From be67d92bccb0f085a35b4e797c1f5dab97d3e249 Mon Sep 17 00:00:00 2001 From: LinasKo Date: Wed, 3 Jul 2024 10:50:33 +0300 Subject: [PATCH] Updates --- .../{entry.CFEbw8AJ.js => entry.NeV8fPgx.js} | 2 +- _app/immutable/chunks/paths.CHuV8m5c.js | 1 + _app/immutable/chunks/paths.DlIhxnJZ.js | 1 - .../{app.D0JKdPNc.js => app.A3OzJbZe.js} | 4 +- _app/immutable/entry/start.9tDZHVgU.js | 1 - _app/immutable/entry/start.DbUNUlZH.js | 1 + .../nodes/{1.DiDDlYrt.js => 1.CcAuloRU.js} | 2 +- _app/immutable/nodes/2.C0d6rBmj.js | 265 ------------------ _app/immutable/nodes/2.Cskh4VA0.js | 265 ++++++++++++++++++ _app/version.json | 2 +- index.html | 16 +- 11 files changed, 280 insertions(+), 280 deletions(-) rename _app/immutable/chunks/{entry.CFEbw8AJ.js => entry.NeV8fPgx.js} (99%) create mode 100644 _app/immutable/chunks/paths.CHuV8m5c.js delete mode 100644 _app/immutable/chunks/paths.DlIhxnJZ.js rename _app/immutable/entry/{app.D0JKdPNc.js => app.A3OzJbZe.js} (95%) delete mode 100644 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Supervision simplifies the process of working with vision models. It offers connectors to popular model libraries, a plethora of visualizers (annotators), powerful post-processing features and an easy learning curve.

Load image or video » Load the model » Run the model » Annotate';return{c(){e=I("div"),e.innerHTML=t,this.h()},l(s){e=T(s,"DIV",{class:!0,"data-svelte-h":!0}),oe(e)!=="svelte-10ber7h"&&(e.innerHTML=t),this.h()},h(){L(e,"class","text-xs xl:text-sm px-2 pb-2")},m(s,i){R(s,e,i)},p:z,d(s){s&&p(e)}}}function Ms(n){let e,t,s,i,a,r,o,l,c,u;return e=new D({props:{isBash:!0,code:"pip install supervision inference -q"}}),s=new D({props:{isBash:!0,code:"wget https://media.roboflow.com/notebooks/examples/dog.jpeg"}}),a=new D({props:{code:`import cv2 -import supervision as sv -from inference import get_model`}}),o=new D({props:{code:`image = cv2.imread("dog.jpeg") -model = get_model(model_id="yolov8n-640") -results = model.infer(image)[0] -detections = sv.Detections.from_inference(results)`}}),c=new D({props:{code:`annotated_image = sv.BoundingBoxAnnotator().annotate( - scene=image.copy(), detections=detections -) -annotated_image = sv.LabelAnnotator().annotate( - scene=annotated_image, detections=detections -) -sv.plot_image(annotated_image) -`}}),{c(){h(e.$$.fragment),t=y(),h(s.$$.fragment),i=y(),h(a.$$.fragment),r=y(),h(o.$$.fragment),l=y(),h(c.$$.fragment)},l(f){b(e.$$.fragment,f),t=S(f),b(s.$$.fragment,f),i=S(f),b(a.$$.fragment,f),r=S(f),b(o.$$.fragment,f),l=S(f),b(c.$$.fragment,f)},m(f,g){v(e,f,g),R(f,t,g),v(s,f,g),R(f,i,g),v(a,f,g),R(f,r,g),v(o,f,g),R(f,l,g),v(c,f,g),u=!0},p:z,i(f){u||($(e.$$.fragment,f),$(s.$$.fragment,f),$(a.$$.fragment,f),$(o.$$.fragment,f),$(c.$$.fragment,f),u=!0)},o(f){_(e.$$.fragment,f),_(s.$$.fragment,f),_(a.$$.fragment,f),_(o.$$.fragment,f),_(c.$$.fragment,f),u=!1},d(f){f&&(p(t),p(i),p(r),p(l)),E(e,f),E(s,f),E(a,f),E(o,f),E(c,f)}}}function Os(n){let e,t,s,i,a,r,o;return t=new K({props:{header:"Basic Principles",$$slots:{default:[As]},$$scope:{ctx:n}}}),r=new K({props:{header:"Quickstart",$$slots:{default:[Ms]},$$scope:{ctx:n}}}),{c(){e=I("div"),h(t.$$.fragment),s=y(),i=I("div"),a=y(),h(r.$$.fragment),this.h()},l(l){e=T(l,"DIV",{slot:!0});var c=H(e);b(t.$$.fragment,c),s=S(c),i=T(c,"DIV",{class:!0}),H(i).forEach(p),a=S(c),b(r.$$.fragment,c),c.forEach(p),this.h()},h(){L(i,"class","mt-2"),L(e,"slot","col1")},m(l,c){R(l,e,c),v(t,e,null),A(e,s),A(e,i),A(e,a),v(r,e,null),o=!0},p(l,c){const u={};c&1&&(u.$$scope={dirty:c,ctx:l}),t.$set(u);const f={};c&1&&(f.$$scope={dirty:c,ctx:l}),r.$set(f)},i(l){o||($(t.$$.fragment,l),$(r.$$.fragment,l),o=!0)},o(l){_(t.$$.fragment,l),_(r.$$.fragment,l),o=!1},d(l){l&&p(e),E(t),E(r)}}}function Rs(n){let e,t=`
