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"display = VideoDisplayGizmo([\"Original\", \"Resized\"]) # two-input display gizmo\n",
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"resizer = ResizingGizmo(300, 200) # resizing gizmo\n",
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"\n",
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"# Create pipeline: the image source is connected to a display input 0. \n",
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"# Also it is connected to the image resizing gizmo which is then connected to display input 1.\n",
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"# Both pipelines are passed to the Composition object constructor.\n",
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"# Note, how `[]` operator is used to select the input of display gizmo to connect to.\n",
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"Composition(source >> display[0], source >> resizer >> display[1]).start()"
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]
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},
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{
@@ -162,16 +147,13 @@
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"source": [
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"from degirum_tools.streams import *\n",
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"\n",
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"c = Composition()\n",
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"\n",
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"source = c.add(VideoSourceGizmo(video_source))\n",
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"display = c.add(VideoDisplayGizmo())\n",
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"saver = c.add(VideoSaverGizmo(\"temp/mycapture.mp4\"))\n",
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"\n",
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"source >> display\n",
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"source >> saver\n",
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"# create gizmos\n",
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"source = VideoSourceGizmo(video_source)\n",
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"display = VideoDisplayGizmo()\n",
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"saver = VideoSaverGizmo(\"temp/mycapture.mp4\")\n",
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"\n",
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"c.start()"
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"# create pipeline and composition, then start it\n",
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"Composition(source >> display, source >> saver).start()"
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]
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},
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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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"execution_count": 18,
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"id": "193a0c25",
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"metadata": {},
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"outputs": [],
@@ -228,25 +210,20 @@
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"from degirum_tools.streams import *\n",
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"\n",
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"# load some object detection AI model\n",
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"model=dg.load_model(\n",
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"model = 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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")\n",
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"c = Composition()\n",
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"\n",
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"# create gizmos\n",
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"source = c.add(VideoSourceGizmo(video_source)) # video source\n",
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"detection = c.add(AiSimpleGizmo(model)) # AI model\n",
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"display = c.add(\n",
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" VideoDisplayGizmo(\"Detection\", show_ai_overlay=True, show_fps=True)\n",
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") # display\n",
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"source = VideoSourceGizmo(video_source) # video source\n",
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"detection = AiSimpleGizmo(model) # AI model\n",
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"display = VideoDisplayGizmo(\"Detection\", show_ai_overlay=True, show_fps=True) # display\n",
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"\n",
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"# create pipeline\n",
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"source >> detection >> display\n",
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"\n",
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"c.start()"
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"# create pipeline and composition, then start it\n",
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"Composition(source >> detection >> display).start()"
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]
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},
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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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"execution_count": 20,
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"id": "2d31645f",
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"metadata": {},
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"outputs": [],
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"from degirum_tools.streams import *\n",
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"\n",
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"# load some object detection AI model\n",
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"model=dg.load_model(\n",
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"model = 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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")\n",
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"\n",
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"c = Composition()\n",
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"\n",
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"# create gizmos\n",
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"source = c.add(VideoSourceGizmo(video_source)) # video source\n",
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"preprocessor = c.add(AiPreprocessGizmo(model))\n",
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"detection = c.add(AiSimpleGizmo(model))\n",
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"display = c.add(\n",
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" VideoDisplayGizmo(\"Objects\", show_ai_overlay=True, show_fps=True)\n",
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") # display\n",
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"\n",
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"# create pipeline\n",
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"source >> preprocessor >> detection >> display\n",
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"source = VideoSourceGizmo(video_source) # video source\n",
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"preprocessor = AiPreprocessGizmo(model) # AI model preprocessor\n",
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"detection = AiSimpleGizmo(model) # AI model\n",
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"display = VideoDisplayGizmo(\"Objects\", show_ai_overlay=True, show_fps=True) # display\n",
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"\n",
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"c.start()"
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"# create pipeline and composition, then start it\n",
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"Composition(source >> preprocessor >> detection >> display).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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"id": "596abdcf",
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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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},
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"nbformat": 4,

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