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path: root/src/client/views/nodes/VideoBox.tsx
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-rw-r--r--src/client/views/nodes/VideoBox.tsx89
1 files changed, 42 insertions, 47 deletions
diff --git a/src/client/views/nodes/VideoBox.tsx b/src/client/views/nodes/VideoBox.tsx
index 4d85b4942..f994bdbb5 100644
--- a/src/client/views/nodes/VideoBox.tsx
+++ b/src/client/views/nodes/VideoBox.tsx
@@ -110,56 +110,51 @@ export class VideoBox extends ViewBoxAnnotatableComponent<FieldViewProps>() {
return this._videoRef;
}
+ autoTag = async () => {
+ if (this.Document.$tags_chat) return;
+ try {
+ if (!this.player) throw new Error('Video element not available.');
+
+ // 1) Extract a frame at the video's midpoint
+ const videoDuration = this.player.duration;
+ const snapshotTime = videoDuration / 2;
+
+ // Seek the video element to the midpoint
+ await new Promise<void>(resolve => {
+ const onSeeked = () => {
+ this.player!.removeEventListener('seeked', onSeeked);
+ resolve();
+ };
+ this.player!.addEventListener('seeked', onSeeked);
+ this.player!.currentTime = snapshotTime;
+ });
- autoTag = async () => {
- try {
- if (!this.player) throw new Error('Video element not available.');
-
- // 1) Extract a frame at the video's midpoint
- const videoDuration = this.player.duration;
- const snapshotTime = videoDuration / 2;
-
- // Seek the video element to the midpoint
- await new Promise<void>((resolve, reject) => {
- const onSeeked = () => {
- this.player!.removeEventListener('seeked', onSeeked);
- resolve();
- };
- this.player!.addEventListener('seeked', onSeeked);
- this.player!.currentTime = snapshotTime;
- });
-
- // 2) Draw the frame onto a canvas and get a base64 representation
- const canvas = document.createElement('canvas');
- canvas.width = this.player.videoWidth;
- canvas.height = this.player.videoHeight;
- const ctx = canvas.getContext('2d');
- if (!ctx) throw new Error('Failed to create canvas context.');
- ctx.drawImage(this.player, 0, 0, canvas.width, canvas.height);
- const base64Image = canvas.toDataURL('image/png');
-
- // 3) Send the image data to GPT for classification and descriptive tags
- const raw = await gptImageLabel(
- base64Image,
- `Classify this video frame as either a PERSON or LANDSCAPE.
+ // 2) Draw the frame onto a canvas and get a base64 representation
+ const canvas = document.createElement('canvas');
+ canvas.width = this.player.videoWidth;
+ canvas.height = this.player.videoHeight;
+ const ctx = canvas.getContext('2d');
+ if (!ctx) throw new Error('Failed to create canvas context.');
+ ctx.drawImage(this.player, 0, 0, canvas.width, canvas.height);
+ const base64Image = canvas.toDataURL('image/png');
+
+ // 3) Send the image data to GPT for classification and descriptive tags
+ const label = await gptImageLabel(
+ base64Image,
+ `Classify this video frame as either a PERSON or LANDSCAPE.
Then provide five additional descriptive tags (single words) separated by spaces.
Finally, add one detailed summary phrase using underscores.`
- );
-
- // 4) Normalize and store labels in the Document's tags
- const label = raw.trim().toUpperCase();
- const tokens = label.split(/\s+/);
- this.Document.$tags_chat = new List<string>();
- tokens.forEach(tok => (this.Document.$tags_chat as List<string>).push(tok));
- const aspect = this.player!.videoWidth / (this.player!.videoHeight || 1);
- (this.Document.$tags_chat as List<string>).push(`ASPECT_${aspect}`);
- // 5) Turn on tag display in layout
- this.Document._layout_showTags = true;
-
- } catch (err) {
- console.error('Video autoTag failed:', err);
- }
-};
+ ).then(raw => raw.trim().toUpperCase());
+
+ // 4) Normalize and store labels in the Document's tags
+ const aspect = this.player!.videoWidth / (this.player!.videoHeight || 1);
+ this.Document.$tags_chat = new List<string>([...label.split(/\s+/), `ASPECT_${aspect}`]);
+ // 5) Turn on tag display in layout
+ this.Document._layout_showTags = true;
+ } catch (err) {
+ console.error('Video autoTag failed:', err);
+ }
+ };
componentDidMount() {
this.unmounting = false;