  • sv.Detections: Common class for model both object detection and segmentation. - Contains fields: xyxy, mask, class_id, tracker_id, data. -
  • import supervision as sv: All useful functions available in global scope
  • A selection of models: - Load popular, - fine-tuned, - or Universe models.
  • sv.Detections.from_X: Load from one of 12 sources.
  • Annotators: Draw the detections with one of 20 annotators.
  • More features: This sheet contains < 50% of supervision's features. Find others here!
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-model = get_model("yolov8s-640") -tracker = sv.ByteTrack(frame_rate=video_info.fps) -smoother = sv.DetectionsSmoother() - -trace_annotator = sv.TraceAnnotator() - -with sv.VideoSink(target_path="out.mp4", video_info=video_info) as sink: - for frame in frames_generator: - results = model.infer(frame)[0] - detections = sv.Detections.from_inference(results) - detections = tracker.update_with_detections(detections) - detections = smoother.update_with_detections(detections) - - annotated_frame = trace_annotator.annotate( - frame.copy(), detections) - - sink.write_frame(frame=frame)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:z,i(s){t||($(e.$$.fragment,s),t=!0)},o(s){_(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function Ks(n){let e,t;return e=new D({props:{code:`frames_generator = sv.get_video_frames_generator(source_path=) -model = get_model("yolov8s-640") -tracker = sv.ByteTrack() - -start, end = sv.Point(x=0, y=500), sv.Point(x=200, y=1000) -line_zone = sv.LineZone(start=start, end=end) - -for frame in frames_generator: - results = model.infer(frame)[0] - detections = sv.Detections.from_inference(results) - detections = tracker.update_with_detections(detections) - crossed_in, crossed_out = line_zone.trigger(detections) -print(line_zone.in_count, line_zone.out_count)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:z,i(s){t||($(e.$$.fragment,s),t=!0)},o(s){_(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function Ws(n){let e,t,s,i,a,r,o;return t=new D({props:{code:`import cv2 -import supervision as sv -from inference import get_model - -`}}),i=new K({props:{header:"Track Object Movement",$$slots:{default:[Gs]},$$scope:{ctx:n}}}),r=new K({props:{header:"Count objects crossing a LineZone",$$slots:{default:[Ks]},$$scope:{ctx:n}}}),{c(){e=I("div"),h(t.$$.fragment),s=y(),h(i.$$.fragment),a=y(),h(r.$$.fragment),this.h()},l(l){e=T(l,"DIV",{slot:!0});var c=H(e);b(t.$$.fragment,c),s=S(c),b(i.$$.fragment,c),a=S(c),b(r.$$.fragment,c),c.forEach(p),this.h()},h(){L(e,"slot","col1")},m(l,c){R(l,e,c),v(t,e,null),A(e,s),v(i,e,null),A(e,a),v(r,e,null),o=!0},p(l,c){const u={};c&1&&(u.$$scope={dirty:c,ctx:l}),i.$set(u);const f={};c&1&&(f.$$scope={dirty:c,ctx:l}),r.$set(f)},i(l){o||($(t.$$.fragment,l),$(i.$$.fragment,l),$(r.$$.fragment,l),o=!0)},o(l){_(t.$$.fragment,l),_(i.$$.fragment,l),_(r.$$.fragment,l),o=!1},d(l){l&&p(e),E(t),E(i),E(r)}}}function Ys(n){let e,t;return e=new D({props:{preface:"InferenceSlicer breaks the image into small parts and runs the model on each one",code:`import cv2 -import supervision as sv -from inference import get_model - -image = cv2.imread("dog.jpeg") -model = get_model("yolov8s-640") - -def callback(image_slice: np.ndarray) -> sv.Detections: - results = model.infer(image_slice)[0] - return sv.Detections.from_inference(results) - -slicer = sv.InferenceSlicer( - callback=callback, - overlap_filter_strategy=sv.OverlapFilter.NON_MAX_SUPPRESSION, -) - -detections = slicer(image)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:z,i(s){t||($(e.$$.fragment,s),t=!0)},o(s){_(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function Xs(n){let e,t;return e=new D({props:{code:`frames_generator = sv.get_video_frames_generator(source_path=) -model = get_model("yolov8s-640") -tracker = sv.ByteTrack() - -polygon = np.array([[100, 200], [200, 100], [300, 200], [200, 300]]) -polygon_zone = sv.PolygonZone(polygon=polygon) - -for frame in frames_generator: - results = model.infer(frame)[0] - detections = sv.Detections.from_inference(results) - detections = tracker.update_with_detections(detections) - is_detections_in_zone = polygon_zone.trigger(detections) - print(polygon_zone.current_count)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:z,i(s){t||($(e.$$.fragment,s),t=!0)},o(s){_(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function qs(n){let e,t,s,i,a;return t=new K({props:{header:"Detect Small Objects",$$slots:{default:[Ys]},$$scope:{ctx:n}}}),i=new K({props:{header:"Count objects inside PolygonZone",$$slots:{default:[Xs]},$$scope:{ctx:n}}}),{c(){e=I("div"),h(t.$$.fragment),s=y(),h(i.$$.fragment),this.h()},l(r){e=T(r,"DIV",{slot:!0});var o=H(e);b(t.$$.fragment,o),s=S(o),b(i.$$.fragment,o),o.forEach(p),this.h()},h(){L(e,"slot","col2")},m(r,o){R(r,e,o),v(t,e,null),A(e,s),v(i,e,null),a=!0},p(r,o){const l={};o&1&&(l.$$scope={dirty:o,ctx:r}),t.$set(l);const c={};o&1&&(c.$$scope={dirty:o,ctx:r}),i.$set(c)},i(r){a||($(t.$$.fragment,r),$(i.$$.fragment,r),a=!0)},o(r){_(t.$$.fragment,r),_(i.$$.fragment,r),a=!1},d(r){r&&p(e),E(t),E(i)}}}function Zs(n){let e,t;return e=new D({props:{code:`frames_generator = sv.get_video_frames_generator() -model = get_model("yolov8s-640") - 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label_annotator.annotate( + scene=annotated_image, detections=detections) + +sv.plot_image(annotated_image)`],vs=[`mask_annotator = sv.MaskAnnotator() +label_annotator = sv.LabelAnnotator() + +annotated_image = mask_annotator.annotate( + scene=image.copy(), detections=detections) +annotated_image = label_annotator.annotate( + scene=annotated_image, detections=detections) + +sv.plot_image(annotated_image)`],Es=[`vertex_annotator = sv.VertexAnnotator(radius=10) +edge_annotator = sv.EdgeAnnotator(thickness=5) + +annotated_frame = edge_annotator.annotate( + scene=image.copy(), + key_points=key_points +) +annotated_frame = vertex_annotator.annotate( + scene=annotated_frame, + key_points=key_points +)`],ws=[`from inference import get_model + +model = get_model(model_id="yolov8n-640") +results = model.infer(image)[0] +detections = sv.Detections.from_inference(results)`],ks=[`from ultralytics import YOLO + +model = YOLO("yolov8n.pt") +results = model(image)[0] +detections = sv.Detections.from_ultralytics(results)`],ys=[`import torch +from PIL import Image +from transformers import DetrImageProcessor, DetrForObjectDetection + +processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") +model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") + +image = Image.open("dog.jpeg") +inputs = processor(images=image, return_tensors="pt") + +with torch.no_grad(): + outputs = model(**inputs) + +target_size = torch.tensor([[image.size[1], image.size[0]]]) +results = processor.post_process_object_detection( + outputs=outputs, target_sizes=target_size)[0] + +detections = sv.Detections.from_transformers( + transformers_results=results, + id2label=model.config.id2label)`],Ss=[`from inference import get_model + +model = get_model(model_id="yolov8n-seg-640") +results = model.infer(image)[0] +detections = sv.Detections.from_inference(results)`],xs=[`from ultralytics import YOLO + +model = YOLO("yolov8n-seg.pt") +results = model(image)[0] +detections = sv.Detections.from_ultralytics(results)`];function As(n){let e,t='

    Supervision simplifies the process of working with vision models. It offers connectors to popular model libraries, a plethora of visualizers (annotators), powerful post-processing features and an easy learning curve.

    Load image or video » Load the model » Run the model » Annotate';return{c(){e=T("div"),e.innerHTML=t,this.h()},l(s){e=N(s,"DIV",{class:!0,"data-svelte-h":!0}),se(e)!=="svelte-10ber7h"&&(e.innerHTML=t),this.h()},h(){C(e,"class","text-xs xl:text-sm px-2 pb-2")},m(s,i){R(s,e,i)},p:G,d(s){s&&_(e)}}}function Os(n){let e,t,s,i,a,r,o,l,c,u;return e=new D({props:{isBash:!0,code:"pip install supervision inference -q"}}),s=new D({props:{isBash:!0,code:"wget https://media.roboflow.com/notebooks/examples/dog.jpeg"}}),a=new D({props:{code:`import cv2 +import supervision as sv +from inference import get_model`}}),o=new D({props:{code:`image = cv2.imread("dog.jpeg") +model = get_model(model_id="yolov8n-640") +results = model.infer(image)[0] +detections = sv.Detections.from_inference(results)`}}),c=new D({props:{code:`annotated_image = sv.BoundingBoxAnnotator().annotate( + scene=image.copy(), detections=detections +) +annotated_image = sv.LabelAnnotator().annotate( + scene=annotated_image, detections=detections +) +sv.plot_image(annotated_image) +`}}),{c(){h(e.$$.fragment),t=y(),h(s.$$.fragment),i=y(),h(a.$$.fragment),r=y(),h(o.$$.fragment),l=y(),h(c.$$.fragment)},l(f){b(e.$$.fragment,f),t=S(f),b(s.$$.fragment,f),i=S(f),b(a.$$.fragment,f),r=S(f),b(o.$$.fragment,f),l=S(f),b(c.$$.fragment,f)},m(f,g){v(e,f,g),R(f,t,g),v(s,f,g),R(f,i,g),v(a,f,g),R(f,r,g),v(o,f,g),R(f,l,g),v(c,f,g),u=!0},p:G,i(f){u||(m(e.$$.fragment,f),m(s.$$.fragment,f),m(a.$$.fragment,f),m(o.$$.fragment,f),m(c.$$.fragment,f),u=!0)},o(f){$(e.$$.fragment,f),$(s.$$.fragment,f),$(a.$$.fragment,f),$(o.$$.fragment,f),$(c.$$.fragment,f),u=!1},d(f){f&&(_(t),_(i),_(r),_(l)),E(e,f),E(s,f),E(a,f),E(o,f),E(c,f)}}}function Ms(n){let e,t,s,i,a,r,o;return t=new W({props:{header:"Basic Principles",$$slots:{default:[As]},$$scope:{ctx:n}}}),r=new W({props:{header:"Quickstart",$$slots:{default:[Os]},$$scope:{ctx:n}}}),{c(){e=T("div"),h(t.$$.fragment),s=y(),i=T("div"),a=y(),h(r.$$.fragment),this.h()},l(l){e=N(l,"DIV",{slot:!0});var c=H(e);b(t.$$.fragment,c),s=S(c),i=N(c,"DIV",{class:!0}),H(i).forEach(_),a=S(c),b(r.$$.fragment,c),c.forEach(_),this.h()},h(){C(i,"class","mt-2"),C(e,"slot","col1")},m(l,c){R(l,e,c),v(t,e,null),O(e,s),O(e,i),O(e,a),v(r,e,null),o=!0},p(l,c){const u={};c&1&&(u.$$scope={dirty:c,ctx:l}),t.$set(u);const f={};c&1&&(f.$$scope={dirty:c,ctx:l}),r.$set(f)},i(l){o||(m(t.$$.fragment,l),m(r.$$.fragment,l),o=!0)},o(l){$(t.$$.fragment,l),$(r.$$.fragment,l),o=!1},d(l){l&&_(e),E(t),E(r)}}}function Rs(n){let e,t=`
  • sv.Detections: Common class for model both object detection and segmentation. + Contains fields: xyxy, mask, class_id, tracker_id, data. +
  • import supervision as sv: All useful functions available in global scope
  • A selection of models: + Load popular, + fine-tuned, + or Universe models.
  • sv.Detections.from_X: Load from one of 12 sources.
  • Annotators: Draw the detections with one of 20 annotators.
  • More features: This sheet contains < 50% of supervision's features. Find others here!
    • `;return{c(){e=T("ul"),e.innerHTML=t,this.h()},l(s){e=N(s,"UL",{class:!0,"data-svelte-h":!0}),se(e)!=="svelte-1lcxblo"&&(e.innerHTML=t),this.h()},h(){C(e,"class","list-disc list-inside ml-2 svelte-1li3azr")},m(s,i){R(s,e,i)},p:G,d(s){s&&_(e)}}}function Ds(n){let e,t,s,i,a,r;return e=new D({props:{preface:"Load a single image",code:`import cv2 +image = cv2.imread("dog.jpeg")`}}),s=new D({props:{preface:"Iterate over video frames",code:`for frame in sv.get_video_frames_generator(source_path=): + print(frame.shape)`}}),a=new D({props:{preface:"Run a function over every frame, save output",code:`import numpy as np + +def callback(scene: np.ndarray, index: int) -> np.ndarray: + print(f"Processing frame {index}") + return scene; + +sv.process_video( + source_path=, + target_path="out.mp4", + callback=callback)`}}),{c(){h(e.$$.fragment),t=y(),h(s.$$.fragment),i=y(),h(a.$$.fragment)},l(o){b(e.$$.fragment,o),t=S(o),b(s.$$.fragment,o),i=S(o),b(a.$$.fragment,o)},m(o,l){v(e,o,l),R(o,t,l),v(s,o,l),R(o,i,l),v(a,o,l),r=!0},p:G,i(o){r||(m(e.$$.fragment,o),m(s.$$.fragment,o),m(a.$$.fragment,o),r=!0)},o(o){$(e.$$.fragment,o),$(s.$$.fragment,o),$(a.$$.fragment,o),r=!1},d(o){o&&(_(t),_(i)),E(e,o),E(s,o),E(a,o)}}}function Is(n){let e,t,s,i,a,r,o;return t=new W({props:{header:"Core Concepts",$$slots:{default:[Rs]},$$scope:{ctx:n}}}),r=new W({props:{header:"Read images & Videos",$$slots:{default:[Ds]},$$scope:{ctx:n}}}),{c(){e=T("div"),h(t.$$.fragment),s=y(),i=T("div"),a=y(),h(r.$$.fragment),this.h()},l(l){e=N(l,"DIV",{slot:!0});var c=H(e);b(t.$$.fragment,c),s=S(c),i=N(c,"DIV",{class:!0}),H(i).forEach(_),a=S(c),b(r.$$.fragment,c),c.forEach(_),this.h()},h(){C(i,"class","mt-4"),C(e,"slot","col2")},m(l,c){R(l,e,c),v(t,e,null),O(e,s),O(e,i),O(e,a),v(r,e,null),o=!0},p(l,c){const u={};c&1&&(u.$$scope={dirty:c,ctx:l}),t.$set(u);const f={};c&1&&(f.$$scope={dirty:c,ctx:l}),r.$set(f)},i(l){o||(m(t.$$.fragment,l),m(r.$$.fragment,l),o=!0)},o(l){$(t.$$.fragment,l),$(r.$$.fragment,l),o=!1},d(l){l&&_(e),E(t),E(r)}}}function Ts(n){let e,t,s,i,a,r;return e=new D({props:{code:ws[0]}}),s=new D({props:{code:ks[0]}}),a=new D({props:{code:ys[0]}}),{c(){h(e.$$.fragment),t=y(),h(s.$$.fragment),i=y(),h(a.$$.fragment)},l(o){b(e.$$.fragment,o),t=S(o),b(s.$$.fragment,o),i=S(o),b(a.$$.fragment,o)},m(o,l){v(e,o,l),R(o,t,l),v(s,o,l),R(o,i,l),v(a,o,l),r=!0},p:G,i(o){r||(m(e.$$.fragment,o),m(s.$$.fragment,o),m(a.$$.fragment,o),r=!0)},o(o){$(e.$$.fragment,o),$(s.$$.fragment,o),$(a.$$.fragment,o),r=!1},d(o){o&&(_(t),_(i)),E(e,o),E(s,o),E(a,o)}}}function Ns(n){let e,t,s,i,a,r,o='+9 more connectors ⚡',l;return t=new D({props:{code:`import cv2 +import supervision as sv + +image = cv2.imread("dog.jpeg")`}}),i=new W({props:{header:"Frequent choices: Inference, Ultralytics & Transfomers",$$slots:{default:[Ts]},$$scope:{ctx:n}}}),{c(){e=T("div"),h(t.$$.fragment),s=y(),h(i.$$.fragment),a=y(),r=T("div"),r.innerHTML=o,this.h()},l(c){e=N(c,"DIV",{slot:!0});var u=H(e);b(t.$$.fragment,u),s=S(u),b(i.$$.fragment,u),a=S(u),r=N(u,"DIV",{class:!0,"data-svelte-h":!0}),se(r)!=="svelte-bfew51"&&(r.innerHTML=o),u.forEach(_),this.h()},h(){C(r,"class","flex flex-row justify-center text-lg font-bold"),C(e,"slot","col1")},m(c,u){R(c,e,u),v(t,e,null),O(e,s),v(i,e,null),O(e,a),O(e,r),l=!0},p(c,u){const f={};u&1&&(f.$$scope={dirty:u,ctx:c}),i.$set(f)},i(c){l||(m(t.$$.fragment,c),m(i.$$.fragment,c),l=!0)},o(c){$(t.$$.fragment,c),$(i.$$.fragment,c),l=!1},d(c){c&&_(e),E(t),E(i)}}}function Ls(n){let e,t,s,i='+18 more annotators 🎨',a;return e=new D({props:{code:bs[0]}}),{c(){h(e.$$.fragment),t=y(),s=T("div"),s.innerHTML=i,this.h()},l(r){b(e.$$.fragment,r),t=S(r),s=N(r,"DIV",{class:!0,"data-svelte-h":!0}),se(s)!=="svelte-1lcfdfr"&&(s.innerHTML=i),this.h()},h(){C(s,"class","flex flex-row justify-center text-lg font-bold")},m(r,o){v(e,r,o),R(r,t,o),R(r,s,o),a=!0},p:G,i(r){a||(m(e.$$.fragment,r),a=!0)},o(r){$(e.$$.fragment,r),a=!1},d(r){r&&(_(t),_(s)),E(e,r)}}}function Bs(n){let e,t='For inference and ultralytics, you only need to change the model ID:',s,i,a,r,o;return i=new D({props:{code:Ss[0]}}),r=new D({props:{code:xs[0]}}),{c(){e=T("div"),e.innerHTML=t,s=y(),h(i.$$.fragment),a=y(),h(r.$$.fragment),this.h()},l(l){e=N(l,"DIV",{class:!0,"data-svelte-h":!0}),se(e)!=="svelte-1x6ubdi"&&(e.innerHTML=t),s=S(l),b(i.$$.fragment,l),a=S(l),b(r.$$.fragment,l),this.h()},h(){C(e,"class","flex flex-row justify-center text-sm font-bold")},m(l,c){R(l,e,c),R(l,s,c),v(i,l,c),R(l,a,c),v(r,l,c),o=!0},p:G,i(l){o||(m(i.$$.fragment,l),m(r.$$.fragment,l),o=!0)},o(l){$(i.$$.fragment,l),$(r.$$.fragment,l),o=!1},d(l){l&&(_(e),_(s),_(a)),E(i,l),E(r,l)}}}function Cs(n){let e,t;return e=new 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g={};c&1&&(g.$$scope={dirty:c,ctx:l}),r.$set(g)},i(l){o||(m(t.$$.fragment,l),m(i.$$.fragment,l),m(r.$$.fragment,l),o=!0)},o(l){$(t.$$.fragment,l),$(i.$$.fragment,l),$(r.$$.fragment,l),o=!1},d(l){l&&_(e),E(t),E(i),E(r)}}}function Hs(n){let e,t;return e=new D({props:{code:`from inference import get_model + +model = get_model(model_id="yolov8s-pose-640") + +results = model.infer(image)[0] +key_points = sv.KeyPoints.from_inference(results) +`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:G,i(s){t||(m(e.$$.fragment,s),t=!0)},o(s){$(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function js(n){let e,t;return e=new D({props:{code:`from ultralytics import YOLO + +model = YOLO("yolov8s-pose.pt") + +results = model(image)[0] +key_points = sv.KeyPoints.from_ultralytics(results)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:G,i(s){t||(m(e.$$.fragment,s),t=!0)},o(s){$(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function Us(n){let e,t;return e=new D({props:{code:`import torch +import super_gradients + +device = "cuda" if torch.cuda.is_available() else "cpu" +model = super_gradients.training.models.get( + "yolo_nas_pose_s", pretrained_weights="coco_pose").to(device) + +results = model.predict(image, conf=0.1) +key_points = sv.KeyPoints.from_yolo_nas(results)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:G,i(s){t||(m(e.$$.fragment,s),t=!0)},o(s){$(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function Vs(n){let e,t,s,i,a,r,o,l,c;return t=new D({props:{code:`import cv2 +import supervision as sv + +image = cv2.imread("dog.jpeg")`}}),i=new W({props:{header:"Inference",$$slots:{default:[Hs]},$$scope:{ctx:n}}}),r=new W({props:{header:"Ultralytics",$$slots:{default:[js]},$$scope:{ctx:n}}}),l=new W({props:{header:"Yolo NAS",$$slots:{default:[Us]},$$scope:{ctx:n}}}),{c(){e=T("div"),h(t.$$.fragment),s=y(),h(i.$$.fragment),a=y(),h(r.$$.fragment),o=y(),h(l.$$.fragment),this.h()},l(u){e=N(u,"DIV",{slot:!0});var f=H(e);b(t.$$.fragment,f),s=S(f),b(i.$$.fragment,f),a=S(f),b(r.$$.fragment,f),o=S(f),b(l.$$.fragment,f),f.forEach(_),this.h()},h(){C(e,"slot","col1")},m(u,f){R(u,e,f),v(t,e,null),O(e,s),v(i,e,null),O(e,a),v(r,e,null),O(e,o),v(l,e,null),c=!0},p(u,f){const g={};f&1&&(g.$$scope={dirty:f,ctx:u}),i.$set(g);const d={};f&1&&(d.$$scope={dirty:f,ctx:u}),r.$set(d);const w={};f&1&&(w.$$scope={dirty:f,ctx:u}),l.$set(w)},i(u){c||(m(t.$$.fragment,u),m(i.$$.fragment,u),m(r.$$.fragment,u),m(l.$$.fragment,u),c=!0)},o(u){$(t.$$.fragment,u),$(i.$$.fragment,u),$(r.$$.fragment,u),$(l.$$.fragment,u),c=!1},d(u){u&&_(e),E(t),E(i),E(r),E(l)}}}function Fs(n){let e,t,s,i='+1 more annotator 🎨',a;return e=new D({props:{code:Es[0]}}),{c(){h(e.$$.fragment),t=y(),s=T("div"),s.innerHTML=i,this.h()},l(r){b(e.$$.fragment,r),t=S(r),s=N(r,"DIV",{class:!0,"data-svelte-h":!0}),se(s)!=="svelte-9ymj2a"&&(s.innerHTML=i),this.h()},h(){C(s,"class","flex flex-row justify-center text-lg font-bold")},m(r,o){v(e,r,o),R(r,t,o),R(r,s,o),a=!0},p:G,i(r){a||(m(e.$$.fragment,r),a=!0)},o(r){$(e.$$.fragment,r),a=!1},d(r){r&&(_(t),_(s)),E(e,r)}}}function zs(n){let e,t,s,i,a;return t=new D({props:{preface:"⚠️ Available in pre-release: pip install git+https://github.com/roboflow/supervision.git@develop ",code:`import mediapipe as mp + +image = cv2.imread("dog.jpeg") +image_height, image_width, _ = image.shape +mediapipe_image = mp.Image( + image_format=mp.ImageFormat.SRGB, + data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + +options = mp.tasks.vision.PoseLandmarkerOptions( + base_options=mp.tasks.BaseOptions( + model_asset_path="pose_landmarker_heavy.task" + ), + running_mode=mp.tasks.vision.RunningMode.IMAGE, + num_poses=2) + +PoseLandmarker = mp.tasks.vision.PoseLandmarker +with PoseLandmarker.create_from_options(options) as landmarker: + pose_landmarker_result = landmarker.detect(mediapipe_image) + +key_points = sv.KeyPoints.from_mediapipe( + pose_landmarker_result, (image_width, image_height))`}}),i=new W({props:{header:"Annotate KeyPoints",$$slots:{default:[Fs]},$$scope:{ctx:n}}}),{c(){e=T("div"),h(t.$$.fragment),s=y(),h(i.$$.fragment),this.h()},l(r){e=N(r,"DIV",{slot:!0});var o=H(e);b(t.$$.fragment,o),s=S(o),b(i.$$.fragment,o),o.forEach(_),this.h()},h(){C(e,"slot","col2")},m(r,o){R(r,e,o),v(t,e,null),O(e,s),v(i,e,null),a=!0},p(r,o){const l={};o&1&&(l.$$scope={dirty:o,ctx:r}),i.$set(l)},i(r){a||(m(t.$$.fragment,r),m(i.$$.fragment,r),a=!0)},o(r){$(t.$$.fragment,r),$(i.$$.fragment,r),a=!1},d(r){r&&_(e),E(t),E(i)}}}function Gs(n){let e,t;return e=new D({props:{code:`video_info = sv.VideoInfo.from_video_path(video_path=) +frames_generator = sv.get_video_frames_generator(source_path=) + +model = get_model("yolov8s-640") +tracker = sv.ByteTrack(frame_rate=video_info.fps) +smoother = sv.DetectionsSmoother() + +trace_annotator = sv.TraceAnnotator() + +with sv.VideoSink(target_path="out.mp4", video_info=video_info) as sink: + for frame in frames_generator: + results = model.infer(frame)[0] + detections = sv.Detections.from_inference(results) + detections = tracker.update_with_detections(detections) + detections = smoother.update_with_detections(detections) + + annotated_frame = trace_annotator.annotate( + frame.copy(), detections) + + sink.write_frame(frame=frame)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:G,i(s){t||(m(e.$$.fragment,s),t=!0)},o(s){$(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function Ks(n){let e,t;return e=new D({props:{code:`frames_generator = sv.get_video_frames_generator(source_path=) +model = get_model("yolov8s-640") +tracker = sv.ByteTrack() + +start, end = sv.Point(x=0, y=500), sv.Point(x=200, y=1000) +line_zone = sv.LineZone(start=start, end=end) + +for frame in frames_generator: + results = model.infer(frame)[0] + detections = sv.Detections.from_inference(results) + detections = tracker.update_with_detections(detections) + crossed_in, crossed_out = line_zone.trigger(detections) 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D({props:{code:`frames_generator = sv.get_video_frames_generator(source_path=) +model = get_model("yolov8s-640") +tracker = sv.ByteTrack() + +polygon = np.array([[100, 200], [200, 100], [300, 200], [200, 300]]) +polygon_zone = sv.PolygonZone(polygon=polygon) + +for frame in frames_generator: + results = model.infer(frame)[0] + detections = sv.Detections.from_inference(results) + detections = tracker.update_with_detections(detections) + is_detections_in_zone = polygon_zone.trigger(detections) + print(polygon_zone.current_count)`}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:G,i(s){t||(m(e.$$.fragment,s),t=!0)},o(s){$(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function qs(n){let e,t,s,i,a;return t=new W({props:{header:"Detect Small Objects",$$slots:{default:[Ys]},$$scope:{ctx:n}}}),i=new W({props:{header:"Count objects inside 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+) +`}}),{c(){h(e.$$.fragment),t=y(),h(s.$$.fragment)},l(a){b(e.$$.fragment,a),t=S(a),b(s.$$.fragment,a)},m(a,r){v(e,a,r),R(a,t,r),v(s,a,r),i=!0},p:G,i(a){i||(m(e.$$.fragment,a),m(s.$$.fragment,a),i=!0)},o(a){$(e.$$.fragment,a),$(s.$$.fragment,a),i=!1},d(a){a&&_(t),E(e,a),E(s,a)}}}function tr(n){let e,t;return e=new D({props:{code:`dataset = sv.DetectionDataset.from_yolo("") + +model = get_model("yolov8s-640") +def callback(image: np.ndarray) -> sv.Detections: + results = model.infer(image)[0] + return sv.Detections.from_inference(results) + +confusion_matrix = sv.ConfusionMatrix.benchmark( + dataset=dataset, callback=callback +) +print(confusion_matrix.matrix) + `}}),{c(){h(e.$$.fragment)},l(s){b(e.$$.fragment,s)},m(s,i){v(e,s,i),t=!0},p:G,i(s){t||(m(e.$$.fragment,s),t=!0)},o(s){$(e.$$.fragment,s),t=!1},d(s){E(e,s)}}}function nr(n){let e,t,s,i,a;return t=new W({props:{header:"Run a fine-tuned LMM",$$slots:{default:[er]},$$scope:{ctx:n}}}),i=new W({props:{header:"Compute 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